Distributed computing: a locality-sensitive approach
Distributed computing: a locality-sensitive approach
A taxonomy of wireless micro-sensor network models
ACM SIGMOBILE Mobile Computing and Communications Review
A coverage-preserving node scheduling scheme for large wireless sensor networks
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
A directionality based location discovery scheme for wireless sensor networks
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Approximating the Domatic Number
SIAM Journal on Computing
WCA: A Weighted Clustering Algorithm for Mobile Ad Hoc Networks
Cluster Computing
PEAS: A Robust Energy Conserving Protocol for Long-lived Sensor Networks
ICNP '02 Proceedings of the 10th IEEE International Conference on Network Protocols
Faster and Simpler Algorithms for Multicommodity Flow and other Fractional Packing Problems.
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Distributed Clustering for Ad Hoc Networks
ISPAN '99 Proceedings of the 1999 International Symposium on Parallel Architectures, Algorithms and Networks
Batteries and Power Supplies for Wearable and Ubiquitous Computing
ISWC '99 Proceedings of the 3rd IEEE International Symposium on Wearable Computers
Differentiated surveillance for sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Energy-efficient surveillance system using wireless sensor networks
Proceedings of the 2nd international conference on Mobile systems, applications, and services
On k-coverage in a mostly sleeping sensor network
Proceedings of the 10th annual international conference on Mobile computing and networking
Maximizing the Lifetime of Dominating Sets
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 12 - Volume 13
On the upper bound of α-lifetime for large sensor networks
ACM Transactions on Sensor Networks (TOSN)
A fast localized algorithm for scheduling sensors
Journal of Parallel and Distributed Computing - Special issue: Algorithms for wireless and ad-hoc networks
Coverage by randomly deployed wireless sensor networks
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
Constant-factor approximation for minimum-weight (connected) dominating sets in unit disk graphs
APPROX'06/RANDOM'06 Proceedings of the 9th international conference on Approximation Algorithms for Combinatorial Optimization Problems, and 10th international conference on Randomization and Computation
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
A Localized Algorithm for Target Monitoring in Wireless Sensor Networks
ADHOC-NOW '09 Proceedings of the 8th International Conference on Ad-Hoc, Mobile and Wireless Networks
EURASIP Journal on Wireless Communications and Networking - Special issue on theoretical and algorithmic foundations of wireless ad hoc and sensor networks
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Given n sensors and m targets, a monitoring schedule is a partition of the sensor set such that each part of the partition can monitor all targets. Monitoring schedules are used to maximize the time all targets are monitored when there is no possibility of replacing the batteries of the sensors. Each part of the partition is used for one unit of time, and thus the goal is to maximize the number of parts in the partition. We present distributed algorithms for Monitoring Schedule under the following assumptions: 1) identical sensors can each monitor all targets within a certain radius, 2) the n sensors are randomly distributed uniformly in a large square containing the targets, 3) the number of sensors is high enough given the area the square, and 4) the communication range is twice the sensing range (thus any two sensors which can monitor the same target can communicate in one hop). Our results hold with high probability. With the further assumptions that the sensors are capable (for example, by GPS) of knowing their exact geographic position, and targets fill out the square, our schedule has at least (1-ε) opt parts, where opt is the optimum solution. Without geographic position we show that a previously proposed distributed algorithm can be modified to achieve a constant approximation ratio. Our algorithms run in a polylogarithmic number of communication rounds, with the exact running time depending on assumptions on the information a sensor receives when packets collide.