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Register a new userRandomly Roving Agents in Wireless Sensor Networks
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Hakob Aslanyan Hakob Aslanyan
Publication date
2011
fields
Informatics Engineering
and research's language is
English
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Quantitative characterization of randomly roving agents in wireless sensor networks (WSN) is studied. Below the formula simplifications, regarding the known results and publications, it is shown that the basic agent model is probabilistically equivalent to a similar simpler model and then a formula for frequencies is achieved in terms of combinatorial second kind Stirling numbers. Stirling numbers are well studied and different estimates are known for them letting to justify the roving agents quantitative characteristics.
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Quantitative characterization of randomly roving agents in Agent Based Intrusion Detection Environment (ABIDE) is studied. Formula simplifications regarding known results and publications are given. Extended Agent Based Intrusion Detection Environment (EABIDE) is introduced and quantitative characterization of roving agents in EABIDE is studies.
To save energy and alleviate interferences in a wireless sensor network, the usage of virtual backbone was proposed. Because of accidental damages or energy depletion, it is desirable to construct a fault tolerant virtual backbone, which can be modeled as a $k$-connected $m$-fold dominating set (abbreviated as $(k,m)$-CDS) in a graph. A node set $Csubseteq V(G)$ is a $(k,m)$-CDS of graph $G$ if every node in $V(G)backslash C$ is adjacent with at least $m$ nodes in $C$ and the subgraph of $G$ induced by $C$ is $k$-connected. In this paper, we present an approximation algorithm for the minimum $(3,m)$-CDS problem with $mgeq3$. The performance ratio is at most $gamma$, where $gamma=alpha+8+2ln(2alpha-6)$ for $alphageq4$ and $gamma=3alpha+2ln2$ for $alpha<4$, and $alpha$ is the performance ratio for the minimum $(2,m)$-CDS problem. Using currently best known value of $alpha$, the performance ratio is $lndelta+o(lndelta)$, where $delta$ is the maximum degree of the graph, which is asymptotically best possible in view of the non-approximability of the problem. This is the first performance-guaranteed algorithm for the minimum $(3,m)$-CDS problem on a general graph. Furthermore, applying our algorithm on a unit disk graph which models a homogeneous wireless sensor network, the performance ratio is less than 27, improving previous ratio 62.3 by a large amount for the $(3,m)$-CDS problem on a unit disk graph.
An energy cooperation policy for energy harvesting wireless sensor networks (WSNs) with wireless power transfer is proposed in this paper to balance the energy at each sensor node and increase the total energy utilization ratio of the whole WSNs. Considering the unbalanced spatio-temporal properties of the energy supply across the deployment terrain of energy harvesting WSNs and the dynamic traffic load at each sensor node, the energy cooperation problem among sensor nodes is decomposed into two steps: the local energy storage at each sensor node based on its traffic load to meet its own needs; within the energy storage procedure sensor nodes with excess energy transmit a part of their energy to nodes with energy shortage through the energy trading. Inventory theory and game theory are respectively applied to solving the local energy storage problem at each sensor node and the energy trading problem among multiple sensor nodes. Numerical results show that compared with the static energy cooperation method without energy trading, the Stackelberg Model based Game we design in this paper can significantly improve the trading volume of energy thereby increasing the utilization ratio of the harvested energy which is unevenly distributed in the WSNs.
To address the problem of unsupervised outlier detection in wireless sensor networks, we develop an approach that (1) is flexible with respect to the outlier definition, (2) computes the result in-network to reduce both bandwidth and energy usage,(3) only uses single hop communication thus permitting very simple node failure detection and message reliability assurance mechanisms (e.g., carrier-sense), and (4) seamlessly accommodates dynamic updates to data. We examine performance using simulation with real sensor data streams. Our results demonstrate that our approach is accurate and imposes a reasonable communication load and level of power consumption.
We propose an algorithm which produces a randomized strategy reaching optimal data propagation in wireless sensor networks (WSN).In [6] and [8], an energy balanced solution is sought using an approximation algorithm. Our algorithm improves by (a) when an energy-balanced solution does not exist, it still finds an optimal solution (whereas previous algorithms did not consider this case and provide no useful solution) (b) instead of being an approximation algorithm, it finds the exact solution in one pass. We also provide a rigorous proof of the optimality of our solution.
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