Improved Priority-based Congestion Control Protocol for Multi-Access Edge Computing (MAEC) Using IoT-based Wearable Devices for Neurological Diseases Diagnosis

Stroke is one of the fatal diseases that affect the brain and causes death within 3 to 10 hours. However, most of the deaths caused by a stroke can be avoided with the identification of the nature of stroke and react to it in a timely manner by intelligent health systems. Internet of Things (IoT) has become an important aspect in medical industry for monitoring of stroke related data/information using various wearable devices. Moreover, Multiple-Access Edge Computing (MAEC) is playing a major role for processing, analysing, and storing of data which leads to several researchers to compete in improving the mechanism of congestion control. In this paper, an improved Priority-based Congestion Control Protocol (iPCCP) is proposed for obtaining increased throughput, decreasing delay, effective resource utilization, and longer network lifetime by optimal energy consumption among IoT based sensor nodes. The proposed method categorizes the data-traffic into emergency and normal data. The packet delivery rate is considered for the normal data-traffic and retains the size of the buffer to improve the throughput and avoids the packet drops due to congestion. The energy consumption and network traffic load is reduced using the data aggregation and filtration technique. For emergency situations, priority-based routing scheme is used to have greater throughput and lesser delay. The performance of the proposed technique outperforms in term of traffic load, lifespan, energy consumption, and network throughput and simulation results are compared with other existing methods to show the improvement of the proposed work.