ENCODING FOR EFFICIENT DATA DISTRIBUTION IN MULTIHOP AD HOC NETWORKS in Visual Studio .NET

Generator QR Code 2d barcode in Visual Studio .NET ENCODING FOR EFFICIENT DATA DISTRIBUTION IN MULTIHOP AD HOC NETWORKS
ENCODING FOR EFFICIENT DATA DISTRIBUTION IN MULTIHOP AD HOC NETWORKS
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content of several packets they have received. Simulation results show that this algorithm achieves the reliability and robustness of ooding at a small fraction of the overhead. Two interesting topics have been discussed, namely generations and redundancy. The solution for generation recognition which has been proposed in reference 19 j is as follows. Let X(i) be the jth information vector that originates at the ith source j node Si . Then a function f (X(i) ) determines which generation the packet belongs to j j and = {X(i) |f (X(i) ) = } is the set of all the source vectors of a generation . Namely, the generation membership is determined through hashing over the sender address and the packet identi er. A new hash function is generated whenever the matrix becomes too big. The hash function is then used at a node to determine which generation to insert a given packet into, provided that the size of this generation does not exceed a certain max threshold. It has been demonstrated that managing generations through hashing works better than simply incrementing the generation index from time to time, especially in ad hoc networks. Another interesting topic raised in reference 19 is on the forwarding strategy and, speci cally, on how to decide when to send a new packet. The solution that has been proposed relies on a so-called forwarding factor d > 0 and establishes that whenever an innovative packet is received or generated at a node for a given generation, this has to forward a certain number of packets depending on the value of d and on whether the node is a source or an intermediate node. Speci cally, in case of an intermediate node, it rst generates d vectors from the corresponding matrix and rebroadcasts them to the neighbors, and then it generates and sends a further vector with probability d d . In the case of a source node, whenever it generates a new original packet it encodes and broadcasts to the neighbors max(1, d ) vectors. It then produces and sends out a further vector if d > 1 with probability d d . The delivery policy at source nodes is obviously a bit different, since at least one packet must be generated from each newly produced packet. In other words, a new packet is sent out by the source at least once. With the network coding model described above, a node sends out, on average, dG + 1 packets where G is the maximum generation size (G = m in the simplest case). Since receivers can decode all original vectors when they receive a number of innovative packets equal to the generation size, a good value for d strongly depends on the number of neighbors (i.e., the node density). Similar to probabilistic routing, a high forwarding factor results in a high decoding probability at the expense of a high network load. In references 50 and 51, network coding is used in a peer-to-peer content distribution network named Avalanche. In a peer-to-peer content distribution network, a server splits a large le into a number of blocks, and then peer nodes try to retrieve the original le by downloading blocks from the server but also distributing downloaded blocks among them. When using network coding, the blocks sent out by the server are random linear combinations of all the original blocks. Similarly, peers send out random linear combinations of all the blocks available to them. Network coding in Avalanche minimizes download times with respect to the case where it is not used. Network coding gives the system more robustness in case the server leaves early or when peers only join for short periods.
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