Energy Optimization Approaches for Internet of Things

Abstract

Internet of Things (IoT) has become a reality with the emergence of interconnected devices in heterogeneous networks. However, there are several challenges which are need to be addressed to enhance the robustness of IoT, including energy optimization, routing, load balancing and communication overhead. Two research methodologies are proposed novel solutions for IoT networks to address these challenges. The first research methodology proposes a QoS-aware Energy- efficient Multipath Routing (QEMR) protocol for IoT networks using a hybrid optimization algorithm. The second research methodology proposes a Communication Overhead aware Optimal Cluster-based (COOC) routing algorithm for IoT networks based on a hybrid heuristic technique. In the first research methodology, optimal clustering is performed using a Modified Teaching-Learning-based Optimization (MTLO) algorithm, and the cluster head is computed using a Nonlinear Regression-based Pigeon Optimization (NR-PO) algorithm. The Deep Kronecker Neural Network (DKNN) for routing and optimal path selection is used. In the second research methodology, load-balanced clusters are formed using k-means clustering, fuzzy logic and genetic algorithm and the rank of each node in a cluster is computed using multiple design constraints optimized using the Improved COOT (I-COOT) bird optimum search algorithm. The cluster head is selected based on the rank condition and the optimal best path between IoT nodes are chosen using the Chaotic Golden Search Optimization (CGSO) algorithm. In the first research methodology, the proposed QEMR scheme is implemented in the NS-2 simulation tool. It is compared with existing state-of-the-art REER, Rumor and EOMR schemes in terms of the impact of node density, node speed and network traffic. In the second research methodology, the proposed COOC routing algorithm is validated against different simulation scenarios and compared with existing state-of-the-art routing algorithms in terms of communication costs, end- to-end delay, packet loss ratio,