Energy efficient fast predictor for WSN-based target tracking

Mirsadeghi, Maryam; Mahani, Ali

doi:10.1007/s12243-014-0430-y

Cited by 20 publications

(11 citation statements)

References 20 publications

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“…With continuous technological development, the amount of data generated by an application is also very large. However, the research on data storage processing is flawed [20].…”

Section: Related Workmentioning

confidence: 99%

An optimized method of HDFS for massive small files storage

et al. 2018

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The development of the Internet-of-Things (IoT) and the Cyber-Physical System (CPS) has greatly facilitated many aspects of technological applications and development. This may lead to significant data growth, especially for small files. The analysis and processing of a large number of small files has become a crucial part of the development of IoT and CPS. Hadoop Distributed File Systems have become powerful platforms to store a larger amount of big data. However, this method has a number of issues when dealing with small files, such as substantial memory consumption and poor access. In this paper, a Dynamic Queue of Small Files (DQSF) algorithm is proposed to solve these problems. DQSF differentiates small files into different categories using an analytical hierarchal process that examines the performance of small files with different ranges across four indexes and determines the size of the dynamic queue according to the best system performance. Additionally, period classification is applied to preprocess the small files before storage, and the prefetching mechanism of the secondary index is used to process index tables. Experimental results show that this method could effectively reduce memory use and improve the storage efficiency of massive small files, which optimizes system performance.

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“…With continuous technological development, the amount of data generated by an application is also very large. However, the research on data storage processing is flawed [20].…”

Section: Related Workmentioning

confidence: 99%

An optimized method of HDFS for massive small files storage

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Prediction relies on heuristics and attempts to anticipate the upcoming position of the moving object based on its historical positions observed over time and the spatial and temporal knowledge of sensors (Zhenga et al, 2014). Based on this prediction, sensor nodes get scheduled to be either active or asleep (Ren et al, 2008) during each defined time step (Mirsadeghi and Mahani, 2014). Due to the inevitable prediction mistakes, these algorithms have recovery mechanisms in order to make up for the inaccuracy of object localization.…”

Section: Hybrid Architecturementioning

confidence: 99%

“…In Mirsadeghi and Mahani (2014), the author presented a high-precision and energy efficient tracking scheme that is based on clustering architecture and object speed prediction. In each time step, just a few nodes in the vicinity of the predicted position of object get activated as tracker nodes by considering three parameters: distance, remaining energy of nodes and energy needed to send a packet to the cluster head while others remain in power-saving mode.…”

Section: Hybrid Architecturementioning

confidence: 99%

Object Tracking in Wireless Sensor Networks: Challenges and Solutions

Ismail¹,

Alkhader²,

Elnaffar³

2016

Journal of Computer Science

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Wireless Sensor Networks (WSNs) are small, inexpensive and battery-operated sensor nodes that are deployed over a geographical area. WSNs are used in many applications such border patrolling, military intrusion detection, wildlife animal monitoring, surveillance of natural disasters and healthcare systems. Mobile object tracking is a vital task in all these applications. The goal of this work is to highlight the most important challenges in the field of object tracking and provide a survey of the WSN architectural design and implementation approaches for tackling this problem.To that end, we analyze how each approach responds to each challenge and where it falls short. This analysis should provide researchers with a state-of-the-art review and inspire them to propose novel solutions.

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“…Another up-to-date proposal for a fast and energy efficient target tracking model based on location prediction is presented in [22]. It is pointed that the proposed method leads to a good accuracy with low energy consumption and it has low missing rates compared to linear and extended Kalman filter predictors.…”

Section: Literature Reviewmentioning

confidence: 99%

An Energy Conservative Wireless Sensor Network Model for Object Tracking

Peynirci

Korkmaz

Gurgen

2014

Annals of Computer Science and Information Systems

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This study aims to find the relationship between energy consumption level and object tracking success in an object tracking sensor network (OTSN). Convenient use of energy proposes a great challenge for wireless sensor network (WSN) design and the balance between successful object tracking and low energy consumption is a tight one. To address this issue, we propose a new network operation scheme for object tracking, implement this scheme in Network Simulator 2 (ns-2) and present the obtained results of the conducted simulation experiments. The simulation results show that the proposed method can be used to track objects in a WSN network in an energy conservative manner.

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Energy efficient fast predictor for WSN-based target tracking

Cited by 20 publications

References 20 publications

An optimized method of HDFS for massive small files storage

An optimized method of HDFS for massive small files storage

Object Tracking in Wireless Sensor Networks: Challenges and Solutions

An Energy Conservative Wireless Sensor Network Model for Object Tracking

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