Abstract
Abstract In mobile ad hoc network (MANET), the most important issue is to provide a stable and reliable route for the uninterrupted communication. In order to overcome these issues, in this paper, we propose a stable and energy-efficient routing technique. In the proposed method, quality of service (QoS) monitoring agents collect and calculate the link reliability metrics such as link expiration time (LET), probabilistic link reliable time (PLRT), link packet error rate (LPER) and link received signal strength (LRSS). In addition, residual battery power (RBP) is implemented to maintain the energy efficiency in the network. Finally, route selection probability (RSP) is calculated based on these estimated parameters using fuzzy logic. Simulation results show that the proposed routing technique improves the packet delivery ratio and reduces the energy consumption.
Highlights
A mobile ad hoc network (MANET) is a collection of wireless nodes that is self-configured to form a network without the aid of any established infrastructure
The link expiration time (LET), probabilistic link reliable time (PLRT), link packet error rate (LPER) and link received signal strength (LRSS) and residual battery power (RBP) are taken as input for the fuzzy logic engine and based on the results of fuzzy rules, the route selection probability is estimated as output
3.2 Estimation of metrics in order to find the reliable routes in the network, we estimate LET, PLRT, LPER, LRSS and RBP, which are related to the links between the nodes
Summary
A mobile ad hoc network (MANET) is a collection of wireless nodes that is self-configured to form a network without the aid of any established infrastructure. 2 Literature review Weng and Yang [3] have proposed a cross-layer stabilitybased routing mechanism for ultra wideband networks (LS_AODV) where a computing model is established to map the received signal strength into link stability factor and route stability factor, which serves as a routing metric for path selection. Sargolzaey et al [17] have proposed a new cross-layer metric (CLM) and compared the efficiency of link reliability metrics for finding reliable links in MANET It reduces the number of route reconstructions in this network. The received signal strength indicator value predicts a link breakage in its link with its hop to the source node of this active route It reduces the probability of constructing a route with bad links and increases the packet delivery ratio with less packet loss and end-to-end delay. The theory of learning automata (LA) has been used for optimizing the selection of paths, reducing the overhead in the network, and for learning about the faulty nodes present in the network
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