Efficient and Adaptive Node Selection for Target Tracking in Wireless Sensor Network

Feng, Jufu; Zhao, Hongwei; Lian, Baowang

doi:10.1155/2016/9152962

Cited by 8 publications

(14 citation statements)

References 16 publications

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“…There are number of researchers in literature that have worked on estimating the next position of an object [107], [30], [108], [60], [36], [54], [109], [62]. Position estimation is used to cut down the number of sensor nodes that are used for monitoring or reporting the information near the lost object.…”

Section: Object Recovery Position Estimationmentioning

confidence: 99%

“…According to literature, position estimation is commonly based on Kalman filter KF, Particle filter, PF [30] and Extend KF [36] methods. The work in [107] chooses a node by considering the residual energy and spatial correlation of the sensor nodes located at different locations within the sensing range of the node. Thus, it balances energy consumption with accurate position estimation that ensures tracking reliability with optimal sensor nodes.…”

Section: Object Recovery Position Estimationmentioning

confidence: 99%

“…Researchers have proposed many works for prediction of the object [107]. However, the time that prediction algorithm takes to process or predict the future position of the object is not negligible.…”

Section: Object Prediction Delaymentioning

confidence: 99%

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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 Recovery Position Estimationmentioning

confidence: 99%

Section: Object Recovery Position Estimationmentioning

confidence: 99%

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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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“…Fourth, few efforts has been conducted based on distributed target tracking [18] under ideal binary sensor model. Finally, a few target tracking approaches have used adaptive node selection [19] for reducing the energy cost and computational complexity as well as ensuring the tracking accuracy. A voronoi-based distributed sensor handover protocol [20] has been designed using localized algorithm with the objective of target tracked ratio, i.e., the quality of target tracking service.…”

Section: Related Workmentioning

confidence: 99%

Bayesian Localized Energy Optimized Sensor Distribution for Efficient Target Tracking

Sumathy*¹,

Alonshia²

2019

IJITEE

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In wireless sensor network application, the localization of nodes are carried out for extended life time of the node. Many applications in wireless sensor network perform localization of nodes over an extended period of time with energy variance. However, optimal selection algorithm poses new challenges to the overall transmission power levels for target detection, and thus localized energy optimized sensor management strategies are necessary for improving the accuracy of target tracking. In this work, it is proposed to develop a Bayesian Localized Energy Optimized Sensor Distribution (BLEOSD) scheme for efficient target tracking in Wireless Sensor Network. The sensor node localized with Bayesian average scheme thatestimates the sensor node’s energy are optimized as per data transfer capacity verification. The Bayesian average energy level of the sensor network is compared with the energy of each sensor node. The sensor nodes are localized and energy distribution based on the Bayesian energy estimate for efficient target tracking. The sensor node distribution strategy improves the accuracyto identify the targets effectively. Experiments are conducted using simulation of WSN by varying number of nodes, energy levels of the node and target object density using the Network Simulator Tool (NS2) The proposed BLEOSD technique is compared with various recent methods by evaluating accuracy of target tracking, energy consumption rate, localized node density and time for target tracking. The experimental results shows that the performance of BLESOD is more encouraging compared to contemporary methods.

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“…Jiang et al 31 propose an energy efficient node selection algorithm for bearings-only target tracking WSNs, in which the residual energy of a node is incorporated into the objective function of node selection. Feng et al 32 design a novel spatial-correlated node selection strategy, which selects the node according to the residual energy and the spatial correlation of the nodes located at the different positions. Shi et al 33 propose a centralized algorithm to determine the approximate minimum set of nodes based on the calculation of node redundancy degree and the partition of the target area.…”

Section: Related Workmentioning

confidence: 99%

Distributed and morphological operation-based data collection algorithm

Nie

Wang

Qin

et al. 2017

International Journal of Distributed Sensor Networks

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When monitoring the environment with wireless sensor networks, the data sensed by the nodes within event backbone regions can adequately represent the events. As a result, identifying event backbone regions is a key issue for wireless sensor networks. With this aim, we propose a distributed and morphological operation-based data collection algorithm. Inspired by the use of morphological erosion and dilation on binary images, the proposed distributed and morphological operation-based data collection algorithm calculates the structuring neighbors of each node based on the structuring element, and it produces an event-monitoring map of structuring neighbors with less cost and then determines whether to erode or not. The remaining nodes that are not eroded become the event backbone nodes and send their sensing data. Moreover, according to the event backbone regions, the sink can approximately recover the complete event regions by the dilation operation. The algorithm analysis and experimental results show that the proposed algorithm can lead to lower overhead, decrease the amount of transmitted data, prolong the network lifetime, and rapidly recover event regions.

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Efficient and Adaptive Node Selection for Target Tracking in Wireless Sensor Network

Cited by 8 publications

References 16 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

Bayesian Localized Energy Optimized Sensor Distribution for Efficient Target Tracking

Distributed and morphological operation-based data collection algorithm

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