Resource aware medical IoT data management system using Social IoT based edge computing

Resource aware medical IoT data management system using Social IoT based edge computing

연구 분야: Cryptography

학회: Cluster Computing

IoT is gradually taking on greater significance in our lives and is pervasive in our surroundings. Our daily lives are undergoing a significant transition in tandem with the growing use of IoT devices and technology. Medical IoT has developed into a significant IoT application space that links a range of medical IoT devices worldwide. Numerous wearable and stationary medical IoT devices are used in various medical IoT applications, producing vast amounts of data. However, these devices are driven by tiny sensors that have limited resources and must be used as efficiently as possible. Managing data in Medical IoT systems is a multifaceted challenge involving resource constraints, security, real-time processing, compliance, data analytics and heterogeneity of devices. Medical IoT uses a variety of resource types that fit into the physical and virtual resource categories. Computational, networking, storage, and energy resources are examples of physical resources. On the other hand, protocols and algorithms used for data processing and communication are considered virtual resources. Medical IoT devices create vast amounts of data, which places a heavy demand on processing and storage capacity to enable improved connectivity and efficient communication. One of the ongoing challenges facing healthcare organizations is resource limitation. Finding resource limits in medical IoT devices and minimizing their impact are necessary to increase the efficiency of the medical IoT system. Therefore, our objective is to propose social IoT based data management framework capable of addressing resource optimization taking heterogeneity of devices into consideration. As demonstrated by the findings, the suggested system performs better than traditional cloud-based medical IoT systems that disregard data processing at edge using Social IoT approach by (i) reducing cloud bandwidth usage by 43.4% (ii) reducing cloud storage by 33.4% (iii) cloud processing power by 53.4% (iv) reducing cloud processing delay by 63.4% and (v) device energy by 23.4%.