| Wireless sensor networks are expected to be used in a wide range of applications from environment monitoring to event detection. The key challenge is to provide energy efficient communication; however, latency remains an important concern for many applications that require fast response.;The central thesis of this work is that energy efficient medium access and sleep scheduling mechanisms can be designed without necessarily sacrificing application-specific latency performance. We validate this thesis through results from four case studies that cover various aspects of medium access and sleep scheduling design in wireless sensor networks.;Our first effort, DMAC, is to design an adaptive low latency and energy efficient MAC for data gathering to reduce the sleep latency. We propose staggered schedule, duty cycle adaptation, data prediction and the use of more-to-send packets to enable seamless packet forwarding under varying traffic load and channel contentions. Simulation and experimental results show significant energy savings and latency reduction while ensuring high data reliability.;The second research effort, DESS, investigates the problem of designing sleep schedules in arbitrary network communication topologies to minimize the worst case end-to-end latency (referred to as delay diameter). We develop a novel graph-theoretical formulation, derive and analyze optimal solutions for the tree and ring topologies and heuristics for arbitrary topologies.;The third study addresses the problem of minimum latency joint scheduling and routing (MLSR). By constructing a novel delay graph, the optimal joint scheduling and routing can be solved by M node-disjoint paths algorithm under multiple channel model. We further extended the algorithm to handle dynamic traffic changes and topology changes. A heuristic solution is proposed for MLSR under single channel interference.;In the fourth study, EEJSPC, we first formulate a fundamental optimization problem that provides tunable energy-latency-throughput tradeoffs with joint scheduling and power control and present both exponential and polynomial complexity solutions. Then we investigate the problem of minimizing total transmission energy while satisfying transmission requests within a latency bound, and present an iterative approach which converges rapidly to the optimal parameter settings. |
