Sensor placement for lifetime maximization in monitoring oil pipelines

Guo, Yifeng; Kong, Fanxin; Zhu, Dakai; Tosun, Ali Şaman; Deng, Qingxu

doi:10.1145/1795194.1795204

Cited by 57 publications

(69 citation statements)

References 17 publications

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“…Some previous works propose time-and channel-optimal scheduling methods for networks with linear topology or tree topology [31]- [34], whereas we focused on a network with mesh topology. A previous study proposed scheduling algorithms for mesh networks; however, their networks do not support graph routing, and a transmission is not periodically scheduled in different periods [8].…”

Section: Related Workmentioning

confidence: 99%

Reliability and Temporality Optimization for Multiple Coexisting WirelessHART Networks in Industrial Environments

Jin

Kong

et al. 2017

IEEE Trans. Ind. Electron.

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Abstract-WirelessHART is a networking technology that is widely used in industrial wireless sensor networks. Its reliability and real-time performance are essential to industrial production. Many works have studied these two aspects, primarily focusing on a single WirelessHART network. However, multiple WirelessHART networks usually coexist in a real industrial environment. Applying existing approaches to such coexisting networks would cause performance degradation due to communication interference among these networks. In this paper, we propose a holistic framework that optimizes both reliability and temporality for multiple coexisting networks. The framework consists of two levels. The upper level targets communication channel management, and the lower level addresses data flow scheduling. For the upper level, we propose a network isolation algorithm that improves the data transmission reliability through dynamically adjusting channel assignments to different WirelessHART networks. For the lower level, we propose data flow scheduling algorithms that guarantee the temporality of data flows within each isolated network. These algorithms minimize the number of channels reserved by each isolated network and further enhance the transmission reliability through alleviating channel resource contention. We conduct trace-driven simulations of the channel management algorithm, and the results demonstrate that our algorithm exhibits stable performance and reduces packet loss by 36%. For the scheduling algorithms, the simulations demonstrate that in contrast with existing algorithms, the greater the number of coexisting networks, the fewer resources our algorithms use. When eight networks coexist, our algorithms outperform existing ones by consuming up to 63% fewer channel resources.

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Section: Related Workmentioning

confidence: 99%

Reliability and Temporality Optimization for Multiple Coexisting WirelessHART Networks in Industrial Environments

Jin

Kong

et al. 2017

IEEE Trans. Ind. Electron.

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“…Following this idea, some research works, such as Liu and Mohapatra 21 and Guo et al, 22 have addressed the linear sensor deployment problem, where nodes can be placed anywhere along a continuous pipeline. However, nodes can locate segmentally in boxcars for train monitoring to meet the requirements above.…”

Section: Sensor Deployment For Lifetime Maximizationmentioning

confidence: 99%

“…By this assumption, we use the algorithm L2H or H2L (Algorithm 1 or 2 in Guo et al 22 ) to deploy nodes to cover the range of cov.…”

Section: An Efficient Heuristicmentioning

confidence: 99%

Maximizing the lifetime of wireless sensor networks in trains for monitoring long-distance goods transportation

Jin

Kong

et al. 2017

International Journal of Distributed Sensor Networks

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One key issue in designing battery-powered wireless sensor networks is to properly control the energy consumption of the sensor nodes in order to prolong their operation time (i.e. lifetime). In this article, we present a real-life application of wireless sensor networks in trains to monitor the goods conditions in a long-distance transportation. We study the wireless sensor network deployment problem in developing a monitoring system with the goal of maximizing the network lifetime under constraints derived from the real application scenario. The key technical problem to solve is to determine the sensor placement and the transmission level for each sensor node, as well as the appropriate number of sensor nodes. We first formulate the problem with a realistic discrete power model as a mixed integer linear programming problem. Then, a two-step efficient deployment heuristic is proposed to satisfy these constraints step by step. The evaluation results indicate that the proposed heuristic performs almost the same as the optimal mixed integer linear programming solution. Moreover, the wireless sensor network with appropriate number of nodes can improve its lifetime up to 10.6% for a train with 80 boxcars. We also discussed a tested experiment in a laboratory environment, as well as the real implementation of the whole monitoring system.

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“…In [16], for sensor nodes that are utilized to monitor oil pipelines, authors study the linear sensor placement problem with the goal of maximizing their lifetime.…”

Section: B Pipeline Monitoring Networkmentioning

confidence: 99%

Float-based Pipeline Monitoring Network

Barzegar¹,

Hashemi²,

Akbarizadeh³

2014

IJCATR

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Wireless sensor networks are used to monitor water/oil pipelines. The excising schemes for pipeline monitoring are not energy efficient. Network operators have to daily travel across pipelines to change batteries of sensor nodes. In this paper, we propose a novel sensorbased pipeline monitoring protocol called FPMN. FPMN utilizes float nodes to reduce energy consumption in sensor nodes by reducing tasks of sensor nodes. FPMN tries to spread best route information along the pipelines in a way to reduce bandwidth and energy consumption. Our simulation experiments show that FPMN reduces energy consumption by 68.3% without change in packet delay and network traffic.

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Sensor placement for lifetime maximization in monitoring oil pipelines

Cited by 57 publications

References 17 publications

Reliability and Temporality Optimization for Multiple Coexisting WirelessHART Networks in Industrial Environments

Reliability and Temporality Optimization for Multiple Coexisting WirelessHART Networks in Industrial Environments

Maximizing the lifetime of wireless sensor networks in trains for monitoring long-distance goods transportation

Float-based Pipeline Monitoring Network

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