An Adaptive Data Reporting Scheme Considering Node Contexts in Wireless Sensor Networks

Advancements in sensor technology have enabled the development of extremely small, low-power, low-cost sensing devices. Recently, wireless sensor networks (WSNs) with thousands of those tiny sensors have become increasingly common. The existing and potential WSN applications span a wide spectrum across various domains. In terms of data reporting schemes, WSNs can be categorized as time-driven and event-driven networks, both with their own pros and cons. In this paper, we propose a novel technique to combine these two data reporting schemes in such a way that the network identifies sensor nodes that will potentially detect an event in the near future and enables them to be proactively involved in the data reporting process. When selecting such nodes, our approach takes into consideration node contexts such as locality and past history of the sensed attributes. In so doing, both energy efficiency and proactive analysis of the environment, which is a major limitation of the event-driven data reporting scheme, can be achieved. To verify the effectiveness of our approach, we conducted several experiments with various synthetic scenarios, the results show that our approach is able to provide sufficient information for proactive actions from users with moderate consumption of energy.