Energy-Efficient Data Gathering in Wireless Sensor Networks Using Hybrid Oppositional Fruitfly and Bacterial Foraging Optimization

Abstract Wireless sensor networks (WSNs) are very useful in all areas such as health care, environmental sensing, surveillance, automation, etc. and are capable of performing real time data collection. One of the main limitations of WSNs is their limited life span because they have limited power source. Also, an efficient method for collecting data has not been developed yet which also limits the life cycle of WSNs. In this paper we propose a hybrid algorithm using two meta-heuristics called Bacterial Foraging Optimization (BFO) and Oppositional Fruitfly Optimization (OFF). The goal of our proposal is to improve the efficiency of data collection while minimizing the energy consumption of WSNs. We use Weighted K-means Clustering (WKMC) to cluster WSN nodes based on their remaining energy; OFF will be used to select the Cluster Head (CH); and BFO will be used to calculate the route of a moving sink. Our approach eliminates hotspots, prevents congestions, minimizes energy waste and provides better scalability. To minimize idle energy loss we also add an adaptive cluster head sleeping strategy. Simulations were performed with NS-2 over the UNSW-NB15 dataset that includes benchmarks of common network attacks and other standard configurations for WSNs. These results clearly show that our proposed OFF-BFO model performs much better than others (LEACH, PSO-based clustering, ACO-LTAWSN, AVOA) in terms of network lifetime, PDR (Packet Delivery Ratio), Average Residual Energy per Node, Hop Count, Number of Active Nodes, End-To-End Delay. Compared to LEACH, our proposed method increases the WSN’s lifetime by 68%. Additionally, our proposed method demonstrates a high packet delivery ratio (PDR) of 92.4% when deployed at scale in energy-constrained environments.