Parallel program design: a foundation
Parallel program design: a foundation
Directed diffusion: a scalable and robust communication paradigm for sensor networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Data Gathering Algorithms in Sensor Networks Using Energy Metrics
IEEE Transactions on Parallel and Distributed Systems
Fundamental Protocols on Wireless Sensor Networks
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
A Hierarchical Model for Distributed Collaborative Computation in Wireless Sensor Networks
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
A Survey on Leader Election Protocols for Radio Networks
ISPAN '02 Proceedings of the 2002 International Symposium on Parallel Architectures, Algorithms and Networks
The number of neighbors needed for connectivity of wireless networks
Wireless Networks
Optimization-based dynamic sensor management for distributed multitarget tracking
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Sequential use of wireless sensors for target estimation and tracking
MILCOM'03 Proceedings of the 2003 IEEE conference on Military communications - Volume I
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In this paper we present a distributed, application-morphable, algorithm for waking up appropriate sensor nodes in a heterogeneous sensor network. We assume a sensor field consisting of a large number of low power, limited functionality, tripwire nodes and a smaller number of powerful, energyhungry, tracker nodes. Our problem is that when an event is detected by a set of tripwire nodes a specific number of appropriate tracker nodes needs to be woken up. These tracker nodes will subsequently collaborate to perform the sensing task required by the application. Waking up non-suitable tracker nodes or employing more trackers than necessary for a specific task, can lead to significant waste of network resources (e.g. energy). The application indicates the number of nodes that are needed for a sensing task, as well as an optimization function to be used by the algorithm. Therefore, our algorithm is isolated from most application details and is simple and general enough to accommodate a wide range of sensing applications. We prove that our algorithm converges to a uniform optimal global decision for specific classes of optimization functions. Furthermore, we show that it is fast enough (