An Adaptive Sampling Technique for Massive Data Collection in Distributed Sensor Networks

Wireless sensor networks are becoming very popular nowadays. Sensors in such networks are used to gather data periodically about a given zone area and send the collected data to the sink. However, such networks face several challenges especially the limited energy source and the data management. Hence, data sampling approach is becoming one of the essential techniques that saves he sensor energies and extend the network lifetime. Adaptive algorithms are created to allow each sensor to adapt its sampling rate to the application under surveillance, which leads to reduced data collection thus, reducing energy consumption. In this paper, we propose a new adaptive sampling technique that is dedicated to periodic sensor network applications. Our technique consists of two stages: aggregation and adapting stages. The first stage is applied at sensor level and aims to reduce the amount of data collected by the sensor. The second stage is applied at an intermediate level node called cluster-head (CH). The CH receives data periodically from the sensors and computes the new sampling rate for each sensor based on the spatio-temporal correlation between the sensors. Our technique is evaluated based on real sensor data collected in the Intel lab. The obtained results show the effectiveness of our technique in terms reducing the energy consumption while ensuring a high level of data accuracy and coverage network.