An Efficient ACO-based Routing and Data Fusion Approach for IoT Networks

The ant colony optimization (ACO) is an evolutionary algorithm that tries to imitate the usual biological behavior of ants. Since Internet of Things (IoT) works by integrating and connecting devices of heterogeneous architecture, the size of the network increases rapidly. Therefore, in such situations ACO can be used to attain ideal solutions for large-scale optimization problems. As wireless sensors network (WSN) can integrate itself with IoT, the routing challenges faced by both of WSN and IoT are similar. To cope with the dynamics of the environment many intelligent routing algorithms have been designed. In this paper, an ACO-based routing algorithm for IoT networks has been proposed to analyze and enhance the scalability of the network, by minimizing the delay of the time critical applications. This would help in finding the optimal path for data transmission, and improve the efficiency of IoT communications. The proposed algorithm is simulated using network simulators (NS-2) that showed improvement in conserving energy when compared to the traditional ACO-based routing. Our proposed scheme prolonged the network lifetime and was found to have a 20% more packet delivery ratio, 19% reduced end-to-end delay and almost consumed 78% less energy.