WSN in Monitoring Oil Pipelines Using ACO and GA

Elnaggar, Ola E.; Ramadan, Rabie A.; Fayek, Magda B.

doi:10.1016/j.procs.2015.05.158

Cited by 20 publications

(14 citation statements)

References 8 publications

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“…As can be seen, higher budgets permit higher reliability (Model 3 rather than 2 or 1). (3,3), (4, 2), (5, 2), (6, 3), (7,3), (8,3), (9,3), (10,1), (11,3), (12,3), (13,3) 0.285 Figure 8 illustrates the selected optimal sensor locations (from Figure 7 and Table 6) with respect to the BN. It can be seen that the optimization methodology preferences sensors at the third layer.…”

Section: Resultsmentioning

confidence: 99%

“…Table 1 lists and categorizes current literature in this regard. Single Linear Not considered Mixed integer linear programming solver Oil Pipeline [5] Single Linear Not considered Worst case energy balance strategy Oil Pipeline [6] Single Linear Considered CPLEX Gas detector [7] Single Nonlinear Not considered Genetic algorithm, ant colony algorithm Oil Pipeline [8] Single Nonlinear Not considered Dynamic programming, simulated annealing, Particle swarm optimization algorithm, ant colony optimization algorithm…”

Section: Introductionmentioning

confidence: 99%

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An Entropy Based Bayesian Network Framework for System Health Monitoring

Parhizkar

Balali

Mosleh

2018

Entropy

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Oil pipeline network system health monitoring is important primarily due to the high cost of failure consequences. Optimal sensor selection helps provide more effective system health information from the perspective of economic and technical constraints. Optimization models confront different issues. For instance, many oil pipeline system performance models are inherently nonlinear, requiring nonlinear modelling. Optimization also confronts modeling uncertainties. Oil pipeline systems are among the most complicated and uncertain dynamic systems, as they include human elements, complex failure mechanisms, control systems, and most importantly component interactions. In this paper, an entropy-based Bayesian network optimization methodology for sensor selection and placement under uncertainty is developed. Entropy is a commonly used measure of information often been used to characterize uncertainty, particularly to quantify the effectiveness of measured signals of sensors in system health monitoring contexts. The entropy based Bayesian network optimization outlined herein also incorporates the effect that sensor reliability has on system information entropy content, which can also be related to the sensor cost. This approach is developed further by incorporating system information entropy and sensor costs in order to evaluate the performance of sensor combinations. The paper illustrates the approach using a simple oil pipeline network example. The so-called particle swarm optimization algorithm is used to solve the multi-objective optimization model, establishing the Pareto frontier.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Entropy Based Bayesian Network Framework for System Health Monitoring

Parhizkar

Balali

Mosleh

2018

Entropy

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“…The mark sign of "√" is used to show where there exist tapped areas and the null shows that there is need for more attention from researchers. [75], [97], [113], [178], [197], [221], [222] [139], [152], [154], [156], [173], [208] [64], [93], [120], [131], [183] 5 Data mining [43], [65], [66], [116], [148], [160], [166], [188], [228] [74], [104], [123], [146], [206], [211] [35], [68], [103], [121], [142], [148], [161], [184], [186], [190], [216] 13…”

Section: Applications Of Soft Computingmentioning

confidence: 99%

A Holistic Review of Soft Computing Techniques

Philip¹,

Mom²,

Igwue³

2017

ACM

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Due to notable technological convergence that brought about exponential growth in computer world, SoftComputing (SC) has played a vital role with automation capability features to new levels of complex applications. In this research paper, the authors reviewed journals related to the subject matter with the aim of striking a convincing balance between a system that is capable of tolerance to uncertainty, imprecision, approximate reasoning and partial truth to achieve tractability, robustness, economy of communication, high machine intelligence quotient (MIQ), low cost solution and better rapport with reality to conventional techniques. This paper gives an insight on four major consortiums of SC that sprang from the concept of cybernetics, explores and reviews the different techniques, methodologies; application areas and algorithms are formulated to give an idea on how these computing techniques are applied to create intelligent agents to solve a variety of problems. The mechanisms highlighted can serve as an inspiration platform and awareness to new and old researchers that are not or fully grounded in this unique area of research and to create avenue in order to fully embrace the techniques in research communities.

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“…Rabei et al [ 27 ], used nature-inspired techniques for node placement and lifetime optimization of a Linear Wireless Sensor Network specifically used in monitoring of oil pipelines. The problem of the optimal number of sensors to be deployed with a given initial energy of each sensor node and message buffering limitations has been discussed.…”

Section: Introductionmentioning

confidence: 99%

“… An efficient mechanism for data transfer for such pipeline has been designed. The performance comparison of the proposed scheme with three techniques namely greedy approach, ACO and GA [ 27 ] with respect to lifetime, throughput and end-to-end delay has been performed. …”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Management of Pipelines in Buildings Using Linear Wireless Sensor Networks

Varshney

Kumar

Swaroop³

et al. 2018

Sensors

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The efficient and safe management of air conditioner (AC), Piped Natural Gas (PNG) and water pipelines in large buildings is a major challenge for the safety of these buildings. In recent years, Linear Wireless Sensor Networks (LWSN) are being used extensively for monitoring of long natural gas, water, and oil pipelines. LWSNs can also be used for efficient and safe management of AC, PNG and water pipelines in large buildings. In this paper, a scheme for optimal placement of sensors and base stations in a linear fashion to monitor the various pipelines used in large buildings has been proposed. The proposed scheme utilizes the Lion Optimization Algorithm (LOA) and has been compared with three strategies, namely Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Greedy Approach with respect to throughput, lifetime and end-to-end delay. The simulation results show that the proposed scheme exhibits better performance in comparison to the other three considered techniques for all the three parameters. The most striking feature of the proposed approach is that optimization is more effective when the length of the pipeline is more as far as end-to-end delay is concerned. The lifetime of the network is significantly improved using the proposed approach, especially when the length of the pipeline is of medium size, which makes the proposed scheme suitable for energy efficient buildings.

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WSN in Monitoring Oil Pipelines Using ACO and GA

Cited by 20 publications

References 8 publications

An Entropy Based Bayesian Network Framework for System Health Monitoring

An Entropy Based Bayesian Network Framework for System Health Monitoring

A Holistic Review of Soft Computing Techniques

Energy Efficient Management of Pipelines in Buildings Using Linear Wireless Sensor Networks

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