Dynamic topology and power-controlled lightweight mobile devices usually affect the performance of wireless ad hoc networks and have always been a major focus of research. Data transmission between a source-destination pair is governed by different types of routing protocols like proactive, reactive, hybrid, geographical, etc. Reactive routing techniques are highly preferred due to on-demand source-based routing with limited space requirements. The routing process should consider the link availability between the communicating nodes to get a stable route. If any two nodes along the route become unreachable, the communication could be interrupted. This could cause the receiver to lose data and experience longer delays. In this paper, we suggest a way to improve the existing DSR protocol. The focus is on choosing a stable route with less control overhead. During the route request phase, every participating node calculates its link availability time (LAT) for each adjacent link based on the current position of the neighbor and
receiving signal strength. Links with lesser LATs than a threshold are not considered for route setup hence the flooding of control packets is reduced. Simulation has been performed over the basic DSR and proposed improvements over DSR in terms of control overhead, successful data transmission rate, and the number of route errors generated in the network. The findings show that previous knowledge
of link availability during the routing phase aids in the discovery of more stable routes with lower control overhead, thereby promoting reliable data delivery and improving the overall network performance.
Particle Swarm Optimization algorithm (PSO) is found to be an effective meta-heuristic swarm-based algorithm in solving modern time problems. Various improvements have been proposed in this algorithm in terms of internal computation, acceleration coefficients, stopping criteria, hybridization, velocity upgradation etc. The objective of this paper is to implement hybrid weights and, therefore, improve the quality of PSO algorithm. In the case of hybrid weights, we have combined two weights at a time. These weights are mixed in various but not in equal proportions and are tested against ten standard testing functions along with the pre-existing weights. By using this collection, we have analysed them on three parameters-mean, standard deviation, and minimum value achieved. Later on, after analysing the data, we found out that hybrid weights are an overall better option with respect to the pre-existing weights.
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