One of the great challenges in Internet service fault management under noisy and uncertain environment lies in the difficulty of fault priori distribution acquisition. To address the problem, an active probing based approach is proposed for the Internet service in this paper. A hidden Markov model(HMM) based dynamic probabilistic dependency model is chosen to be the fault propagation model (FPM). A forward-backward(F-B) learning procedure is employed for the estimation of FPM. F-B fully takes both uncertainty and excessive probing traffic load into account, revising the FPM with active probing and online learning techniques. Detection probes and diagnosis probes were employed separately in fault detection phase and fault diagnosis phase. The selection of diagnosis probes is integrated into the online model learning procedure. As for fault diagnosis, a Viterbi N-best based approach is proposed to record N most likely faulty components, utilizing the probing information gain in the F-B learning procedure. As a result it can reduce the complexity of the fault priori distribution acquisition, further enhancing the accuracy of the detection rate. Simulation results prove the validity and efficiency of the HMM-based FPM model and proposed approaches.Index Terms-fault diagnosis, hidden Markov model, forward-backward learning, Viterbi N-best inference, active probing
Battery recovery effect is a phenomenon that the available capacity of a battery could increase if the battery can sleep for a certain period of time since its last discharging. Accordingly, the battery can work for a longer time when it takes some rests between consecutive discharging processes than when it works all the time. However, this effect has not been considered in the design of energy-efficient topology control algorithms for wireless sensor networks. In this paper, we propose a distributed battery recovery effect aware connected dominating set constructing algorithm (BRE-CDS) for wireless sensor networks. In BRE-CDS, each network node periodically decides to join the connected dominating set or not. Nodes that have slept in the preceding round have priority to join the connected dominating set in the current round while nodes that have worked in the preceding round are encouraged to take sleep in the current round for battery recovery. Detailed algorithm design is presented. The computational complexity of BRE-CDS is deduced to be O(D 2 ), where D is node degree. Simulation results show that BRE-CDS can significantly prolong the network lifetime as compared with existing work.
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