In mobile ad hoc networks (MANETs), there is no fixed infrastructure. One of the most widely used routing protocols for an ad hoc network is the Ad hoc On Demand Distance Vector routing protocol, abbreviated as AODV. In the conventional AODV routing protocol, source node forwards RREQ (Route Request) packet to find out path to the destination node. The intermediate node having less lifetime or energy, also forwards RREQ. As lifetime expires after some time i.e. node goes down; it could not forward RREP (Route Reply) on reverse path. Hence, source node has to restart RREQ rebroadcast to communicate with destination, which results in unnecessary RREQ rebroadcast, less Packet Delivery Ratio (PDR) as well as throughput and more end to end delay.Solution to above problem is given in this paper, by Optimized AODV (OAODV) routing protocol. In this, the node does not forward RREQ unless there is sufficient energy (battery lifetime), and until the node density in its surrounding exceeds a particular threshold. These two parameters are defined taking into consideration various statistics. Optimized AODV analyzes these two parameters, when implementing routing discovery, and avoiding the unnecessary information sending efficiently. By comparing AODV with optimized AODV in the same scenario, the new protocol is much better than AODV in terms of battery lifetime and throughput. General TermsAd hoc network routing protocol, Wireless ad hoc networks et. al.

Abstract:In this paper, we propose a novel method for iris recognition system using enhanced isocentric segmentor (EISOS) and wavelet rectangular coder (WRC). At first, we locate the center of the eye within the area of the pupil on low resolution images using EISOS method. Once the iris region is successfully segmented, the next stage is to transform the iris region into the fixed dimensions. Then, we propose a novel feature vector generation method namely, wavelet rectangular coder (WRC). Finally, we recognize the iris image using fuzzy logic classifier to identify whether the iris image is present in the dataset or not. The iris recognition performance is measured using different dataset such as, CASIA, MMU and UBIRIS dataset. Experimental results indicate that the proposed method of EISOS+WRC based iris segmentation and recognition framework have outperformed by having better accuracy of 99.75% which is 94% and 93% for using existing approaches.

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