Routing with guaranteed delivery in ad hoc wireless networks
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Almost all existing routing and broadcasting protocols for ad hoc networks assume an ideal physical layer model. In this article, we present new greedy, localized algorithms for ad hoc wireless networks for maximizing probability of delivery, without acknowledgements, considering a realistic physical layer. Using the log normal shadowing model to represent a realistic physical layer, we use the probability p(x) for receiving a packet successfully as a function of distance x between two nodes and define the transmission radius R as the distance at which p(R)=0.5. The probability p(x) of receiving a packet of length L is computed as b(x)^L where b(x) the probability of successfully receiving a bit. In Expected Progress Routing, without acknowledgements (referred as nEPR), a node S currently holding message, destined for node D, will forward to a neighbor A (closer to destination than itself) that maximizes p(|SA|)(|SD|-|AD|). In Iterative Expected Progress Routing (referred as InEPR), we first find a node A that maximizes p(|SA|)(|SD|-|AD|) as in nEPR and then iteratively find an intermediate neighbor node B of S and A (B is closer to D than S) with maximum p(|SB|)p(|BA|) measure, while satisfying p(|SB|)p(|BA|)p(|SA|). In Projection Progress algorithm, a neighbor that maximizes p(|SA|)(SD.SA) is selected to forward the message. An improved variant, Iterative Projection Progress, similar to InEPR is also presented. We also present the EER (end-to-end routing) protocol, where the probability of end-to-end successful delivery is maximized and show that it is same as the NC (Nearest Closer) method proposed earlier. The algorithms, nEPR, InEPR, Projection Progress, Iterative Projection Progress and EER (NC) schemes are implemented and their performance evaluated and compared with that of shortest path and tR greedy schemes. Our newly proposed localized routing protocols are performing better than the greedy routing schemes which forwards the packet to neighbor closest to destination.