Optimising quality of information in data collection for mobile sensor networks

Abstract

Wireless sensor networks have become increasingly popular for environmental and activity monitoring, such as temperature, pollution, parking space, traffic, and crowd monitoring. Mobile users can collect and visualise sensing data by communicating with wireless sensors along their walks using Bluetooth or NFC. They can also share the sensing data on the Internet through 3G or WiFi connectivity. Nevertheless, mobile users may not be able to collect all the data from the sensors due to limited contact times and batteries. It is crucial to collect data with a maximum amount of information from the available resources. In this paper, we tackle the problem by prioritising the sensing data to maximise the data utility considering the quality of information of the sensing data and the communication overhead. We formulate the optimisation problem and propose a greedy algorithm for clustering the sensors and scheduling the data collection. Our greedy algorithm coordinates the mobile users in the sensing field in order to avoid the collection of redundant sensing data. We evaluate the data utility and energy consumption of the proposed algorithm using real mobility traces from the North Carolina state fair. The results demonstrate that our algorithm can significantly improve data utility at low communication overhead compared with an existing algorithm.