Tiny, low-cost sensor devices are expected to be failure-prone and hence in many realistic deployment scenarios for sensor networks these nodes are deployed in higher than necessary densities to meet operational goals. In this paper we address the question of how nodes should be managed in such dense sensor deployments so that the network topology formed by the active sensors is able to provide connected-coverage to the entire area of interest and at the same time increase the lifetime of the network. In particular, we propose and study distributed, low-coordination node wakeup schemes to efficiently construct multiple independent (node-disjoint) sensor network topologies to achieve good fault tolerance. We propose and evaluate different distributed, random and pattern-based wakeup policies for sensor nodes to construct connected-covered topologies. Through analysis and simulations we demonstrate that in dense sensor deployment scenarios, these policies can construct near-optimal topologies (within 2.7% of the optimal) with zero coordination between nodes, as long as location information is available at the individual sensor nodes. Based on these observations, we develop and evaluate a few simple distributed, wakeup based topology construction algorithms that can realize similar performance bounds in realistic sensor deployments, with varying node densities. These algorithms differ in terms of the required level of coordination and the use of sensor location information, and generate connected-covered topologies efficiently, with very low message-exchange overhead.

Accurate positioning mechanisms are important in large scale sensor networks to achieve a number of functionalities like location aware routing, efficient coordination of resources and other application specific requirements. This paper proposes a distributed and scalable GPS free positioning algorithm for wireless sensor networks. This approach is an effort in the direction of finding a solution to the positioning problem which minimizes the number of messages exchanged and the coordinate setup time. We use a clustering based approach for the coordinate formation wherein a small subset of the nodes can successfully establish the coordinate system for the whole network. We also compare the performance of this system against existing mechanisms and show that our system scales linearly as the number of nodes in the network increases in contrast to the exponential increase in current mechanisms. Additionally, our mechanism takes considerably lower convergence times. The proposed mechanism is scalable, distributed and able to support the ad hoc deployment of large scale sensor networks quickly and efficiently.

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Abstract-We consider the uplink channel assignment problem in a multi-channel access point wireless network, with the goal of attaining maximum system throughput. In this setup, a set of orthogonal channels must be assigned to a set of users, where each user splits its power optimally across the channels allocated to it. While the optimal power allocation solution has a "water-filling" type structure, the optimal channel assignment problem is very challenging due to the non-linear dependence of user throughput on the set of channels assigned to it. Since the optimal channel allocations is computationally intensive to obtain in general, we analyze the system in the two extremal SINR regimes (very high and very low SINR) and show how the optimal solutions can be obtained in these regimes in a computationally efficient manner. Finally, we demonstrate that the best of the optimal solutions obtained for the two extremes show excellent (close to optimal) performance over the entire SINR range.

We study the resource allocation problem in OFDMA based 802.16 broadband wireless access systems. Frequency and time resources must be allocated by a central controller (Base Station) to a number of users. We consider variations of a resource allocation problem, some of which are difficult to solve. Situations in which only the objective of the Base Station need to be maximized are easily dealt with as are cases where all the users perceive the same channel conditions. Scenarios where both the objectives of the BS as well as those of the end users must be met simultaneously require more complicated solutions since individual users experience different channel conditions. We present linear programming relaxations for the resource allocation problem. While solving the LP using standard techniques like ellipsoidal algorithm can provide optimal allocations for all users, it can be expensive in terms of computing overhead as the number of users in the system increase. Therefore we present an efficient algorithm which performs well even as the number of clients in the system increases. We also present a heuristic based on the interpretation of the linear programming relaxation as a concurrent flow problem. We note that in numerical experiments, the performance of the heuristic closely matches the optimal solution to the linear programming relaxation.