Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
TEEN: ARouting Protocol for Enhanced Efficiency in Wireless Sensor Networks
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Lightweight sensing and communication protocols for target enumeration and aggregation
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Wireless Sensor Networks: An Information Processing Approach
Wireless Sensor Networks: An Information Processing Approach
Dynamic Clustering for Acoustic Target Tracking in Wireless Sensor Networks
IEEE Transactions on Mobile Computing
HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks
IEEE Transactions on Mobile Computing
An Energy-Efficient Voting-Based Clustering Algorithm for Sensor Networks
SNPD-SAWN '05 Proceedings of the Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks
A cluster-based energy balancing scheme in heterogeneous wireless sensor networks
ICN'05 Proceedings of the 4th international conference on Networking - Volume Part I
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
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The clustering is a key routing method for large-scale wireless sensor networks, which effective extends the lifetime and the expansibility of network. In this paper, a node model is defined based on the structure and transmission principle of neuron, and a dynamic-clustering reactive routing algorithm is proposed. Once the event emergences, the cluster head is dynamic selected in the incident region according to the residual energy. The data collected by the cluster head is sent back to the Sink along the network backbone. Two kinds of accumulation ways are designed to increase the efficiency of data collection. Meanwhile through the fluctuation of action-threshold, the cluster head can trace the changing speed of incident; the nodes outside the incident region use this fluctuation to send data periodically. Finally, the simulation results verify that the DCRR algorithm extends the network's lifetime considerably and adapts to the change of network scale. The analysis shows that DCRR has more prominent advantages under low and middle load.