Method for replica selection in the Internet of Things using a hybrid optimisation algorithm

Abstract

Internet of Things (IoT) as a new technological revolution has been proposed recently wherein the things are connected over the Internet. Because of the inherent characteristics of IoT for storage of data at untrusted and heterogeneous hosts, data replication across large geographic distances for efficient data management is unavoidable. The selection of appropriate replication things in the IoT, which reduces response time and cost is one of the most important issues of data management. Since this problem is an NP-hard problem, classic approaches are not efficient to solve this issue, and evolutionary algorithm such as ant colony optimisation (ACO) and genetic algorithm (GA) seems to be very useful. This study offers a method based on a combination of ACO and a GA to solve this problem. In the proposed method, the ACO has been used to create diversity, and afterwards, the GA is performed to provide a full search over the search space. The obtained results have shown the better performance of the proposed method in comparison with ACO, the High-QoS First-Replication (HQFR), and the Response Time-based Replica Management algorithms with regard to waiting time. In addition, the obtained results have revealed the better performance of the hybrid method in comparison with HQFR and the Dynamic Cost-aware Re-replication and Re-balancing Strategy.