Focal design issues affecting the deployment of wireless sensor networks for pipeline monitoring

Owojaiye, Gbenga; Sun, Yichuang

doi:10.1016/j.adhoc.2012.09.006

Cited by 66 publications

(28 citation statements)

References 64 publications

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“…For communication and energy efficiency a MAC protocol with low duty cycle is designed that allows idle listening avoidance. In [7], the authors study the impact of the choice of MAC protocol (TDMA or CDMA) on the behavior of LWSNs in terms of lifetime and congestion avoidance. They suggest cooperative communications of sensor nodes sharing their physical resources particularly their antennas to create virtual multiple transmission paths.…”

Section: Related Workmentioning

confidence: 99%

Investigating Energy Efficiency and Timeliness for Linear Wireless Sensor Networks

Sokullu

Demir

2014

Procedia Computer Science

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Wireless sensor networks (WSNs) keep attracting the attention of researchers due to the newly emerging areas of application fueled by the development of inexpensive sensors and advanced communication technologies. A large group of applicationsmonitoring of pipelines, roads and bridges, as well as recently evolving IoT applications -specifically requires linear topology. Compared to traditional WSNs, these linear wireless sensor networks (LWSN) pose additional challenges in terms of overall delay and energy efficiency. Even though there are numerous and also very successful MAC protocols for WSN, very few of them take into consideration the specifics of linear topologies. Thus our aim is to address these challenges by proposing LINE-MAC, a MAC protocol tailored especially for LWSN. Our simulation results show that LINE-MAC achieves significant improvement in end-to-end delay, energy consumption and packet delivery ratio (PDR) compared to a general, energy and delay efficient MAC protocol for WSN.

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Section: Related Workmentioning

confidence: 99%

Investigating Energy Efficiency and Timeliness for Linear Wireless Sensor Networks

Sokullu

Demir

2014

Procedia Computer Science

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“…Wireless sensors, together with actuators, can be used for factory automation, inventory management, and detection of liquid/gas leakages. These applications require accurate supervision of shock, noise and temperature parameters in remote or inaccessible locations such as tanks, turbine engines or pipelines [15,16].…”

Section: Industry: Manufacturing and Smart Gridsmentioning

confidence: 99%

Energy efficiency in wireless sensor networks: A top-down survey

Rault¹,

Bouabdallah²,

Challal³

2014

Computer Networks

789

452

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The design of sustainable wireless sensor networks (WSNs) is a very challenging issue. On the one hand, energyconstrained sensors are expected to run autonomously for long periods. However, it may be cost-prohibitive to replace exhausted batteries or even impossible in hostile environments. On the other hand, unlike other networks, WSNs are designed for specific applications which range from small-size healthcare surveillance systems to large-scale environmental monitoring. Thus, any WSN deployment has to satisfy a set of requirements that differs from one application to another. In this context, a host of research work has been conducted in order to propose a wide range of solutions to the energysaving problem. This research covers several areas going from physical layer optimization to network layer solutions. Therefore, it is not easy for the WSN designer to select the efficient solutions that should be considered in the design of application-specific WSN architecture.We present a top-down survey of the trade-offs between application requirements and lifetime extension that arise when designing wireless sensor networks. We first identify the main categories of applications and their specific requirements. Then we present a new classification of energy-conservation schemes found in the recent literature, followed by a systematic discussion as to how these schemes conflict with the specific requirements. Finally, we survey the techniques applied in WSNs to achieve trade-off between multiple requirements, such as multi-objective optimisation.

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“…In underwater environment, acoustic communication is considered an ideal but the range of underwater sensor nodes is not preferred more than 1 km. However, if we divide the pipeline length into subzones of 1000 meter each, then less number of nodes is required to deliver the data packets from the middle of the pipeline and from bottom to the surface at different ocean depths [3].…”

Section: Related Workmentioning

confidence: 99%

“…Although in underwater, sound waves travel longer and faster than the air but still they are five times slower than the electromagnetic waves [2]. Most of deployment algorithms and routing protocols do not remain suitable for such environments as they necessitate random nodes deployment, joint topology, 2D area, higher data rate, and outcomes in large end-to-end delays [3]. Although significant areas of improvement are highly required to be there in UW-LSN monitoring techniques like scalable nodes deployment, distribution of large network, minimization of the delay in communication and efficient data deliveries to the sink; but the available data rates for the long range underwater applications is very low and not feasible for the real time communication.…”

Section: Introductionmentioning

confidence: 99%

Scalable Nodes Deployment Algorithm for the Monitoring of Underwater Pipeline

et al. 2016

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Underwater Wireless Linear Sensor Networks (UW-LSNs Keywords: scalability, heterogeneity, nodes deployment algorithm, underwater pipeline monitoringCopyright © 2016 Universitas Ahmad Dahlan. All rights reserved. IntroductionUnderwater environments do not remain feasible for human operators due to the harsh underwater activities, high water pressure, hazardous underwater creatures, vast areas for exploration and lack of high frequency signals propagation [1]. Due to the underwater acoustic communication, the signal propagation speed is decreased to the speed of sound. Although in underwater, sound waves travel longer and faster than the air but still they are five times slower than the electromagnetic waves [2]. Most of deployment algorithms and routing protocols do not remain suitable for such environments as they necessitate random nodes deployment, joint topology, 2D area, higher data rate, and outcomes in large end-to-end delays [3]. Although significant areas of improvement are highly required to be there in UW-LSN monitoring techniques like scalable nodes deployment, distribution of large network, minimization of the delay in communication and efficient data deliveries to the sink; but the available data rates for the long range underwater applications is very low and not feasible for the real time communication. In short, it seems to be hard to increase the data rate for the long range underwater communication where scalable and efficient nodes deployments in distributed topology are highly supportive in this regard.In fact, UW-LSNs and terrestrial sensor networks have many common properties including deployment of large number of nodes and energy constraints; but UW-LSN stays unique in many aspects from the terrestrial sensor networks [4]. Firstly, most of the times homogenous types of sensor nodes are deployed randomly that are not scalable for extension of underwater pipelines monitoring coverage. Secondly, UW-LSN requires special deployment algorithms that assign proper nodes positions in 3D dynamic underwater environment. Thirdly, sensors are linearly deployed to maintain the linear topology and distributed topology network [5] that divides the pipeline length into sub-zones and ranges of heterogeneous sensors.

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Focal design issues affecting the deployment of wireless sensor networks for pipeline monitoring

Cited by 66 publications

References 64 publications

Investigating Energy Efficiency and Timeliness for Linear Wireless Sensor Networks

Investigating Energy Efficiency and Timeliness for Linear Wireless Sensor Networks

Energy efficiency in wireless sensor networks: A top-down survey

Scalable Nodes Deployment Algorithm for the Monitoring of Underwater Pipeline

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