Intelligent probabilistic broadcasting in mobile ad hoc network: a PSO approach

Abstract

MANETs are collection of independent nodes, which communicate with each other to perform a task. Broadcasting methods are widely used in this infrastructureless networks. Although broadcasting is easy to implement and a method to perform routing and safety functions, in a wide and high mobility MANET it is a difficult and expensive task to achieve. It is required that the underlying algorithm used for communication must consider parameters such as neighborhood density, the size and shape of the network, and the efficient use of channel. Probabilistic strategies are frequently used, as they do not introduce additional latency. Several researchers have proposed using various parameter instances which are managed dynamically, for instance, the change in the number of neighbor nodes and corresponding change in retransmission probability. But the authors did not optimize the parameters for specific environments. The proposed work in this research article suggests and determines the most efficient strategy for each node to decide the retransmission probability according to its neighborhood density, available bandwidth and remaining energy of a node. It describes a tool combining a network simulator (ns-2) and a particle swarm optimization algorithm. Then, it is applied to the MANET broadcasting problem. The simulation results show that the proposed particle swarm optimization probabilistic broadcasting (PSOPB) scheme is reliable and efficient in comparison with the other artificial intelligence broadcasting schemes such as elitist simulated binary evolutionary algorithm (ESBEA), multi-objective problems with Pareto front solution (MOP_PF) and efficient fuzzy logic-based probabilistic broadcasting (EFPB).