Selective forwarding for energy-efficient target tracking in sensor networks

Pino-Povedano, Sara; Arroyo-Valles, Rocio; Cid-Sueiro, J.

doi:10.1016/j.sigpro.2013.07.018

Cited by 22 publications

(7 citation statements)

References 28 publications

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“…A reward is given if the transportation agent delivers the asset to the destination. 5) Prioritized Data Delivery: In [123], Pino-Povedano et al discussed the operation of selectively dropping unimportant data samples in target tracking applications. To maximizing the probability of delivering important messages over normal ones.…”

Section: B Target Tracking and Localizationmentioning

confidence: 99%

Markov Decision Processes With Applications in Wireless Sensor Networks: A Survey

Alsheikh

Hoang

Niyato

et al. 2015

IEEE Commun. Surv. Tutorials

187

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Abstract-Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs.

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Section: B Target Tracking and Localizationmentioning

confidence: 99%

Markov Decision Processes With Applications in Wireless Sensor Networks: A Survey

Alsheikh

Hoang

Niyato

et al. 2015

IEEE Commun. Surv. Tutorials

187

View full text Add to dashboard Cite

show abstract

“…• x k : importance assigned to a message which is generated at epoch k. For mathematical convenience, we assume x k = 0 when y k = 0. The importance value is application-dependent (see [20] or [21] for example).…”

Section: Network Statementioning

confidence: 99%

Asymptotic analysis of cooperative censoring policies in sensor networks

2015

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“…In general, wireless sensor networks (WSNs) are potential in facilitating adaptive communication, flexible arrangement, low cost, and energy consumptions 1 . The sensor nodes of WSNs are tiny and numerous, which possess the potential of sensing, communication, and computing.…”

Section: Introductionmentioning

confidence: 99%

Markov Modulated Bernoulli Prediction Process‐based Cluster Head Selection Mechanism for improving resilience in wireless sensor networks

Arulmurugan

Amuthan

2021

Int J Communication

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Summary The clustering process in wireless sensor networks is considered to be predominant for facilitating minimized energy consumptions through the selection of potential cluster heads that are efficient in energy and trust. The significant cluster head selection is deterministic in topology control with the view to manage the sensor member nodes of the cluster with the view to enhance energy efficiency in the network. In this paper, a Markov Modulated Bernoulli Prediction Process‐based Cluster Head Selection Mechanism (MMBPP‐CHSM) is proposed by modeling the success probability of sensor nodes' state into a Bernoulli process that dynamically evolves over time based on Markov chain for extending the lifetime of the network. This proposed MMBPP‐CHSM scheme is vital in quantifying the reliability and availability of the sensor nodes in a dynamically evolving wireless sensor network through the estimation of trust and energy in order to facilitate effective cluster head selection. The simulation results of the proposed MMBPP‐CHSM scheme confirmed a superior enhancement in percentage of alive nodes and death nodes in the network to a maximum level of 28% and 23% on par with the baseline cluster head selection approaches used for investigation.

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Selective forwarding for energy-efficient target tracking in sensor networks

Cited by 22 publications

References 28 publications

Markov Decision Processes With Applications in Wireless Sensor Networks: A Survey

Markov Decision Processes With Applications in Wireless Sensor Networks: A Survey

Asymptotic analysis of cooperative censoring policies in sensor networks

Markov Modulated Bernoulli Prediction Process‐based Cluster Head Selection Mechanism for improving resilience in wireless sensor networks

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