Unbalanced Hybrid AOA/RSSI Localization for Simplified Wireless Sensor Networks

Le, Anh Tuyen; Tran, Le Chung; Huang, Xiaojing; Ritz, Christian; Dutkiewicz, Eryk; Phung, Son Lam; Bouzerdoum, A.; Franklin, Daniel

doi:10.3390/s20143838

Cited by 20 publications

(19 citation statements)

References 33 publications

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“…The CRLB of the hybrid RSS/AOA method is given in Ref. [30]. In addition to the RSS, the CSI has been used for the range-based localization as well.…”

Section: Unbalanced Hybrid Rss/aoamentioning

confidence: 99%

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An ensemble approach to deep‐learning‐based wireless indoor localization

Wisanmongkol

Taparugssanagorn

Tran

et al. 2022

IET Wireless Sensor Systems

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The authors investigate the use of deep learning in wireless indoor localization to address the shortcomings of the existing range-based (e.g. trilateration and triangulation) and range-free (e.g. fingerprinting) localization. Instead of relying on geometric models and hand-picked features, deep learning can automatically extract the relationship between the observed data and the target's location. Nevertheless, a deep neural network (DNN) model providing a satisfactory accuracy might perform differently when it is retrained in the deployment. To mitigate this issue, the authors propose an ensemble method where DNN models obtained from multiple training sessions are combined to locate the target. In the authors' evaluation, several DNN models are trained on the data, which consists of the received signal strength (RSS), angle of arrival (AOA), and channel state information (CSI), used in the existing hybrid RSS/AOA and RSS/CSI fingerprinting, and their rootmean-square error (RMSE) values are compared accordingly. The results show that the proposed method achieves the lower RMSE than the existing methods, and the RMSE can be lowered by up to 1.47 m compared with the ones obtained from a single model. Moreover, for some DNN models, the RMSE values are even lower than the minimum RMSE obtained by their single-model counterparts.

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“…The CRLB of the hybrid RSS/AOA method is given in Ref. [30]. In addition to the RSS, the CSI has been used for the range-based localization as well.…”

Section: Unbalanced Hybrid Rss/aoamentioning

confidence: 99%

“…We denote NRNA as the hybrid RSS/ AOA methods, which are based on N RSS and N AOA measurements, and LS, WLS, and CRLB as the least-squares method, the weighted least-squares method, and the CRLB given in Ref. [30], respectively.…”

Section: Effects Of the Training Sample Sizementioning

confidence: 99%

An ensemble approach to deep‐learning‐based wireless indoor localization

Wisanmongkol

Taparugssanagorn

Tran

et al. 2022

IET Wireless Sensor Systems

Self Cite

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“…To overcome this issue, authors introduced improved Distance Vector Hop approach which uses RSSI model values of links between one-hop neighbors. Similar to this hybrid scheme, the authors in [19] presented hybrid approach using AOA/RSSI because these techniques do not require any synchronization among anchor and unknown nodes.…”

Section: Secure Node Localizationmentioning

confidence: 99%

An Efficient Routing Mechanism for Node Localization, Cluster Based Approach and Data Aggregation to Extend WSN Lifetime

2022

IJIES

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The Last decade the demand for wireless communication has increased massively. Wireless Sensor Networks (WSN) have turned out as a potential solution for various real-time applications namely environmental monitoring, health and military applications. These networks follow the random placement strategy hence, identifying the location of sensor nodes is a tedious task. Moreover, these networks suffer from various issues such as limited power resources. This work, focusses on the sensor node localization and network lifetime enhancement of Wireless Sensor Networks (WSN). In localization, security is an important issue, so a trust-based model to secure localization is investigated in this work. Further, energy aware clustering and Cluster Head (CH) selection has been undertaken to prolong the lifetime of the network. Finally, a data aggregation model which decreases the network energy depletion by discarding the redundant data is presented. The outcome of the model which is proposed has been assessed and expressed in the form of localization error, energy depletion and alive node. The comparative analysis shows that the localization error for varied anchor nodes is obtained as 1.51, 2.28, 2.52 and 1.25 using existing techniques DMA (Distance Mapping Algorithm), MDS-Map (Multidimensional Scaling), DV-Hop (Distance Vector-Hop) and Proposed Approach, respectively. Similarly, the average energy consumption is obtained as 1.02, 1.67, 1.90 and 0.82 using DMA (Distance Mapping Algorithm), MDS-Map (Multidimensional Scaling), DV-Hop (Distance Vector-Hop) and Proposed Hybrid Approach. The comparative analysis shows that proposed hybrid approach of localization, CH (Cluster Head) formation and aggregation attains better performance when compared with existing techniques in WSN(Wireless Sensor Networks).

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“…e by a 2D CF LS algorithm using θ i , x i , and y i as defined by Equation (6); and (2) usinĝ p 2D e , r i , and ϕ i as indicated by Equation (8) to estimateẑ e .…”

Section: Two-step Closed-form Solutionmentioning

confidence: 99%

Emitter Location with Azimuth and Elevation Measurements Using a Single Aerial Platform for Electronic Support Missions

Khalaf-Allah

2021

Sensors

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Passive ground emitter geolocation techniques are essential to electronic warfare systems, as they provide threat warnings in hostile environments, while ensuring the electronic silence of the mission platform. Geolocation of enemy emitters indicates the position of and type of adversary troops, and allows for the use of guided munition against enemy targets. Three-dimensional geolocation solutions based on least squares and particle filter estimation, using only azimuth and elevation measurements, were considered. Three batch-processing and one instantaneous solution algorithms, i.e., using a single pulse or a single observation point, were developed and investigated. The performance of the proposed solutions was demonstrated by simulations. Results showed that the batch-processing solutions achieved acceptable accuracies with a sufficient number of observation points. The performance degraded with fewer observation points. The instantaneous geolocation solution improved performance with increasing observation points, i.e., working in the sequential mode, and therefore could approach the accuracy of the batch-processing solutions.

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Unbalanced Hybrid AOA/RSSI Localization for Simplified Wireless Sensor Networks

Cited by 20 publications

References 33 publications

An ensemble approach to deep‐learning‐based wireless indoor localization

An ensemble approach to deep‐learning‐based wireless indoor localization

An Efficient Routing Mechanism for Node Localization, Cluster Based Approach and Data Aggregation to Extend WSN Lifetime

Emitter Location with Azimuth and Elevation Measurements Using a Single Aerial Platform for Electronic Support Missions

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