Application Scheduling with Multiplexed Sensing of Monitoring Points in Multi-purpose IoT Wireless Sensor Networks

TL;DR

This paper addresses application scheduling in IoT wireless sensor networks by proposing multiplexed sensing and novel algorithms, significantly improving scheduling efficiency and resource utilization.

Contribution

It introduces a shared-data multiplexed sensing approach and new scheduling algorithms, including a genetic algorithm, for better application management in WSNs.

Findings

Proposed algorithms outperform standard methods in key metrics.

Genetic algorithm GABAS is highly effective for scheduling.

Multiplexed sensing reduces network load and improves performance.

Abstract

Wireless sensor networks (WSNs) have many applications and are an essential part of IoT systems. The primary functionality of a WSN is gathering data from specific points that are covered with sensor nodes and transmitting the collected data to remote units for further processing. In IoT use cases, a WSN infrastructure may need to be shared by many applications, which requires scheduling those applications to time-share the node and network resources. In this paper, we investigate the problem of application scheduling in WSN infrastructures. We focus on the scenarios where applications request a set of monitoring points to be sensed in the region a WSN spans and propose a shared-data approach utilizing multiplexed sensing of monitoring points requested by multiple applications, which reduces sensing and communication load on the network. We also propose a genetic algorithm called GABAS, and three greedy algorithms for scheduling applications onto a WSN infrastructure considering different criteria. We performed extensive simulation experiments to evaluate our algorithms and compare them to some standard scheduling methods. The results show that our proposed methods perform much better than the standard scheduling methods in terms of makespan, turnaround time, waiting time, and successful execution rate metrics. We also observed that our genetic algorithm is very effective in scheduling applications with respect to these metrics.