An Energy-efficient Duty-cycle Scheduling Algorithm in Delay-constrained Wireless Sensor Networks

Abstract

In this study, we consider the delay-constrained problem in duty-cycled wireless sensor networks (WSNs). In WSNs, a large number of sensor nodes are deployed in a region to collect information about the environment. Sensors are usually equipped with a limited capacity battery. Therefore, reducing energy consumption to prolong network lifetime is a crucial challenge in WSNs. One of the most effective solutions for saving energy is duty cycling mechanism, in which the sensor turns on the radio to wake up for an active period and turns off the radio for the rest of the duty cycle interval. Since sensor nodes usually have a large duty cycle interval to prolong network lifetime, duty-cycled WSNs can suffer from a long end-to-end (E2E) delay. Because delay-sensitive applications have a certain E2E delay requirement, a lot of studies have tried to tackle the long E2E delay problem. However, most existing studies focused on simply reducing the E2E delay rather than considering the delay bound requirement, which makes it hard to achieve balanced performance between E2E delay and energy efficiency. Although a few studies took into consideration both the delay bound requirement and energy consumption, they required specific node deployment or strict time synchronization between nodes in the network. In order to address the limitations in the existing studies, we propose an energy-efficient dutycycle scheduling algorithm in delay-constrained WSNs, namely delay-constrained duty-cycle scheduling (DDS) algorithm. The objective of DDS is to achieve low energy consumption while satisfying the delay bound requirement in various node deployment scenarios depending on user demands. First, based on network topology information collected by the sink, one-hop delay distribution is derived as a function of the duty cycle interval. Then, the E2E delay distribution is estimated using the Lyapunov central limit theorem, which allows each node group to have a different delay distribution. Finally, the duty cycle interval is determined using the estimated E2E delay distribution such that energy consumption is minimized while meeting the delay bound requirement. Practical WSN deployment scenarios are considered to evaluate the proposed algorithm. The simulation results show that DDS can guarantee the given delay bound requirement and outperform existing algorithms in terms of energy efficiency.