Affiliation:
1. Department of Computer Science and Engineering National Institute of Technology Uttarakhand Pauri Uttarakhand India
2. Department of Computer Science and Engineering SGT University Gurugram Haryana India
3. Department of Information Technology IGDTUW New Delhi India
Abstract
SummaryIn wireless sensor networks, sensor nodes (SNs) are placed throughout a wide area and gather information from the surroundings. SN used to detect and send data consumes a lot of energy and dies instantly, which causes network overhead issues. Due to network overhead, network faults occur and do not cover a significant region for data transmission. A meta‐heuristic‐based adaptive routing for large‐scale opportunistic networks (MH‐ARO) is proposed in this work to overcome these problems. The network exploits the dragonfly approach (DA) with opportunistic routing in this protocol. The DA is based on local and global search (GS) optimization. In local search (LS), each node assigns/uses a rank for data transmission and selects the forwarder node after applying the decision level matrix in a single group. In GS, an opportunistic network has multiple groups. Each group sends optimal data to the next group. Each group assigns a relay ranking based on the forwarder node's highest rank. The BS receives critical data from the relay and increases the survival of the node. Based on LS and GS, MH‐ARO is categorized into two parts: (1) optimal forwarder opt (OFO) and (2) optimal route selection (ORS). In OFO, forwarder node selection is based on the following factors: relay ranking, node density, group ranking, residual energy, forwarder distance, and relay distance. ORS follows the adaptive routing and sends the data to BS using the best optimal route. The BS receives critical data without network failure and increases the survival of the node. The maximum active node participates in the network and consumes less energy so that a node covers a large region for communication. Comparing MH‐ARO's performance to competing routing protocols, key performance indicators such as PDR, MSR, alive node, survivability, AEC, AD, and throughput are examined. The results demonstrate that the MH‐ARO performs noticeably better than its rivals regarding energy efficiency.