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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.

The Electro Cardiogram Signals (ECG) are utilized for detecting cardiac abnormalities for more than a century, since cardiac-related diseases are considered as the primary cause of death for the increasing degree of patients over the years around the world. Heart Variability investigation schemes are determined to be the potential cardiac rhythms automatic classification techniques for precise assessments of rhythm disorders. However, accurate QRS and T wave detection techniques are identified for effective and reliable HRV interpretation process. This review presents the role, merits and limitations of the most recent QRS and T Wave detection techniques proposed in the literature in the last three years for confirming Cardiac Abnormalities. This survey presents a diversified number of evaluation metrics that could used for determining the predominance of the reviewed schemes. This survey also presents a list of open research issues and challenges that could be focused by the growing researchers around the world.The major outcome of the survey concentrated on the determination of merit and limitations of the existing QRS and T Wave detection techniques in order to propose future research that confirms cardiac abnormalities with minimized overhead.