Software Defined Mission-Critical Wireless Sensor Network: Architecture and Edge Offloading Strategy

Xu, Fangmin; Ye, Huanyu; Yang, Fan; Zhao, Chenglin

doi:10.1109/access.2019.2890854

Cited by 40 publications

(17 citation statements)

References 16 publications

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“…Wang et al in [16] defined a resource allocation and power control method to optimize spectrum efficiency and system capacity in a D2D enabled MEC system of IWCN. A MEC based missioncritical wireless sensor network architecture and a kind of centralized computing resource management strategy were studied in [17]. Qiu et al in [18] explained how to manage and orchestrate resources jointly in software-defined satellite-terrestrial networks.…”

Section: B Computational Offloading and Resource Allocation Schemes mentioning

confidence: 99%

Regional Intelligent Resource Allocation in Mobile Edge Computing Based Vehicular Network

Wang

2020

IEEE Access

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The advancement of 5G technology has brought the prosperous development of Internet of Vehicles (IoV). IoV services are not only computational intensive but also extremely sensitive to the delay. As a promising computing paradigm, mobile edge computing (MEC) can be applied to IoV scenarios. However, due to the limited resources of a single MEC server, it is difficult to cope with the suddenly increased computation loads caused by emergencies, or the intensive resource requests from busy regions. Therefore, we propose a novel regional intelligent management vehicular system with dual MEC planes, in which MEC servers in the same region cooperate with each other to achieve resource sharing. We classify computing tasks into different types according to their delay tolerances and focus on the optimization problem of resource allocation for different type tasks. And then, we design a resource allocation algorithm based on deep reinforcement learning, which can adapt to the changeable MEC environment to process high-dimensional data. Simulation results confirm that our proposed scheme is feasible and effective. INDEX TERMS Internet of vehicles, mobile edge computing, deep reinforcement learning, resource allocation, delay tolerance.

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Section: B Computational Offloading and Resource Allocation Schemes mentioning

confidence: 99%

Regional Intelligent Resource Allocation in Mobile Edge Computing Based Vehicular Network

Wang

2020

IEEE Access

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“…The sensors' location data are conduced to calculate network consumption and attain route management etc. Therefore, localization proves to be a significant research direction in WSNs [5]- [7]. The characteristics of numerous sensor nodes, limited power, random distribution and complex communication JIAXING CHEN and WEI ZHANG are co-first authors and they have contributed equally to this work.…”

Section: Introductionmentioning

confidence: 99%

CWDV-Hop: A Hybrid Localization Algorithm With Distance-Weight DV-Hop and CSO for Wireless Sensor Networks

et al. 2021

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“…Recent sensor networks have the capability to handle huge data like image or visual sensing, which is in turn integrated with the cloud to enhance the capability of the internet of things (IOT) [3]. Furthermore, the WSN has been implemented in mission critical applications like body sensor networks, security, space, underwater communication, and military [4,5]. Often, critical sensor networks are installed in harsh, inaccessible, and uncontrolled environments, which may affect the sensor performance and cannot be attended immediately.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Continuous Density Hmm-Based Ensemble Neural Networks for Sensor Fault Detection and Classification in Wireless Sensor Network

Emperuman

Chandrasekaran

2020

Sensors

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Sensor devices in wireless sensor networks are vulnerable to faults during their operation in unmonitored and hazardous environments. Though various methods have been proposed by researchers to detect sensor faults, only very few research studies have reported on capturing the dynamics of the inherent states in sensor data during fault occurrence. The continuous density hidden Markov model (CDHMM) is proposed in this research to determine the dynamics of the state transitions due to fault occurrence, while neural networks are utilized to classify the faults based on the state transition probability density generated by the CDHMM. Therefore, this paper focuses on the fault detection and classification using the hybridization of CDHMM and various neural networks (NNs), namely the learning vector quantization, probabilistic neural network, adaptive probabilistic neural network, and radial basis function. The hybrid models of each NN are used for the classification of sensor faults, namely bias, drift, random, and spike. The proposed methods are evaluated using four performance metrics which includes detection accuracy, false positive rate, F1-score, and the Matthews correlation coefficient. The simulation results show that the learning vector quantization NN classifier outperforms the detection accuracy rate when compared to the other classifiers. In addition, an ensemble NN framework based on the hybrid CDHMM classifier is built with majority voting scheme for decision making and classification. The results of the hybrid CDHMM ensemble classifiers clearly indicates the efficacy of the proposed scheme in capturing the dynamics of change of statesm which is the vital aspect in determining rapidly-evolving instant faults that occur in wireless sensor networks.

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Software Defined Mission-Critical Wireless Sensor Network: Architecture and Edge Offloading Strategy

Cited by 40 publications

References 16 publications

Regional Intelligent Resource Allocation in Mobile Edge Computing Based Vehicular Network

Regional Intelligent Resource Allocation in Mobile Edge Computing Based Vehicular Network

CWDV-Hop: A Hybrid Localization Algorithm With Distance-Weight DV-Hop and CSO for Wireless Sensor Networks

Hybrid Continuous Density Hmm-Based Ensemble Neural Networks for Sensor Fault Detection and Classification in Wireless Sensor Network

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