Continuous object tracing in wireless sensor networks

Hao-li, Luan; Zhang, Yiying; Gao, Dequan; Zhen, Yan; Ma, Xiyuan

doi:10.1109/iceceng.2011.6057259

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…Finally, the backbone node can estimate the position and shape of the target by analyzing the subset of the boundary nodes. In [30], Luan et al proposed a scheme based on ring architecture for tracing continuous objects in WSNs, that is, ring-based continuous object tracing. The sensor is used as a ring to detect the target.…”

Section: B Methods For Detecting Continuous Object Boundarymentioning

confidence: 99%

Energy-Efficient Boundary Detection of Continuous Objects in Internet of Things Sensing Networks

et al. 2020

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Internet of Things (IoT) has been widely used to facilitate environmental perception, where detecting the boundary regions for continuous objects with energy-efficient manner is a challenge to be explored to support domain applications. This paper proposes a novel approach for continuous objects boundary prediction and detection in IoT sensing network, which is called Cloud Model-IoT Sensing Network Collaborative (CM-IoTSNC). Specifically, when an event is potentially occurred, the atmospheric dynamic diffusion model deployed on the cloud is adopted to predict gas diffusion trend in ideal and complex environments, moreover prediction results are transmitted to the IoT devices in the real-time fashion for scheduling security plans in advance. One-hop neighbor nodes are activated by abnormal nodes to determine a more accurate boundary region. Compared our technique with two traditional methods, namely Wireless Sensor Monitoring and Activating One-hop Neighbor Nodes, experimental results show that our method has a good performance in reducing the energy consumption and prolonging the lifetime of the network. INDEX TERMS Boundary detection, continuous objects, IoT sensing networks, energy efficiency, gas dynamic diffusion model.

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Section: B Methods For Detecting Continuous Object Boundarymentioning

confidence: 99%

Energy-Efficient Boundary Detection of Continuous Objects in Internet of Things Sensing Networks

et al. 2020

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“…Moreover, the denser of the WSNs, the more difficult the maintenance and administration, and the higher the risk of error. In order to decrease the dependency on the density of the WSNs, the most direct way is to decrease the dimension of the final result such as using the boundary information instead of global distribution information [2]. The drawback of this solution is that we will lose too much information about the continuous objects, and on the other hand the boundary of a continuous object highly relies on the people's logical decision, hence it has indeterminacy.…”

Section: Introductionmentioning

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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“…Continuous objects can be defined as objects whose characteristic parameters are continuous both in time and space, such as smog, gas, combustion zones, radiation zones and etc. The two primary factors for continuous objects monitoring(COM) are (1) boundary and (2) distributed density [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Optimal Fusion Set based Clustering in WSN for continuous objects monitoring

Chen

Liu

et al. 2014

9th International Conference on Communications and Networking in China

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Based on the application of continuous objects monitoring (COM) for Wireless Sensor Networks (WSNs), the sampling data collected by the sensors have relatively higher correlation and continuity due to the reason that the characteristic parameters of the monitored objects are continuous both in time and in space. In this paper, an Optimal Fusion Set based Clustering (OFSC) algorithm is presented to address the network clustering problem when monitoring the continuous objects. Different from the traditional clustering algorithms which cluster the network only after the cluster heads have been determined, OFSC algorithm, based on the global routing protocol in which the entire network information can be acquired by each node, the cluster head selection is carried out individually by nodes after the clustering results are firstly determined according to the Optimal Fusion Set theory. The performance evaluation results show that our OFSC algorithm can remarkably reduce the data traffic. On the other hand, our OFSC algorithm is a distributed clustering algorithm, which eliminates the computation of the communication cost during the cluster head selection, hence, decreases the computational complexity. Moreover, compared with the traditional LEACH and LEACH-C, our results show that the energy consumption of the entire network can be better balanced when monitoring the continuous objects.

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Continuous object tracing in wireless sensor networks

Cited by 5 publications

References 9 publications

Energy-Efficient Boundary Detection of Continuous Objects in Internet of Things Sensing Networks

Energy-Efficient Boundary Detection of Continuous Objects in Internet of Things Sensing Networks

BP neural network based continuous objects distribution detection in WSNs

Optimal Fusion Set based Clustering in WSN for continuous objects monitoring

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