Wireless Sensor Networks in oil and gas industry: Recent advances, taxonomy, requirements, and open challenges

Aalsalem, Mohammed Y.; Khan, Wazir Zada; Gharibi, Wajeb; Khan, Muhammad Khurram; Arshad, Quratulain

doi:10.1016/j.jnca.2018.04.004

Cited by 130 publications

(63 citation statements)

References 33 publications

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“…In the final leaf branch, when the packet loss was less than 22, eight cases were considered non-optimal, and one case was misclassified. Based on this road map of DT and the IF-THEN statement used to find the optimal node, the best optimal nodes among all nodes were 3 The DT prediction in Figure 6 presents a tree graph after applying the J48 (C4.5) DT algorithm on the enhanced SCTP congestion control alternative. The root node is cwnd, and the arches represent the conditions of value comparison to determine the optimal node.…”

Section: Machine Learning Implementationmentioning

confidence: 99%

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Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

et al. 2019

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The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-traffic networks with multiple nodes/sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G offers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall.

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Section: Machine Learning Implementationmentioning

confidence: 99%

“…Wireless sensor networks have played a crucial role in communication in the past few decades due to their unique features (e.g., mobility and ease of connection), which make them a major carrier of data across networks [1][2][3]. Consequently, the communication revolution embraces various standard wireless networks.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

et al. 2019

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“…In water industries, these I/O devices and sensors can be powered by solar panels or battery packs [11]. The transmission capability of the node is restricted due to its communication range and hence the information data is transmitted several times through transitional nodes (Hop) via which fallout in flooding of packets which can cause the conflict between the packets, information idleness, and energy dissipation [12]. On the other hand, the queuing and delay issues are mainly universal features for the technical surroundings, such as in networking and telecommunications [13].…”

Section: Fig5 Different Paths In Wsnmentioning

confidence: 99%

Efficient Energy Routing Protocol based on Energy & Buffer Residual Status (EBRS) for Wireless Sensor Networks

2019

IJEAT

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Wireless networks consist of nodes, having the ability that, they can sense and collect the information from the nearby surroundings. It has the responsibility of designed protocol to send this collected information by data gathering and forward it to the outside network via a sink node. Furthermore, WSNs doesn’t need any predetermined network structure; all the nodes used in WSN can operate as a router as well as the host. It uses multiple hops to send information to the node outside the communication range through different neighbor nodes. All the sensor nodes in WSN have their range of communication and can send and collect messages straight to each other until they were in the communication range. Moreover, the Self-organizing property of nodes in the network made WSN outstanding amongst the major applications. Nevertheless, the wireless nodes there in the network have a battery with restricted energy and can’t be recharge or change once deployed. Hence, the node energy must be utilized efficiently for various functions as sensing the information, processing the sensed information, and transmitting the processed information to another node. With the enhancements of the innovation and cost-effective hardware, our visualization presents a tremendous life enhancement of WSN into several new applications. To modify following such background, the energy-efficient routing protocol is extremely desirable and can be achieved by clustering in WSN. In the literature survey, various energy-efficient routing techniques based on cluster have been given to attain the energy-efficiency and enhance the lifetime of the network. However, these protocols were suffering from the bottleneck node issue. It is the situation in the network where the router node subjected to heavy traffic due to its presence in energy-efficient routing path or high remaining energy. This paper aims to moderate the possibility of the node to become a bottleneck node throughout the application. Thus, we attain the objective by design and develop the cluster-based efficient-routing protocol by selecting the head nodes of the cluster based on their residual energy and buffer status. Performance outcome shows that the projected work out-performs in contrast with present cluster-based routing protocols.

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“…Recently, research efforts around the globe are focusing on exploiting the fine-grained perception of IWSNs to track the movement of continuous objects [8] [9]. Detecting the boundary of diffusing continuous objects by identifying the sensor nodes located closest to the evolving front line attracts intensive attention [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

Boundary Tracking of Continuous Objects Based on Binary Tree Structured SVM for Industrial Wireless Sensor Networks

Liu

Han

et al. 2022

IEEE Trans. on Mobile Comput.

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Due to the flammability, explosiveness and toxicity of continuous objects (e.g., chemical gas, oil spill, radioactive waste) in the petrochemical and nuclear industries, boundary tracking of continuous objects is a critical issue for industrial wireless sensor networks (IWSNs). In this article, we propose a continuous object boundary tracking algorithm for IWSNs-which fully exploits the collective intelligence and machine learning capability within the sensor nodes. The proposed algorithm first determines an upper bound of the event region covered by the continuous objects. A binary tree-based partition is performed within the event region, obtaining a coarse-grained boundary area mapping. To study the irregularity of continuous objects in detail, the boundary tracking problem is then transformed into a binary classification problem; a hierarchical soft margin support vector machine training strategy is designed to address the binary classification problem in a distributed fashion. Simulation results demonstrate that the proposed algorithm shows a reduction in the number of nodes required for boundary tracking by at least 50%. Without additional fault-tolerant mechanisms, the proposed algorithm is inherently robust to false sensor readings, even for high ratios of faulty nodes (≈ 9%).

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Wireless Sensor Networks in oil and gas industry: Recent advances, taxonomy, requirements, and open challenges

Cited by 130 publications

References 33 publications

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

Efficient Energy Routing Protocol based on Energy & Buffer Residual Status (EBRS) for Wireless Sensor Networks

Boundary Tracking of Continuous Objects Based on Binary Tree Structured SVM for Industrial Wireless Sensor Networks

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