Reward and punishment based cooperative adaptive sampling in wireless sensor networks

Abstract

Energy conservation is one of the main concerns in wireless sensor networks. One of the mechanisms to better manage energy in wireless sensor networks is adaptive sampling, by which instead of using a fixed frequency interval for sensing and data transmission, the wireless sensor network employs a dynamic scheme based on how frequent pattern of sensed data changes. Selecting an appropriate sampling rate for wireless sensor networks to ensure both long network life-time and high data quality is challenging. Lack of cooperation between sensor nodes to enable them to adapt their sampling rates while having an eye on the overall energy use is one of the main drawbacks of the current data gathering techniques in wireless sensor networks. Through cooperation, sensor nodes can obtain enough knowledge about resources available in the network and environmental conditions they observe. This information can help them to better and more intelligently select their own sampling rates. In this paper, we propose a cooperative adaptive sampling mechanism based on the award and punishment concept to motivate sensor nodes to cooperate with each other. We define a utility function for every sensor node, which aims at finding a good balance between its data prediction error and remaining energy. When some sensor nodes in a neighbourhood experience frequent environmental changes, other nodes lower down their sampling rates to enable them to increase their sampling rate to keep the overall network data quality high and energy consumption low.