Cooperative Services in 4G

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

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

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 thegroup, 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

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

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.