A new learning automata-based approach for maximizing network lifetime in wireless sensor networks with adjustable sensing ranges

Mohamadi, Hosein; Salleh, Shaharuddin; Razali, Mohd Norsyarizad; Marouf, Sara

doi:10.1016/j.neucom.2014.11.056

Cited by 33 publications

(15 citation statements)

References 24 publications

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“…al. [37] by developing a learning automatabased algorithm to find the optimal cover sets. Additionally, the MNLAR problem was extended to include directional (e.g., camera) sensor networks [34].…”

Section: B Maximum Network Lifetime Problemmentioning

confidence: 99%

POSE: Prediction-Based Opportunistic Sensing for Energy Efficiency in Sensor Networks Using Distributed Supervisors

Hare

Gupta

Wettergren

2018

IEEE Trans. Cybern.

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This paper presents a distributed supervisory control algorithm that enables opportunistic sensing for energy-efficient target tracking in a sensor network. The algorithm called Prediction-based Opportunistic Sensing (POSE), is a distributed node-level energy management approach for minimizing energy usage. Distributed sensor nodes in the POSE network self-adapt to target trajectories by enabling high power consuming devices when they predict that a target is arriving in their coverage area, while enabling low power consuming devices when the target is absent. Each node has a Probabilistic Finite State Automaton which acts as a supervisor to dynamically control its various sensing and communication devices based on target's predicted position. The POSE algorithm is validated by extensive Monte Carlo simulations and compared with random scheduling schemes. The results show that the POSE algorithm provides significant energy savings while also improving track estimation via fusion-driven state initialization.

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Section: B Maximum Network Lifetime Problemmentioning

confidence: 99%

POSE: Prediction-Based Opportunistic Sensing for Energy Efficiency in Sensor Networks Using Distributed Supervisors

Hare

Gupta

Wettergren

2018

IEEE Trans. Cybern.

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“…In paper [8], the authors discussed the effect of adjustable radius for the network lifetime. They proposed an algorithm based on learning-automata and equipped with a pruning rule, the algorithm is aimed to select a sensor which can adjust radius to satisfy a certain target coverage.…”

Section: Related Workmentioning

confidence: 99%

Estimation and Healing of Coverage Hole in Hybrid Sensor Networks: A Simulation Approach

Zhang

et al. 2017

Sustainability

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Nowadays, wireless sensor network which consists of numerous tiny sensors has been widely used. One of the major challenges in such networks is how to cover the sensing area effectively and maintain longer network lifetime with limited energy simultaneously. In this paper, we study hybrid sensor network which contains both static and mobile sensors. We divide monitoring area into Delaunay Triangulation (DT) by using of Delaunay theory, estimate static sensors coverage holes, calculate the number of assistant mobile sensors and then work out the positions of assisted mobile nodes in each triangle. Next, mobile sensors will move to heal the coverage holes. Compared with the similarity methods, the algorithm HCHA we proposed is simpler, the advantages of our algorithm mainly represents in the following aspects. Firstly, it is relatively simple to estimate coverage hole based on Delaunay in our proposed algorithm. Secondly, we figure out the quantitative number range of assisted sensors those need to heal the coverage holes. Thirdly, we come up with a kind of deployment rule of assisted sensors.

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“…Learning automata have a vast variety of applications in combinatorial optimization problems [5][6][7][8][9][10], computer networks [8,[11][12][13][14][15], queuing theory [16,17], signal processing [18,19], information retrieval [20][21][22], adaptive control [23][24][25], neural networks engineering [26,27] and pattern recognition [28][29][30].…”

Section: Theory Of Learning Automatamentioning

confidence: 99%

A Michigan memetic algorithm for solving the community detection problem in complex network

Mirsaleh

Meybodi

2016

Neurocomputing

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A new learning automata-based approach for maximizing network lifetime in wireless sensor networks with adjustable sensing ranges

Cited by 33 publications

References 24 publications

POSE: Prediction-Based Opportunistic Sensing for Energy Efficiency in Sensor Networks Using Distributed Supervisors

POSE: Prediction-Based Opportunistic Sensing for Energy Efficiency in Sensor Networks Using Distributed Supervisors

Estimation and Healing of Coverage Hole in Hybrid Sensor Networks: A Simulation Approach

A Michigan memetic algorithm for solving the community detection problem in complex network

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