The Implementation of an Adaptive Data Reduction Technique for Wireless Sensor Networks

Abstract

Wireless sensor networks (WSN) have gained significant attention due to their ability to monitor physical phenomena within a wide range of applications. These networks are generally deployed in remote areas and are battery powered. This means that the lifetime of the network depends on the energy consumption, thus necessitating careful hardware and software design to sustain the long period of operation without human intervention. One way of reducing the energy required is to minimize the number of data transmissions. A prediction-based data reduction algorithm based on the least-mean-square (LMS) algorithm was implemented on a field programmable gate array (FPGA) to reduce the communication between the sensor nodes and the base station. Measurement results show that communication can be reduced by as much as 90% in a temperature monitoring application if an error of 0.5 degree is acceptable. This has a large impact on the lifetime of the wireless sensor network since the transceiver can be switched off during non-communication periods saving precious energy.