Matrix analysis
Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
A Markov-based channel model algorithm for wireless networks
MSWIM '01 Proceedings of the 4th ACM international workshop on Modeling, analysis and simulation of wireless and mobile systems
Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Denial of Service in Sensor Networks
Computer
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Medium access control with coordinated adaptive sleeping for wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Maximum lifetime routing in wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Fine-grained network time synchronization using reference broadcasts
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
PEDAMACS: Power Efficient and Delay Aware Medium Access Protocol for Sensor Networks
IEEE Transactions on Mobile Computing
Statistical location detection with sensor networks
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
Smart Sleeping Policies for Energy Efficient Tracking in Sensor Networks
IEEE Transactions on Signal Processing
Duty-cycle optimization for IEEE 802.15.4 wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
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We consider wireless sensor networks with nodes switching ON (awake) and OFF (sleeping) to preserve energy, and transmitting data over channels with varying quality. The objective is to determine the best path from each node to a single gateway. The performance metrics we are interested in are: the expected energy consumption, and the probability that the latency exceeds a certain threshold. Under Markovian assumptions on the sleeping schedules and the channel conditions, we obtain the expected energy consumption of transmitting a packet on any path to the gateway. We also provide an upper (Chernoff) bound and a tight large deviations asymptotic for the latency probability on each path. To capture the trade-off between energy consumption and latency probability, we formulate the problem of choosing a path to minimize a weighted sum of the expected energy consumption and the exponent of the latency probability. We provide two algorithms to solve this problem: a centralized stochastic global optimization algorithm, and a distributed algorithm based on simulated annealing. The proposed methodology can also optimize over the fraction of time that sensor nodes remain ON (duty cycle).