Linear sensor networks: Applications, issues and major research trends

Varshney, Sudeep; Kumar, Chiranjeev; Swaroop, Abhishek

doi:10.1109/ccaa.2015.7148418

Cited by 27 publications

(15 citation statements)

References 13 publications

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“…The simulation setting and parameters for DAT-FTCAM, AODV+ and DSDV+ is as described in Table-I The simulated results are from an average of five cycle runs with a selection of seed values between 1-15. The results obtained for DAT-FTCAM incorporated with DI-LSR were compared with AODV+ (reactive protocol) and DSDV+ (proactive protocol) using the fairness mechanism proposed in [7] on the following metrics:…”

Section: Simulation and Evaluationmentioning

confidence: 99%

“…The transportion of a valuable data from the chain-like sensor arrays to the monitoring station, a routing algorithm predefines a communication path between sensor nodes to a destination node on a multi-hop linear topology. Thus, full deployment of WSN on pipeline network prompts to a series of performance related issues especially on resources sharing among source nodes [4][5][6][7]. Generally, a multi-hop linear WSN has severe performance degradation issues especially on network fairness using the Transmission Control Protocol (TCP) [8][9][10].…”

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confidence: 99%

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Tcp Performance and Throughput Fairness Optimization in a Multi-Hop Pipeline Network

2019

IJRTE

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Node starvation wireless sensor network (WSN) is a critical factor that affects the overall performance in a typical multi-hop linear network especially in an extensive scale network. The unfairness of sharing network resources with all source nodes in a multi-hop linear network amplifies the node starvation that often results in passive nodes in a network. This factor becomes critical with the increasing network density, aggressive data transfer, single destination node and inadequate data scheduling. This paper highlights the Delayed acknowledgement timeout for flat one-tier throughput critical application model (DAT-FTCAM) a mathematical fairness model that ensure maximum throughput fairness for pipeline network scenario. The DAT-FTCAM enables the users to calculate the maximum delayed acknowledgement timeout for transmission control protocol (TCP) proportional to the travel time or difference between a source and a destination node. The implementation of DAT-FTCAM technique with modified TCP parameters on NS2 has revealed a network fairness index of above 0.99 with optimum network performance in a scalable pipeline network. The DAT-FTCAM decreases data packet collision and eliminates passive nodes in a pipeline network with optimum throughput fairness.

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Section: Simulation and Evaluationmentioning

confidence: 99%

mentioning

confidence: 99%

Tcp Performance and Throughput Fairness Optimization in a Multi-Hop Pipeline Network

2019

IJRTE

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“…Those tasks mostly have characteristics of sensing environmental conditions using their embedded sensors, with data packets generated and forwarded to the sink node [ 3 ]. This ability of WSNs has been widely applied to monitor linear environmental conditions around nodes for gas/oil/water pipelines, railway tracks, national borders, AC power converters, and seacoast surveillance [ 4 ]. These scenarios are usually one-dimensional in fashion, meaning they have a linear structure; therefore, sensor nodes should be deployed linearly along them, resulting in a linear sensor network (LSN) [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

RDCPF: A Redundancy-Based Duty-Cycling Pipelined-Forwarding MAC for Linear Sensor Networks

Zhang

Fei

et al. 2020

Sensors

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Existing duty-cycling and pipelined-forwarding (DCPF) protocols applied in battery-powered wireless sensor networks can significantly alleviate the sleep latency issue and save the energy of networks. However, when a DCPF protocol applies to a linear sensor network (LSN), it lacks the ability to handle the bottleneck issue called the energy-hole problem, which is mainly manifested due to the excessive energy consumption of nodes near the sink node. Without overcoming this issue, the lifespan of the network could be greatly reduced. To that end, this paper proposes a method of deploying redundant nodes in LSN, and a corresponding enhanced DCPF protocol called redundancy-based DCPF (RDCPF) to support the new topology of LSN. In RDCPF, the distribution of energy consumption of the whole network becomes much more even. RDCPF also brings improvements to the network in terms of network survival time, packet delivery latency, and energy efficiency, which have been shown through the extensive simulations in comparison with existing DCPF protocols.

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“…For such types of environments, many deployment schemes have been proposed to monitor the underwater pipelines. Among these, most of them need special network setups like automotive tools/robots/vehicles and they generally are divided in different categories [1][2], [6]. Deployment Schemes are classified as those that require special network setups and extra automotive tools [7][8][9][10][11] and use homogenous types of sensors.…”

Section: Related Workmentioning

confidence: 99%

“…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].…”

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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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Linear sensor networks: Applications, issues and major research trends

Cited by 27 publications

References 13 publications

Tcp Performance and Throughput Fairness Optimization in a Multi-Hop Pipeline Network

Tcp Performance and Throughput Fairness Optimization in a Multi-Hop Pipeline Network

RDCPF: A Redundancy-Based Duty-Cycling Pipelined-Forwarding MAC for Linear Sensor Networks

Scalable Nodes Deployment Algorithm for the Monitoring of Underwater Pipeline

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