Coordinated and Adaptive Information Collecting in Target Tracking Wireless Sensor Networks

Feng, Jufu; Lian, Baowang; Zhao, Hongwei

doi:10.1109/jsen.2014.2388234

Cited by 21 publications

(14 citation statements)

References 15 publications

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“…The network lifetime is increased. CAICS [91] No Discrete Single No Yes: It uses a dynamical selection of the best set of nodes for tracking missions. [92] No Discrete Single No Yes: They proposed a target-tracking mechanism that minimizes the sensing redundancy and maximizes the number of sleeping nodes in the network.…”

Section: Discretementioning

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: Discretementioning

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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“…Several works have been done in the field of target tracking, but most of them only considered the single-target problem. [11][12][13][14][15][16][17][18][19] Different approaches are performed in single-target tracking, in Masazade and Kose, 11 an algorithm is proposed for multiple access among network nodes, and an adaptive quantization threshold is considered. Mansouri et al 16 have proposed a sensor selection mechanism for target tracking in a quantized data transmitting scenario, based on quantized variational filtering.…”

Section: Related Workmentioning

confidence: 99%

“…Two targets could be placed on two crosses located on a line between two sensors. Let s1 have the maximum possible distance to t1, that is d 11 = d m , then its distance to t2 must be at least d 12…”

Section: Individual Mode Trackingmentioning

confidence: 99%

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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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“…This leads to loss of object, and large amount of energy may be wasted in recovering the object. Additionally, in object tracking sensor networks, the data aggregation model, nodes activation model, and data reporting model play an essential role that could maximize the lifetime of network [12][13][14][15]. Those models should be designed to reduce the communication overhead in the network taking in to consideration the hardware limitations of nodes.…”

Section: Introductionmentioning

confidence: 99%

GLT: Grouping Based Location Tracking for Object Tracking Sensor Networks

Hawbani

Wang

Karmoshi

et al. 2017

Wireless Communications and Mobile Computing

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The use of wireless sensor networks (WSN) in tracking applications is growing rapidly. In these applications, the nodes detect, monitor, and track a target, object, or event. In this paper, we consider the problem of tracking mobile objects in wireless sensor networks (WSN). We present a novel tracking model, named Grouping based Location Tracking (GLT), scaling well with the number of nodes and the number of mobile objects. GLT is based on the Grouping Hierarchy Structure, GHS. In GHS, nodes are partitioned into groups (not clusters) according to their maximum covered region (MCR) such that each group contains a number of nodes and a number of leaders. GLT consists of two tiers. The first tier, which is called the Notification Tree (NT), enhances the activation mechanism, the data cleaning mechanism, and the energy balancing mechanism. On the other hand, the second tier, which is called the Hierarchical Spanning Tree (HST), supports the data reporting mechanism and the lifetime prolonging mechanism. Simulations results show that GLT reduces the communication node selections overhead without diminishing object tracking accuracy and achieves a significant energy consumption reduction and network lifetime extension compared with the state-of-the-art approaches.

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Coordinated and Adaptive Information Collecting in Target Tracking Wireless Sensor Networks

Cited by 21 publications

References 15 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

GLT: Grouping Based Location Tracking for Object Tracking Sensor Networks

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