Simple LTE for 4G Mobile Broadband

Improving Coverage for a Given Service Area

2015-08-25T08:02:00.000-07:00

Some common practices to improve the coverage for a given service area are:

Receiver selection. Selecting an eNodeB with a better receiver sensitivity will help to improve the coverage for a service area.

Implementing receiver diversity. In UL highers, the chances of correctly decoding the received signal from the UE improve the coverage.

Beamforming. For uneven heights, beamforming can be a very handy feature to compensate for any coverage hole.

Improving the antenna gain. This is particularly useful for smaller cells, wherein the DL cell radius is limited in comparison with the UL cell radius.

Adding more sites. In case none of these techniques can be used to compensate for a coverage hole, the last option would be to add a new site.

Initiation, Formation and Usage of Spontaneous PN-F

2012-05-16T01:12:00.000-07:00

Since in the spontaneous PN-F, the federation is formed in the absence of a fixed infrastructure; therefore we extend our proposal for PN cluster formation i.e. PNRP, for the initiation, formation and usage of spontaneous PN-Fs. The Personal Network Routing Protocol (PNRP) extension for PN-F is a variant of on-demand multi-hop routing protocol such as AODV and DSR, which adapts to the personal networking environments for communication among PNs. The PN-F creator decides the PN-F members and sends them, the PN-F profile. On the reception of PN-F profile, if the initially proposed members decide to be the part of PN-F, they exchange their PN-F participation profile with the PN-F creator.

Therefore, PN-F initiation becomes an intrinsic capability of PN-F formation owed by PNRP. Moreover, PN-F routing mechanisms assist to determine the routes towards the desired PN-F members and provide means to form and to use PN Federations.

PN-F Topology Discovery, Initiation and Formation

PN-F routing builds routes using a join request/reply query cycle. When a PN desires to create a PN-F with certain defined PN-F members, it creates a PN-F profile and sends to its FMs, piggybacked with a Join-Request (JR) message, leveraging the inter-cluster routes; thanks to PNRP's PN-cluster formation capability. As show in Figure 1, in order to create a PN-F with PN2, PN3 and PN4, the PN1 (i.e. PN-F creator) sends the JR message to its FMs. The Join-Request (JR) message contains two lists of PN-IDs such as "destination-list", which stores the potential PN-F participants and "destination-attained-list", which represents the PNs already attained by the JR message. As FMs receive the JR, they investigate whether the PN-ID of their neighbouring PN is mentioned in JR message destination-list. In case of positive response, the JR is forwarded to the FM of the neighbouring PN. In contrast, if the FM does not find the adjacent PN in the destination-list and the potential participant PNs are not accessible through other FMs of the same PN, a "Connectivity PN-F" can be formed with the adjacent PN in order to relay the PN-F information towards the potential PN-F members.

Figure 1: PN-F topology discovery in PNRP

On the reception of JR message, the neighbouring PN's FM first removes its PN-ID from JR's destination-list and then put it in the destination-attained-list. Moreover it also sets up backward pointers in PN-F routing table, towards the PN which sent the JR, as a next-hop to reach all the PNs mentioned in the destination-attained-list. PN1's FM (i.e. A) forwards the JR to PN2's FM (i.e. B), which sets the entries in its PN-F routing table that PN1 is reachable through its FM B.

The above presented mechanisms are repeated at each next PN until all the PNs mentioned in the JR destination-list are reached. Finally, on the reception of JR if the PN finds the JR's destination-list is empty, it will send a Joint-Request-Ack (JRA) message (by replacing the entries of destination-attained-list with destination-list) backwards to the PN which forwarded the JR message. As shown in Figure 1, the JRA is initiated by PN4, which receives the empty destination-list. The mechanisms of setting backward pointers to the PNs declared in destination-attained-list and moving the PN-IDs from destination-list to destination-attained-list at every next PN is also repeated for JRA message until it reaches the PN-F creator, who triggered the PN-F formation. The exchange of JR and JRA messages facilitate the establishment of PN-F routing tables at the Federation Managers of PNs, which are participating in the PN-F.

Data Forwarding and PN-F Use

During the integrated PN-F topology discovery process, the entire participant PNs learns the routes not only towards the PN-F creator but also towards each other. These routes are stored in the PN-F routing table and are leveraged to exchange, initially the PN-F profiles and then the PN-F participation profiles in order to form the PN-F. To this end, the data packets destined to any other PN are first forwarded to the FMs, which further routes the data with the help of PN-F routing table. The profiles are stored at the FMs (the entry points of PNs), which are used to enforce PN-F policies on the PN-F routing in order to ensure secure PN-F overlay concept.

PN Cluster Formation and Personal Network Routing Protocol (PNRP)

2012-05-12T05:06:00.000-07:00

The initial step towards the PN formation is the formation of PN clusters i.e. all the personal nodes that are in the close vicinity of each other discover the routes towards each other. Subsequently, the different geographically separated clusters of a PN are glued together to form a PN with the help of PN Agent. To this end, we propose the Personal Network Routing Protocol (PNRP) which helps in determining the routes among all the personal nodes in a PN cluster.

PNRP is a variant of link-state multi-hop routing protocol that adapts to the personal networking environments. It maintains the proactive topology of nodes which lie within the PN cluster boundary, at every personal node of the PN cluster. Moreover, the information on functionalities of the personal nodes such as Gateway Node and/or Federation Manager is also exchanged within the PN topology in order to facilitate PN's access to the outside world. In the following subsections, we discuss different steps that PNRP performs towards the formation of a PN cluster.

Integrated Topology Discovery

The role of Integrated Topology Discovery mechanism is to determine how the personal nodes are connected (using which interfaces in single/multi hop) in order to provide routes for any source/destination pair in the PN cluster. It first determines the direct connectivity among nodes and further exchanges this information to form a unified cluster topology.

Neighbor discovery is incorporated into PNRP by allowing every personal node to periodically transmit "Hello" packets on all of its interfaces. "Hello" packet contains the PN-ID and the Node-ID of the source node which is processed at the destination node to identify the source of the "Hello" packet. It is possible that the personal nodes may discover the non-personal nodes; therefore PN level authentication is indispensable. Every node maintains a 1-hop neighbour table and associated costs to each link with its direct neighbours and their PN identification. For the current implementation of PNRP, we have considered number-of-hops as a cost metric.

Once the 1-hop topology is formed, it is exchanged with other personal nodes in order to form a complete snapshot (table) of the PN cluster on its every single node. The topology information is only exchanged with the personal nodes i.e. among the nodes which belong to the same PN. To this end, every personal node periodically transmits the "Cluster topology (Ctopo)" message towards all its personal nodes (neighbours). On the reception of "Ctopo", the PN cluster routing tables are formed/updated and further exchanged with the other neighbouring nodes. Figure 1 shows the PN routing table at a personal node constructed after the exchange of "Hello" and "Ctopo" messages.

Figure 1: PNRP for PN routing

Gateway and Neighboring PN's Discovery

In PNRP, each Gateway Node (GN) advertises in the "Hello" message, whether it has connectivity with the infrastructure network or not. As can be seen in Figure 1, the exchange of integrated PN cluster topology with the help of "Ctopo" message permits each node to maintain routes to all the existing GNs and the cost to reach them, in the PN routing table.

Discovery of the neighbouring PNs is also intrinsic to Integrated Topology Discovery mechanism. During the exchange of "Hello" messages, if the destination node finds out that it's not the part of the source node's PN (with the help of PN-ID), the destination node sets itself as a Federation Manager (FM) to the source node's PN. The connectivity among the PNs is realised with the help of FMs. Once the integrated topology information is exchanged among all the nodes of the PN, every node knows the exit points (FMs) to communicate with other neighbouring PNs, which further helps in PN to PN (PN-F) routing. In case of multiple GNs or FMs, the minimum cost option is selected.

Route Discovery

PNRP differentiates the route discovery procedure when the destination is the part of same PN as the source and/or from the case when the destination is the part of different PN. In latter case, "PNRP for PN-F" mechanisms are triggered. In former case, since a proactive PN cluster-level topology is maintained at each PN node, the route to all the destinations in the PN will be known before time.

TECHNICAL DIMENSION: ROUTING, A PRACTICAL ENABLER OF COOPERATION IN 4G

2012-05-08T17:02:00.000-07:00

The key to successful realization of user-centric (Personal Network (PN)) and group-centric (PN Federation) cooperation is the general connectivity architecture which can seamlessly bridge heterogeneous personal devices, placed both in the close vicinity and at remote locations. The development, implementation and integration of global Personal Network architecture for connecting the devices geographically separated across the interconnection structures (intranet or Internet, for instance) has been explored recently in Hoebeke (2006). However, the ad-hoc seamless connectivity among the heterogeneous PN devices present in the close vicinity of each other, is still open to research. Moreover, the extension of PN concept to realize the group-centric Personal Network Federation (PN-F) i.e. connecting multiple PNs, towards PUE is a research theme that emerged recently. In this section, we present general connectivity architecture i.e. a routing protocol, which enables cooperation in PUE. At first, it facilitates the cooperation between the user's heterogeneous devices in order to form a Personal Network (PN). Moreover, at second, our cooperative routing protocol enables multiple PNs to join hands to form a PN Federation. In order to summarize, the routing solution enables the cooperation not only within the devices of a distinct user, but also among the devices of different users, making the vision of PUE, a reality.

Preliminaries

1 Terminology

As described previously, we define a "personal node (pn)" to be the node that belongs to the owner of a PN. Each node is identified by its Personal Network Identification (PN-ID) and Node Identification (NID). All personal nodes of a PN owner share the same PN-ID. A "Gateway Node (GN)" is a personal node that enables the connectivity to the infrastructure network such as Internet or corporate LAN. A personal node is defined to be "Federation Manager (FM)", if it enables connectivity to the personal nodes of the other PN(s). A "PN Cluster" is a network of personal nodes located within a limited geographical area (such as house, office or car). One or more than one "PN cluster" of a single owner contributes his PN.

2 Cooperative PN Federation Profiles

In order to interact among the PNs and to create trustable PN Federations (PN-Fs), rules and polices are needed to determine for instance, who is or can become member of the federation and how and which resources are made available to the PN-F members. Based on this, two different types of profiles have been identified to realise the concept of PN-F (PN to PN interaction), such as "PN-F profile" and "PN-F participation profile". As shown in Figure 1, the PN-F profile is common to the federation, created by the PN-F creator, which reflects the global information about the PN-F. Whereas, the "participation profile" is bound to the individual PN-F member and it reflects his local view regarding the PN-F. The PN-F is initiated by the PN-F profile, which is further updated with the help of participation profiles during the course of PN-F's existence.

Figure 1: Personal network federation (PN-F)

COOPERATIVE PERSONAL/GROUP SERVICES IN 4G

2012-05-04T01:11:00.000-07:00

Personal computing paradigm flourished faster than any other domain and with its marriage with the networking world, it gave birth to a new era of computing called ubiquitous computing. 4G is not the name of a single technology, rather it is a cooperative platform where a large range of heterogeneous wireless networks and services coexist. The diverse devices, network and service elements find their way into the life of the end-user and this integration of 4G elements into the end-user environment should ideally go unnoticed to the user; so that the technology eventually focuses over the user and not the user focuses on the diversity of technology around him. Calm 4G technology integrated into user's world is only possible with the essence of cooperation, sharing, openness and trust, within the user's own devices and among the users. The notion of cooperation in personal/group services may take various dimensions ranging from technology and services to socio-physiological aspects.

There is a large array of actors in 4G service arena such as user, service/content provider, network operator, regulatory bodies, and so on, who bind their own proper stakes with 4G's success. However, economically speaking, user is a major player; a center of the entire 4G globe, whereas the other actors join hands to meet the expectations of the end-user. Taking the technological dimension, in the last few years, number of heterogeneous devices emerged and networked, ranging from mobile communication equipments to home electronics. This proliferation results into the availability of large range of choices to the user to communicate in highly diverse environments. As a result, in a 4G system, the user is surrounded by a variety of devices offering a multiplicity of different services, as shown in Figure 1. Moreover, the utilization of these devices and services dramatically changes with the change in user's environment. Therefore, the devices and services in the 4G world should have a high deal of adaptation capabilities. "Personalization" is a key word in this context. Since every user is unique in his roles, taste and likings; the 4G systems should be intelligent enough to fully understand the user and adapt the network and service elements according to user's preferences.

Figure 1: User-centric cooperation

In a user-centric model, the user is the focus of the whole system. The cooperation among his heterogeneous devices and his environment is vital for the seamless working of the entire 4G system. Here, we refer to the cooperation in two dimensions. At first, the devices themselves need to cooperate, for instance, while the user is busy working on his laptop and he receives an important voice message on his mobile phone, the mobile phone should track the activity of the user in order to notify him about the voice message. To this end, irrespective of their specifications, the user's devices should be able to cooperate in order to help the user in his daily life. And second, the devices should cooperate with the user's environment. Since the user preferences vary with the change in his environment therefore the devices should be capable to dynamically adjust themselves accordingly. For instance, if the user receives a video call while at home sitting in his TV lounge, the mobile phone should intelligently detect the activity/mood of the user and should propose to transfer the video flow on the higher resolution screen placed in front of the user. These both dimensions of cooperation are only possible when the 4G systems encircling the distinct end-user, fully understand the socio-physiological and the technological potentials and limitations of cooperation.

In 4G, towards personalization and user-centric cooperation, we generalize the concept of Personal Computers (PCs) and extend it towards Personal Networks (PN). It is a system/network owned and operated by one person i.e. the PN owner. The PN owner is the sole authority in his personal interconnected devices and can use the PN in a way he wants. The personal devices may be located, both in his close vicinity (forming a PAN) and at remote locations. Figure 2 presents the PN of Bob, which is composed of his home, office and car clusters. The owner of the PN can add new devices or personalized services in his personal network according to his will. The PN for its owner is a heaven of personalized services in the cyberspace and appears as a black box to the outside world.

Figure 2: Bob ‘s personal network

Group-centric cooperation is also referred as cooperation among the end-users who are organized in groups. This is somehow fundamentally opposite to the user-centric cooperation, where only the user's devices and environments cooperate, and this cooperation appears as a dark cloud for the outside world (for other users). In fact, the 4G services which can be made available to a single user (with user-centric cooperation) are limited and the users need to cooperate with the each other to extend their global services repository. In addition, many service-oriented patterns need to extend the boundaries of "user-centric cooperation" and involve the secure interaction of multiple users having common interests for various professional and private services. Moreover, in this federated users environment towards group-centric cooperative model, the distinct users can offer services to each other promoting the concept of "give and take".

In order to promote the group-centric cooperation in 4G systems, the concept of Personal Network Federations (PN-F) has been recently introduced in the European MAGNET Beyond project. PN-F addresses the interactions between multiple PN users with common interests for a range of diverse services. A PN federation can be defined as a secure impromptu, situation-aware or beforehand agreed cooperation between a subset of relevant devices belonging to different PNs for the purpose of achieving a common goal or service by forming an efficient collaboration. Consider the PN-F B in Figure 3, a simple example of PN-F is the federation of PNs belonging to a group of students in a classroom, sharing lecture notes.

Figure 3: Personal network federation architectures

Based on how cooperation between devices in different PNs is realized in order to establish the federation, we can differentiate between infrastructure and spontaneous PN federations. In an infrastructure based federation, PN-F is established between devices in PN clusters that are all connected to an infrastructure network. As shown in Figure 3, the infrastructure PN-F i.e. PN-F A is formed between the user 1 and user 2, who are located across the infrastructure network. On the other hand, in a spontaneous/ad-hoc PN-F, the federation is formed in the absence of a fixed infrastructure. This type of federation mostly occurs when nearby users collaborate within a federation.

The cooperation among the users, their devices and environments results into the development of a "Personal Ubiquitous Environment" around the user, which permits the "ubiquitous global access" to a vast number and variety of information resources. This uniform and comprehensive sense of cooperation results into a vast base of services for all the users who are part of this personal ubiquitous environment village. In the language of Personal Networking, we can collectively define PN and PN-F as a Personal Ubiquitous Environment. As shown in Figure 4, three users come closer to share devices, services and environments to form the cooperative group (PUE/PN-F). In PUE environment, the users believe in the essence of openness and sharing not only for their self-centric goals but also for the global benefits of the entire cooperative community. Those users, who are satisfied with their own proper resources and do not have any intention to cooperate; stays in their own user-centric environments i.e. PN, as shown in Figure 4.

Figure 4: Personal ubiquitous environment

Cooperative Services in 4G

2012-04-30T04:41:00.000-07:00

The widely agreed upon rule for success in 4G telecommunication markets is to visualize a cooperative service chain of multiple suppliers to satisfy the ever-growing requirements of end customers (Roussos, 2003). The evolution of 4G systems in a multi-dimensional facet provided a scrupulous platform for deriving advanced and innovative user-oriented and cooperative services. Embossed to high level perspectives and equally leveraging on technical dimensions, we recognize several aspects of cooperative services; those related to personal (or group centric) services, intelligent transport network services, cooperative community networks and large scale ad hoc network services. As shown in Figure 1, these cooperative and heterogeneous services accounts for the efficient 4G convergence platforms that renders clear cut benefits in terms of bandwidth, coverage, power consumption and spectrum usage.

Figure 1: Cooperation in 4G, services perspective

The personal and group-centric communication models put forth a multitude of interesting services, benefiting from the "cooperative clouds" formed as a result of multi-level social groups based on self-organizing common objectives. Within this context, various compelling services for smart-home networking, cooperative health care etc. are shaping up. One such service is the cooperative distribution of media content in stationary home networks, where the transparency enabled by the seamless and intelligent platform equips the home network to converge into an interdependent service ecosystem for the consumers. Other services in group communication which exploits collaborative behavior include symbolic resource sharing among communication groups (for example, user-centric dynamic content sharing similar to popular web services like MySpace or YouTube), ubiquitous and collaborative healthcare monitoring at home or hospitals etc. The intelligent transport network is also a setting for providing collaborative 4G services from a user perspective. The most interesting among them is the development of evolutionary cooperative multi-player games as a massive collaborative constellation for vehicular networks. These self-evolving games are targeted at intelligent transport networks which range from private vehicle owners to public transportation system users. Other envisaged services include varying location-based services in offer on a cooperative basis, where the consumers could either locate their intended footage leveraging on the collaborative platform or the customers could market their business availing on cooperative advertisement options. This creates an open service ecosystem beneficial for the entire service value chain in vehicular transportation networks.

Wireless community networks (commercial, public and non-profit), have matured enough through the continuing evolution of mesh networks, which are now exploiting heterogeneity in a third generation mesh context with the use of multiple-radios (including different radios for downlink-uplink), dynamic interference detection and avoidance mechanisms, automatic location updating mechanisms etc. This, along with the introduction of inter-community networking aspects has given new dimensions to collaborative service distribution in community networks. This includes community-based IPTV services, cooperative web-radio, collective surveillance etc apart from common service attributes like resource sharing among users. In general, large-scale user cooperation is an important aspect to the success of community networks triggering the collaborative service-profit chain and introducing competitive differentiation. Mobile Ad Hoc networks applications have made appealing progress, particularly in the field of wireless sensor networks. Many distributed applications are envisaged in sensor networks where collaborative computing assumes the center stage; smart messaging services for sensors, collaborative objects tracking etc to name a few.

In the search for niche markets and opportunity for 4G, large organizations and policy makers converge to accept that the 4G landscape will not just be about defining higher data rates or newer air interfaces, but rather will be shaped by the increasing integration and interconnection of heterogeneous systems, with different devices processing information for a variety of purposes, a mix of infrastructures supporting transmission and a multitude of applications working in parallel making the most efficient use of the spectrum. On the contrary, users are getting more vary about the services that they require and the modes with which they prefer to communicate and cooperate, which also hugely influences the future of 4G commercialization. These developments has led us to think in the lines of personal/group services as the most appealing and predominant platform for the development of 4G; where the users collaborate in a distributed and cooperative fashion. This user-centric cooperation and supporting issues which accounts for the development of cooperative, ubiquitous, personal communication models

Towards Cooperative 4G Services

2012-04-26T02:00:00.000-07:00

The goal of the original Internet was to provide a unified communication platform for different kind of devices and networks as well as future technologies, where every single host would be an equal player. However, this fundamental design radically changed over time with the emergence of the client/server architecture, with relatively small number of privileged servers serving a huge mass of consumer hosts. This emerged architecture was totally opposite to the fundamental design of the Internet i.e. "a cooperative network of peers". However, in late 90s, with the appearance of the music-sharing application, Napster, the Internet experienced another drastic change, where the architectural design of the Internet reverted and pushed back to its original "peer to peer" notion. The millions of hosts connected to the Internet, inspired by the culture of cooperation and openness, started connecting to each other directly, forming collaborative groups, sharing their resources to become user-created powerful information clusters. Currently, the peer to peer applications are using the Internet much as it was originally dreamed for; a common platform for hosts to collaborate and to share information as equal computing peers.

Wireless communication has simply revolutionized the way we communicate today and is not less than a magic for someone who does not know how it works. It enables us to communicate anytime, anywhere in any form (data, voice). However, wireless technology is not only limited to communication, it can offer much more than just a phone call. The limits of wireless communication are still unpredictable and unimaginable. The father of radio communication Heinrich Hertz once said "I do not think that the wireless waves I have discovered will have any practical applications." The inventor of first wireless telegraph system Guglielmo Marconi said "Have I done the world good; or have I added a menace?" These early giants of wireless communications were not so sure about the usefulness of their work and were underestimating the power of wireless. They might have envisaged that without the essence of cooperation and sharing, no technology can be economically and socially viable.

The cooperation in wireless technologies is a key to discover avariety of unforeseen innovative applications. This latter is the core reason, why the cooperation is gradually increasing with the progress in the generation of mobile systems. Cooperative and distributed wireless techniques have received significant attention in the past decade and a large body of research both highly useful and contradicting has emerged. Today we are at the doorstep of 4G systems, where collaborative services, technologies, environments and so on, are the major areas of research concern.

As it was originally expected, the future is not limited to cellular systems and 4G should not be exclusively understood as a liner extension of 3G In concrete terms 4G is more about services than ultra-high speed broadband wireless connectivity. As predicted in Frattasi (2005), keeping the cellular core, the network architecture will be predominantly extended to short-range cooperative communication systems. Apart from the coverage extension, power and spectral efficiency, increased capacity and reliability, this enormous flexibility at the user end will help in the development of "personal ubiquitous environment" around the user. The 4G service and technology infrastructure will induce the user's devices to form cooperative groups and share information and resources in order to attain mutual socio-technical benefits. The whole bunch of unforeseen 4G cooperative services would enable the 4G technologies to recede into the background of our lives, making us a part of an intelligent and ubiquitous personal substrate.

Until recently, the cooperative services in 4G systems have received significant attention due to their high degree of technological and social flexibility, infinite freedom of choice and cooperation for the user and more importantly, a potential mega-revenue source for the industrial players. Focus on the services side of the cooperation in 4G systems and discuss how these personalized services would make use of the multitude of wireless systems and networks available under the auspices of 4G in a cooperative manner.

COOPERATION IN THE PUE | 4G Services

2012-04-25T02:32:00.000-07:00

In PUEs, we consider a scenario in which a group of users are located within each other's spatial proximity and are open to cooperate and share services and applications. However, some basic questions may arise here; why the user wants to extends his PUE in order to accommodate the other users, what he is interested in and more importantly, what he would be able to get after forming the PN-F with other users and what price the user may have to pay for these services. These questions would be answered and discussed throughout this section. We base our discussion around three fundamental stances outlined in the following:

Before the Cooperation Begins

The PUE of a user first constitutes his own devices and services available in his PN. The user is the sole authority to extend his PUE (to form a PNF) in order to accommodate the services and the devices available to other users in their own PNs. However, before really moving towards cooperating and forming groups, the user first looks at his motivation to cooperate. Adam Smith, the father of modern economics said, "Every man, as long as he does not violate the laws of justice, is left perfectly free to pursue his own interest his own way". In terms of cooperative groups, if the user feels satisfied with the services he has in his own PN, no desire to cooperate and to from groups will come on his way. The user shall only devise ways into cooperation when he looks for some service which his own PN (current PUE) can not offer. The user's intent to cooperate can be classified in several ways: purpose-driven cooperation vs. opportunity-driven cooperation, short-lived cooperation vs. longer-term cooperation and proactive cooperation vs. reactive cooperation.

Purpose-driven cooperation means that the cooperative strategies are explicitly defined beforehand, whereas opportunity driven means that the users cooperate spontaneously when interesting circumstances to do so arise. In both cases, and especially in the second case, information about the user's context/environment/activities can play an important role. Next, depending on the lifetime of the cooperative groups, we can make the distinction between very short-lived cooperation and longer term cooperation. This distinction will have its implications on the complexity of the solutions to establish the cooperative groups. In the case of short-lived cooperative groups, solutions to setup and manage the cooperation need to be lightweight and simple. Longer term cooperation open up much more opportunities to introduce more complex and powerful management and definition mechanisms. Finally, based on the way the cooperation process is carried out, both proactive and reactive cooperative groups are possible. Proactive implies that the cooperative groups are established in anticipation of the use of the common goals or services provided by the cooperation strategies of each group user. Last but not the least, reactive cooperative groups are established upon request or when the opportunity arises.

Formation of Cooperative Groups

In precise terms, a cooperative group is a function of cooperation strategies defined by each participant of the group. First the group members define their local strategies and exchange them with the other members. The exchange of strategies is similar to negotiation between the end-users i.e. what each of the user wants to provide and consume as a part of the cooperative group. For instance, as shown in Figure 1, there are three distinct PNs who want to form a cooperative group (a PN Federation). Before forming the group, they negotiate on the terms and conditions of the PN-F. As an outcome of this negotiation, all of the potential cooperative group (PN-F) members converge at a certain point (a group of strategies), referred in Figure 1 as "convergence" point. Once the convergence point is attained i.e. the common strategies for the cooperative groups are defined, and further on the cooperative groups are actually formed.

Figure 1: Cooperation among PNs

Cooperative groups may vary on different scales such as age, profession, liking, needs, culture, so on and so forth. Therefore it is very less likely at times that they converge on a single point. The derivation of common strategies for the entire group gets more complicated and nontrivial with the increase in number of members of the cooperative group. Moreover, even if they finally converge to certain agreed upon strategies of the group, the time it would take to form a group would be considerably very high. Therefore, it would be quite efficient that the some group members converge on some strategies and does not converge on others. It is also possible that the cooperative group defines one single strategy as a "general" strategy for the group and other "specific" strategies for cooperation among group members. To this end, a cooperative group can have multiple convergence points. As shown in Figure 1, PN-1 defines two disjoint convergence points with each of the other PNs (i.e. PN-2 and PN-3) in the group. In concrete service terms, the cooperative group is formed by the PN-1 to consume/provide service to each of the other PNs, whereas other PNs i.e. PN-2 and PN-3 might not be interested in each others services. Therefore, in order for the group to achieve its goal, the convergence points of PN-1 with other PNs are essential. However, in this case, a much complex problem is to provide a secure and efficient interface between each of the convergence points defined within the scope of the cooperative groups. Moreover, during the lifetime of the cooperative group, due to the dynamism of the group and its members, the individual strategies can change. This dynamic nature of the group would certainly have its effect on the restructuring and reformation of the global group strategies. In this respect, coping up with the dynamism in the cooperative group environments is also a hard nut to crack.

Sharing Strategies in Cooperative Groups

In order to fully understand the sharing strategies in cooperative groups, it could be interesting to see how the economics of cooperation works in the society. Cooperation refers to the practice of people or greater entities working in common with commonly agreed upon goals and possibly methods, instead of working separately in competition. In the society, we cooperate when we want to accomplish something that we can not achieve working alone. In contrast, sometimes we cooperate not for obvious short-term benefits but for long-term gains. For instance, User A relays the traffic of User B so that in future, User B would be in a position to ask User A to relay his traffic. This type of cooperation involves the business, cultural and relationship development aspects. Even, sometimes in the society people cooperate just for social reasons and no obvious quantitative gains. Whatsoever the reason behind the cooperative behaviors is, the cooperation does not come for free and we always have to pay a certain price for it. The cost and the gains of cooperation can take many forms ranging from resources (man, money, machines) to moral and ethical support, referred as the potential of cooperation.

Even if all members of a group benefit from the cooperative group, individual self-interest may not favor cooperation. This theory of non-cooperative behaviors for self-interest in a cooperative group is referred as "prisoner's dilemma". There can be several reasons to be non-cooperative in a group. One of the major reasons is associated with the utility of being the part of the group. Everyone wants to have the best thing under the cost constraints he has. Therefore, the user would be cooperative to a certain limit where he sees that his total utility of being cooperative is greater or equal to the cost he is paying as a part of the cooperative group. Since the total utility and associated cost is associated with the satisfaction of the user, once the cost bypasses the total utility the user's satisfaction starts decreasing, and he becomes egoist or less cooperative member of the group.

FUTURE RESEARCH DIRECTIONS

2012-04-23T01:11:00.000-07:00

Untraditional routing algorithms remain an open field for future applications and networks. New research can seek to integrate different routing mechanisms, or improve the untraditional algorithms, or yet to develop new routing proposals based on such social, biological and epidemic concepts. For example, there is a limited routing experience based on the Wasp model which may also be applied in the allocation of radio resources when there are multiple interfaces, as well as the optimization of routing and selection of networks.

The social model was presented as a way to extract the relationship and mobility pattern in order to improve the security and optimization over routing. Another interesting issue, not presented here, is with regard to building negotiation models. What information should be used to negotiate the communication between social networks? How to negotiate and apply policy routing among theses environment?

Although this chapter presented and evaluated a number of non-traditional routing stratégie s that future 4G networks could dwell from, there is still a considerable need for more studies of routing stimulus. This could be especially important for the integration of different 4G environments that internally may work with varying routing algorithms. For this reason, future work should seek to make a better analysis of some of the proposed routing ideas such as diffusion, chemotaxy, stigmergy and percolation. Such evaluations should be more complete in looking for the behavior of each algorithm in different 4G environments, including as disruptive network, delay tolerant network, short-range network, disaster recovery and overlay network. Moreover, new mathematical and possibly better calibrated models should accurately describe the result of blending and breeding this new class of routing algorithms.

A deeper knowledge of stimulus could be a first step to define a new policy architecture to control and manage future 4G networks. Further, work is also needed to understand the impact of policies on stimulus in order to enable their dynamic adjusting and routing customization. One expects to see studies showing how these classes of routing protocols may be calibrated and taken advantage of to suit different operating environments and requirements.

Self-Organization | COMPARING INNOVATION ROUTING

2012-04-03T04:02:00.000-07:00

Communication in 4G is expected to live with frequent disconnections due to wide ranging mobility patterns supported including vehicular, user planned disconnection, cellular, short-range and delay tolerant networks. Hence, 4G devices should always be on the lookout to find the best alternative for message delivery depending on availability and application requirements.

Both epidemic and Ant models are leading models for dealing with these challenges by continuously adapting and self organizing. The epidemic approach has proven efficiency for spreading information to all devices with high delivery rates. It nonetheless suffers from some QoS restrictions that could be reduced through buffer management and the use of adequate traffic engineering and routing policies. The probabilistic, random and social metrics can be used to decide whether and when a node must send some information.

On the other hand, the Ant model mimics the search for food, providing a mechanism for finding interesting routing paths according to QoS, security and others requirements. Moreover, the Ant model can increase or decrease the update process of the routing table according to traffic and network stability. When the scenario at hand is unstable, then the algorithm sends many "Forward Ants" to find new paths. Hence, the Ant model may then increase throughput while decreasing delay.

Given that a 4G device may be part of several networks, then it can execute a vertical handoff in order to obtain and exchange information from these different environments, improve its routing and also collaborate with other network users.

In the social routing approach, the nodes can exchange messages in order to identify popular ones and similarity behavior and consequently self-organize to improve their security and performance levels as seen in sections about social routing and social overlay networks.

Routing Optimization | COMPARING INNOVATION ROUTING AND 4G REQUIREMENTS

2012-03-31T08:59:00.000-07:00

At the early days of the Internet, routing took into account the number of hops an as important metric for path selection. This was a wise decision at the time as most of the Internet was still homogenous in terms of its links, router capacity and traffic. Soon later, weights were associated to links giving autonomous systems a new criterion to decide on the best routes and a mean to engineer their traffic and balance this. The creation of labels by MPLS provided a similar traffic engineering mechanism capable of controlling and improving the routing and service delivery through path selection.

In the new context of 4G networks, routers must deal with different dynamic link stability levels, security and QoS levels and network handoff It is for such reasons that the 4G networks will certainly need to also consider new routing metrics and change these according to their environment or context. Under some scenarios, reachability could be more important than performance whereas QoS may become the metric of choice in other circumstances. This may also be service and application driven. Electronic mail transfer is a store and forward application that requires information integrity mainly whereas video conferencing considers low delay and bandwidth as primordial network resources.

Future 4G will certainly embrace disruptive connections and delay tolerant networks, high mobility users resulting in a challenging mix with different routing metaphors and techniques thriving within a single 4G unifying architectures. A simple, "one hat fits all" approach to routing cannot be the way forward. Therefore 4G needs to consider multi-metric optimization following different innovative routing approaches instead of merely reusing traditional strategies. It is believed that new routing and resource management insights borrowed from areas as diverse as biology, social phenomena, random and probabilistic diffusion models are expected to lead the way ahead.

But this is nonetheless not a complete breakaway from routing as we know it. In fact one expects to continue making use of useful traditional concepts such as clustering and hierarchical structures to simplify, organize and improve 4G routing. Following a dynamic approach, social algorithms exchange messages to find popular nodes and establish similarity among them in order to create clusters and hierarchical structures. Similarly to traditional routing algorithms from fixed networks, messages can be forwarded from any social node to a popular one judged to be in a better position to disseminate the information and capable of increasing the probability of a message reaching its destination. This offers ways to increase the delivery rate, but differently from the flooding algorithm, the social strategy reduce s the number of message replication as these messages only are forwarding among a restricted number of nodes. Moreover, approaches such as SOLAR and (Leguay, Friedman & Conan, 2006) work by extracting location information to identify mobility patterns in order to improve their routing efficiency. They rely therefore on the understanding of user's behaviors in terms of mobility patterns 4G networks are expected to collaborate with each other independently of their underlying technologies. For example, a user with Bluetooth and GPRS devices can choose one or another technology to disseminate a given type of information according to application level criteria such as urgency and destination distance. One could use an epidemic algorithm to send a simple message to a friend through a Bluetooth interface while selecting a GPRS interface to transfer credit (possible future money) to a distant family member.

Advanced Scheduling Schemes and Resource Allocation

2012-03-27T02:28:00.000-07:00

Although the fourth generation is homogeneous under the IP umbrella, the participating 4G devices may have multiple interfaces and radios. A 4G device could bind each one of its interfaces to a distinct network, or use multiple interfaces to access a single network. With these features it will be possible to increase traffic bandwidth using link aggregation or execute hand-off without latency and instability, and maximize processing time available for functions such as data-error correction. Hence future routing protocols cannot assume the presence of static links between device interfaces and networks. They must deal with these heterogeneities under the IP layer and dynamic binding. A new class of challenges emerges, mainly in terms of QoS guarantees. So, how could the user achieve good acceptable performance when using several interfaces submitted to varying working conditions seen at several networks?

Mobile wireless devices often need to maintain data or voice communication across different access points and radio base stations. This process is known as hand-off Current cellular systems implement handoff over a single interface and only for phone calls. However, the next generation wireless system (4G) supports seamless handoff for data traffic and should be able to manage radio resources efficiently. VoIP continuity is another requirement in LTE especially when using the 3GPP IP Multimedia Service (IMS). In such a multi-radio environment, there is space to optimize bandwidth radio resources usage, signal quality and reduce information loss.

An important role for nontraditional routing approaches to play in improving future 4G communication systems is foreseen. The Wasp model has shown to be able to schedule tasks and reallocate resources, following a hierarchical and threshold based approach. Each wasp is stimulated to execute its task when its variable value becomes bellow a given threshold. We have seen this model being applied in the dynamic routing of vehicles, a prominent component of future 4G networks. Here, each vehicle is seen as a wasp with a threshold that waits before finding new optimized paths. When a node receives a request from two or more wasps with the same threshold, it then reserves the necessary resources and improves the routing path to the wasp with the highest hierarchy.

The wasp model may be used to resolve another important problem: that of interface selection. The individual force variation (F) is used for decision making, and to determine the best interface to use. This same wasp characteristic has also been associated to model signal strength, stability, efficiency and power consumption.

Since the binding between network and interface could be seen as a task, then wasp routing (Song, Hu, Tian & Xu, 2005) could also be a good approach to improve routing performance. Moreover, this scheme could also be used to manage and improve robustness by allocating messages to different networks when some paths may become unreachable.

Case Study | ANALYSES OF UNTRADITIONAL ROUTING

2012-03-23T00:56:00.000-07:00

The 4G routing algorithms presented in this Chapter are evaluated in terms of their delivery success rate, level of message overhead and delay they incur. One or more scenarios are defined according to some parameters chosen mostly as fixed variables or a combination of factors (various values). The selected scenarios depict a number of situations such future networks may operate in. Not only are these metrics going to tell the reader more about the relationship between some existent and future routing approaches for 4G, but these are also expected to give some helpful and explicit insights into the understanding of routing issues and solutions in the new 4G context. As a result, the simulations are going to span a number of configurations in an attempt to identify and separate those states that offer better performance while reducing overhead and resource requirements. For instance, some specific factor value may be set to induce chemotaxy, stigmergy, diffusion or percolation, used in biological, social and physics modeling.

One objective is to show how percolation stimulus may be discovered for a given 4G scenario. The next step is then to determine the routing improvement a network engineer may obtain once this understands and knows how to handle the stimulus. Given that this is a case study, public domain software and solutions will be used whenever possible to allow the reader to repeat parts of the study. The algorithms and simulation tools may be openly obtained from the Web as well as information on the traffic and topology models used in the scenarios. With this in mind, the OMNet simulator, the PRoPHET algorithm and its set of data – mobility and traffic models have been selected to compose the network scenario. This scenario contains 50 nodes with random mobility and its simulation takes around 3995 seconds to run. The initial topology is shown in Figure 1.

Figure 1: Initial scenario

To evaluate percolation in the PRoPHET scenario, both the buffer size and the number of message replication in routing information were chosen as the percolation variables that should establish a percolation threshold in the model. Next, it was necessary to check the stimulus selected and see if it was able to percolate in this scenario. In other works, this work checked if there is a buffer size limit and a message replication level that determine a stable number or a successful delivery rate of the network. In this case, messages were associated to the fluid, and both buffer and message replication to the surface when considering the analogy with the liquid percolation model used to establish when a fluid starts running through a given surface.

Firstly, the buffer size is a variable that was changed between 2 and 100, with increments that also varied between 2 to 10 with an increment of 2, while all the other variables were maintained fixed. Message replication was also set to 1. Figure 2 shows the results of this first evaluation with a stable state reached for a buffer size of 12. Hence this is the limit value of the percolation model, but the best configuration, the one with highest delivery rate, is obtained with an even smaller buffer size equal to 6.

Figure 2: Percolation of one node according to set up buffer size for all node

Next, the number of the message replication was changed in the simulations from 2 to 100, with increments of 2. In turn these increments where also set to values from 2 until 10 with a step of 2. Similarly the other scenario variables were maintained constant including the buffer size set to 100. Figure 3 illustrates the results with stable state when the number of message copies is 24 or any other larger value. Hence, 24 is seen as the percolating number of copies, but the best delivery rate occurs when the number of copies is 2.

Figure 3: Percolation of one node according to set up message replication for all node

Both the variation of buffer size and message replication of only one node are also evaluated. Firstly, the buffer size of only one node is a variable that was changed from 2 to 100, incrementing this by 2 to 10 increments, while all other variables were maintained. Message replication was also maintained with a single value and buffer size of 100 for all nodes. Figure 4 presents the results of this evaluation with a stable state reached when the buffer size is 10. So this value represents the limit value of the percolation model.

Figure 4: Percolation of one node according to set up buffer size of the only one node

Next, the effect of message replication is studied. In these simulations, a single node was selected and subjected to changes in its level of message replication. In this case, node 24 had his message replication mechanism changed to use values between 2 and 100, with increments between 2 and 10. In the scenario presented here, the buffer size was set to 100 for this special node while all variables were left unchanged. For the other nodes, message replication was fixed at 1, i.e. a single replication was used, while their buffer size had the value of 100. One can see from the results that, for example, when node 24 (the differentiated node in this scenario) sends 2 replications, it already obtained almost a 50% delivery rate. Percolation was achieved when the level of copies reached 4 (Figure 5).

Figure 5: Single node percolation according to its own message replication

In a different evaluation, the number of message replication and nodes that may replicate messages were changed. The number of nodes allowed to replicate messages was incremented until reaching 50 nodes representing the total nodes of the network scenario. The other nodes continued with their default behavior where only a single copy of any given message was injected into the network. So, initially, a unique node was making 20 copies of messages it was sending, in the next round of simulation two nodes could make 20 copies of a message and send this, then 3, 4… nodes until all the 50 nodes where doing this. Strangely, the results have shown that even when there were different numbers of nodes with the same number of replication (heterogeneous network), when the number of copies was 50,100 and 150, the delivery rate of node 24 presented the same behavior as in Figure 6 (a special and identical function). There is a stable behavior when the number of copies is 50, 100 and 150. For example, when there are 3 nodes with 50, or 100, or 150 copies, the delivery rate is 27%. When the number of copies for 7 nodes is 20, 50, 100 or 150, the delivery rate is 43%. This shows that it is necessary to run extensive simulations, for any given scenario, in order to determine the minimum number of copies as well as the minimum number of nodes making message copying that offer a good delivery level that ensures a required quality of service level to node 24 in this case. It is clear that one needs to establish the percolation pair (replication level, number of nodes that do message replication) in order to ensure that:

All the nodes of a network are able to achieve their minimum QoS requirements in terms of message delivery in this scenario. Other QoS parameters such as bandwidth, delay and jitter may also be observed as reference levels to achieve;
To avoid clogging the network with unnecessary message copies and possibly leading to the congestion of parts of the network, one needs to consider the traffic overhead and network occupation as new metrics for choosing the right percolation pairs that may achieve similar levels of message delivery or other QoS requirements.

Figure 6: Delivery rate for 20, 50, 100 and 150 replicas

Actually, by observing only node 24 delivery rate, the simulations have shown that a configuration where 8 nodes make 20 message replicas each gives the best delivery to this node.

New studies and simulations are needed to gain more insights on how to build some rules of thumb for the optimized routing configuration for this type of algorithms. Although each topology may need a different setup, but one expects to hopefully find some general rules that apply across the spectrum of 4G topologies and provide adequate routing performance.

ANALYSES OF UNTRADITIONAL ROUTING | Improve Routing and Future 4G Networks

2012-03-19T08:46:00.001-07:00

The restrictions imposed by traditional network technologies were presented and we showed how new ways for thinking about routing have emerged to overcome these. They include insights and parallels made from observing a number of biological, social and epidemic behaviors. A number of proposals, associated to these metaphors, make use of mobility patterns, pheromone levels, user habits and profiles, relationships and other types of stimulus to offer self-organization, load balancing, adaptability and advanced technology dependent routing. This section is going to perform some concrete evaluations to show and determine the impact of some of network and other important parameters and examine their configuration. To achieve this, the reader is invited to review some optimization and evaluation techniques that are very much relevant to the context of routing in future networks,

Percolation

The percolation theory is inspired from the observation that there is a limit value for a physical material to make a transition between two states called by "critical phenomenon". For instance, water (a fluid) has two states: liquid and gas. A bottle of water may transition from the state liquid to gas when submitted to a higher temperature, namely, at 100°C at sea level. Another example is that of a filter where there is a given alpha number of porous in a stone. When the number of porous reaches a threshold, water, then, passes to the other side of this stone. These probabilistic changes of states are defined according to a percolation model that uses a threshold to determine such transitions. Hence, such strategy would help determining which routing parameter values would cause percolation, or successful knowledge sharing in the context of future 4G networks.

Some works set up a static percolation coefficient value in order to improve routing. The spatial gossip is an example of a routing algorithm that used this to select the forwarding node. Other works chose to evaluate the environment to discover when such algorithm percolates. For instance, one could seek the relation between buffer size and the success delivery rate. Otherwise, one could check if there is a limit buffer size that determines success or no delivery of messages. The analogy in this example associates messages to a fluid in a percolation scenario and nodes to the surface. Consequently, when all the messages start going from the source and reach their destination, one says that routing has percolated.

Diffusion and Chemotaxy

Adolph Fick was among the pioneering researchers who studied extensively the diffusion process. He observed that salt movement occurs from high to low concentration in liquids and defined an equation to express the proportionality between the flow and the spatial gradient of diffusion. Other researchers also studied the diffusion observing a spontaneous particles movement from low to high concentration. However, there is a common concept among these equations: they expressed the movement of cell or substance to obtain equilibrium, considering, in general, the position as a variable or both time and position.

Similarly, Chemotaxy is a movement behavior according to the gradient of concentration, but it is not a spontaneous event. Chemotaxy represents the attraction or retraction among cells due to some substance. It is commonly used in biology to analyze the behavior of human cell, virus or bacteria. However, such behavior has been analyzed and shown to also benefit the routing environment. Routing policies could be seen as the substance that modifies spontaneous movement.

Given that some message forwarding is based on a probabilistic mechanism set according to the encounter frequency of nodes (i.e. PRoPHET). We could evaluate the diffusion by modifying node movement in order to verify whether node mobility could be a stimulus to influence this behavior or not. In other words, we could check whether node mobility increases or not the message delivery rate.

Considering that PRoPHET could also be executed in sensor networks, policies are likely to move a node by several spaces in order to increase the encounter frequency and as a result may be used to improve the delivery ratio. Alternatively, one could set fake information altering encounter frequency, the message delivery decreases, because messages are removed from a buffer before actually finding their destination.

Stigmergy

Pierre-Paul Grassê introduced the Stigmergy concept after studying nest building. He observed that there is an indirect communication used by social individuals in order to coordinate their efforts towards some objective. For example, Ants lay down more pheromone when they find food to enable other ants to detect and react to this stimulus. In summary, they indirectly interact and cooperate to feed (or finding a path in the routing analogy). Although it is a comprehensive behavior, there is lack of mathematical models or equations to describe Stigmergy. Typically, the stimulus is not reached by some well established known equation, one may consider a given variable as stimulus to Stigmergy behavior and verify whether only a node with a fake variable can modify the Stigmergy of all individuals of a group and consequently the environment

Given that the decision mechanism of PRoPHET routing evaluates the number of encounters of neighbors, we could setup the encounter frequency for a single given node with fake information and next observe the success ratio. The encounter frequencies are used by such node as a Stigmergy where the nodes collaborate with this information to route the information.

POLICIES FOR UNTRADITIONAL ROUTING

2012-02-09T06:52:00.000-08:00

The untraditional routing algorithms, deal with different quality metrics associated with nodes, location or other information used to choose a suitable relay node. On the other hand, most traditional routing policies deal with only address and interface metrics, where the objective is the management of network traffic in order to decrease its latency, cost, congestion and other metrics. For example, policies for BGP, the current inter-domain routing protocol in the Internet, may be used to update routing tables and consequently to control the traffic forwarding, taking into account path lengths, local preference values, traffic origin among others factors. In terms of software, the IP Filter, iproute2 and net filter are utilities commonly used to create and apply network policies in order to control the routing in local area networks.

However, these approaches are based on the origin-destination pair of IP addresses and interfaces and are not suitable for 4G networks where routing involves often multi-homed end-hosts, intermittent connection, unpredicted mobility and other dynamic characteristics. We need new policies and mechanisms to deal with buffer, message replication and scheduling schemes.

In untraditional routing algorithms, information forwarding can be based on replication (usually broadcast) or on the basis of the pair: source and destination nodes. When using replication, one needs to evaluate and establish the necessary number of replicas according to the underlying environment being used (network size, mobility model, etc.). This is important to avoid network congestion and unnecessary overhead and resource usage. Note that a replication level or value must be carefully set as a small number of replicas may very well become insufficient for the dissemination of information and discovering all nodes and paths. Changes to the number of neighbors over a short period of time indicate a high mobility scenario and consequent frequent topology updates including the network size. This information is a very determinant factor in setting the replication policy and its parameters.

One may choose to apply a policy over the forwarding mechanism based on source and destination information, whereby a node is not required to send all messages. Forwarding may be subject to priority in order to give preferential treatment to some messages for example or offer differentiated traffic. Work in (Lindgren & Phanse, 2006) evaluated four such strategies: GRTR, GR-TRSort, GRTRMax and COIN. GRTR defines a mechanism that allows forwarding to a neighbor if the encountered node has higher delivery predictability to the message destination. When a node A uses GRTR Sort with its neighboring node B, node A subtracts both delivery predictability values for this message destination and then forwards the message for node B using the highest subtraction result it could find. The GRTR Max strategy is also a little similar to GRTR and the difference between these lies in the ordering executed by GRTRMax which is before comparing the delivery predictability. The last policy example is given by COIN which generates a random value for each message. Depending on whether this is higher than 0.5, then the node forwards the message which is similar to tossing a coin and taking a decision according to the outcome.

Independently of whether the message forwarding policy is based on broadcast, replication use, or according to source and destination, the buffer (where messages are stored before being forwarded) is obviously not an infinite resource. Therefore, one needs to have one or more policies in place to determine what to do with the messages. Common per hop behavior or queue management techniques such as FIFO (First In First Out), MOFO (Most Forwarded first) – messages, MOPR (Most favorably forwarded first), SHLI (Shortest Life Time First) or LEPR (Least Probable First) may apply.

Policies may also determine for how long a message could be stored. For example, the Data Mule project considers that a mobile node must store information until finding or coming into contact with a destination (in this case, a base station within a sensor network). Moreover, Data Mule chooses the next hop according to the mobile's behavior, all the way from the fixed nodes, by forwarding the data to intermediary mobile nodes that collect and store the information until the base station is reached.

Another routing work involving policies. In this social overlay shaped by groups, a node needs to decide which query could be dropped, when the buffer is full or simply choose randomly queries to swap. However the authors suggest that the nodes should firstly exchange meta-information, before deleting and adding queries (buffer management). Although it uses a simple decision mechanism, it follows the policy approach to choose what queries must be dropped from the buffer and exchanged with its neighbors. Instead of using such a simple policy model, it could adopt a social policy that gives higher priority to messages forwarded from friends or partners over strangers or unknown senders.

In summary, this section presented how important it is to work towards reducing costs and the requirement for policy changes according to behavior modification in order to provide more flexibility for routing mechanisms. Moreover, it has shown that policies may be applied to control several device features (i.e. buffer, message replication and message scheduling schemes) to determine, configure and improve 4G routing.

SOCIAL OVERLAY NETWORKS

2012-02-06T07:30:00.000-08:00

The overlay network approach allows the creation of several virtual networks over physical ones. Here one hop in a virtual network may correspond to several hops from one or more physical networks (underlays). In turn, a virtual network may even be built over other virtual networks. However the overlay routing does not have direct control over how the underlay forwarding of packets is actually performed. As a result one may not classify solutions such as Buble and SimBet as mechanisms to build overlay networks, since their nodes have routing control over the physical layer. Other routing algorithms found in widely spread Peer-to-Peer (P2P) social networks such as Gnutella, Chord and Tapestry are examples of peer-to-peer protocols that can be used to create overlay networks. In order to improve these overlay networks, and other works applied social mechanisms and consequently we named these proposals as social overlay networks. They deal with security and optimization using social ideas extracted from the underlays.

DSL (Davis Social Links) is an example of a proposal that applied social approaches to deal with security. It has a social overlay mechanism that creates trust relationships of social networks based on the small-world phenomenon in order to control, trace and separate address and identity information. The idea is to allow communication between nodes with a direct link or a social path linking them. Messages in the DSL social network contain a set of keywords describing node properties. The social path is created by the exchange of keywords between nodes connected by direct links. The nodes can accept or drop messages from some social path or modify these along another one. There is consequently a recipient controllability being exerted. For example, nodes A and B have "red" and "yellow" as part of their keyword sets respectively. These strings are cryptographically exchanged prior to the exchange of data. A Node can propagate the keywords in order to increase routability.

Work is a bit similar to social routing while not applied at the physical layer however. SOLAR, seen earlier, imports a user's profile information to forward data. In SOLAR, the profile is defined by a node itself and describes node mobility and its likely locations. Differently from SOLAR, acts at the application layer where the profile manipulates the dissemination and routing of messages. An even greater difference is to do with the fact that the profile in may be updated and created by friends and acquaintances of a given node instead of being the sole responsibility of the node as in SOLAR. When a node creates a link, the user must attribute one or more keywords to describe the new friend. This one, the friend, needs then to agree with such description to allow for the effective link creation. As described by an application in, it is important to find a best person to answer some questions about some given keywords. This idea for creating profiles by friends could be implemented at the network level in terms of performance and made available to view by other nodes. Consequently, a profile that describes the options and possibilities for other nodes could be an interesting mechanism to help the node decide if another encountered node is a good relay to forward its messages.

PeopleNet is another social mechanism that propagates information using overlay networks. It has only three types of messages: request propagation, request and response. The request and response messages are always forwarded over long range connectivity such as over a cellular infrastructure; the propagation messages are always broadcasted with shorter range connections until some node matches the request propagation with some response. Whenever a propagation message matching occurs, the user who placed the request message receives the response message via long range connectivity. Moreover, the users can pre-determine the type of queries to handle in some specific geographic context, called Bazaar. So, any person close or distant to a Bazaar can send requests through the cellular infrastructure to other users in a specific geographic (Bazaar). Information is spread around to users in a specific geographic location, but it does not benefit from the number of meetings among nodes in a specific geographic area as in SOLAR.

PeopleNet differs from the proposal defined in as it does not concern itself with any mobility pattern, similarity of mobility and the ability of nodes to learn about their own mobility. PeopleNet relies on the innovative idea of using the widely deployed cellular infrastructure and Bluetooth devices to propagate information search. A second peculiar contribution involves the overlay routing according to the meta-information (i.e. Bazzar and message type) to choose what connection must be used to forward the messages. So, one could extract the importance from high level information that could be used in routing.

SPROUT presents a social mechanism to route messages in overlay networks such as Distributed Hash Tables (DHT). It is based on the use of the knowledge of a trust relationship among social nodes to choose what node must receive a message and to associate message priority. SPROUT presents possible trust function according to the number of hops in a social overlay (the distance d_ij between social nodes n₁ and n_j). The relation in (46) is one of its trust functions used to choose the next hop, where f is a static probability for two nodes to be trusted friends. The reader may note that the probability of two nodes being friends is limited by the value r. For example, if f = 0.95 and r = 0.6, then the trust function assumes that the friends with high proximity of node n i are best friends (reflecting a high trust relationship) and consequently very likely to correctly route a message and when d_ij >8 the trust will be maintained as 0.6. The objective of this work is to reduce the number of several network attack types that may drop the packets or forward the data to any different node other than the correct destination. For instance, in a DHT structure, a malicious node may exchange messages in order to disseminate unwanted information. SPROUT locates the trustiest friend of a given node that has a closer identifier, but not greater than, a key value until finding the destination node for that given key. Should this fail, then the source node executes the traditional DHT process. Although this work has been implemented in overlays, it could also be applied at the network layer. A node should evaluate the trust relationship of its neighbor instead of choosing the shortest path, which may be an unsafe path in terms of optimization and integrity. Further, trust identification remains a hard undertaking.

On the other hand, the trust relationship is not immune to attacks completely. Malicious nodes may convince a small number of honest ones trough the creation of several and false identities to increase their influence and credibility in the network. Looking at this problem SybilGuard and Syb-ilLimit map users and nodes, separating the network in two groups: a honest region with nodes with only one identity and a Sybil region populated by malicious nodes with more than a single identity. They also established that the number of links between these regions (called attack edges) is independent of the number of malicious identities. Moreover, if a trust route contains only nodes into the honest region then all the routes that cross the same node or edge will converge. Therefore, one may observe that there are several works applying social approaches in order to improve information propagation in overlay networks, security and optimization.

Propagation in social overlay and underlay is very similar, but there is a little difference. Both the optimization and security are interlaced in the context of social networks. This may be the case when messages are dropped or wrongly forwarded. A number of security enhancements have been suggested in to improve underlay security. User's devices are used to route data in DTN scenarios, increasing the likelihood to disseminate worms and viruses, as their users are often inexperienced with regard to such security threats. Moreover, networks with high-degree nodes tend to connect to other high-degree node networks (the famous often move in the same circles) and are therefore more likely to be subject to epidemics. Indeed a single infected high degree node will quickly infect other high-degree ones. On the other hand, networks where high-degree nodes tend to connect to low-degree nodes show the opposite behavior; a single infected high-degree node will not spread an epidemic very far.

Challenges for Future 4G Networks

2012-02-03T09:09:00.000-08:00

We have established that 4G convergence is expected to deal with several environments, including short range networks, which, in general, involve devices with capabilities subject to CPU, memory and disk limitations. Thus, with regard to routing, the next generation network should consider strategies that conserve battery power not only from a device survival point of view but more importantly in an attempt to use greener environment friendly solutions. These are also known as power-aware routing. For example, these devices should avoid the use of highly demanding table updates.

Routing mechanisms using location information have been proposed in order to improve energy consumption and data delivery. Note that the excessive use of location information from devices such as GPS for routing decisions can lead to a considerable increase in energy consumption when compared to location agnostic solutions. Moreover, vehicular networks are also expected to maintain location servers for vehicles to help in information routing.

In existing 2G and 3G networks, there is the horizontal cell connectivity handoff, one that involves cell changes by a terminal within the same type of network. 4G introduces also the vertical handoff. Here devices are expected to change networks they attach to. Hence, the challenge is to design routing protocols that are capable of handling vertical handoffs between pairs of different types of networks, while maintaining QoS requirements and optimizing common radio resources availability.

Under 4G networks, security is also seen as a paramount concern. Traditional attacks on existing IP networks will certainly migrate to 4G networks encouraged by both the heterogeneity and the open air interfaces of such networks. Routing information may be targeted in such attacks on system security, there is a need to design secure routing approaches to ensure the integrity of routing in 4G systems. This is not however the object of this chapter.

In summary, among the many routing-related challenges this work has identified, we mentioned QoS, security, energy and location information. The following section will present the details of new routing algorithms, with new network metrics and routing mechanisms, which motivate the extension of traditional classification to include epidemic, biological and social class of routing ideas, one expects to see in 4G networks.

WHY DO WE NEED NEW ROUTING MECHANISMS?

2012-02-01T00:28:00.000-08:00

If anything is certain, the fact that 4G system will be completely IP-based is one. Although homogeneous at the higher levels of the architecture. 4G most likely will integrate several nestled networks with heterogeneous offered services, features, applications and service providers represented by mobile operators, companies or even individual users and content providers. The role of 4G systems in the wireless arena will be parallel to that of the Internet for fixed networks. It will be a common place to find the same service being offered by different sub-networks though maybe under varying security, bandwidth, delay, availability and pricing features.

While the prices for multi-radio and multi-interface devices are continuing to drop, new network topologies are also emerging including ad-hoc, mesh, hybrid and multi-dimensional ones. The latter are networks where a node may take part into different technology dependent networks at the same time, leading hence to a multi-layer view of the new topology. For this reason, new routing algorithms are designed to lead with new topologies and network challenges.

New Network Topologies

One expects that existing traditional network topologies such as those based on fixed infrastructure, ad hoc networks and others, to blend into 4G networks in a highly dynamic cooperating environment. 4G is also embracing highly dynamic networks such as delay tolerant or disruptive networks in addition to opportunistic communications which may be exploited in disaster recovery scenarios and military applications. The emergence of vehicular networks also presents new challenges to the design of 4G edge capacity and mobility support. VANs are expected to have bit rates close to those of fixed networks. New VAN topologies depict dynamic multi-hop short range vehicle-to-vehicle and vehicle-with-infrastructure communications.

Similarly to fixed networks, overlay topologies can run over 4G to offer virtual high level structures among individuals across 4G networks. Examples of such overlays are emerging new social networks, where topology information is primordial to improving data forwarding, privacy and security as seen in (Bernardos, Casar & Tarrio, 2006). Unlike current networks, a 4G one should open up further possibilities for individuals and groups to work, discuss and play together often by self-structuring driven by common interests, needs and incentives, instead of mere connectivity. The success of future 4G systems may depend to a great extent on their capacity to accommodate these new data services using social, biological and other nature inspired.

Proactive or Reactive Routing

2012-01-28T06:26:00.000-08:00

Proactive and Reactive are ways to classify a routing mechanism in terms of when an algorithm announces the routing route selection information. The routing mechanism mayanticipate having this information or select a route on-demand due to the high dynamicity and unpredictability of a network environment.

The proactive routing announces periodically network information generating a predictability amount of messages. In a worst case, the time to select the correct path t_s = t_period(period used to update paths). For example, formula (8) presents a possible generic function that may be adopted to calculate the number of messages generated by each node k when using proactive algorithms, where T is a set of announcement periods t independently of node or network faults. Hence, if there is large interval between path announcements, the number of messages will be small, nonetheless leading to a routing scenario practically lacking reactivity to possible network failures. Examples of pro-active routing include: DSDV (Destination-Sequenced Distance Vector) and WRP (Wireless Routing Protocol).

On the other hand, the algorithms classified as reactive do not issue their route announcement messages following any periodicity. They rely on observed failures or user demand to update routes. Consequently the number of messages announced could be much lower than that of proactive algorithms. Given that they do not need to wait a whole period to update their routing information, they may have offer better performance in unpredicted environment. They may also however suffer from low select the correct path may be given as the sum of t_s = t_fault (time needed to identify a failure)+t_request(to search for a correct path) + t_response (someone response the node with the correct path). For example, formula (9) presents a possible generic function to calculate the number of messages generated by each node k using a reactive algorithm, where F is a set of independent failures. So, one cannot know the number of message without knowing the number of failures. When some fault occurs, node k is notified, next it announces a request to find the route and others nodes announce or send back their responses. Examples that fall into this routing category are AODV (Ad-Hoc On-Demand Distance Vector), DSR (Dynamic Source Routing) and LMR (Lightweight Mobile Routing).

Distance Vector or Link-State Routing

2012-01-25T06:21:00.000-08:00

Many routing strategies rely on mechanisms for sharing and announcing their routes among routing nodes. Traditional routing algorithms may be classified as Distance Vector and Link State under this angle.

Distance Vector algorithms are characterized by the fact that each network node announces the content of its routing table, known as a vector in this context, to "only" its neighbors. Although such limited scope announcement saves networking resources, it is also the root for some known route convergence latency related problems suffered by large networks. Each vector entry contains local information on some node k and how this may be reached in terms of number of hops and delay for example. Each neighbor for node k receives its advertisement (routing vector or information from node k) to build a routing database with size m.m_etric′, where m is the number of routers known by node k and m_etricis a number of metrics announced about each node. So, the traffic size generated by each node is given by formula (6) in order for a network node to get to know the network's topology. Once, a node acquires this knowledge through these advertisements, each node locally calculates the importance of each other node in the network according to its own vector and those received vector from the neighbors. Hence there is a combination of both local and external information to perform shortest paths selection. The result of such regular calculus updates the routing table based on algorithms such as Bellman-Ford and Ford-Fulkerson.

Given that the advertisement is relayed from neighbor to neighbor until all network nodes acquire network topology and path information, the protocols classified as Distance Vector suffer from slow convergence. The following protocols are example of Distance Vector: the Routing Internet Protocol (RIP) executed in intra-domain, Border Gateway Protocol (BGP) executed in inter-domain, DVMRP (Distance Vector Multicast Routing Protocol) for multicast applications, AODV (Ad-hoc On-demand Distance Vector) for mobile ad hoc networks (MANETs) and DSDV (Destination-Sequence Distance-Vector) also for MANETs.

link-state algorithms are different from distance vector ones in terms of the scope of their announcements. Unlike the former one where each node notifies the neighbors only about its vector, link-state routing floods such announcement to all the network nodes. Further, link-state updates are conveyed over small messages to all nodes. Each message contains the link-state of its neighbor's node such as occupied bandwidth, throughput, delay and so forth. A network node then builds a routing database with size m.n.m_etric where m is the number of neighbor nodes, n is the number of routers in the network and m _etric the number of metrics announced for each node. As a result, the traffic size generated by each node is given by formula (7) in order for network node to build its database and get to know the current topology. With this knowledge at hand, each node locally then calculates the importance of each neighbor to forward messages over the shortest paths determined for each destination. This may be achieved usually using Dijkstra's algorithm, although there are others as described in. The most common and widespread link state protocols used are the OSPF (Open Shortest Path Frist) and IS-IS. Similarly, Multicast OSPF (MOSPF) has been developed to lead with multicast transmission. Other link state protocols include SPF

Optimal or Shortest Path Routing | 4G Networks

2012-01-12T01:41:00.000-08:00

In terms of route choices, the traditional routing may be classified as Optimal or Shortest Path routing. An algorithm is said to present optimal routing, only when it has a convex function f _routing. (X): X → D, where D is always the best routing for all X, the convex set of parameters for this function. Although seemingly highly desirable, it suffers from some design limitations. It is difficult to build an optimized routing function that accurately captures all the performance measures and balances all data flows in a network. This task could lead to the use of routing parameters with incorrect or insufficient information, generating a probable unbalanced environment.

In general, many works define a function f (x) which expresses how to measure the network performance using metrics such as delay, latency, packet loss, link throughput and so on. Next, f(x) is minimized over variable x by applying one or more derivatives and defining the inequality of the form g_i(x) ≤ α and/or equality of the form h_i(x) = ω asconstraints, where g_i is a convex function and h_i is an affine function (i.e. one that consists of a linear transformation then a translation). As a result, an optimal routing algorithm controls the traffic and load balancing according to constraints of the minimum global value. The formula (4) could be a generic optimal function defined to choose the best route r_i(one with minimal load among neighbors of node k expressed by N_k) in terms of network load (expressed using constraints over the total mean and variance flow over the network).

Although not strictly a routing protocol, acting between the link and network layers, the IETF multiprotocol label switching (MPLS) may be seen as one that falls into the optimal routing class. MPLS has emerged as a very promising traffic engineering tool used in the control of traffic within current broadband core networks.

The routing protocols, classified as Shortest Path Routing (SPF), are characterized as those where each network node k selects routes following a formula such as the one in (5): here node k chooses the neighbor that can route its message with the shortest metric between source (node k) and destination. The number of hops is the usually used metric, but others also may used in addition to the attribution of weight for each metric such delay, throughput and others. In this case, the shortest path is the route with lowest weight instead to be the one with smallest number of hops. Consequently, we could classify SPF as an optimal routing when the network traffic is always stable, where, for example, the user's network maintains and σ² ≤ ω.

However, this scenario is not a common one. Given that each node leads with estimated costs of link and does not worry whether its choices will unbalance the network traffic, SPF nodes tend to generate points of congestion on the network on a shortest path, along which most traffic is directed. Consequently, this class of algorithms is not an interesting one when dealing with highly dynamic network and disconnections. Dynamicity may turn the environment unstable with some nodes underutilized and others overloaded.

Routing algorithms 4G Networks

2012-01-08T08:37:00.000-08:00

Routing algorithms are mechanisms to build paths and forwarding messages by selecting one or more intermediary nodes between source and destination of data messages. At the early days of the Internet, theses algorithms were designed to often lead with a single routing metric: the number of hops between routers. This was primarily due to the homogenous nature of the early IP backbone of the ARPANET. A general rule of thumb was adopted: the fewer hops a packet goes through, the less time it stays in the network and the fewer resources it uses. With time, the Internet evolved to a heterogeneous melting pot where such simple hop routing could no longer be sufficient. Let us consider that a network, expressed by N, is constituted by a set of heterogeneous routers (n nodes), then N = {n_1′ n_2′…n_n_-1, n_n}, where each node n_i can have a distinct total network capacity c_i and a set of known routes {r_1′ r_2′…r_n-1, r_n} that consumes the node's resources. Given that each router can have different capacities and routes known to it, its available capacity to forward messages can be represented by formula (1). Consequently, the network is constituted by a set of available capacities of nodes X= {x_1′ x_2′… x_n-1′x_n }, where the network balance can be measured by the average and variance according to formulas (2) and (3) respectively.

However, performance measurements itself generate overhead that should be known and controlled. Node and network measurement, as well as, the routing algorithms also consume resources. Overhead control may be subject to what is being evaluated, the operating capacities of each node, its dynamicity, mobility, constraints on the resources and the underlying network switching, transmission capabilities among other factors. As a result, several routing algorithms have emerged, where each one is better when executed in specific environment(s) according to some pre-established. On the other hand, there are some common characteristics among them, allowing their grouping and classification. The traditional classification of routing algorithms looks for similarity in terms of three routing aspects: the process of route selection, whether it concerns itself or not with network balancing and process of route building, when routes are announced, including also what is announced and to whom.

Data & Results | 4G READINESS ASSESSMENT

2011-12-28T07:00:00.000-08:00

Data

The data for the different countries were collected from different, and to the extent possible, reliable resources. However in some cases the authors have used industry experts opinion, when the data was incomplete or missing.

In the first category, the mobile wireless penetration was collected and the HSPA standards status from 3G Americas report. The data for the business environment and also for the social and cultural categories were collected from the Economist's study (2008). The spectrum law effectiveness is calculated from the subscribers served per MHz of spectrum allocated. The available spectrum used for commercial use was collected from CTIA(2008) and the GSM world (2008) reports. In developed countries the available spectrum was usually over 300 MHz and in the emerging ones around 200MHz. To calculate the rate, the number of mobile wireless subscribers was divided to the available radio spectrum and then the data were normalized to the highest value. In the next category, the data on consumer spending on telecom services per household, was collected from Euromonitor database and then normalized. The active mobile wireless services were collected from Netsizeguide (2008). Finally in the last category both variables were collected from the OECD (2008) database.

Results

The results are presented in Figure 1. Most of the countries' score is in a scale between 2 and 5. The developed countries as expected are ranking in the top-10 list, with the rest of the countries, Brazil, Russia, China and India in the lower part.

	Country	Overall score	Connectivity and technology	Business environment	Social and cultural	Legal	Consumer	ICT spending
Category Weight			20%	15%	15%	15%	20%	15%
1	USA	3.619	1.86	8.53	9.00	1	1.49	1.13
2	Denmark	3.606	2.22	9.65	8.67	0.664	1.28	0.39
3	Australia	3.432	1 99	8.59	9.13	0.846	0.94	0.40
4	Finland	3.382	2.10	8.4	8.3	0.658	1.55	0.33
5	Germany	3.323	2.13	8.36	8.00	0.361	1.58	0.48
6	Netherlands	3.240	1.09	8.55	8.07	0.217	2.00	0.64
7	Sweden	3.227	1.12	8.52	8.6	0.658	1.65	0.28
8	Switzerland	3.357	1.06	8.57	8.27	0.292	1.80	1.44
9	UK	3.192	1.23	8.61	8.13	0.245	1.51	0.64
10	Japan	3.186	1.81	7.39	7.87	0.359	1.89	0.68
11	Canada	3.184	1.59	8.63	8.13	0.128	1.36	0.40
12	Hong Kong	3.176	2.22	8.64	7.47	0.188	1.18	0.34
13	Brazil	2.732	1.78	7.01	6.13	0.200	1.36	0.70
14	Russia	2.264	1.16	6.19	5.33	0.656	0.88	0.20
15	China	2.219	0.88	6.49	5.53	0.266	0.42	0.78
16	India	2.116	0.19	6.53	5.33	0.229	1.01	0.42

Figure 1: 4G-readiness ranking

The emerging markets such as China, and India, are expected to move up. Russia is surprisingly lagging behind the developing markets, revealing inefficiency to the LTE path

Categories | 4G READINESS ASSESSMENT

2011-12-25T08:29:00.000-08:00

In this post, we are describing the 6 different category and the weights as shown in Figure 1.

Category Weight	Connectivity and technology infrastructure	Business environment	Social and cultural environment	Legal environment	Consumer and business trend	Government policy and vision
Criteria	mobile phone penetration current industry standards	Overall political environment; macroeconomic environment; market opportunities policy toward private enterprise; foreign investment policy; foreign trade and exchange regime; tax regime; financing; the labour market.	Level of education and literacy; level of Internet literacy; degree of entrepreneurship; technical skills of workforce; degree of innovation.	spectrum laws efficiency, Subscribers served per MHz of Spectrum	Consumer spending on communications services; availability of mobile wireless services for citizens and businesses	Telecom investing per GDP Total ICT national spending rate
Percentage	20%	20%	15%	10%	20%	15%

Figure 1: 4G readiness categories and weights

Using several resources and databases as listed in the Appendix, we build an index for each country and based on its value, we rank it in our list into a certain position out of the 16. The index, when calculated is expected to be on a scale 1-10

The first of the categories is about connectivity and technology infrastructure, which is one of the most significant drivers, adding 20% to the overall score. The connectivity measures the extent to which businesses can access mobile networks, integrating the 4G path. The effective access uses the mobile-phone penetration that includes both 2G and 3G technology per country. Regarding the 4G path, most of the countries chose High Speed Download Packet Access (HSDPA) and High Speed Upload Packet Access (HSUPA) deployment before migrating to the LTE technology by. Most of the countries prefer not to deploy advanced HSPA or HSPA plus, but instead to spend the money on LTE testing as T-Mobile did in the USA. Thus in the figure if in a country both technologies are deployed, HSDPA and HSUPA the value ‘1’ is assigned otherwise if only one of the two is in-service then value ‘0.5’ is assigned. These two technologies are considered as 3.5G and 3.75G respectively.

For this category, the metrics are mobile phone penetration and industry standards in-service.

The second category is the business environment, weighing 15% in the overall score. As explained in the criteria "cover such factors as the strength of the economy, political stability, taxation, competition policy, the labour market, and openness to trade and investment."

The third category refers to the social and cultural environment, weighing a 15% in the overall score. The environment again is expected to be similar to the one defined in the Economist's study, since the 4G evolution will not at least at this point and with the current knowledge to bring any major changes into that environment different from the digital study. Thus "e-literacy and basic education" could define this category's criteria.

The fourth category describes the legal environment, weighing a 15% in the overall score. It is very important to identify the legal and regulatory environment per country. The spectrum law policy with a combination of the spectrum's availability is considered to be the most important metric in that case, quantifying its effectiveness for each market. More specifically, the number of subscribers served per MHz is calculated. This shows the market's efficiency to adjust to the regulatory environment keeping up with growth and profits for the operators. Finally, all the values are .normalized to the US one, which is the highest, proving that the US wireless carriers are able to get so much more use of the spectrum available.

The next category to be included is the Consumer's or the demand side, weighing an overall score of 20% into the index calculation.

Except for the supply side, we need to describe the demand side and the roadmap for the adoption of technology. Consumer spending on communications services is very important on each country's trends and the amount of money the subscribers are willing to spend. Additionally the market's efficiency as the consumers are demanding more services is described from the number of services that are activated in 3 general areas, mobile games, music and TV. If all three services are offered in the market then the value "1" is assigned, if one of the three services "0.33" and two of the three "0.66". A market is more efficient and is converging faster into new services and technologies, when it offers all of the three services, showing the consumer's demand and advanced needs. The category metrics are consumer spending on telecom services per household and active mobile services.

Finally, the last category is the R&D spending, accounting for 15% of the overall score.

The Telecom investing as a rate of the overall Gross Domestic Product (GDP) index is a significant metric that should be considered as part of the country's strategy especially in the long run. The investment could include trials, new networks and R&D projects etc. Additionally a country's profile could be completed with the Information and Communications Technology national spending rate. That metric could differentiate the countries and the special weight they put on the ICT sector overall. All this spending should be expected to pay back or to bring some results to the market after a certain amount of time that could range from 4-5 years. Thus the volume spent today, could pay back by 2011 and contribute into the 4G commercial deployment.

Methodology | 4G READINESS ASSESSMENT

2011-12-22T04:57:00.000-08:00

The need for strategic planning and new services has urged the need for new studies that could give an idea of the current 4G status of the countries and their move towards the future 4G deployment.

The new 4G readiness concept could be defined as "state of play" of a country's mobile wireless 4G preparation status, and the ability of its potential and existing consumers, businesses and governments to use in the future the mobile wireless and broadband to their benefit. Based on the 4G readiness criteria we will rank the countries as a first step in estimating how soon they will close the gap to new 4G technological environment.

To measure each country's innovation using the Motivation/Ability framework (Figure 1). In this framework, we are describing the 4 different quadrants and how they are adjusted into our problem. The Motivation means that the 4G including the digital convergence should be the "pot of gold" and the new opportunity awaiting for the winners that most likely would be the first movers. The Ability describes the resources needed to develop 4G and craft them into business models for new products and services.

Figure 1: Motivation/ability framework

In the "Looking for a target" section, the operators are still undecided regarding the more beneficial choice or are lacking the spectrum to develop a new market. This hesitation also can derive from the "Looking for the Money" section, since the players are still expecting the 3G to pay off before they move into a new investment or wait for the LTE, in order to upgrade the GSM networks that might also include lower cost, much less than developing a WiMAX solution. "The Dilemma" is what we can quantify using our 4G readiness metrics and estimate per country, assuming basic innovation and ability. Finally "The Hotbed" is addressing all the innovative countries that feel confident and in the right path for the 4G adoption in the near future.

Other important non-market factors for 4G based on the framework development are:

Industry standards
Cultural norms
State of technological development
Government regulation
Country's intellectual property infrastructure

We are applying non-market metrics and factors, because there are no markets structured shaped yet and the current 3G markets provide very little knowledge to support the new landscape for 10 years from today.

More specifically we will rank the following list of countries that are included in the top 20 of the referred study: Canada, Hong Kong, Netherlands, Switzerland UK, Denmark, Germany, USA, Japan, Sweden and Finland.

Also, some other countries will be added, such as China due to the China Mobile 4G activity and trials as mentioned before. Other strategic countries added in the sample: India, Russia, Brazil due to the following facts or figures:

Almost represent half of the world's population
Showing record in wireless adoption in the last few years

In our methodology we are using as input countries with the most efficient digital users, assuming they are more technologically innovative, demanding more advanced services than the rest of the world. The suggested categories and the data collected will be similar to the e-readiness study, adjusted more specifically when needed into the mobile wireless dataset, trying to include important non-market factors as already described. We assume in most cases that the 4G evolution can be considered as a digital subcategory developing similar categories and criteria.