QoS Routing enhancement using metaheuristic approach in mobile ad-hoc network

• Published in: Computer networks (Amsterdam, Netherlands : 1999) Vol. 110; pp. 180 - 191
• Main Authors: Mandhare, V.V., Thool, V.R., Manthalkar, R.R.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 09-12-2016; Elsevier Sequoia S.A
• Subjects: ACO; Ad hoc networks; Algorithms; Ant colony optimization; AODV; Computer networks; Computer simulation; Cuckoo search; Fitness; Heuristic; Heuristic methods; MANET; Optimization; PSO; QoS; Quality; Quality of service; Quality of service architectures; Residual energy; Routing; Routing (telecommunications); Search algorithms; Shortest-path problems; Studies; Swarm intelligence; Wireless networks; Cuckoo search; MANET; QoS; Routing; ACO; PSO; AODV
• ISSN: 1389-1286; 1872-7069
• DOI: 10.1016/j.comnet.2016.09.023

The Quality of Service Routing (QoSR) is always a tricky problem, due to dynamic nature of network, which is always Non-deterministic Polynomial-time (NP) hard. To resolve the problem, multi-constrained QoSR in Mobile Ad-hoc Network (MANET), an intelligent algorithm have been proposed to find the feasible path. This paper focuses on, satisfying the constraint of QoS in MANET inspiring Cuckoo Search(CS) algorithm, based on enhancing conventional CS technique using on-demand protocol. This approach select QoS path based on computation of best fitness value instead of shortest path for Route Replay (RRPLY) packet of Ad-hoc On-Demand Distance Vector (AODV) protocol. The fitness value is computed using three different parameters namely, routing load, residual energy and hop count. The algorithm is applied on AODV protocol for RRPLY, where multiple routes are available. The Cuckoo Search Optimization AODV (CSO-AODV) protocol gives better QoS routing metrics, satisfying QoS constraint. The obtained results of proposed CSO-AODV protocol are compared with, Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and basic AODV protocol, tested for three different condition i.e. mobility, scalability and congestion. The simulation results of the proposed algorithm is superior compared to ACO, PSO, and AODV algorithms.