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Wireless sensor networks (WSNs) have many applications and are an essentialpart of IoT systems. The primary functionality of a WSN is gathering data fromspecific points that are covered with sensor nodes and transmitting thecollected data to remote units for further processing. In IoT use cases, a WSNinfrastructure may need to be shared by many applications, which requiresscheduling those applications to time-share the node and network resources. Inthis paper, we investigate the problem of application scheduling in WSNinfrastructures. We focus on the scenarios where applications request a set ofmonitoring points to be sensed in the region a WSN spans and propose ashared-data approach utilizing multiplexed sensing of monitoring pointsrequested by multiple applications, which reduces sensing and communicationload on the network. We also propose a genetic algorithm called GABAS, andthree greedy algorithms for scheduling applications onto a WSN infrastructureconsidering different criteria. We performed extensive simulation experimentsto evaluate our algorithms and compare them to some standard schedulingmethods. The results show that our proposed methods perform much better thanthe standard scheduling methods in terms of makespan, turnaround time, waitingtime, and successful execution rate metrics. We also observed that our geneticalgorithm is very effective in scheduling applications with respect to thesemetrics.

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