A Cluster-Based Continuous Object Tracking Scheme in Wireless Sensor Networks

Lee, Wanseop; Yim, Yongbin; Park, Soochang; Lee, Jeongcheol; Park, Hosung; Kim, Sang-Ha

doi:10.1109/vetecf.2011.6093203

Cited by 17 publications

(10 citation statements)

References 7 publications

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“…Continuous object tracking: in the literature, some works focus on continuous mobile objects [41]. Because of the nature of the objects, they can expand and increase in size to occupy a vast area or even split into fractions due to monitoring a continuous object is energy consuming.…”

Section: Imentioning

confidence: 99%

“…Because of the nature of the objects, they can expand and increase in size to occupy a vast area or even split into fractions due to monitoring a continuous object is energy consuming. The authors in [41] have provided a solution for energy consumption by using prediction, selective wake-up and structured clustering schemes. The proposed work reduced the energy consumption and increased the accuracy.…”

Section: Imentioning

confidence: 99%

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Object tracking sensor networks in smart cities: Taxonomy, architecture, applications, research challenges and future directions

Adam

Anisi

Ali

2020

Future Generation Computer Systems

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The development of pervasive communication devices and the emergence of the Internet of Things (IoT) have acted as an essential part in the feasibility of smart city initiatives. Wireless sensor network (WSN) as a key enabling technology in IoT offers the potential for cities to get smatter. WSNs gained tremendous attention during the recent years because of their rising number of applications that enables remote monitoring and tracking in smart cities. One of the most exciting applications of WSNs in smart cities is detection, monitoring, and tracking which is referred to as object tracking sensor networks (OTSN). The adaptation of OTSN into urban cities brought new exciting challenges for reaching the goal of future smart cities. Such challenges focus primarily on problems related to active monitoring and tracking in smart cities. In this paper, we present the essential characteristics of OTSN, monitoring and tracking application used with the content of smart city. Moreover, we discussed the taxonomy of OTSN along with analysis and comparison. Furthermore, research challenges are investigated concerning energy reservation, object detection, object speed, accuracy in tracking, sensor node collaboration, data aggregation and object recovery position estimation. This review can serve as a benchmark for researchers for future development of smart cities in the context of OTSN. Lastly, we provide future research direction. Highlights One of the most interesting applications of wireless sensor networks in smart cities would be detecting and monitoring or tracking of objects based on object tracking sensor networks (OTSN).  The review delineates the advantages as well as the weakness of existing OTSN and conduct a comparative view.  Furthermore, research challenges in OTSNs and existing methods are investigated with a focal point of energy constraints, object detection, object speed, tracking accuracy, sensor node collaboration, data aggregation and object recovery position estimation in WSN and smart cities.  Open research issues that required further improvement are summarized.

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Section: Imentioning

confidence: 99%

Section: Imentioning

confidence: 99%

Object tracking sensor networks in smart cities: Taxonomy, architecture, applications, research challenges and future directions

Adam

Anisi

Ali

2020

Future Generation Computer Systems

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“…In this way, the wake-up scheme can avoid the void area, and maintain the success ratio of continuous objects detection and tracking. For the problems of energy efficiency and accuracy of continuous object tracking, Lee et al [10] proposed a novel prediction and selective wake-up scheme by using structured clustering. In this paper, they claim that the prediction results of most of the prediction-based selective wake-up algorithm are very inaccurate, since for these algorithms, the nodes calculates next boundary line with only a few data, and it is difficult to cope with flexibility of continuous objects.…”

Section: Related Workmentioning

confidence: 99%

BP neural network based continuous objects distribution detection in WSNs

Chen

Wang

et al. 2015

Wireless Netw

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WSNs (Wireless Sensor Networks) are widely applied in environment monitoring. Especially, in large scale environment monitoring, its flexibility in deployment and self-organization are strong points. However for distribution detection of continuous objects in large scale environment monitoring, there are two primary constraints: energy consumption and the accuracy of the detection which relies on the density of the WSNs. Currently, almost all of the continuous object monitoring are based on the boundary detection, and all the energy efficiency solutions only focus on the WSNs itself. Unfortunately, with the boundary detection method, the accuracy of the continuous objects detection highly relies on the density of the sensor nodes. What is worse, it is even impossible to make sure of the density of the sensor nodes in real situation. In order to deal with these issues, we proposed the Optimal Fusion Set based Clustering algorithm based on the continuous characteristics of the targets to enhance the energy efficiency and Global Distribution Status Monitoring (GDSM) algorithm to implement the monitoring with finite sensor nodes. Firstly, a dynamic diffusion model based on the Gaussian Puff model is proposed, and then the characteristics of continuous objects are analyzed. According to the theoretic analysis and simulation results, the GDSM algorithm can achieve stable accuracy with limited sensor nodes.

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“…When these states are studied, these objects are generally looked at continuous objects. Continuous objects [4][5][6] generally refers to the volume and shape can not be ignored, cannot be regarded as the object of particles. The main task of monitoring the continuous object is to monitor the boundary of the continuous object.…”

Section: Introductionmentioning

confidence: 99%

Continuous Object Monitoring Based on Wireless Sensor Network and RBF

Peng¹,

Liu²

2017

Proceedings of the 2nd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2017)

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Abstract. Monitoring of continuous object distribution in large-scale environmental monitoring depends on the problem of high-density sensor networks. The RBF neural network is used to fit the distributed data of continuous objects at low sensor network density. Firstly, based on the distribution characteristics of continuous objects, a continuous evolution model of continuous objects based on Gaussian smoke model [1] is established. Secondly, the RBF neural network is trained based on the known distributed node data. Thirdly, RBF neural network is used to fit the distribution data of continuous objects. Finally, the fitting error of different number of training nodes is calculated. Through the experiment of matlab simulation, the practicability of RBF neural network for continuous object distribution has been validated.

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A Cluster-Based Continuous Object Tracking Scheme in Wireless Sensor Networks

Cited by 17 publications

References 7 publications

Object tracking sensor networks in smart cities: Taxonomy, architecture, applications, research challenges and future directions

Object tracking sensor networks in smart cities: Taxonomy, architecture, applications, research challenges and future directions

BP neural network based continuous objects distribution detection in WSNs

Continuous Object Monitoring Based on Wireless Sensor Network and RBF

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