Energy Efficient Received Signal Strength-Based Target Localization and Tracking Using Support Vector Regression

Pasha, M. Jahir; Dhabliya, Dharmesh; Jondhale, Satish R.; Sivakumar, S A; Rajawat, Anand Singh; Goyal, S. B.; Răboaca, Maria Simona; Mihălţan, Traian Candin; Verma, Chaman; Suciu, George

doi:10.3390/en16010555

Cited by 12 publications

(10 citation statements)

References 24 publications

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“…Pre-processing: The data collected from the healthcare institution contained a lot of noise. Thus, the raw data were preprocessed to remove noise and other unwanted features before the final analysis can begin [25,26].…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Longevity Recommendation for Root Canal Treatment Using Machine Learning

Choudhari,

Rajawat,

Goyal

2024

RAiSE-2023

Self Cite

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Section: Methodsmentioning

confidence: 99%

“…Key considerations in configuring an SVM model include the choice of kernel function, such as the linear kernel for linearly separable data or the RBF kernel, a versatile option used for capturing intricate relationships within complex datasets, like those shown in [24,25].…”

Section: Building Machine Learning Modelsmentioning

confidence: 99%

Longevity Recommendation for Root Canal Treatment Using Machine Learning

Choudhari,

Rajawat,

Goyal

2024

RAiSE-2023

Self Cite

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show abstract

“…It is found that the proposed OSS-SVR scheme is robust enough for variations in system noise and need very smaller amount of labeled data during training. The SVR-based target localization model can also be fused with KF to smoothen the target location estimates [27]. The proposed SVR model utilizes linear, Sigmoid, RBF, and polynomial kernel functions to estimate the moving target locations in indoor.…”

Section: Related Workmentioning

confidence: 99%

Energy-Constrained Target Localization Scheme for Wireless Sensor Networks Using Radial Basis Function Neural Network

K¹,

Nandini

Jondhale

et al. 2023

International Journal of Distributed Sensor Networks

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The indoor object tracking by utilizing received signal strength indicator (RSSI) measurements with the help of wireless sensor network (WSN) is an interesting and important topic in the domain of location-based applications. Without the knowledge of location, the measurements obtained with WSN are of no use. The trilateration is a widely used technique to get location updates of target based on RSSI measurements from WSN. However, it suffers with high location estimation errors arising due to random variations in RSSI measurements. This paper presents a range-free radial basis function neural network (RBFN) and Kalman filtering- (KF-) based algorithm named RBFN+KF. The performance of the RBFN+KF algorithm is evaluated using simulated RSSIs and is compared against trilateration, multilayer perceptron (MLP), and RBFN-based estimations. The simulation results reveal that the proposed RBFN+KF algorithm shows very low location estimation errors compared to the rest of the three approaches. Additionally, it is also seen that RBFN-based approach is more energy efficient than trilateration and MLP-based localization approaches.

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“…The most classic localization algorithms for WSNs include ToA [22], TDoA [23], AoA [24] and RSSI [25]. However, these algorithms cannot guarantee the accuracy and energy of localization when they are used in sparse and harsh underwater environments.…”

Section: Related Workmentioning

confidence: 99%

A Secure Localization Scheme for UASNs Based on Anchor Node Self-Adaptive Adjustment

Ding

Zhou

et al. 2023

JMSE

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The UASNs are widely used in underwater communications and monitoring, and many applications require accurate information regarding the position of nodes. However, intentional attacks against devices or information transmission may exist in the network, and the localization process is periodic, so it is necessary to quickly address attacks and optimize the network structure. This paper proposed an anchor node self-adaptive adjustment localization scheme (ASAL), in which the anchor node can adjust the state and depth of its participation. Two filters were used to adjust the states of referable nodes. The first filter was based on the distance difference of reverse information transmission after direct localization based on anchor nodes. The second was based on the error of the anchor node’s reverse localization after network localization was completed. In addition, a depth-adjustment mechanism of anchor nodes was proposed to optimize the network structure, the virtual force vector was introduced to describe the cost of depth adjustment, and the whale optimization algorithm was used to converge to the depth with the minimum total cost. The simulation results showed that the scheme can ensure localization accuracy and coverage in attack scenarios and reduce localization energy consumption.

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Energy Efficient Received Signal Strength-Based Target Localization and Tracking Using Support Vector Regression

Cited by 12 publications

References 24 publications

Longevity Recommendation for Root Canal Treatment Using Machine Learning

Longevity Recommendation for Root Canal Treatment Using Machine Learning

Energy-Constrained Target Localization Scheme for Wireless Sensor Networks Using Radial Basis Function Neural Network

A Secure Localization Scheme for UASNs Based on Anchor Node Self-Adaptive Adjustment

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