Abstract-In wireless ad hoc networks, nodes communicate with far off destinations using intermediate nodes as relays. Since wireless nodes are energy constrained, it may not be in the best interest of a node to always accept relay requests. On the other hand, if all nodes decide not to expend energy in relaying, then network throughput will drop dramatically. Both these extreme scenarios (complete cooperation and complete noncooperation) are inimical to the interests of a user. In this paper we address the issue of user cooperation in ad hoc networks. We assume that nodes are rational, i.e., their actions are strictly determined by self interest, and that each node is associated with a minimum lifetime constraint. Given these lifetime constraints and the assumption of rational behavior, we are able to determine the optimal throughput that each node should receive. We define this to be the rational Pareto optimal operating point. We then propose a distributed and scalable acceptance algorithm called Generous TIT-FOR-TAT (GTFT). The acceptance algorithm is used by the nodes to decide whether to accept or reject a relay request. We show that GTFT results in a Nash equilibrium and prove that the system converges to the rational and optimal operating point.

In this paper, we address the problem of energy-conscious cache placement in wireless ad hoc networks. We consider a network comprising a server with an interface to the wired network, and some nodes requiring access to the information stored at the server. In order to reduce access latency in such a communication environment, an effective strategy is caching the server information at some nodes distributed across the network. Caching, however, can considerably impact the system energy expenditure; for instance, disseminating information incurs additional energy burden. Since wireless devices have limited amounts of available energy, we need to design caching strategies that optimally trade-off between energy consumption and access latency. We pose our problem as an integer linear program. We show that this problem is the same as a special case of the connected facility location problem, which is known to be NP-hard. We devise a polynomial time algorithm which provides a sub-optimal solution. The proposed algorithm applies to any arbitrary network topology and can be implemented in a distributed and asynchronous manner. In the case of a tree topology, our algorithm gives the optimal solution. In the case of an arbitrary topology, it finds a feasible solution with an objective function value within a factor of 6 of the optimal value. This performance is very close to the best approximate solution known today, which is obtained in a centralized manner. We compare the performance of our algorithm against three candidate caching schemes, and show via extensive simulation that our algorithm consistently outperforms these alternative schemes. * This work was partially funded by

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Abstract-In this paper, we address the problem of efficient cache placement in multi-hop wireless networks. We consider a network comprising a server with an interface to the wired network, and other nodes requiring access to the information stored at the server. In order to reduce access latency in such a communication environment, an effective strategy is caching the server information at some of the nodes distributed across the network. Caching, however, can imply a considerable overhead cost; for instance, disseminating information incurs additional energy as well as bandwidth burden. Since wireless systems are plagued by scarcity of available energy and bandwidth, we need to design caching strategies that optimally trade-off between overhead cost and access latency. We pose our problem as an integer linear program. We show that this problem is the same as a special case of the connected facility location problem, which is known to be NP-hard. We devise a polynomial time algorithm which provides a suboptimal solution. The proposed algorithm applies to any arbitrary network topology and can be implemented in a distributed and asynchronous manner. In the case of a tree topology, our algorithm gives the optimal solution. In the case of an arbitrary topology, it finds a feasible solution with an objective function value within a factor of 6 of the optimal value. This performance is very close to the best approximate solution known today, which is obtained in a centralized manner. We compare the performance of our algorithm against three candidate cache placement schemes, and show via extensive simulation that our algorithm consistently outperforms these alternative schemes.Index Terms-Heuristic optimization, web cache placement, wireless multi-hop networks.

In this paper, we address the problem of energy-conscious cache placement in wireless ad hoc networks. We consider a network comprising a server with an interface to the wired network, and some nodes requiring access to the information stored at the server. In order to reduce access latency in such a communication environment, an effective strategy is caching the server information at some nodes distributed across the network. Caching, however, can considerably impact the system energy expenditure; for instance, disseminating information incurs additional energy burden. Since wireless devices have limited amounts of available energy, we need to design caching strategies that optimally trade-off between energy consumption and access latency. We pose our problem as an integer linear program. We show that this problem is the same as a special case of the connected facility location problem, which is known to be NP-hard. We devise a polynomial time algorithm which provides a sub-optimal solution. The proposed algorithm applies to any arbitrary network topology and can be implemented in a distributed and asynchronous manner. In the case of a tree topology, our algorithm gives the optimal solution. In the case of an arbitrary topology, it finds a feasible solution with an objective function value within a factor of 6 of the optimal value. This performance is very close to the best approximate solution known today, which is obtained in a centralized manner. We compare the performance of our algorithm against three candidate caching schemes, and show via extensive simulation that our algorithm consistently outperforms these alternative schemes. * This work was partially funded by