Information-Driven Adaptive Sampling Strategy for Mobile Robotic Wireless Sensor Network

Nguyen, Linh; Kodagoda, Sarath; Ranasinghe, Ravindra; Dissanayake, Gamini

doi:10.1109/tcst.2015.2435657

Cited by 61 publications

(68 citation statements)

References 33 publications

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“…On the other hand, it has been presented that the GP based mutual information is not totally monotone [9], [43]. Nonetheless, by representing the mutual information via the GMRF, we prove that the proposed function F (C) satisfies monotonicity as following lemma.…”

Section: B a Solution Boundmentioning

confidence: 79%

“…Although it has been shown that the greedy algorithm is very effective to solving a combinatorial NP-hard issue in spatial prediction using mobile robotic wireless sensor networks [43], a theoretical bound on the approximated solution of the spatial sensor selection problem is paramount. Mathematically, Nemhauser et al in their work [44] presented that the solution obtained by a greedy heuristic algorithm can be guaranteed by a specific level of the optimum if the objective optimization function is monotonic and submodular.…”

Section: B a Solution Boundmentioning

confidence: 99%

See 1 more Smart Citation

Spatial Sensor Selection via Gaussian Markov Random Fields

Nguyen

Kodagoda

Ranasinghe

2016

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-The paper addresses the problem of selecting the most informative sensor locations out of all possible sensing positions in prediction of spatial phenomena by using a wireless sensor network. The spatial field is modelled by Gaussian Markov random fields (GMRF), where sparsity of the precision matrix enables the network to benefit from computation. A new spatial sensor selection criterion is proposed based on mutual information between random variables at a selected locations and those at unselected locations and interested but unlikely sensor placed positions, which enhances resulting prediction. The GMRF based optimality criterion is then proved to be very computationally efficiently resolved, especially in a large-scale sensor network, by a polynomial time approximation algorithm. More importantly, with demonstrations of monotonicity and submodularity properties of the mutual information set function in the proposed selection criterion, our near-optimal solution is also guaranteed by at least within (1 − 1/e) of the optimal performance. The effectiveness of the proposed approach is compared and illustrated using two real-life large data sets with promising results.

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Section: B a Solution Boundmentioning

confidence: 79%

Section: B a Solution Boundmentioning

confidence: 99%

Spatial Sensor Selection via Gaussian Markov Random Fields

Nguyen

Kodagoda

Ranasinghe

2016

IEEE Trans. Syst. Man Cybern, Syst.

Self Cite

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“…In the literature, further ASAs for more specific applications in WSN are proposed. In particular, in the work of Nguyen et al optimality criteria for mobile robotic wireless sensor network is suggested to the most informative location of interest. The adaptive sampling strategy for mobile sensors in the environment monitoring context was proposed by Xu et al, where the sequential Bayesian prediction algorithm minimizes the prediction error variance.…”

Section: Background In Adaptive Sampling For Sensing Devicesmentioning

confidence: 99%

Energy modeling and adaptive sampling algorithms for energy‐harvesting powered nodes with sampling rate limitations

Gindullina

Badia

Vilajosana

2019

Trans Emerging Tel Tech

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This article explores the implementation of different sampling strategies for a practical energy‐harvesting wireless device (sensor node) powered by a rechargeable battery. We look for a realistic yet effective sampling strategy that prevents packet delivery failures, which is simple enough to be implemented in low‐complexity hardware. The article proposes methods that balance erratic energy arrivals and include advantages of dynamic data‐driven approaches based on historical data. Due to the industrial requirements in terms of minimum acceptable sampling frequency, we also integrate sampling rate limits and verify the proposed methods. To do so, we simulated the operation of an industrial data logger powered with a solar panel relying on the enhanced state of the model for battery charging. Finally, the proposed methods are compared based on energy consumption over a year and the amount of packet delivery failures, thus showing how some modifications of available strategies achieve satisfactory performance in this sense.

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“…R ECENTLY, given technological advancements in microelectromechanical systems and wireless communications, particularly in the Internet of Things (IoT) era, a mobile wireless sensor network (MWSN) [1], [2] plays a significant impact on in situ observations in variety of environmental and event monitoring applications such as exploring spatial phenomena [3]- [5], monitoring natural habitats [6], tracking a target [7], [8], observing traffic [9] or battlefield [10] and detecting forest fire [11]. In terms of architecture, mobility in a MWSN can be presented by mobile sensor nodes and/or mobile sink(s).…”

Section: Introductionmentioning

confidence: 99%

Mobility based network lifetime in wireless sensor networks: A review

Nguyen

2020

Computer Networks

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Increasingly emerging technologies in microelectromechanical systems and wireless communications allows a mobile wireless sensor networks (MWSN) to be a more and more powerful mean in many applications such as habitat and environmental monitoring, traffic observing, battlefield surveillance, smart homes and smart cities. Nevertheless, due to sensor battery constraints, energy-efficiently operating a MWSN is paramount importance in those applications; and a plethora of approaches have been proposed to elongate the network longevity at most possible. Therefore, this paper provides a comprehensive review on the developed methods that exploit mobility of sensor nodes and/or sink(s) to effectively maximize the lifetime of a MWSN. The survey systematically classifies the algorithms into categories where the MWSN is equipped with mobile sensor nodes, one mobile sink or multiple mobile sinks. How to drive the mobile sink(s) for energy efficiency in the network is also fully reviewed and reported.

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Information-Driven Adaptive Sampling Strategy for Mobile Robotic Wireless Sensor Network

Cited by 61 publications

References 33 publications

Spatial Sensor Selection via Gaussian Markov Random Fields

Spatial Sensor Selection via Gaussian Markov Random Fields

Energy modeling and adaptive sampling algorithms for energy‐harvesting powered nodes with sampling rate limitations

Mobility based network lifetime in wireless sensor networks: A review

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