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Efficient approximation approaches to minimal exposure path problem in probabilistic coverage model for wireless sensor networks

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APPLIED SOFT COMPUTING 2019年 76卷 726-743页

作者： Huynh Thi Thanh Binh Nguyen Thi My Binh Nguyen Hong Ngoc Dinh Thi Ha Ly Nguyen Duc Nghia Hanoi Univ Sci & Technol Hanoi Vietnam Hanoi Univ Ind Hanoi Vietnam Natl Inst Informat Tokyo Japan

A well-known method for evaluating the coverage quality of Wireless Sensor Networks (WSNs) is using exposure as a measure, especially in barrier coverage problems. Among all studies related to exposure, discussions regarding the Minimal Exposure Path (MEP) problem have dominated research in recent years. The problem aims to find a path on which an intruder can penetrate through the sensing field with the lowest probability of being detected. This path along with its exposure value enables network infrastructure designers to identify the worst-case coverage of the WSN and make necessary improvements. Most prior research worked on the MEP problem under the assumption that there are no environmental factors such as vibration, temperature, etc., which causes errors in practical WSN systems. To overcome this drawback, we first formulate the MEP problem based on probabilistic coverage model with noise (hereinafter PM-based-MEP) and introduce a new definition of the exposure metric for this model. The PM-based-MEP is then converted into a numerically functional extreme with high dimension, non-differentially and non-linearity. Adapting to these characteristics, we propose two approximation methods, GB-MEP and GA-MEP, for solving the converted problem. GB-MEP is based on the traditional grid-based method which is fine-tuned by several tweaks, and GA-MEP is formed by the genetic algorithm with a featured individual representation and an effective combination of genetic operators. Experimental results on numerous instances indicate that the proposed algorithms are suitable for the converted PM-based-MEP problem and perform well regarding both solution accuracy and computation time compared with existing approaches. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Minimal exposure path probabilistic coverage model Wireless sensor network Genetic algorithm Grid-base algorithm

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Sleep-wake up scheduling with probabilistic coverage model in sensor networks

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INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS 2014年第1期29卷 1-16页

作者： Chen, Pingsheng Hu, Weidong Shaoxing Vocat & Tech Coll Dept Comp Sci Shaoxing 312000 Peoples R China

Energy optimisation is one of the important issues in the research of wireless sensor networks (WSNs). In the application of monitoring, a large number of sensors are scattered uniformly to cover a collection of points of interest (PoIs) distributed randomly in the monitored area. Since the energy of battery-powered sensor is limited in WSNs, sensors are scheduled to wake up in a large-scale sensor network application. In this paper, we consider how to reduce the energy consumption and prolong the lifetime of WSNs through wake-up scheduling with probabilistic sensing model in the large-scale application of monitoring. To extend the lifetime of sensor network, we need to balance the energy consumption of sensors so that there will not be too much redundant energy in some sensors before the WSN terminates. The detection probability and false alarm probability are taken into consideration to achieve a better performance and reveal the real sensing process which is characterised in the probabilistic sensing model. Data fusion is also introduced to utilise information of sensors so that a PoI in the monitored area may be covered by multiple sensors collaboratively, which will decrease the number of sensors that cover the monitored region. Based on the probabilistic model and data fusion, minimum weight probabilistic coverage problem is formulated in this paper. We also propose a greedy method and modified genetic algorithm based on the greedy method to address the problem. Simulation experiments are conducted to demonstrate the advantages of our proposed algorithms over existing work.

关键词： sleep-wake up scheduling probabilistic coverage model sensor networks

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coverage Prediction for Target coverage in WSN Using Machine Learning Approaches

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WIRELESS PERSONAL COMMUNICATIONS 2024年第2期137卷 931-950页

作者： Chaturvedi, Pooja Daniel, A. K. Narayan, Vipul Nirma Univ Ahmedabad Inst Technol Ahmadabad Gujarat India Madan Mohan Malaviya Univ Technol Gorakhpur India Galgotias Univ Greater Noida India

Mathematical programming techniques are widely used in the determination of optimal functional configuration of a wireless sensor network (WSN). But these techniques have usually high computational complexity and are often considered as Non Polynomial complete problem. Therefore, machine learning techniques can be utilized for the prediction of the WSN parameters with high accuracy and lesser computational complexity than the mathematical programming techniques. This paper focuses on developing the prediction model for determination of the node status to be included in the set cover based on the coverage probability and trust values of the nodes. The set covers are defined as the subset of nodes which are scheduled to monitor the region of interest with the desired coverage level. Several machine learning techniques have been used to determine the node activation status based on which the set covers are obtained. The results show that the random forest based prediction model yields the highest accuracy for the considered network setting.

关键词： Wireless sensor network Target coverage probabilistic coverage model Network lifetime Machine learning

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A Movement Control Policy Based On probabilistic coverage model for Mobile Sensor Networks

A Movement Control Policy Based On Probabilistic Coverage Mo...

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7th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM)

作者： Yang, Zhi Li, Qian-Nan Yu, Min Peng, Ya-Li Jiangxi Normal Univ Coll Comp Informat Engn Nanchang 330022 Jiangxi Peoples R China Jiangxi Normal Univ Sch Software Nanchang 330022 Jiangxi Peoples R China

ISBN: (纸本)9781424462520

The design problems of localization algorithm, distribution density estimation and node's moving path for data collection in multi-layers mobile wireless senor network are investigated. To ensure estimating accurately the node density, this paper proposes a new movement control strategy for fusion nodes based on probabilistic coverage model. This method completed the unknown node localization through beacon nodes traversing the monitoring area, and estimated distribution density of nodes in the network based on probabilistic coverage model, and divided the nodes into groups as little as possible, and constructed the short path of the fusion node's data collection according to the centers of these limited groups. The results of the experiments indicated that the control policy of mobile fusion nodes can reduce greatly network energy, and prolong network lifetime.

关键词： Mobile wireless sensor network probabilistic coverage model Localization algorithm Data collection

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Performance Analysis of Characteristic Parameters for coverage model in Wireless Sensor Networks 3

Performance Analysis of Characteristic Parameters for Covera...

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3rd International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Chaturvedi, Pooja Daniel, A. K. MMM Univ Technol Dept Comp Sci & Engn Gorakhpur Uttar Pradesh India

ISBN: (纸本)9789380544199

The major challenge in wireless sensor networks having limited resources is to maximize the network lifetime and coverage level. The effectiveness of sensor network operation depends significantly on the level of coverage achieved. The coverage level depends on the sensing capability and quality of the data transmitted. The sensor coverage models are abstraction models which measure sensors' observation capability of the environmental phenomenon. The proposed probabilistic coverage model is based on the optimal parameter values of hardware parameter (lambda), the characteristic of the sensing unit (alpha) and the characteristic of the computing unit (beta). The proposed model is compared with the existing probabilistic Sensor coverage model (PSCM) and the results from the simulation conclude that better performance is achieved in terms of the coverage probability and coverage distance for a given network.

关键词： sensor networks coverage probabilistic coverage model performance

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