Energy efficiency is critical for wireless sensor networks. The data-gathering process must be carefully designed to conserve energy and extend network lifetime. For applications where each sensor continuously monitors the environment and periodically reports to a base station, a tree-based topology is often used to collect data from sensor nodes. In this work, we first study the construction of a data-gathering tree when there is a single base station in the network. The objective is to maximize the network lifetime, which is defined as the time until the first node depletes its energy. The problem is shown to be NP-complete. We design an algorithm that starts from an arbitrary tree and iteratively reduces the load on bottleneck nodes (nodes likely to soon deplete their energy due to high degree or low remaining energy). We then extend our work to the case when there are multiple base stations and study the construction of a maximum-lifetime data-gathering forest. We show that both the tree and forest construction algorithms terminate in polynomial time and are provably near optimal. We then verify the efficacy of our algorithms via numerical comparisons.

Energy efficiency is critical for wireless sensor networks. The data gathering process must be carefully designed to conserve energy and extend the network lifetime. For applications where each sensor continuously monitors the environment and periodically reports to a base station, a tree-based topology is often used to collect data from sensor nodes. In this work, we study the construction of a data gathering tree to maximize the network lifetime, which is defined as the time until the first node depletes its energy. The problem is shown to be NP-complete. We design an algorithm which starts from an arbitrary tree and iteratively reduces the load on bottleneck nodes (nodes likely to soon deplete their energy due to high degree or low remaining energy). We show that the algorithm terminates in polynomial time and is provably near optimal.

We study sleep/wake scheduling for low duty cycle sensor networks. Our work is different from prior work in that we explicitly consider the effect of synchronization error in the design of the sleep/wake scheduling algorithm. In our previous work, we have studied sleep/wake scheduling for single hop communications, e.g., intra-cluster communications between a cluster head and cluster members. We showed that the there is an inherent trade-off between energy consumption and message delivery performance (defined as the message capture probability). We proposed an optimal sleep/wake scheduling algorithm, which satisfies a message capture probability threshold (assumed to be given) with minimum energy consumption. In this work, we consider multi-hop communications. We remove the previous assumption that the capture probability threshold is already given, and study how to decide the per-hop capture probability thresholds to meet the quality of services (QoS) requirements of the application. In many sensor network applications, the QoS is decided by the amount of data delivered to the base station(s), i.e., the multi-hop delivery performance. We formulate an optimization problem, which aims to set the capture probability threshold at each hop such that the network lifetime is maximized, while the multi-hop delivery performance is guaranteed. The problem turns out to be non-convex and hard to solve exactly. By investigating the unique structure of the problem and using approximation techniques, we obtain a solution that achieves at least 0.73 of the optimal performance.

We study sleep/wake scheduling for low-duty cycle sensor networks. Our work is different from previous work in that we explicitly consider the effect of the synchronization error. We study a widely used synchronization scheme and show that the synchronization error is non-negligible, and using a conservative guard time is energy wasteful. Hence, we formulate an optimization problem to minimize the expected energy consumption, with the constraint that the message capture probability should be no less than a threshold. We find that the problem is non-convex, hence cannot be solved by conventional convex optimization techniques. By investigating the unique structure of the problem, we transform the problem into a convex equivalent, and solve it using an efficient search method. Simulations show that our scheme significantly outperforms schemes that do not intelligently consider the synchronization error. We also remove the assumption that the capture probability threshold is given, and study how to decide it to meet the quality of services (QoS) requirements of the application

Fair bandwidth allocation is critical in wireless communication networks, since the wireless channel is often shared by a number of stations in the same neighborhood. With fair scheduling, bandwidth can be shared by competing flows in proportion to their assigned weights. In this paper, we propose a credit-based distributed protocol for fair allocation of bandwidth in IEEE 802.11 wireless LANs. Our protocol is derived from the distributed coordination function in the IEEE 802.11 medium access control (MAC) protocol. Analytical and simulation results demonstrate that the protocol achieves the desired bandwidth allocations. An important feature of our protocol is its backward compatibility, which allows legacy IEEE 802.11 stations to coexist with stations adopting the new MAC protocol.