An analysis of stochastic shortest path problems
Mathematics of Operations Research
Location-aided routing (LAR) in mobile ad hoc networks
MobiCom '98 Proceedings of the 4th annual ACM/IEEE international conference on Mobile computing and networking
Wireless integrated network sensors
Communications of the ACM
GPSR: greedy perimeter stateless routing for wireless networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Analysis of a cone-based distributed topology control algorithm for wireless multi-hop networks
Proceedings of the twentieth annual ACM symposium on Principles of distributed computing
Constructing minimum energy mobile wireless networks
MobiHoc '01 Proceedings of the 2nd ACM international symposium on Mobile ad hoc networking & computing
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Finite State Markovian Decision Processes
Finite State Markovian Decision Processes
Ad-hoc On-Demand Distance Vector Routing
WMCSA '99 Proceedings of the Second IEEE Workshop on Mobile Computer Systems and Applications
Minimum energy mobile wireless networks
IEEE Journal on Selected Areas in Communications
Continuous neighbor discovery in asynchronous sensor networks
IEEE/ACM Transactions on Networking (TON)
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We consider sensor networks in which individual nodes with on-board sensing and low-power transmitters and receivers establish connections with neighboring nodes. The overall objective is to enable energy-efficient data communication, relayed between arbitrary nodes on the network. We develop a distributed algorithm which minimizes the power required for neighbor discovery.Initially nodes do not have deterministic knowledge of the location of their neighbors, and we model the distribution of the nodes as a two-dimensional Poisson process with known intensity. This corresponds to a situation in which a large number of nodes are randomly distributed over a given area. The process of neighbor discovery is modeled as a Markov decision process, and the resulting control policy is a finite automaton, driven by the underlying probability distribution, that minimizes the average power consumed. This policy can be computed offline and stored in each node with very low requirements for online memory and processor capability.