Topology Control Strategy for Movable Sensor Networks in Ultradeep Shafts

Zhou, Gongbo; Wang, Peng-Hui; Zhu, Zhencai; Wang, Houlian; Li, Wei

doi:10.1109/tii.2017.2780319

Cited by 8 publications

(5 citation statements)

References 24 publications

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“…In order to make comparative studies between existing discovery concepts, the presented formula based on Ci parameters is used which will permit us to assess any web services discovery method based on the overall consumed power. Furthermore, three methods are presented in 165 to gain their objectives, namely, a source‐aware redundant packet forwarding system, increasing the extensive parameters, and Monte Carlo method 166 . However, due to tough physical characteristics, the existing deployment designs in the coal mine are not proper for CWSNs in ultradeep shafts.…”

Section: Service Discovery and Cluster‐based Routing Protocolsmentioning

confidence: 99%

Service redundancy and cluster‐based routing protocols for wireless sensor and mobile ad hoc networks: A survey

Yagoub

Rodrigues

Khalifa

et al. 2020

Int J Communication

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SummaryService discovery (SD) is the cornerstone of wireless and mobile systems. The discovery of effective service is the key to achieve a higher level of ubiquity, which ensures the availability of services to users and applications significantly, and the high use of services. The research in this field is limited, with a few SD protocols available for specific purposes specified in the 6LoWPAN region. This paper discusses the various issues and challenges facing the design and selection of the appropriate service discovery and cluster‐based mechanism. Moreover, attention has been drawn to the cross‐layer solution by discussing different clustering techniques used to solve the redundancy problems. Furthermore, the service discovery protocols and cluster‐based routing protocols are critically investigated considering the redundancy problem, methods, mechanisms, and architectures by categorizing them into different categories, comparing them with fundamental parameters in the WSNs and MANETs environments. In addition, a modern form is proposed to show the life cycle of the service discovery process, and a new figure has been designed to clarify to the researchers various of the open issues related to the service discovery and cluster‐based routing protocols, corresponding objectives and the techniques of solving problems. Through the discussion of the scientific papers under survey, important results and valuable recommendations were reached. The survey is concluded with a summary, open research issues, outlook, and directions for future research on the topic.

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Section: Service Discovery and Cluster‐based Routing Protocolsmentioning

confidence: 99%

Service redundancy and cluster‐based routing protocols for wireless sensor and mobile ad hoc networks: A survey

Yagoub

Rodrigues

Khalifa

et al. 2020

Int J Communication

View full text Add to dashboard Cite

show abstract

“…Differing from the above DBCT schemes, Ref. [ 100 ] exploited a topological control method for Chain-type Wireless Sensor Networks (CWSNs) which can evidently improve the coverage of the whole network. The literature [ 101 ] exploited a new robustness enhancing algorithm for scale-free WSNs, which can improve the robustness of the network.…”

Section: Future Aspects Of Iosbmentioning

confidence: 99%

The Shared Bicycle and Its Network—Internet of Shared Bicycle (IoSB): A Review and Survey

Shen

Wei

Sun

et al. 2018

Sensors

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With the expansion of Intelligent Transport Systems (ITS) in smart cities, the shared bicycle has developed quickly as a new green public transportation mode, and is changing the travel habits of citizens heavily across the world, especially in China. The purpose of the current paper is to provide an inclusive review and survey on shared bicycle besides its benefits, history, brands and comparisons. In addition, it proposes the concept of the Internet of Shared Bicycle (IoSB) for the first time, as far as we know, to find a feasible solution for those technical problems of the shared bicycle. The possible architecture of IoSB in our opinion is presented, as well as most of key IoT technologies, and their capabilities to merge into and apply to the different parts of IoSB are introduced. Meanwhile, some challenges and barriers to IoSB’s implementation are expressed thoroughly too. As far as the advice for overcoming those barriers be concerned, the IoSB’s potential aspects and applications in smart city with respect to technology development in the future provide another valuable further discussion in this paper.

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“…Online monitoring: design the hardware and software of the monitoring system, and apply them to online monitoring. The monitoring system framework is composed of three parts, including the image acquisition system, the vertical shaft movable sensor network [28] and the upper computer. The image acquisition system includes a light source, CCD (charge coupled device) cameras, an acquisition card and memory, and can realize the real-time collection of the BTRs image data.…”

Section: Image Data-driven Monitoring System Frameworkmentioning

confidence: 99%

Health Monitoring for Balancing Tail Ropes of a Hoisting System Using a Convolutional Neural Network

Zhou

Zhu

et al. 2018

Applied Sciences

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Abstract:With the arrival of the big data era, it has become possible to apply deep learning to the health monitoring of mine production. In this paper, a convolutional neural network (CNN)-based method is proposed to monitor the health condition of the balancing tail ropes (BTRs) of the hoisting system, in which the feature of the BTR image is adaptively extracted using a CNN. This method can automatically detect various BTR faults in real-time, including disproportional spacing, twisted rope, broken strand and broken rope faults. Firstly, a CNN structure is proposed, and regularization technology is adopted to prevent overfitting. Then, a method of image dataset description and establishment that can cover the entire feature space of overhanging BTRs is put forward. Finally, the CNN and two traditional data mining algorithms, namely, k-nearest neighbor (KNN) and an artificial neural network with back propagation (ANN-BP), are adopted to train and test the established dataset, and the influence of hyperparameters on the network diagnostic accuracy is investigated experimentally. The experimental results showed that the CNN could effectively avoid complex steps such as manual feature extraction, that the learning rate and batch-size strongly affected the accuracy and training efficiency, and that the fault diagnosis accuracy of CNN was 100%, which was higher than that of KNN and ANN-BP. Therefore, the proposed CNN with high accuracy, real-time functioning and generalization performance is suitable for application in the health monitoring of hoisting system BTRs.

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Topology Control Strategy for Movable Sensor Networks in Ultradeep Shafts

Cited by 8 publications

References 24 publications

Service redundancy and cluster‐based routing protocols for wireless sensor and mobile ad hoc networks: A survey

Service redundancy and cluster‐based routing protocols for wireless sensor and mobile ad hoc networks: A survey

The Shared Bicycle and Its Network—Internet of Shared Bicycle (IoSB): A Review and Survey

Health Monitoring for Balancing Tail Ropes of a Hoisting System Using a Convolutional Neural Network

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