Secured SDN Based Task Scheduling in Edge Computing for Smart City Health Monitoring Operation Management System

Abstract

Health monitoring systems (HMS) with wearable IoT devices are constantly being developed and improved. But most of these gadgets have limited energy and processing power due to resource constraints. Mobile edge computing (MEC) must be used to analyze the HMS information to decrease bandwidth usage and increase reaction times for applications that depend on latency and require intense computation. To achieve these needs while considering emergencies under HMS, this work offers an effective task planning and allocation of resources mechanism in MEC. Utilizing the Software Denied Network (SDN) framework; we provide a priority-aware semi-greedy with genetic algorithm (PSG-GA) method. It prioritizes tasks differently by considering their emergencies, calculated concerning the data collected from a patient’s smart wearable devices. The process can determine whether a job must be completed domestically at the hospital workstations (HW) or in the cloud. The goal is to minimize both the bandwidth cost and the overall task processing time. Existing techniques were compared to the proposed SD-PSGA regarding average latency, job scheduling effectiveness, execution duration, bandwidth consumption, CPU utilization, and power usage. The testing results are encouraging since SD-PSGA can handle emergencies and fulfill the task’s latency-sensitive requirements at a lower bandwidth cost. The accuracy of testing model achieves 97 to 98% for nearly 200 tasks.