Approximate Self-Adaptive Data Collection in Wireless Sensor Networks

Bin Wang, Xiaochun Yang, Wanyu Zang, Meng Yu

Abstract: To extend the lifetime of wireless sensor networks, reducing and balancing energy consumptions are main concerns in data collection due to the power constrains of the sensor nodes. Unfortunately, existing data collection schemes mainly focus on energy saving while overlook balancing the energy consumption of the sensor nodes. In addition, most of them assume that each sensor has a global knowledge about the network topology. However, in many real applications, such a global knowledge is not desired due to the dynamic features of the wireless sensor network. In this paper, we propose an Approximate Self-Adaptive data collection technique (ASA), to approximately collect data in a distributed wireless sensor network. ASA investigates the spatial correlations between sensors to provide an energy-efficient and balanced route to the sink, while each sensor does not know any global knowledge on the network. Based on our synthetic experiences, we demonstrate that ASA can provide significant communication (and hence energy) savings and equal energy consumption of the sensor nodes.