PRS-LD: priority-based rank selection load distribution protocol for routing in MANETs

Internet of things (IoT) and fifth generation (5G) both are rapidly flourishing the demand for the development of ad hoc networks to meet the communication requirement of wireless communication. Mobile ad hoc multipath routing protocols offer the facility to build multiple paths for communication between a source node (SN) and the destination node (DN). As the number of paths has been maintained between the two nodes of the network, multipath routing protocols add the reliability feature to the network. Initially, multipath routing protocols make use of a single path for data transfer at a particular point of time. Switching to the next alternate path happens only when the previous one fails. Multipath variant of ad hoc routing protocols is gaining importance nowadays due to the reliability and consistent wireless communication offer by them. It is a difficult task to efficiently distribute the data traffic along multiple available paths with the parallel transmission of packets and is an open research problem that has been addressed by the paper. Load balancing or load distribution feature allows exploitation of the multipath routing in an efficient way. It allows the transfer of data traffics simultaneously along multiple available paths. This not only reduces the energy of nodes along a specific path, but also helps in reducing the end-to-end delay caused by the failure of nodes or paths. In this paper, a priority-based rank selection load distribution (PRS-LD) protocol for performing load-balanced routing has been proposed. It makes use of an optimized priority-based ad hoc on-demand multipath distance vector energy efficient (OPAOMDV-EE) routing protocol for the route discovery phase and rank based selection technique for distributing load or data traffics along the multiple obtained routes. The protocol has been simulated on a network simulator (NS)-2.35 and the analysis of the result proves the effectiveness of the protocol in high-load scenarios by reducing the value of route discovery frequency, routing overhead, the average end-to-end delay, and the number of energy exhausted nodes.