Clustering with a high-performance secure routing protocol for mobile ad hoc networks

• Published in: The Journal of supercomputing Vol. 78; no. 6; pp. 8830 - 8851
• Main Authors: Srinivas, Maganti, Patnaik, M. Ramesh
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-04-2022; Springer Nature B.V
• Subjects: Clustering; Compilers; Computer Science; Energy consumption; Geographical locations; Heuristic methods; Interpreters; Mathematical analysis; Mobile ad hoc networks; Mobile and Intelligent Sensing on High Performance Computing; Optimization; Physical properties; Processor Architectures; Programming Languages; Route selection; Routing (telecommunications); Search algorithms; Wireless sensor networks; Routing; Gravitational search algorithm; Clustering; Metaheuristics; Glowworm swarm optimization
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-021-04258-6

Mobile ad hoc networks (MANETs) comprise a collection of independent, compact-sized, and inexpensive sensor nodes, which are commonly used to sense the physical parameters in geographical locations and transmit them to base stations (BSs). Since clustering and routing are considered commonly used energy efficient techniques, several metaheuristic algorithms have been employed to determine optimal cluster heads (CHs) and routes to destinations. However, most metaheuristic techniques have failed to achieve effective clustering and routing solutions in a large search space, and the probability of generating optimal solutions is also considerably reduced. To resolve these issues, this paper presents a new metaheuristic quantum worm swarm optimization-based clustering with a secure routing protocol for MANETs, named QGSOC-SRP. The presented QGSOC-SRP technique follows a two-stage process, namely optimal CH selection and route selection. First, the QGSO algorithm derives a fitness function using four variables, the energy, distance, node degree, and trust factor, for the optimal selection of secure CHs. Second, the SRP using the oppositional gravitational search algorithm (OGSA) is applied for the optimal selection of routes to the BS. The traditional GSA is inspired by the law of gravity and interaction among masses. To improve the effectiveness of the GSA, the OGSA is derived based on the opposition-based learning concept for population initialization and generation jumping. To validate the results regarding the effectiveness of the presented OGSOC-SRP technique, a set of experiments was performed, and the results were determined using distinct measures such as the energy consumption, network lifetime, throughput, and end-to-end delay.