An Efficient eNB Selection and Traffic Scheduling Method for LTE Overlay IoT Communication Networks

Smart or electronic healthcare is undergoing rapid change from the traditional specialist and hospital-centered style to a disseminated patient-centered using Internet of Things (IoT). Presently, 4G and other advanced communication standards are utilized in healthcare for intelligent healthcare services and applications. Traffic handling is an essential feature for the flexible interoperability of the internet of things (IoT) with other heterogeneous communication networks. Efficient traffic handling controls latency and communication failures due to random access and collision in cellular network overlay IoT. It is challenging for existing communication technology to achieve the necessities of time-sensitive and very dynamic healthcare applications of the future. In this manuscript, adaptive eNB selection with traffic scheduling (AeS-TS) is proposed to improve the efficiency of IoT-long term evolution (LTE) networks. AeS-Tsworks in two phases: adaptive eNB selection and gateway traffic scheduling. In eNB selection, traffic-aware radio infrastructure selection with the offloading feature is presented. eNB selection is preceded by using a preference function to improve the acceptance rate of incoming IoT traffic and minimize transmission loss. In the traffic scheduling phase, sequential and level-based slot transmission is adapted to improve traffic forwarding quality. The slots are selected by analyzing the error in time function using the recurrent learning process.