An Adaptive Activity Cycling Technique for Energy Management in Wireless Sensor Networks (WSNs)

Abstract

Wireless Sensor Networks (WSNs) consist of large number of small nodes that sense the surround environment and transmit the data to the central collection points. Since these nodes rely on batteries as power source, the effective power management considered as a vital to ensuring that the network continues to operate over long periods. In this research, we offer innovative adaptive activity cycling technique aims to enhance power management in Wireless Sensor Networks. The suggested technique rely on duty cycling concept, where the node is turned on for a certain period and turned off for another period to save energy. Our approach is characterized by dynamically adjusting the on and off periods based on changing network conditions. The adaptive cycling setup according network traffic, so that, in the low traffic cases, the off periods is increased to save power, while these periods decreases in the high traffic cases to ensure transmit data efficiently. In addition, the technique, regard remaining node battery level, which ensure node continue for longer possible period by modifying adapting cycles based on remaining battery level. Simulation tools NS3 and MATLAB are used to evaluate the performance of innovative technique. The results showed, the technique achieve significant enhancements in term energy consumption efficiency comparing with traditional techniques. Furthermore, the technology was able to maintain the quality of service in terms of response time reduction and increase packet delivery ratio. In conclusion, this research demonstrates that using adaptive activity cycling technique can contribute significantly in terms of prolong network lifetime and enhance energy efficiency in Wireless Sensor Networks without sacrificing the quality of service. This technology is promising and can be used in a variety of applications like procession agriculture, environmental monitoring, health care, which support the effectiveness and sustainability of wireless sensor networks in different environments.