JOURNAL ARTICLE

Bootstrapping a very low power, beacon enabled, wireless sensor network

Nicola AltanErwin P. Rathgeb

Year: 2007 Journal:   Proceedings - IEEE Symposium on Computers and Communications/IEEE Symposium on Computers and Communications Vol: 3713 Pages: 955-962   Publisher: Institute of Electrical and Electronics Engineers

Abstract

Wireless sensor networks (WSN) consisting of a large number of inexpensive sensor nodes are a viable solution for many problems in the field of building automation, especially if the expected lifetime of the network permits to synchronize the network maintenance with the schedule for routine maintenance of the building. A challenging and up to now only marginally addressed problem concerns the power-on phase and the automatic topology discovery and maintenance process of the sensor network. We suggest a simple heuristic algorithm, which permits the integration of all sensor nodes into a tree-like failure tolerant routing structure and also provides continuous adaptation of the structure to possible changes. We developed a simulation model to reproduce the problems of a realistic indoor placement of the nodes to test the proposed algorithm. Simulation results show that the suggested algorithm converges quickly to a structure with the desired properties even in the worst case.

Keywords:
Wireless sensor network Computer science Heuristic Schedule Network topology Key distribution in wireless sensor networks Distributed computing Computer network Process (computing) Bootstrapping (finance) Routing (electronic design automation) Topology control Mobile wireless sensor network Wireless network Real-time computing Wireless Artificial intelligence

Metrics

3
Cited By
0.64
FWCI (Field Weighted Citation Impact)
8
Refs
0.70
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Topics

Energy Efficient Wireless Sensor Networks
Physical Sciences →  Computer Science →  Computer Networks and Communications
Modular Robots and Swarm Intelligence
Physical Sciences →  Engineering →  Mechanical Engineering
Context-Aware Activity Recognition Systems
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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