A TAXONOMY OF ROUTING PROTOCOLS IN SENSOR NETWORKS in .NET framework

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A TAXONOMY OF ROUTING PROTOCOLS IN SENSOR NETWORKS
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coordinates in such a scenario in which any other geographic routing protocols can be deployed without any location information. The protocol has three stages. The initial assumptions are gradually removed as the protocol proceed further. It is essential to point out that the virtual coordinates assigned to the nodes do not have to accurately match to the geography of the places, but they should re ect the underlying connectivity. The local connectivity information is used to set these coordinates. The three stages of the protocol are described below in detail. 1. Perimeter Nodes Know Location: Initially, it is assumed that all perimeter nodes know their locations and the nonperimeter nodes are assigned virtual coordinates. The analogy used, which is borrowed from graph theoretic approaches, states that each neighbor relation is represented by a force that pulls the neighbors together. Thus, the force in the x and y direction is proportional to the difference in x and y coordinates of the nodes, respectively. Iteratively, each node updates its virtual coordinates by calculating the average of x and y coordinates of the neighbor set of each node. As the number of iterations increase, the nodes tend to move toward the perimeter nodes closest to them, and ultimately the algorithm converges to a steady state where the nodes are spread throughout the region. 2. Perimeter Nodes Are Known: The perimeter nodes are aware that they are on the perimeter; but unlike the rst step, they do not know their location. Each perimeter node broadcasts a HELLO packet to the entire network to discover the hop distance to every other perimeter node. These distance pairs are stored in a perimeter vector in which each perimeter node in turn broadcasts its perimeter vector to the entire network. As a result, every perimeter node becomes aware of its hop distance from other perimeter nodes. A triangulation algorithm is executed to enable the nodes to compute the coordinates of all the perimeter nodes in the network. The triangulation algorithm could fail under some conditions. As a part of the solution to this problem, two nodes are designated as bootstrap beacons. These nodes ood the network with HELLO messages. The perimeter nodes include these beacons in their triangulation algorithm to compute the coordinates of all the perimeter nodes. 3. No Location Information: The nodes use the following criteria as stated in reference 20 to decide if they are perimeter nodes: If a node is the farthest away, among all its two-hop neighbors from the rst bootstrap node, then the node decides that it is on the perimeter. Energy-Ef cient Forwarding Strategies for Geographic Routing in Lossy Wireless Sensor Networks [21]. The main aspect of geographic forwarding includes the greedy forwarding of message packets by a node, usually to the neighbor located closest to the destination. This strategy works only under the following assumptions: (i) suf ciently dense network; (ii) accurate localization; and (iii) high link reliability irrespective of the distance within the physical radio ranges. It has been
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SENSOR NETWORKS ROUTING PROTOCOLS
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observed that even though the rst two assumptions may hold for most types of systems, the nal assumption regarding high reliable links will not be fully satis ed in a realistic scenario. This is because wireless links are inherently unreliable in nature and are subject to various environmental conditions causing the weak links to drop a high number of packets. These packets are then retransmitted, which leads to high energy consumption. In order to alleviate the problem, the neighbors are classi ed based on link reliability. Not only could some neighbor links be weaker than the others, their loss characteristics could also be different. Hence, a blacklisting/neighbor selection scheme is devised to avoid the weak and unreliable links. The distancehop energy tradeoff performance metric is proposed for geographic forwarding. If the forwarding scheme aims at minimizing hops as in greedy forwarding, a significant energy consumption can occur due to transmitting and/or retransmitting the packets on long and possibly unreliable weak links. On the other hand, if smaller distances are covered across stronger links, more hops would be visited again, causing increased energy usage. The optimal choice is generally in the transitional region between these two strategies. However, let us note that not too many links should be blacklisted since this would cause greater route disconnections and lower delivery rates.
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Geographic Routing with Limited Information in Sensor Networks [22]. This protocol shows that even for instances where there is erroneous or limited location information due to faulty GPS, the order of routing delays is within a constant factor of straight-line greedy routing strategies. The limited information means that nodes only know the relative quadrant or zone where the destination node is situated. Nodes can be forwarded in the wrong direction if the GPS at nodes are faulty or biased even when the nodes have the correct destination coordinates. The four basic scenario models are discussed below. 1. Location Errors (Imprecise GPS): An angular error is introduced within greedy straight-line routing so that the packet is forwarded anywhere in the zone within the angle. 2. Limited Destination Information: It is assumed that the node has only a coarse estimate of the destination location such as the quadrant or half-plane information. 3. Small Fraction of Nodes with Routing Information: In this scenario, only a small fraction of nodes know about the destination quadrant. If a node that has no information about the destination has a packet to send, it simply selects a neighbor randomly to forward the packet. 4. Throughput-Capacity Networks: The packet is forwarded in the right direction but not in a straight line along the shortest path. It uses speci c progressive routing strategies to selectively forward the packets toward the destination. This leads to spatial hot spots within the network because many paths may intersect due to suboptimal selection of routes.
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