Adaptive Data Collection protocol for Extending Lifetime of Periodic Sensor Networks

Abstract

In Periodic Sensor Network (PSN), the periodic energy efficient collection of a large amount of sensed data by the sensor nodes is considered as one of the main challenges in this type of network. An adaptive sampling approach to periodic data collection is needed for energy optimization due to the limited lifetime nature of the sensor batteries. In this paper, we propose a protocol, called an Adaptive Data Collection protocol (ADaC), which collects periodically sensor readings and prolong the lifetime of a Periodic Sensor Network (PSN). The lifetime of ADaC protocol is divided into cycles. Each cycle is composed of two stages.
First, data collection. Second, sampling rate adaptation based on the similarity between periods of one cycle using Euclidean distance measure to adapt its rate of sampling according to the dynamic modification of the monitored environment. ADaC allows each sensor to adapt its sampling rate in accordance with the monitored environment conditions. We conduct extensive simulation experiments on real sensor data by applying OMNeT++ network simulator to explain the effectiveness of the ADaC protocol in comparison with a method without using adaptive sampling.