Optimization of Handover Events for an Efficient Resource Allocation in an Internet of Things Network Through Computational Simulation Based on The Moivre-Laplace Theorem

When the Internet of Things (IoT) network is established and running in large cities is crucial to measure the quality of service (QoS) associated to it. This clearly demands that the wireless communications connection have competitive network capabilities such as efficient radio resource allocation which should be coherently optimized in order to maintain an acceptable QoS and allow users to perform soft and hard handover without major limitations. In this paper we measure the through-put for uplink and downlink events through a computational scheme based essentially in a mathematical formalism derived from the Moivre-Laplace theorem as well as the Shannon’s entropy. All this formalism and the computational methodology are done under the assumption that users are carrying out interference each other. We have estimated probabilities for accessing the network including in our methodology the blocking probabilities expected to be realized in hybrid networks that are running IoT with unexpected saturation of requests for cells occupation. From the results of this paper the throughput error of signal to interference ratio for a concrete scenario of mobile users trying to occupy a cell in a base station have been of order of 5%.