A stochastic process model of the hop count distribution in wireless sensor networks

Beyme, Steffen; Leung, Cyril

doi:10.1016/j.adhoc.2014.01.006

Cited by 15 publications

(16 citation statements)

References 38 publications

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“…Hop counts generated by this model are referred to as independent, empirical hop counts, M3: observations are independent draws from the translated Poisson model (2), and are referred to as idealized, model-generated hop counts. It was shown through simulations of the generative models M1 and M3 in [6], that the smallest average target localization error is achieved using hop count observations generated by M3. In [6], observations are taken at randomly selected nodes of the WSN.…”

Section: A Wireless Sensor Network Modelmentioning

confidence: 99%

“…In [6], it was established that for networks with strongly supercritical mean node degree δ, the hop count at distance r from the target is well approximated by a translated Poisson distribution,…”

Section: A Wireless Sensor Network Modelmentioning

confidence: 99%

“…The parameters θ 1 , θ 2 can be learned in various ways, for example through maximum likelihood estimation [6]. It is worth noting that θ 1 , θ 2 are invariant to the common mean node-to-node delay (which may be sensitive to operating conditions such as the average available energy), i.e.…”

Section: A Wireless Sensor Network Modelmentioning

confidence: 99%

“…In such a network, provided that the node density exceeds a critical threshold, a random cluster of all nodes that have received the broadcast message grows over time, starting with the source node that initiated the broadcast upon detection of an event of interest, in a process known as first-passage percolation [13]. For such a network, it was shown in [6] that the message hop count distribution, parameterized by the distance from the source node, can be approximated by a simple stochastic process model.…”

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confidence: 99%

“…We are motivated by the problem of the optimal control of a mobile, autonomous searcher which is given the task of locating a target, that is, the source node of a broadcast message in a WSN of the type studied in [6], relying on observations of the message hop count alone. We focus on the question of how the searcher can home in and make contact with the target in the shortest expected time, given the hop count observations.…”

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confidence: 99%

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Rollout Algorithms for Wireless Sensor Network-Assisted Target Search

Beyme

Leung

2015

IEEE Sensors J.

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We consider a mobile, autonomous searcher that aims to find the source of a broadcast message in a network of location-agnostic wireless sensor nodes. In certain types of networks, the hop count of the broadcast message, given the distance from the source node, is well approximated by a simple parametric distribution. The mobile searcher can interrogate a nearby node to obtain, with a given success probability, the hop count of the broadcast message. The search is modeled as an infinite horizon, undiscounted cost, and partially observable Markov decision process. A computationally efficient approximate online solution is obtained through policy rollout using a novel heuristic. Simulation results show that our rollout approach outperforms commonly used search methods based on a mutual information utility. We quantify the loss due to the use of an approximate hop count observation model and study the effect of statistical dependence between observations. Furthermore, we discuss how to account for this dependence by adapting an integer autoregressive model for the hop count.Index Terms-Sensor decision and fusion, networkable sensors-actuators, wireless sensor networks, robotics and automation applications, localization, POMDP, Monte Carlo methods, rollout algorithms.

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Section: A Wireless Sensor Network Modelmentioning

confidence: 99%

Section: A Wireless Sensor Network Modelmentioning

confidence: 99%

Section: A Wireless Sensor Network Modelmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Rollout Algorithms for Wireless Sensor Network-Assisted Target Search

Beyme

Leung

2015

IEEE Sensors J.

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Connections Management Strategies in Satellite Cellular Networks

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Random adaptive control for cluster synchronization of complex networks with distinct communities

Tang

Park

Lee

et al. 2015

Adaptive Control & Signal

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SummaryIn this paper, we investigate the cluster synchronization for complex networks with time‐varying delayed couplings, stochastic disturbance, and non‐identical nodes in different clusters. Based on randomly occurring controllers, some Bernoulli stochastic variables are introduced to describe the controllers, then, a fraction of nodes in clusters, which have direct connections to the other clusters, is controlled, and the states of the whole dynamical networks can be globally forced to the objective cluster states. Sufficient conditions are derived to guarantee the realization of the mean square cluster synchronization pattern for all initial values by means of Lyapunov stability theory, It differential formula, and LMI approach. Besides, by designing the randomly occurring adaptive update law, some suitable control gains are obtained. Finally, numerical simulations are also given to demonstrate the effectiveness and validity of the main result. Copyright © 2015 John Wiley & Sons, Ltd.

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A stochastic process model of the hop count distribution in wireless sensor networks

Cited by 15 publications

References 38 publications

Rollout Algorithms for Wireless Sensor Network-Assisted Target Search

Rollout Algorithms for Wireless Sensor Network-Assisted Target Search

Bibliography

Random adaptive control for cluster synchronization of complex networks with distinct communities

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