Generation of Surveillance Networked Nodes for Oil Pipelines’ Theft
International Journal of Recent Engineering Science (IJRES) | |
|
© 2021 by IJRES Journal | ||
Volume-8 Issue-5 |
||
Year of Publication : 2021 | ||
Authors : Rahmon Ariyo Badru, Azeez Ajani Waheed, Oluseye Ayobami Akinmoluwa, Olaniyi Ralph Obayemi |
||
DOI : 10.14445/23497157/IJRES-V8I5P104 |
How to Cite?
Rahmon Ariyo Badru, Azeez Ajani Waheed, Oluseye Ayobami Akinmoluwa, Olaniyi Ralph Obayemi, "Soil Investigation of a Collapsed Building Site in Jos, Nigeria," International Journal of Recent Engineering Science, vol. 8, no. 5, pp. 21-26, 2023. Crossref, https://doi.org/10.14445/23497157/IJRES-V8I5P104
Abstract
Despite the use of military task forces and some old detection systems along oil pipelines, sabotage is on the rise, especially in developing countries. This has led to various socio-economic losses and damage to the ecosystem via oil spills. To solve this problem, the research study generated low-cost surveillance networked nodes for oil pipelines theft via real-time monitoring and reporting. The prototype was simulated along the Lagos-Ilorin pipeline (259km) divided into nodes (26m apart interfaced to a central webserver) based on field view computation of the prototype device (resolution 4618 × 3465 pixels). The model software was developed using a web application, tunneling server, and mobile app. The mobile app at each node sends detected faces using feature detection and tracking via Constrained Local Models Algorithm (CLMA), each image having a unique I.D and location, which will be forwarded to a surveillance email. These images were transmitted immediately (1-2 seconds) and uploaded on the webserver. An email of this report was generated and forwarded to the admin indicating human activity on the pipeline infrastructure. The prototype was evaluated using the manual software non-functional Black Box Testing having a response time of 90%, 80% stability, and 90% reliability.
Keywords
Node, Oil, Pipeline, Prototype, Server, Surveillance.
Reference
[1] Robin Cartwright, and Nicholas Atampugre, Delta field Interviews Coordinated by Chris Newsom and Timipere Allison, Organised Oil Crime in Nigeria. [Online]. Available: http://enact-africa.s3.amazonaws.com/site/uploads/2020-11-26-organised-oil-crime-in-nigeria.pdf
[2] Jiedi Sun, Jinquan Zhang, and Xiaojun Wang, “Feature Extraction and Multi-Sensor Data Fusion in Monitoring and Pre-Warning System for Security of Pipeline Based on Multi-Seismic Sensors,” IEEE International Conference on Mechatronics and Automation, pp. 2595-2600, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[3] G.N Ezeh et al., “Pipeline Vandalization Detection Alert with Sms,” International Journal of Engineering Research and Applications, vol. 4, no. 4, pp. 21-25, 2014.
[Google Scholar] [Publisher Link]
[4] Godswill Ofualagba, O'tega Ejofodomi, “Automated Oil and Gas Pipeline Vandalism Detection System,” SPE Nigeria Annual International Conference and Exhibition, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Fakoyejo Olalekan, Pipeline explosion: Over 45,000 incidents recorded in 18 years – NNPC, 2020. [Online]. Available: https://nairametrics.com/2020/01/21/pipeline-explosion-over-45000-incidents-recorded-in-18-years-nnpc/
[6] Al Chukwuma Okoli, and Sunday Orinya., “Oil Pipeline Vandalism and Nigeria’s National Security,” Global Journal of Human Social Science, vol. 13, no. 5, pp. 67-75. 2013.
[Google Scholar] [Publisher Link]
[7] [Online].Available:https://nnpcgroup.com/NNPCBusiness/BusinessInformation/Pages/MonthlyPerformance-Data.aspx
[8] Ahmed, Tukur Umar, Moh’D. Shahwahid Hajj Othman, Miao Wang, “Causes and Consequences of Crude Oil Pipeline Vandalism in the Niger Delta region of Nigeria: A Confirmatory Factor Analysis Approach,” Cogent Economics & Finance, vol. 5, no. 1, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[9] AJAO Lukman et al., “An Anti-Theft Oil Pipeline Vandalism Detection: Embedded System Development,” International Journal of Engineering Science and Application, vol. 2, no. 2, pp. 55-64, 2018.
[Google Scholar] [Publisher Link]
[10] Jinfeng Sun, Zhiyue Zhang, Xiaoli Sun, “The Intelligent Crude Oil Anti-Theft System Based on IoT Under Different Scenarios,” Procedia Computer Science, vol. 96, pp. 1581-1588, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Gómez, Cristina, and David R. Green, “Small Unmanned Airborne Systems to Support Oil and Gas Pipeline Monitoring and Mapping,” Arabian Journal of Geosciences, vol. 10, no. 202, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Nwazor O Nkolika, and Romanus Obagidi, “A Light Tracking Automated Guided Vehicle for Oil Pipeline Leakage Detection,” International Journal of Scientific & Engineering Research, vol. 10, no. 3, pp. 250-256, 2019.
[Google Scholar] [Publisher Link]
[13] Tadas Baltrušaitis, Peter Robinson, Louis-Philippe Morency, “Openface: An Open-Source Facial Behavior Analysis Toolkit,” IEEE Winter Conference on Applications of Computer Vision, pp. 1-10, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[14] [Online].Available: https://thedora.com/pipeline/Nigeria-oil-gas_and_products_pipeline_map.html.
[15] Kintronics IP Security Solution, Calculating What You Can See with Your IP Camera, 2015. [Online].Available: https://kintronics.com/calculating-can-see-ip-camera