Reliable Task Allocation for Time-Triggered IoT-WSN Using Discrete Particle Swarm Optimization

Abstract

A wireless sensor network (WSN) constitutes of nodes that are used to sense a certain physical environment phenomena and sends these collected data to a remote node for further processing and decision making. With the proliferation of Internet of Things (IoT), WSN is evolving to be used as reliable, energy efficient, cost effective, and scalable network infrastructure for many IoT applications. To have a robust WSN with IoT (IoT-WSN), it is a challenge to allocate the tasks among different sensor nodes because the distribution of dependent tasks among wireless nodes must satisfy precedence and time constraints, minimize energy consumption, and prevent signal interference. The problem becomes more complex if a system requires high reliability in the event of link or node failures. This article presents an offline discrete particle swarm optimization algorithm for reliable task allocation (DPSO-TA) in IoT-WSN. DPSO-TA defines a utility function to optimize the task allocation problem by iteratively trying to improve the solution. The defined function is designed to satisfy goals, including saving energy, reducing task completion time, and minimizing the failure rates. A load balance mechanism is applied to make balancing of the tasks on several hosts. During the task allocation process, the frame replication and elimination for reliability (FRER) approach is used to support flow fault tolerance by replicating transmitted flows over redundant routes. DPSO-TA considers the periodicity of the time-trigged (TT) flows and applies a physical interference model to prevent signal interference on the transmitted flows through assigning them to conflict-free time slots. The analysis and simulation results show the optimization of DPSO-TA on timeliness, deadline missing ratio, failure rates, and energy efficiency comparing it with algorithms that allocate tasks to hosts that produce the minimum completion time or that use a traditional procedure to find the most suitable nodes during the task allocation process.