An Adaptive Energy-Efficient Data Collection System for ZigBee Wireless Sensor Networks

Abstract

Wireless sensor networks (WSNs) are deployed in large areas to monitor a number of events in an area of interest. Monitoring environmental events by distributed sensor networks faces the challenge of high power consumption requirement over time, due to the large number of packets required for multihop data collection. To overcome the scalability issue of large scale WSNs, a proof-of-concept implementation demonstrates that integrating a mobile robot (MR) system with a clustering system for ZigBee WSNs will significantly increase the lifetime of the system, by conserving energy that the sensor nodes otherwise would use for communication. In this paper, two energy-efficient systems have been proposed: clustering and MR systems. The former divides the ZigBee WSN into smaller regions, allocates a cluster-head for each region, and aggregates the collected data, whereas the latter collects the sensed data from cluster-heads. The effectiveness of the proposed system has been demonstrated via simulation and experimental studies and verified that, using a single robot for data collection, the lifetime of the network can be extended by 2.3 times in average.