This paper studies distributed formation of femto-clouds in a UMTS LTE network. Femtocell access points (FAPs) are equipped with computational resources. They share their resources with neighboring FAPs and form local clouds with the aim to avoid the remote cloud costs while improving the user quality of experience (QoE) in terms of handling latency. In exchange for sharing their excess resources, FAPs receive monetary incentives proportional to their contribution in performing computational tasks in the femto-cloud. The resource sharing problem is formulated as an optimization problem and a myopic procedure is presented that enables FAPs to collaboratively find its solution in a distributed fashion. In such an optimal femto-cloud structure, the local computational resources of FAPs are maximally exploited, yet the incentive earned by each femto-cloud is divided among the FAPs in a fair fashion. Numerical simulations using NS-3 verify superior QoE of users as well as higher incentives provided to FAP owners as compared with alternative heuristic schemes. Numerical results also show that the grand femto-cloud-the largest collaborative cloud comprising of all FAPs-is not always the optimal structure.

This paper studies formation of local femto-clouds in a UMTS LTE network via a cooperative game-theoretic formulation. Femtocell access points (FAPs) equipped with processing power form collaborative coalitions, namely, femto-clouds, with neighboring FAPs and share their computational resources in exchange for monetary incentives. Femto-clouds are formed with the aim to avoid the remote cloud costs while improving the quality of experience (QoE) of users in terms of handling latency. The core of the formulated game represents an optimal structure for femto-clouds such that the computational resources of FAPs are maximally exploited, yet the incentive earned by each femto- cloud is divided among the FAPs in a fair fashion. Numerical simulations using NS3 verify superior QoE of users and higher incentives to FAP owners as compared with alternative schemes.

In this paper, formation of stable coalitions of users, each exploiting resources in a femto-cell, and the resource allocation in each femto-cell is investigated in a UMTS long term evolution (LTE) network. We study a downlink scenario where users collaborate to increase network throughput and, simultaneously, attempt to increase their own payoffs. Payoffs to the users are defined as the monetary equivalent of the individual users' achievable throughput in the specified coalition structure. A distributed game-theoretic resource allocation mechanism is developed whereby users autonomously decide which sub-channel in which coalition to join. If each user operates according to the proposed algorithm, the sum throughput of all links converges with probability one to its maximum feasible value.

We present a decentralized adaptive filtering algorithm in a clustered network of agents. Agents receive payoffs partly due to performing localized tasks in clusters and partly due to strategic interaction with agents outside clusters. Each agent is only aware of the actions of others within its cluster and is oblivious to the actions, or even existence, of agents outside the cluster. We show that the global behavior of the network converges to the set of correlated e-equilibria if the agents follow the proposed algorithm. Thus simple behavior by individual agents can result in sophisticated global behavior.

We present a decentralized adaptive filtering algorithm where each agent acts selfishly to maximize its payoff. Agents are only aware of the actions of other agents within their coalitions and have no knowledge of the actions of agents outside the coalition. We show that the global behavior of the system converges to the set of correlated equilibria. Thus simple behavior by individual agents can result in sophisticated global behavior.

Lifetime maximization is a key challenge in the design of sensor-network-based tracking applications. In this paper, formation of optimal coalitions of nodes is investigated for data acquisition in bearings-only target localization such that the average sleep times allocated to the nodes are maximized. Cooperative game theory is utilized as a tool to devise a distributed dynamic coalition formation algorithm in which nodes autonomously decide which coalition to join, while maximizing their feasible sleep times. If each node follows the proposed algorithm, the average sleep time for the entire network eventually converges to its maximum feasible value conditional on the pre-defined localization accuracy. This algorithm can also be employed in tracking slow moving targets.

Cognitive radio networks (CRN) are promising to enrich the connectivity demand of users through exploiting the under-utilized licensed bands. In this paper, the load-balanced resource allocation problem is formulated as a non-superadditive coalition formation game in which the cognitive radios (CR) form collaborative groups to exploit the spectrum available to cognitive base stations (CBS) most efficiently. A distributed decision-making framework is developed for coalition formation among CRs that converges to the core of the defined game, corresponding to the maximum average payoff conditional on feasibility and subject to a fairness rule. Further, we elaborate on the load balancing properties of the proposed scheme and demonstrate its superior performance, when compared to opportunistic scheduling, through numerical examples.