An energy-aware and Q-learning-based area coverage for oil pipeline monitoring systems using sensors and Internet of Things

Abstract

Pipelines are the safest tools for transporting oil and gas. However, the environmental effects and sabotage of hostile people cause corrosion and decay of pipelines, which bring financial and environmental damages. Today, new technologies such as the Internet of Things (IoT) and wireless sensor networks (WSNs) can provide solutions to monitor and timely detect corrosion of oil pipelines. Coverage is a fundamental challenge in pipeline monitoring systems to timely detect and resolve oil leakage and pipeline corrosion. To ensure appropriate coverage on pipeline monitoring systems, one solution is to design a scheduling mechanism for nodes to reduce energy consumption. In this paper, we propose a reinforcement learning-based area coverage technique called CoWSN to intelligently monitor oil and gas pipelines. In CoWSN, the sensing range of each sensor node is converted to a digital matrix to estimate the overlap of this node with other neighboring nodes. Then, a Q-learning-based scheduling mechanism is designed to determine the activity time of sensor nodes based on their overlapping, energy, and distance to the base station. Finally, CoWSN can predict the death time of sensor nodes and replace them at the right time. This work does not allow to be disrupted the data transmission process between sensor nodes and BS. CoWSN is simulated using NS2. Then, our scheme is compared with three area coverage schemes, including the scheme of Rahmani et al., CCM-RL, and CCA according to several parameters, including the average number of active sensor nodes, coverage rate, energy consumption, and network lifetime. The simulation results show that CoWSN has a better performance than other methods.

Development of a Model for Spoofing Attacks in Internet of Things

“…The clusters are monitored through detection and localization method to identify and remove the spoofing attack. The cluster is further divided into intra-cluster and inter-clus- In this paper, the IoT network [23][24][25][26] is divided into clusters for the detection and localization of the spoofing attack accurately because there are limited nodes in the cluster. The cluster forms a group of nodes with similar interests and the cluster is maintained by the core node through a Status Declaration (SD) message or Hello message.…”

An End-to-End Platform for Managing Third-Party Risks in Oil Pipelines

“…Monitoring the areas surrounding oil pipelines is crucial for safeguarding not only these essential energy infrastructure assets but also the environment, public safety, and the economy at large [1], [2]. Serving as the arteries of modern society, oil pipelines facilitate the transportation of vast quantities of petroleum products over extensive distances [1], [3]. However, their operation carries inherent risks.…”

“…Just as industries like textiles and agriculture face sustainability challenges, such as water pollution and resource depletion [4]- [6], the petroleum sector also plays a vital role in environmental protection and economic stability [7]. Ruptures or damages in pipeline systems can trigger a chain reaction of environmental disasters, leading to the uncontrolled release of hazardous substances that pollute ecosystems, contaminate water sources, and devastate wildlife populations [1], [8]. These repercussions extend beyond local communities, causing long-term health hazards, necessitating evacuations, and causing economic turmoil.…”

A cluster-based trusted routing method using fire hawk optimizer (FHO) in wireless sensor networks (WSNs)

“…They control the environment and process the collected data in an efficient and distributed manner 1 , 2 . These networks have many applications, for example, military field (to detect and arrange forces in enemy land), detection of biological, chemical, and nuclear radiation, environmental monitoring and protection, agricultural monitoring, and industrial product control 6 , 7 . In addition, WSNs are used in smart homes, smart transportation, smart cities, and the protection of cultural and commercial monuments 8 , 9 .…”