Study of an adaptive frame size predictor to enhance energy conservation in wireless sensor networks

Technological advances in low-power digital signal processors, radio frequency (RF) circuits, and micromechanical systems (MEMS) have led to the emergence of wirelessly interconnected sensor nodes. The new technological possibilities emerge when a large number of tiny intelligent wireless sensor nodes are combined. The sensor nodes are typically battery operated and, therefore, energy constrained. Hence, energy conservation is one of the foremost priorities in design of wireless sensor networks (WSNs) protocols. Limited power resources and bursty nature of the wireless channel are the biggest challenges in WSNs. Link adaptation techniques improve the link quality by adjusting medium access control (MAC) parameters such as frame size, data rate, and sleep time, thereby improving energy efficiency. In This work, our study emphasizes optimizing WSNs by building a reliable and adaptive MAC without compromising fairness and performance. Here, we present link adaptation techniques at MAC layer to enhance energy efficiency of the sensor nodes. The proposed MAC uses a variable frame size instead of a fixed frame size for transmitting data. In order to get accurate estimations, as well as reducing the computation complexity, we utilize the extended Kalman filter to predict the optimal frame size for improving energy efficiency and goodput, while minimizing the sensor memory requirement. Next, we designed and verified different network models to evaluate and analyze the proposed link adaptation schemes. The correctness of the proposed theoretical models have been verified by conducting extensive simulations. We also prototype the proposed scheme with the MAC protocol on Berkeley Motes. Both prototype and simulation results show that the proposed algorithms improve the energy efficiency by up to 15%.