Knapsack-Based Sensor Selection for Target Localization Under Energy and Error Constraints

Ababneh, Ahmad A.

doi:10.1109/jsen.2021.3123734

Cited by 3 publications

(4 citation statements)

References 45 publications

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“…For the training data, we consider M=285 training samples uniformly distributed and generated by the pathloss model. The basic metric to evaluate the adopted solution is the relative average localization error 𝜌 defined as (18):…”

Section: Resultsmentioning

confidence: 99%

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An accurate target tracking method in wireless sensor networks

Ahmadi

Bouallègue

2022

IJEECS

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<span>Localization is a challenging research issue in various sectors of activity, particularly in dynamic indoor environment with high perturbation. Many existing localization techniques in wireless sensor networks are not efficient because of many constraints such as the high cost, the memory and energy limitation and the environmental noise effects. Thus, the development of novel methods of localization has become a great concern for the wireless sensor networks. In this paper, we propose a tracking method that combines regression tree and Kalman smoother filtering. Previously, regression tree has been suggested for static positioning by means of received signal strength indicator measurements. In this work, we employ this strategy to solve the mapping relation between these measurements and the target position by means of an optimized propagation model. Moreover, the predicted position considered as the observed information is introduced to the Kalman smoother algorithm, to have more precise state of the moving target. The proposed algorithm has been assessed and compared to other existing methods using real measurements of the received power by the moving target in an indoor environment. The evaluation shows that our solution outperforms other methods regarding localization accuracy.</span>

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Section: Resultsmentioning

confidence: 99%

“…The development of smart communication technologies has enabled the emergence of new Location based services (LBSs) applications [16]- [18]. Indeed, indoor localization is a basic process in robotics field [19], [20].…”

Section: Introductionmentioning

confidence: 99%

An accurate target tracking method in wireless sensor networks

Ahmadi

Bouallègue

2022

IJEECS

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“…On the one hand, the RSS-based localization performance is closely related to the qualities of the measured RSSs. Theoretically, the closer the sensor is to the emitter, the more effective it is in improving localization accuracy [26]. This means that the higher RSS, the more reliable the estimation results will be.…”

Section: Sensor Selection For Rss-based Localization 41 Sensor Select...mentioning

confidence: 99%

“…This means that the higher RSS, the more reliable the estimation results will be. On the other hand, the objective function of ML is nonlinear, which leads to a non-linear relationship between the accuracy and the number of sensors used [26]. Consequently, using more sensors does not always improve the localization performance.…”

Section: Sensor Selection For Rss-based Localization 41 Sensor Select...mentioning

confidence: 99%

Uncooperative Emitter Localization Based on Joint Sensor Selection and Semidefinite Programming

Cao¹,

Zhang²,

Liu³

et al. 2022

RADIOENGINEERING

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Radio emitter localization based on Received Signal Strength (RSS) is promising in large-scale Internet of Things (IoT) and wireless sensor networks (WSNs) for its low hardware and computation costs. To improve its localization accuracy and reduce the system energy consumption, we propose an improved RSS localization algorithm based on the joint sensor selection and semidefinite programming (SDP). An initial position estimate is first obtained using RSSs available at a random set of sensors.A refined sensor set is then selected to complete the second estimation by analyzing the geometric structure of sensing network. Performance of the method is evaluated in terms of localization accuracy and execution time, and compared with existing methods. Extensive simulations demonstrate that the proposed approach achieves a localization accuracy of approximately 1.5 m with 8 to 10 sensors. The method outperforms the second-order cone programming (SOCP) and the least squared relative error (LSRE)-based SDP algorithms in terms of both the location and the transmit power estimation accuracy.

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Error and Energy Aware Sensor Deployment for RSS Localization—A Linear Quadratic Regulator Formulation

Ababneh

2022

IEEE Sensors J.

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Knapsack-Based Sensor Selection for Target Localization Under Energy and Error Constraints

Cited by 3 publications

References 45 publications

An accurate target tracking method in wireless sensor networks

An accurate target tracking method in wireless sensor networks

Uncooperative Emitter Localization Based on Joint Sensor Selection and Semidefinite Programming

Error and Energy Aware Sensor Deployment for RSS Localization—A Linear Quadratic Regulator Formulation

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