An exploration of integration of IoT and 5G networks and a fuzzy approach for clustering to enhance the lifetime of such networks

Abstract

Medium access control protocols play an important role for allocating the medium to the smart devices connected on the networks. These networks include IoT and sensor networks, IoT and 5G networks, IoT and optical networks using 5G communication etc. MAC protocols ensure the efficient and fair communication through these networks from devices to base station. Wireless sensor networks are considered as the back bone for the internet controlled applications. These networks set up link between virtual and real world. The selection of the suitable MAC protocol can help in improving the network life time so the data collected by the nodes is delivered to its destination. So this paper explores the various MAC protocols available for IoT and 5G applications and .The parameters of study for these MAC protocols used for the real time applications includes residual energy, throughput, packet delivery ratio, latency and network lifetime. The paper also explores the usages of machine learning and AI used in MAC protocols for these high speed IoT networks. The work addresses the improvement of network lifetime and residual energy by applying Fuzzy approach for the selection of cluster head . The energy consumption increases if the clusters are not balanced in terms of the nodes so we have used the grid formation of the clusters. We used the Fuzzy Logic Inference system (FIS) in the cluster formation technique. The probabilistic method for election of cluster head does not consider the parameters such as residual energy, node position and base station distance. This result in the improper selection of the cluster head .The use of multiple parameters in the FIS improves the network lifetime and residual energy as compared to the classical LEACH protocol. Fuzzy logic approach solves the uncertainties occurring in the system especially when it is handling the larger amount of data. The fuzzy approach shows the prolonged network lifetime in terms of first node die and the Last node Die. We have also used the statistical regression approach to calculate the residual energy and network life time in terms of alive nodes. Finally this chapter ends with the discussion of challenges and future scope of the MAC protocols used for the real time applications. The work is important as it gives opportunity for the research areas in high speed IoT and 5G networks for using AI and ML intelligent to develop QoS MAC protocols.