A Local Channel Blacklisting Method Using Adaptive Channel-Quality Estimation in TSCH-Based Wireless Sensor Networks

Abstract

Owing to the development of the Internet of Things (IoT) paradigm, the energy consumption of devices and the reliability of communication have become important issues. Enhanced TSCH technology introduces a technique to select high-quality channels by using energy detection in the TSCH protocol to improve the reliability of communication in a dynamic environment where interference changes. However, it is difficult to apply ETSCH technology to a multi-hop network because the node that performs energy detection consumes more energy than the node that does not. In this article, we propose an adaptive channel-quality estimation (ACE), which flexibly adjusts the duty cycle of energy detection according to whether interference dynamically changes or not. ACEs are generally applicable regardless of the degree of change of interference, which improves energy efficiency. Also, we present ACE-blacklisting based TSCH (ACEBTSCH) that uses ACE and local channel blacklisting to blacklist the wireless channel based on energy detection in a multi-hop network. Experimental results show that ACEB-TSCH has a performance improvement of 15.94% over TSCH and 8.59% over PDR-blacklisting based TSCH.