A Distributed Cross-Layer Framework for Target Tracking in Three-Dimensional Wireless Sensor Networks

Cheng, Hongju; Su, Guang; Huang, Jiannan

doi:10.1007/978-3-642-27323-0_82

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Although the centralized approaches provide a single control point and a structured command flow suitable for surveillance applications, they limit the scalability of the system and drain the system resources, e.g., communication bandwidth. Other SMAs in the literature address the shortcomings of the centralized architecture, such as holonic [28]- [32] and localized architectures [33], [34]. This section summarizes some of the most relevant SMFs reported in the literature that addressed the SMA problem.…”

Section: Related Workmentioning

confidence: 99%

A Scalable Sensor Management Architecture Using BDI Model for Pervasive Surveillance

Hilal

Basir

2015

IEEE Systems Journal

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Recent world events have amplified the need for improved safety and security to contend with natural and man-made threats. The universality and unpredictability of such threats have stimulated intense interest in smart pervasive surveillance systems. They are built by adopting smart sensor networks that cover large areas and can perform self-contained assessments of situations in the environment. However, such systems rely on a massive number of sensors with diverse capabilities but limited resources, e.g., power, processing, and storage. Thus, successful management of tasks hinges on the systems architecture. Sensor management architectures (SMAs) coordinate the sensor nodes and their resources in a manner that improves system control and situation awareness. This paper introduces a scalable and flexible SMA for many sensor management (SM) applications, particularly, pervasive surveillance. This novel SMA is called the extended hybrid architecture for SM (E-HASM), an architecture that combines the advantages of the holonic, federated, and market-based paradigms. The E-HASM models each node as an intelligent sensor by using the beliefs, desires, and intentions model and defines the interaction and cooperation among the nodes. The simulation results illustrate the performance of the E-HASM over a variety of security threats, background targets, and network sizes. The results prove that the proposed architecture is significantly more scalable and flexible than centralized architectures.Index Terms-Belief-desire-intention (BDI), control framework, holonic architecture, pervasive surveillance, quality of surveillance (QoSv), scalability, sensor management (SM), track continuity.

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Section: Related Workmentioning

confidence: 99%

A Scalable Sensor Management Architecture Using BDI Model for Pervasive Surveillance

Hilal

Basir

2015

IEEE Systems Journal

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“…The features of the target generally include the number, the size, the shape, the coordinates, the speed, and the acceleration of the target. The process of the target tracking generally includes five modules: detection module, node selection module, routing module, prediction module, and positioning module [14]. The process of target tracking is shown as Fig.…”

Section: The Target Tracking Problem Of Underwater Wiseless Sensor Network a Underwater Target Trackingmentioning

confidence: 99%

Sensor-Networked Underwater Target Tracking Based on Grubbs Criterion and Improved Particle Filter Algorithm

Zhang

Gao

2019

IEEE Access

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For target tracking in underwater wireless sensor networks (WSNs), the contributions of the measured values of each sensor node are different for data fusion, so a better weighted nodes fusion and participation planning mechanism can obtain better tracking performance. A distributed particle filter based target tracking algorithm with Grubbs criterion and mutual information entropy weighted fusion (GMIEW) is proposed in this paper. The Grubbs criterion is adopted to analyze and verify the information obtained by sensor nodes before the information fusion, and accordingly some interference information or error information can be excluded from the data set. In the process of calculating importance weight in particle filter, dynamic weighting factor is introduced. The mutual information entropy between the measured value of the sensor nodes and the target state is used to reflect the amount of target information provided by sensor nodes, thus a dynamic weighting factor corresponding to each node can be obtained. The simulation results show that the proposed algorithm effectively improves the accuracy of prediction of target tracking system. INDEX TERMSUnderwater wireless sensor networks, target tracking, particle filtering, Grubbs criterion, mutual information entropy.

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Distributed Probabilistic Search and Tracking of Agile Mobile Ground Targets Using a Network of Unmanned Aerial Vehicles

Sun

Baek

Pack

2014

Human Behavior Understanding in Networked Sensing

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A Distributed Cross-Layer Framework for Target Tracking in Three-Dimensional Wireless Sensor Networks

Cited by 4 publications

References 8 publications

A Scalable Sensor Management Architecture Using BDI Model for Pervasive Surveillance

A Scalable Sensor Management Architecture Using BDI Model for Pervasive Surveillance

Sensor-Networked Underwater Target Tracking Based on Grubbs Criterion and Improved Particle Filter Algorithm

Distributed Probabilistic Search and Tracking of Agile Mobile Ground Targets Using a Network of Unmanned Aerial Vehicles

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