A Voronoi-Based Sensor Handover Protocol for Target Tracking in Distributed Visual Sensor Networks

Sung, Tien-Wen; Yang, Chu Sing

doi:10.1155/2014/586210

Cited by 10 publications

(6 citation statements)

References 34 publications

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“…Finally, a few target tracking approaches have used adaptive node selection [19] for reducing the energy cost and computational complexity as well as ensuring the tracking accuracy. A voronoi-based distributed sensor handover protocol [20] has been designed using localized algorithm with the objective of target tracked ratio, i.e., the quality of target tracking service. A quantized measurement fusion framework has been investigated in [21] using probabilistic quantization scheme to reduce the average communication energy saving.…”

Section: Related Workmentioning

confidence: 99%

Bayesian Localized Energy Optimized Sensor Distribution for Efficient Target Tracking

Sumathy*¹,

Alonshia²

2019

IJITEE

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In wireless sensor network application, the localization of nodes are carried out for extended life time of the node. Many applications in wireless sensor network perform localization of nodes over an extended period of time with energy variance. However, optimal selection algorithm poses new challenges to the overall transmission power levels for target detection, and thus localized energy optimized sensor management strategies are necessary for improving the accuracy of target tracking. In this work, it is proposed to develop a Bayesian Localized Energy Optimized Sensor Distribution (BLEOSD) scheme for efficient target tracking in Wireless Sensor Network. The sensor node localized with Bayesian average scheme thatestimates the sensor node’s energy are optimized as per data transfer capacity verification. The Bayesian average energy level of the sensor network is compared with the energy of each sensor node. The sensor nodes are localized and energy distribution based on the Bayesian energy estimate for efficient target tracking. The sensor node distribution strategy improves the accuracyto identify the targets effectively. Experiments are conducted using simulation of WSN by varying number of nodes, energy levels of the node and target object density using the Network Simulator Tool (NS2) The proposed BLEOSD technique is compared with various recent methods by evaluating accuracy of target tracking, energy consumption rate, localized node density and time for target tracking. The experimental results shows that the performance of BLESOD is more encouraging compared to contemporary methods.

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Section: Related Workmentioning

confidence: 99%

Bayesian Localized Energy Optimized Sensor Distribution for Efficient Target Tracking

Sumathy*¹,

Alonshia²

2019

IJITEE

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“…Driven by microelectromechanical systems technology advances in low-cost networking, there have been rapid development and use of wireless sensor networks in recent years [13,14]. These sensor networks carry the promise of significantly improving and expanding the quality of care across a wide range of applications, which include air pollution monitoring, medicine and public health, and natural disaster prevention.…”

Section: Related Workmentioning

confidence: 99%

An Energy Balancing Strategy Based on Hilbert Curve and Genetic Algorithm for Wireless Sensor Networks

Kong

Pan

Sung

et al. 2017

Wireless Communications and Mobile Computing

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A wireless sensor network is a sensing system composed of a few or thousands of sensor nodes. These nodes, however, are powered by internal batteries, which cannot be recharged or replaced, and have a limited lifespan. Traditional two-tier networks with one sink node are thus vulnerable to communication gaps caused by nodes dying when their battery power is depleted. In such cases, some nodes are disconnected with the sink node because intermediary nodes on the transmission path are dead. Energy load balancing is a technique for extending the lifespan of node batteries, thus preventing communication gaps and extending the network lifespan. However, while energy conservation is important, strategies that make the best use of available energy are also important. To decrease transmission energy cost and prolong network lifespan, a three-tier wireless sensor network is proposed, in which the first level is the sink node and the third-level nodes communicate with the sink node via the service sites on the second level. Moreover, this study aims to minimize the number of service sites to decrease the construction cost. Statistical evaluation criteria are used as benchmarks to compare traditional methods and the proposed method in the simulations.

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“…Here, S i , j , k , l denotes the integrating operation of one-known basis function; thus, the mathematical model of energy optimization surface can be transferred into the quadratic function of control vertexes V [ 46 ]. Based on this surface fitting method and the tablet templates shown in Figure 5 , their corresponding reflective energy surfaces of light intensity can then be established with the resultant details being demonstrated in Figure 6 .…”

Section: Experiments For Drug Tablet Trackingmentioning

confidence: 99%

Image Tracking for the High Similarity Drug Tablets Based on Light Intensity Reflective Energy and Artificial Neural Network

Liang

Zhou

Liu

et al. 2014

Computational and Mathematical Methods in Medicine

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It is obvious that tablet image tracking exerts a notable influence on the efficiency and reliability of high-speed drug mass production, and, simultaneously, it also emerges as a big difficult problem and targeted focus during production monitoring in recent years, due to the high similarity shape and random position distribution of those objectives to be searched for. For the purpose of tracking tablets accurately in random distribution, through using surface fitting approach and transitional vector determination, the calibrated surface of light intensity reflective energy can be established, describing the shape topology and topography details of objective tablet. On this basis, the mathematical properties of these established surfaces have been proposed, and thereafter artificial neural network (ANN) has been employed for classifying those moving targeted tablets by recognizing their different surface properties; therefore, the instantaneous coordinate positions of those drug tablets on one image frame can then be determined. By repeating identical pattern recognition on the next image frame, the real-time movements of objective tablet templates were successfully tracked in sequence. This paper provides reliable references and new research ideas for the real-time objective tracking in the case of drug production practices.

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A Voronoi-Based Sensor Handover Protocol for Target Tracking in Distributed Visual Sensor Networks

Cited by 10 publications

References 34 publications

Bayesian Localized Energy Optimized Sensor Distribution for Efficient Target Tracking

Bayesian Localized Energy Optimized Sensor Distribution for Efficient Target Tracking

An Energy Balancing Strategy Based on Hilbert Curve and Genetic Algorithm for Wireless Sensor Networks

Image Tracking for the High Similarity Drug Tablets Based on Light Intensity Reflective Energy and Artificial Neural Network

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