TDL: Two-dimensional localization for mobile targets using compressive sensing in wireless sensor networks

Sun, Baoming; Guo, Yan; Li, Ning; Peng, Laixian; Fang, Da‐Gang

doi:10.1016/j.comcom.2015.10.006

Cited by 22 publications

(12 citation statements)

References 13 publications

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“…Consider a discrete signal denoted by vector xN×1. If x is sparse enough, that is, K=||boldx||0≪N, it is possible to reconstruct x from M measurements produced by a proper linear transform Φ [14,15]: yM×1=ΦM×NxN×1…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…However, it is NP-hard if directly solving the problem. Fortunately, when boldΦboldΨ satisfies the restricted isometric property (RIP), there is an alternative kind of approach seeking to solve the l1 norm minimization problem instead of the l0 norm-based problem [15]. Therefore, we have boldafalsebold^=argminαfalse˜∈double-struckRN||bold-sans-serifαfalsebold˜|false|1, s.t. boldyM×1=boldΦM×NboldΨN×Nbold-sans-serifαN×1where bold-sans-serifαfalsebold˜ is the recovered signal in the representation basis.…”

Section: Related Work and Motivationmentioning

confidence: 99%

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Adaptive Data Acquisition with Energy Efficiency and Critical-Sensing Guarantee for Wireless Sensor Networks

Rao

Zhao

Wang

et al. 2019

Sensors

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Due to the limited energy budget, great efforts have been made to improve energy efficiency for wireless sensor networks. The advantage of compressed sensing is that it saves energy because of its sparse sampling; however, it suffers inherent shortcomings in relation to timely data acquisition. In contrast, prediction-based approaches are able to offer timely data acquisition, but the overhead of frequent model synchronization and data sampling weakens the gain in the data reduction. The integration of compressed sensing and prediction-based approaches is one promising data acquisition scheme for the suppression of data transmission, as well as timely collection of critical data, but it is challenging to adaptively and effectively conduct appropriate switching between the two aforementioned data gathering modes. Taking into account the characteristics of data gathering modes and monitored data, this research focuses on several key issues, such as integration framework, adaptive deviation tolerance, and adaptive switching mechanism of data gathering modes. In particular, the adaptive deviation tolerance is proposed for improving the flexibility of data acquisition scheme. The adaptive switching mechanism aims at overcoming the drawbacks in the traditional method that fails to effectively react to the phenomena change unless the sampling frequency is sufficiently high. Through experiments, it is demonstrated that the proposed scheme has good flexibility and scalability, and is capable of simultaneously achieving good energy efficiency and high-quality sensing of critical events.

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Section: Related Work and Motivationmentioning

confidence: 99%

Section: Related Work and Motivationmentioning

confidence: 99%

Adaptive Data Acquisition with Energy Efficiency and Critical-Sensing Guarantee for Wireless Sensor Networks

Rao

Zhao

Wang

et al. 2019

Sensors

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“…Sensor nodes effectively sense the environmental and physical situations with greater accuracy and are actively interpreted in the areas of disaster management, tracking the vehicle, geographical routing and Global positioning system [1, 2]. There is a large consumption of energy during the transmission of the data, to maximise the lifetime of the network and minimise the energy consumption, there is a requirement of the even distribution of load, and there should be a reduction in the network communication.…”

Section: Introductionmentioning

confidence: 99%

Optimised routing and compressive sensing based data communication in wireless sensor network

Pasupuleti¹,

Balaswamy²

2020

IET Communications

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“…To deal with this challenge, compressive sensing (CS) [9] has been introduced to locate targets from the less number of measurements in terms of the intrinsic sparse nature of target localization [10]. Formally, the CS-based target localization problem can be transformed into the sparse recovery of l1-norm minimization [11,12].…”

Section: Introductionmentioning

confidence: 99%

Energy-Level Jumping Algorithm for Global Optimization in Compressive Sensing-Based Target Localization

Wang

Guan

Wan

et al. 2019

Sensors

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Target localization is one of the essential tasks in almost applications of wireless sensor networks. Some traditional compressed sensing (CS)-based target localization methods may achieve low-precision target localization because of using locally optimal sparse solutions. Solving global optimization for the sparse recovery problem remains a challenge in CS-based target localization. In this paper, we propose a novel energy-level jumping algorithm to address this problem, which achieves high-precision target localization by solving the globally optimal sparse solution of l p -norm ( 0 < p < 1 ) minimization. By repeating the process of energy-level jumping, our proposed algorithm establishes a global convergence path from an initial point to the global minimizer. Compared with existing CS-based target localization methods, the simulation results show that our localization algorithm obtain more accurate locations of targets with the significantly reduced number of measurements.

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TDL: Two-dimensional localization for mobile targets using compressive sensing in wireless sensor networks

Cited by 22 publications

References 13 publications

Adaptive Data Acquisition with Energy Efficiency and Critical-Sensing Guarantee for Wireless Sensor Networks

Adaptive Data Acquisition with Energy Efficiency and Critical-Sensing Guarantee for Wireless Sensor Networks

Optimised routing and compressive sensing based data communication in wireless sensor network

Energy-Level Jumping Algorithm for Global Optimization in Compressive Sensing-Based Target Localization

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