Abstract
Due to the dynamic nature of mobile ad hoc network (MANET), the quality of service (QoS) requires several improvements. The present papercomeswithin the framework of research to optimize QoS in MANET. In this paper, we propose a novel version of OLSR based on the clustering approach which is inspired from Lin and Chu heuristic and adapted to beimplemented inOLSR. We studied its stability and we compared its performances to those of standard OLSR. The metrics we used in evaluating network performances were average end-to-end delay, control routing overhead, and packet delivery ratio. Experimental results show that our alternative significantly reduces the traffic reserved to monitoring the network, which positively influences other performances such as throughput, delay, and loss.
Highlights
mobile ad hoc network (MANET) are mobile radio networks with no infrastructure, allowing a quick and easy implementation
We present a detailed description of the algorithm and its comparison with standard OLSR in terms of routing overhead, average end-to-end delay, and packet delivery ratio
In our experimental study we validate the stability of our alternative in terms of number of generated clusters and average lifetime duration of cluster. We evaluate it in terms of performances routing control overhead, average end-to-end delay, and packet delivery ratio based on CBR traffic
Summary
MANETs are mobile radio networks with no infrastructure, allowing a quick and easy implementation. Each terminal can be used as router to relay other terminals communications The configuration of these multihop roads is carried out by routing protocol. To be effective, these routing protocols must consider the intrinsic characteristics of the network (topology changing), terminals (memory size and computing capacity limited), and the medium of communication (bandwidth limited, interference). Proactive routing protocols try to collect information about the MANET through proactive exchange of messages about their local topology. These protocols reach rapidly their limits when increasing density and mobility of nodes. Each group, called cluster, is represented and managed by a particular node called cluster head
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