EF-MPR, a new energy eFficient multi-point relay selection algorithm for MANET

Abstract

MultiPoint Relay (MPR) selection algorithm is a flooding technique for propagating a broadcast message inside an ad-hoc network which reduces the number of unnecessary broadcast messages in order to save more energy in the network, minimize the number of packet collisions, and speed up the propagation time. In this paper, we demonstrate that MPR selection is an application of Set Covering Problem (SCP). A few optimization methods are developed in this work to find the optimum solution including Simulated Annealing (SA), Tabu Search (TS), Genetic Algorithm (GA), and a new greedy algorithm. Extensive simulations are set up to evaluate the developed methods. The new algorithm is named Energy eFficient MPR or EF-MPR in short. The simulation results show that EF-MPR can reduce the number of MPR nodes up to 19%. Moreover, EF-MPR algorithm reduces the power-consumption of network up to 12% and speed up the propagation time by 9%.

A Simple and Energy-Efficient Flooding Scheme for Wireless Routing

“…Reference [28] has highlighted the necessity for a new strategy that can dynamically adjust the broadcast probability based on the current state of the node. In [16,31,32], the energy consumption of MPR nodes, which are the targets of flooding in OLSR routing, is reduced based on the same concept described above.…”

The New Multipoint Relays Selection in OLSR using Particle Swarm Optimization

“…The MPRs selection using GA, SA and TS is introduced by Chizari et al [13]. Nguyen and Minet [10] and Guo and Malakooti [14] are proposed the greedy algorithm and NN for achieving the improvement of the OLSR.…”

“…The mathematical model has been influenced many MPRs selection algorithm. However, only few [10,[13][14] has explored the advantages of artificial intelligence method for MPRs selection enhancement.…”

The New Multipoint Relays Selection in OLSR using Particle Swarm Optimization

“…The development of OLSR continues and has created opportunity to apply artificial intelligence algorithm such as genetic algorithm (GA), simulated annealing (SA), tabu search (TS), greedy algorithm and neural network (NN) also contribute to the improvement of MPRs selection algorithm in OLSR. The MPRs selection using GA, SA and TS is introduced by Chizari et al [13]. Nguyen and Minet [10] and Guo and Malakooti [14] are proposed the greedy algorithm and NN for achieving the improvement of the OLSR.…”

“…The mathematical model has been influenced many MPRs selection algorithm. However, only few [10,[13][14] has explored the advantages of artificial intelligence method for MPRs selection enhancement.…”