A Continuous Object Boundary Detection and Tracking Scheme for Failure-Prone Sensor Networks

Imran, Sajida; Ko, Young-Bae

doi:10.3390/s17020361

Cited by 25 publications

(19 citation statements)

References 22 publications

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“…If the network configuration changes, then the minimum spanning tree is no longer valid. In WSNs, the network configuration is highly dynamic because the channel quality varies over time [1,14] and the nodes are prone to failures [15,16]. Thus, as time goes by, the minimum spanning tree loses its validity.…”

Section: Limitations Of the Ghs Algorithm For Wsnsmentioning

confidence: 99%

Dynamic minimum spanning tree construction and maintenance for Wireless Sensor Networks

Diaz

Méndez

2019

Rev.Fac.Ing.Univ.Antioquia

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In a wireless sensor network (WSN), finding the optimal route from each node to the sink is not a straightforward task because of the distributed and dynamic characteristics of the network. For instance, the network suffers frequent changes because the channel quality varies over time and the nodes can leave or join the network at any moment. In order to deal with this variability, we propose the Dynamic Gallager-Humblet-Spira (DGHS) algorithm that builds and maintains a minimum spanning tree for distributed and dynamic networks, and we have found that DGHS reduces the number of control messages and the energy consumption, at the cost of a slight increase in the memory size and convergence time. This paper presents a detailed description of the different stages of the DGHS algorithm (neighbor discovery, tree construction and data collection), an in-depth analysis of extensive results that validates our proposal, and an exhaustive description of the GHS limitations.

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Section: Limitations Of the Ghs Algorithm For Wsnsmentioning

confidence: 99%

Dynamic minimum spanning tree construction and maintenance for Wireless Sensor Networks

Diaz

Méndez

2019

Rev.Fac.Ing.Univ.Antioquia

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“…The boundary problem solutions are presented in [10][11][12][13][14] when target moves along the boundary region. In [10], the proposed algorithm forms dynamic clusters when target moves near to the static cluster's boundary area.…”

Section: Related Workmentioning

confidence: 99%

“…These algorithms require higher energy consumption to solve boundary problem by forming extra clusters. In [14], the authors develop an efficient failure-prone object detection algorithm that detects and recovers from binary node failures. This scheme increases the boundary estimation accuracy.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Constant Gain Kalman Filter Based Tracking in Wireless Sensor Network

Hirpara

Rana

2017

Wireless Communications and Mobile Computing

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Target tracking is one of the most widely used applications of wireless sensor network (WSN). Efficient usage of energy is a key issue in WSN application such as target tracking. Another important criterion is a tracking accuracy that can be achieved by using appropriate tracking mechanism. Because of the special characteristic of WSN, there is a trade-off between tracking accuracy and power consumption. Our aim is to improve tracking accuracy as well as provide energy-efficient solution by integrating the concept of clustering and prediction techniques. This paper presents Energy-Efficient Constant Gain Kalman Filter based Tracking (EECGKFT) algorithm to optimize the energy usage and to increase the tracking accuracy. There is also a need to collect data from network having a mobile Base Station (BS). Hence, performance of proposed algorithm is analyzed for a static BS and also for mobile BS. The results depict that proposed algorithm performs better compared to the existing algorithms in energy efficiency and prediction accuracy. Analysis of results validates that EECGKFT increases energy efficiency by reducing transmission of unnecessary data in the sensor network environment and also provides good tracking results.

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“…In industrial applications like factory monitoring, sensors are deployed to monitor gas, liquid, or other kinds of dangerous objects. In this article, we concentrate on continuous object boundary detection, where traditional techniques have explored this topic [6][7][8]. As shown in Figure 1, wireless sensor networks (WSNs) serve as the foundation of IoT.…”

Section: Introductionmentioning

confidence: 99%

Boundary Region Detection for Continuous Objects in Wireless Sensor Networks

Zhang

Wang

Meng

et al. 2018

Wireless Communications and Mobile Computing

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Industrial Internet of Things has been widely used to facilitate disaster monitoring applications, such as liquid leakage and toxic gas detection. Since disasters are usually harmful to the environment, detecting accurate boundary regions for continuous objects in an energy-efficient and timely fashion is a long-standing research challenge. This article proposes a novel mechanism for continuous object boundary region detection in a fog computing environment, where sensing holes may exist in the deployed network region. Leveraging sensory data that have been gathered, interpolation algorithms have been applied to estimate sensory data at certain geographical locations, in order to estimate a more accurate boundary line. To examine whether estimated sensory data reflect that fact, mobile sensors are adopted to traverse these locations for gathering their sensory data, and the boundary region is calibrated accordingly. Experimental evaluation shows that this technique can generate a precise object boundary region with certain time constraints, and the network lifetime can be prolonged significantly.

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A Continuous Object Boundary Detection and Tracking Scheme for Failure-Prone Sensor Networks

Cited by 25 publications

References 22 publications

Dynamic minimum spanning tree construction and maintenance for Wireless Sensor Networks

Dynamic minimum spanning tree construction and maintenance for Wireless Sensor Networks

Energy-Efficient Constant Gain Kalman Filter Based Tracking in Wireless Sensor Network

Boundary Region Detection for Continuous Objects in Wireless Sensor Networks

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