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Transport Protocols. In general, transport protocols are concerned with the provision of a reliable communication service for the application layer. This is the main objective of the Pump Slowly, Fetch Quickly (PSFQ) protocol [40]. PSFQ is an adaptive protocol that makes local error correction using hop-by-hop acknowledgement. In this case, the adaptation means that under low failure rates, the communication is similar to a simple forward, and when failures are frequent, it presents a store-and-forward scheme. Another transport protocol that aims to provide a reliable communication is the Reliable Data Transport in Sensor Networks (RMST) [41] that also implements a hop-by-hop acknowledgment. However, RMST is designed to operate in conjunction with Directed Diffusion. An interesting approach is introduced by the Event-to-Sink Reliable Transfer (ESRT) protocol [42, 43]. This protocol is designed for event-based sensor networks, and it changes the focus of traditional transport protocols. The authors state that for WSNs a transport protocol should be reliable regarding the event detection task. ESRT assumes that an event must be detected when the sink node receives a minimum number of event reports from sensor nodes. If this threshold is not achieved, the sink node

[ Team LiB ]

broadcasts the message to all its neighbors If m is chosen too large, then there may be too many messages The experimental results show that the most signi cant performance improvement could be obtained with m = 1 Thus, in GOSSIP3 (p, k, m), if a node originally did not broadcast a received message but did get the message from at least m other nodes within some timeout period it will immediately broadcast the message after the timeout period In what follow, we will discuss several gossip schemes Geographic Gossip for Ef cient Aggregation Gossip algorithms have also been used for data aggregation in sensor networks Their forte is their simplicity in approach However, in their basic form they may waste signi cant energy by essentially passing around redundant information The authors in Geographic Gossip [24] propose an alternative gossiping scheme that exploits geographic information.

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In a network of n sensors, a basic solution to the averaging problem (ie, to compute the average of all n sensor measurements) is based on the Gossip algorithms where each node randomly picks a one-hop neighbor and exchanges their current values This is performed in an iterative fashion, and ultimately all nodes converge to the global average in a distributed manner The key issue here is the number of iterations it takes for such a gossip algorithm to converge to a suf ciently accurate estimate Recent works [25 29] have dealt with variants of this problem The convergence time of this algorithm is closely linked with the mixing time of the Markov Chain de ned by a weighted random graph on the network.

[ Team LiB ]

In reference 26, the authors showed how to optimize the neighbor selection probabilities for each node in order to nd the fastest mixing Markov chain However, for sensor network graphs, even an optimized gossip algorithm can result in excess energy consumption The authors of Geographic Gossip exploit geographic information to build a completely randomized and distributed algorithm that requires substantially less communication The idea is to include geographic routing to gossip with random nodes far away in the network Empowered with geographic knowledge, this protocol succeeds in quickly diffusing information everywhere in the network and thus computes the average faster than the standard nearest-neighbor gossip Smart Gossip The authors of Smart Gossip [30] propose an adaptive form of gossiping in sensor networks They propose techniques by which a gossip-based protocol can automatically and dynamically adapt to the network topology.

public class AutoTestNature implements IProjectNature { IProject project; public AutoTestNature() { } public IProject getProject() { return project; } public void setProject(IProject project) { thisproject= project; } } The main effort is contained in the life-cycle methods configure() and deconfigure(), which in our case will install and uninstall the auto-test builder

Smart Gossip copes well with wireless losses and unpredictable node failures that affect network connectivity The adaptivity of the gossiping strategy also extends itself to provide reliability for disseminating messages The authors argue that existing gossip strategies are mostly static, since there is a xed probability for transmitting the received information There are a few variants of the gossip protocol which are adaptive Haas et al [23] proposed an adaptive form of gossip which chooses its probability, based on the number of neighbors The authors of Smart Gossip argue that simply choosing gossip probabilities based on the number of neighbors is not.

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correct For example, in Figure 36, which is a subgraph of a random topology, node C has a high degree and therefore its probability of gossiping would be low This could adversely affect the reception of the information at node F , which is solely dependent on C for receiving messages originating at any node to the left of C From the point of view of gossip percolation, the authors extract out the notion of dependence between a node X and a subset of its neighbors These dependencies give birth to parent child relationships between neighbors based on the direction in which the gossip can travel probabilistically This dependency graph is just logical and also probabilistic in nature In other words, node X does not depend on any particular parent, Y , to receive the gossip.