An Optimized Data Replication Algorithm in Mobile Edge Computing Systems to Reduce Latency in Internet of Things

Abstract

AbstractThe actual amount of data that was created applying the actuators, the sensors, and some other devices for the Internet of Things (IoT) has been showing a substantial level of increase in recent years. The data of IoT are handled using the cloud utilizing computing resources that are located in the data canters at a distance. As a result, the bandwidth of the network and the latency of communication have become major bottlenecks. The technology is known as Mobile Edge Computing (MEC) primarily seeks at encompassing the abilities of the cloud to the very edge of its radio access network thereby achieving low latency, real-time, and high bandwidth to the resources of the radio network. The IoT has been recognized as a key of the MEC with the ability of the MEC to be able to provide a new cloud platform along with gateway services. The MEC further inspired the progress of several masses of services and applications for a low-latency but high Quality of Service (QoS) owing to the geographical distribution and support for mobility. The MEC enables the applications and services of IoT for real-time operations. Replication of data is also suitable for increasing global traffic and response time and helps in data sharing. The nodes thereby continue to get access to the data replicas. This makes the problem of optimization work with many objectives. Flower Pollination Algorithm (FPA) is used to solve unconstrained optimization problems. Researchers are attracted to this algorithm for its processing speed, ease of modifying based on the requirement, and robustness. In this work, FPA is used to optimize the data replication. Experimental results shows the efficacy of the proposed method.KeywordsInternet of Things (IoT)Mobile Edge Computing (MEC)Data replicationCloud computingSimulated Annealing (SA) Algorithm and Flower Pollination Algorithm (FPA)