Energy Efficiency and Payload Size Optimization for Wireless Sensor Networks Employing Convolutional Coding

Abstract

This paper studies the impact of the payload size in the energy efficiency of a point-to-point link in a wireless sensor network using convolutional codes. Two channel models are considered to represent distinct conditions with respect to the severity of the fading: AWGN, which only accounts for the large-scale effects; and Rayleigh, encompassing both small-scale and large-scale effects in a scenario without line-of-sight. In this context, signal-to-noise ratio, code rate and the payload size are optimized. The numeric results obtained through simulations show that there is an optimal payload size, which depends on the transmission range, and provides gains in the overall energy efficiency. More importantly, these energy efficiency gains obtained by the optimization of the payload size are higher than those observed by the optimization of the SNR and code rate, and more present in shorter transmission distances. Finally, results also show that different optimal values are obtained if the optimization problem focus on minimizing the energy consumption or maximizing the energy efficiency.