An improved localization algorithm to replace faulty nodes for an IoT network using weighted grey wolf optimization

Localization algorithms are essential for tracking, fault tolerance, and adding location context to Internet of Things (IoT) networks. Among the various range-free techniques, DV-Hop stands out for its lower computational complexity. Despite efforts to minimize manufacturing costs for sensors, they often find themselves deployed in remote and hard-to-access locations, making battery replacement a challenging prospect. These sensors can be utilized in a hostile environment, such as a field of battle, fires, floods, and earthquakes. In these types of scenarios, the sensors can become dead and stop communicating with each other. Hence, there is a critical need to improve algorithms that can tolerate and detect faults in network nodes. The faults of the sensor can lower the quality of the algorithm if they are not replaced. In such situations, we need to distribute supplementary target nodes to replace faulty nodes. This paper presented an improved DV-Hop based method for localization of additional target nodes using grey wolf optimization (GWO). The proposed method will only localize the position of target nodes and minimizes the localization time, which makes the system more energy efficient. Simulation results conclude that the proposed GWO-based DV-Hop localization performed well as compared with traditional DV-hop, genetic DV-Hop, and Adaptive genetic DV-hop. This paper presented an improved DV-Hop-based methods for localization of additional target nodes using grey wolf optimization (GWO). In a hostile environment, such as field of battle, fires, floods, earthquakes, the sensors can become dead and stops communicating with each other. Hence, there is a critical need to improve algorithms that can tolerate and detect faults in network nodes. In such situations, we need to distribute supplementary target nodes to replace faulty nodes as shown in Figure 1. The simulation outcomes indicate that the GWO-based DV-Hop localization method outperformed the traditional DV-Hop, genetic DV-Hop, and adaptive genetic DV-Hop approaches. image