LAUSR

With the emergence of Internet of Things (IoT) having large scale and generating huge amount of data, Intelligent Decision Support Systems (IDSSs) have attracted a lot of attention for provisioning the required Quality of Service. IoT perception layer is responsible for data dissemination of the “Things”, and energy efficient clustering protocols play an important role in providing them with long-time battery operation. Clustering routing protocols are among the most efficient methods in large scale IoT networks and using location-based decision support can highly simplify the routing problem. Existing literature either assume that the nodes’ location is known, or rely on the expensive and energy consuming GPS modules which are not practical in most IoT use cases. Developing a low-cost and low-energy localization solution is an ongoing challenge. In this paper, an IDSS based clustering routing protocol, named GAPSO-SVM, is proposed for the IoT perception layer utilizing a Support Vector Machine (SVM) based algorithm to estimate the nodes’ locations, and a hybrid Genetic Algorithm-Particle Swarm Optimization (GAPSO) based mechanism for clustering optimization. Simulation results show that, although the exact location of the nodes is not available, compared with recent similar works the convergence rate and network lifetime is enhanced by up to 80% and 11%, respectively.