An Adaptive Framework Coping with Dynamic Target Speed for Device-Free Passive Localization

Zheng, Xiuyuan; Yang, Jie; Chen, Yingying; Xiong, Hui

doi:10.1109/tmc.2014.2347303

Cited by 18 publications

(8 citation statements)

References 36 publications

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“…Objects might have dynamic speed level where they keep on changing from time to time, or they move with static speed level. In [104] authors have proposed work pertaining the variation in speed of the object; however, a standard protocol for object speed is not provided. Node position can be predicted with a predefined speed or last known object speed.…”

Section: Object Speedmentioning

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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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: Object Speedmentioning

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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“…Nuzzer leveraged probabilistic techniques, and had the capability to both localize a single entity and estimate the number of people in the area of interest [15]. Since RSSI is a coarse-grained measurement of wireless networks, many RSSI-based human detection systems deployed multiple pairs of transceivers to achieve a higher accuracy [16]. Another technique of human detection using multiple pairs of transceivers is Radio Tomographic Imaging (RTI) [17].…”

Section: Related Workmentioning

confidence: 99%

Robust Device-Free Intrusion Detection Using Physical Layer Information of WiFi Signals

Man

Yang

et al. 2019

Applied Sciences

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WiFi infrastructures are widely deployed in both public and private buildings. They make the connection to the internet more convenient. Recently, researchers find that WiFi signals have the ability to sense the changes in the environment that can detect human motion and even identify human activities and his identity in a device-free manner, and has many potential security applications in a smart home. Previous human detection systems can only detect human motion of regular moving patterns. However, they may have a significant detection performance degradation when used in intrusion detection. In this study, we propose Robust Device-Free Intrusion Detection (RDFID) system leveraging fine-grained Channel State Information (CSI). The noises in the signals are removed by a Principle Component Analysis (PCA) and a low pass filter. We extract a robust feature of frequency domain utilizing Continuous Wavelet Transform (CWT) from all subcarriers. RDFID captures the changes from the whole wireless channel, and a threshold is obtained self-adaptively, which is calibration-free in different environments, and can be deployed in smart home scenarios. We implement RDFID using commodity WiFi devices and evaluate it in three typical office rooms with different moving patterns. The results show that our system can accurately detect intrusion of different moving patterns and different environments without re-calibration.

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“…We generally define the RSS fluctuation caused by the device-free target as radio frequency tomography (RFT) phenomenon [ 4 ]. Actually, recent research and experiments indicate that the RSS fluctuant can be easily detected when a mobile target walks across a line-of-sight link (LOSL) between pairs of wireless nodes [ 5 , 6 , 7 , 8 ]. Taking advantage of the RFT phenomenon, a passive indoor tracking scheme is proposed to find the crossing point (CP) on the LOSLs using geometrical the constraint method in reference [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…It is well known that power consumption is a serious challenge in the WSN environment; hence, the dynamic networking mechanism should be considered in the tracking system for energy conservation purposes. Moreover, the issue of tracking multiple targets has been neglected, which is an essential problem in DFL scenarios [ 2 , 4 , 7 ]. In addition to that, the RFT phenomenon, which is the foundation knowledge, should be further investigated to guarantee the feasibility of the passive tracking scheme based on the geometric constraint methodology.…”

Section: Introductionmentioning

confidence: 99%

A Passive Tracking System Based on Geometric Constraints in Adaptive Wireless Sensor Networks

Zhou

Ahn

Lee

et al. 2018

Sensors

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Target tracking technologies in wireless sensor network (WSNs) environments fall into two categories: active and passive schemes. Unlike with the active positioning schemes, in which the targets are required to hold cooperative devices, the research on passive tracking, i.e., tracking device-free targets, has recently showed promise. In the WSN, device-free targets can be tracked by sensing radio frequency tomography (RFT) on the line-of-sight links (LOSLs). In this paper, we propose a passive tracking scheme exploiting both adaptive-networking LOSL webs and geometric constraint methodology for tracking single targets, as well as multiple targets. Regarding fundamental knowledge, we firstly explore the spatial diversity technique for RFT detection in realistic situations. Then, we analyze the power consumption of the WSN and propose an adaptive networking scheme for the purpose of energy conservation. Instead of maintaining a fixed LOSL density, the proposed scheme can adaptively adjust the networking level to save energy while guaranteeing tracking accuracy. The effectiveness of the proposed scheme is evaluated with computer simulations. According to the results, it is observed that the proposed scheme can sufficiently reduce power consumption, while providing qualified tracking performance.

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An Adaptive Framework Coping with Dynamic Target Speed for Device-Free Passive Localization

Cited by 18 publications

References 36 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

Robust Device-Free Intrusion Detection Using Physical Layer Information of WiFi Signals

A Passive Tracking System Based on Geometric Constraints in Adaptive Wireless Sensor Networks

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