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Self-organization of wireless sensor networks, which involves network decomposition into connected clusters, is a challenging task because of the limited bandwidth and energy resources available in these networks. In this paper, we make contributions towards improving the efficiency of self-organization in wireless sensor networks. We first present a novel approach for message-efficient clustering, in which nodes allocate local "growth budgets" to neighbors. We introduce two algorithms that make use of this approach. We analyze the message complexity of these algorithms and provide performance results from simulations. The algorithms produce clusters of bounded size and low diameter, using significantly fewer messages than the earlier, commonly used, Expanding Ring approach. Next, we present a new randomized methodology for designing the timers of cluster initiators. This methodology provides a probabilistic guarantee that initiators will not interfere with each other. We derive an upper bound on the expected time for network decomposition that is logarithmic in the number of nodes in the network. We also present a variant that optimistically allows more concurrency among initiators and significantly reduces the network decomposition time.However, it produces slightly more clusters than the first method. Extensive simulations over different topologies confirm the analytical results and demonstrate that our proposed methodology scales to large networks.

Wireless and satellite networks often have non-negligible packet corruption rates that can significantly degrade TCP performance. This is due to TCP's assumption that every packet loss is an indication of network congestion (causing TCP to reduce the transmission rate). This problem has received much attention in the literature. In this paper, we take a broad look at the problem of enhancing TCP performance under corruption losses, and include a discussion of the key issues. The main contributions of this paper are: ( ) a confirmation of previous studies that show the reduction of TCP performance in the face of corruption loss, and in addition a plausible upper bound achievable with perfect knowledge of the cause of loss, ( ) a classification of the potential mitigation space, and ( ) the introduction of a promising new mitigation that employs rich cumulative information from intermediate nodes in a path to form a better congestion response.We first illustrate the performance implications of corruption-based loss for a variety of networks via simulation. In addition, we show a rough upper bound on the performance gains a TCP could get if it could perfectly determine the cause of each segment loss -independent of any specific mechanism for TCP to learn the root cause of packet loss. Next, we provide a taxonomy of potential practical classes of mitigations that TCP end-points and intermediate network elements can cooperatively use to decrease the performance impact of corruption-based loss. Finally, we briefly consider a potential mitigation, called cumulative explicit transport error notification (CETEN), which covers a portion of the solution space previously unexplored. CETEN is shown to be a promising mitigation strategy, but a strategy with numerous formidable practical hurdles still to overcome.£ Pre-print: Accepted for publication in Elsevier Computer Networks.

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This paper introduces a binary neural network-based prediction algorithm incorporating both spatial and temporal characteristics into the prediction process. The algorithm is used to predict short-term traffic flow by combining information from multiple traffic sensors (spatial lag) and time-series prediction (temporal lag). It extends previously developed Advanced Uncertain Reasoning Architecture (AURA) k-nearest neighbour (k-NN) techniques. Our task was to produce a fast and accurate traffic flow predictor. The AURA k-NN predictor is comparable to other machine learning techniques with respect to recall accuracy but is able to train and predict rapidly. We incorporated consistency evaluations to determine if the AURA k-NN has an ideal algorithmic configuration or an ideal data configuration or whether the settings needed to be varied for each data set. The results agree with previous research in that settings must be bespoke for each data set. This configuration process requires rapid and scalable learning to allow the predictor to be setup for new data. The fast processing abilities of the AURA k-NN ensure this combinatorial optimisation will be computationally feasible for real-world applications. We intend to use the predictor to proactively manage traffic by predicting traffic volumes to anticipate traffic network problems.