HyDiLLEch: A WSN-Based Distributed Leak Detection and Localisation in Crude Oil Pipelines

The Evolution of Pervasive Information Systems

Carrillo²

2022

In the era of Industry 4.0, digital manufacturing is evolving into smart manufacturing. This evolution impacts companies in three main areas: organization, people, and technologies. This chapter analyzes the Internet of Things (IoT) and Cyber-Physical Systems (CPS) -key technologies transforming the physical world into a digitalized physical world. IoT and CPS provide factories with sensing capabilities, perform data and context capture and allow them to act/react to optimize the value chain. We survey the recent state-of-the-art development of the Industrial Internet of Things (IIoT) -also known as IoT and CPS in the context of Industry 4.0, from a protocol, architecture, and standard point-of-view. We also explore key challenges and future research directions for extensive industrial adoption of these technologies.

Section: Discussionmentioning

confidence: 99%

PIS: IoT & Industry 4.0 Challenges

The Evolution of Pervasive Information Systems

Carrillo²

2022

“…The three layers comprise sensors at layer one, the gateways (fog nodes) at layer two and the cloud servers at layer three. The proposed architecture allows data sharing amongst predefined neighbourhood sensors at layer one, enabling the implementation of services such as detection and localisation of leakages at that layer as in our earlier works [28,29]. We also extend data placement to the fog layer to facilitate data processing, prioritisation, aggregation, replication, and migration services.…”

Section: Data-driven Resilient and Regionalised Mdp (R-mdp)mentioning

confidence: 99%

“…The publish-subscribe paradigm is used as the sub-layer interaction model to improve efficiency further. The communication aspect is the same as in our previous works [28,29].…”

Section: Data-driven Resilient and Regionalised Mdp (R-mdp)mentioning

confidence: 99%

“…c (connection initialisation): this action is used to realise the connectivity between nodes as defined in our previous work [29].…”

Section: The First Stage: Accuracy Optimisationmentioning

confidence: 99%

“…The fault tolerance, accuracy in each region (r0, r1 and r2) and the corresponding obtainable reward using the detection and localisation algorithm from our previous work [29] is evaluated in this subsection. To achieve this, we used two different types of communication failures, i.e.…”

Section: Accuracy In Detection and Fault Tolerancementioning

confidence: 99%

See 2 more Smart Citations

R-MDP: A Game Theory Approach for Fault-Tolerant Data and Service Management in Crude Oil Pipelines Monitoring Systems

Ahmed

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

Stouls

et al. 2023

Self Cite

Failures in pipeline transportation of crude oil have numerous adverse effects, such as ecological degradation, environmental pollution and a decrease in revenue for the operators, to mention a few. Efficient data and service management can predict and prevent these failures, reducing the downtime of the pipeline infrastructure, among other benefits. Thus, we propose a two-stage approach to data and service management in Leakage Detection and Monitoring Systems (LDMS) for crude oil pipelines. It aims to maximise the accuracy of leakage detection and localisation in a fault-tolerant and energy-efficient manner. The problem is modelled as a Markov Decision Process (MDP) based on the historical incident data from the Nigerian National Petroleum Corporation (NNPC) pipeline networks. Results obtained guarantee detection in at least two deployed nodes with a minimum localisation accuracy of 90%. Additionally, we achieved approximately 77% and 26% reduction in energy consumption compared to a pessimistic strategy and a globalised heuristic approach, respectively.

Development and Analysis of a Distributed Leak Detection and Localisation System for Crude Oil Pipelines

Ahmed

Stouls

et al. 2023

Sensors

Self Cite

Crude oil leakages and spills (OLS) isare some of the problems attributed to pipeline failures in the oil and gas industry’s midstream sector. Consequently, they are monitored via several leakage detectiondetection and localisation techniques (LDTs) comprising classical methods and, recently, Internet of Things (IoT)-based systems via wireless sensor networks (WSNs). Although the latter techniques are proven to be more efficient, they are susceptible to other types of failures such as high false alarms or single point of failure (SPOF) due to their centralised implementations. Therefore, in this work, we present a hybrid distributed leakage detection and localisation technique (HyDiLLEch), which combines multiple classical LDTs. The technique is implemented in two versions, a single-hop and a double-hop version. The evaluation of the results is based on the resilience to SPOFs, the accuracy of detection and localisation, and communication efficiency. The results obtained from the placement strategy and the distributed spatial data correlation include increased sensitivity to leakage detection and localisation and the elimination of the SPOF related to the centralised LDTs by increasing the number of node-detecting and localising (NDL) leakages to four and six in the single-hop and double-hop versionversions, respectively. In addition, the accuracy of leakages is improved from 0 to 32 m in nodes that were physically close to the leakage points while keeping the communication overhead minimal.