Neighbor discovery is an essential step for the self-organization of wireless sensor networks. Many algorithms have been proposed for efficient neighbor discovery. However, most of those algorithms need nodes to keep ...
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Neighbor discovery is an essential step for the self-organization of wireless sensor networks. Many algorithms have been proposed for efficient neighbor discovery. However, most of those algorithms need nodes to keep active during the process of neighbor discovery, which might be difficult for low-duty-cycle wireless sensor networks in many real deployments. In this paper, we investigate the problem of neighbor discovery in low-duty-cycle wireless sensor networks. We give an ALOHA-like algorithm and analyze the expected time to discover all n - 1 neighbors for each node. By reducing the analysis to the classical K Coupon Collector's Problem, we show that the upper bound is ne(log 2 n + (3 log 2 n - 1) log 2 log 2 n + c) with high probability, for some constant c, where e is the base of natural logarithm. Furthermore, not knowing number of neighbors leads to no more than a factor of two slowdown in the algorithm performance. Then, we validate our theoretical results by extensive simulations, and explore the performance of different algorithms in duty-cycle and non-duty-cycle networks. Finally, we apply our approach to analyze the scenario of unreliable links in low-duty-cycle wireless sensor networks.
In this paper, a self-organized algorithm for task allocation, based on the hormone reaction-diffusion mechanism, is proposed for a multi-robot system. Hormone messages are used to coordinate the movement of robots. B...
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Clustering problem is one of the hottest issues in wireless sensor networks (WSNs). The strategy for selection of cluster head has not been sufficiently investigated. In this paper, we propose a hormone-based clusteri...
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Cluster analysis is a major method to study gene function and gene regulation information for there is a lack of prior knowledge for gene data. Many clustering methods existed at present usually need manual operations...
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Reliability is one of the essential attributes of the dependable software, and an important factor for quantitatively characterizing software quality. Conventional methodology is software Reliability Growth Model (SRG...
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Appropriate dealing with boundary conditions is very important for SPH. Current boundary treatment methods like boundary force method, ghost particle method and virtual boundary layer method, when dealing with complex...
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Interactive population-based incremental learning (IPBIL) is an effective method to solve optimization problems with implicit performance indices. It can significantly reduce user fatigue compared with interactive evo...
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Emotions are an important aspect of human intelligence and have been shown to play a significant role in human decision-making process. Researchers in areas such as psychology, neuroscience, cognitive science and arti...
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