On Non-Cooperative Multiple-Target Tracking With Wireless Sensor Networks

Zhu, Ye; Vikram, Anil; Fu, Huirong; Guan, Yong Liang

doi:10.1109/twc.2014.2329306

Cited by 11 publications

(4 citation statements)

References 31 publications

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“…When there are many objects, it would be difficult to know which observation belongs to which object. In [63], [64], [65], [66], [67], [57], [68], [69], [70], [71], [72], [73], [74] authors have proposed different solutions for solving the problem with multiple object tracking approaches. In [63], the authors proposed a rule-based algorithm for multiple objects target tracking by using acoustic sensor nodes.…”

Section: Number Of Objects In Otsnmentioning

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: Number Of Objects In Otsnmentioning

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 expectation term in (26), given the probability density in (14), equals I N × N (identity matrix), and the Fisher information matrix becomes…”

Section: The Cramer-rao Lower Boundmentioning

confidence: 99%

“…Multitarget tracking using a mobile sensor network has been studied in Beaudeau et al and Zhang, where authors propose algorithms for controlling the location and activation of the network nodes. Zhu et al have proposed an algorithm for recovering individual signals of different targets and estimate target locations based on these recovered signals; the algorithm is called blind source separation.…”

Section: Introductionmentioning

confidence: 99%

Dual‐mode multiple‐target tracking in wireless sensor networks based on sensor grouping and maximum likelihood estimation

Adhami

Ghazizade

Majidi

2020

Int J Communication

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Summary The problem of tracking multiple mobile targets, using a wireless sensor network, is investigated in this paper. We propose a new sensor grouping algorithm, based on the maximum sensor separation distances (G‐MSSD), for estimating the location of multiple indistinguishable targets, either jointly or individually, depending on the distances between the generated groups. The joint tracking algorithm is formulated as a maximum likelihood (ML) estimator and solved through a modified version of the well‐known Gauss‐Newton (MGN) iterative method. We propose two candidate initial guesses for MGN based on G‐MSSD in joint tracking mode, while for the individual mode, the information of each group is used to estimate the location of only the corresponding target. The Cramer‐Rao lower bound (CRLB) for the variance of the proposed ML estimator is derived, and the potential conditions for reducing the CRLB are presented. Since tracking efficiency is affected by poor estimates, we present two criteria to evaluate the quality of estimates and detect the poor ones. An approach is also proposed for correcting the poor estimates, based on additional initial guesses. We demonstrate the effectiveness and accuracy of our proposed dual‐mode algorithm via simulation results and compare our results with the Multi‐Resolution search algorithm.

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“…Besides other factors in the indoor environment that affect the localization accuracy, in literature, there exist many studies to observe how the source node communicates with anchor nodes. In [34], researchers proposed an approach to track multiple noncooperative targets by assuming that (1) there is only one target in the field; (2) signal from different cooperative targets can be differentiated, or (3) interferences caused by signals from other targets are negligible because of attenuation. Their methods use separated signal aggregation from the multiple identical targets by blind source node (BSS) algorithm.…”

Section: Related Workmentioning

confidence: 99%

Study of Hybrid Localization Noncooperative Scheme in Wireless Sensor Network

Sumitra

Supatmi

2017

Wireless Communications and Mobile Computing

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In this paper, we evaluated the experiment and analysis measurement accuracy to determine object location based on wireless sensor network (WSN). The algorithm estimates the position of sensor nodes employing received signal strength (RSS) from scattered nodes in the environment, in particular for the indoor building. Besides that, we considered another algorithm based on weight centroid localization (WCL). In particular testbed, we combined both RSS and WCL as hybrid localization in case of noncooperative scheme with considering that source nodes directly communicate only with anchor nodes. Our experimental result shows localization accuracy of more than 90% and obtained the estimation error reduction to 4% compared to existing algorithms.

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On Non-Cooperative Multiple-Target Tracking With Wireless Sensor Networks

Cited by 11 publications

References 31 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

Dual‐mode multiple‐target tracking in wireless sensor networks based on sensor grouping and maximum likelihood estimation

Study of Hybrid Localization Noncooperative Scheme in Wireless Sensor Network

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