Analysis of Distributed Wireless Sensor Systems with a Switched Quantizer

Sun, Hao; Wang, Xianyu; Yang, Kaixin; Peng, Tongrui

doi:10.1155/2021/6690761

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…In addition, the algorithm’s efficacy, repeatability, and ability to support continuous improvement depend heavily on thorough validation, benchmarking, and the promotion of transparent cooperation [ 57 ]. By taking these factors into account when developing the algorithm, the algorithm’s applicability and effect in real-world healthcare settings will be increased, bringing methodological developments into line with complete and long-term solutions for illness detection via wireless sensor networks [ 58 , 59 ].…”

Section: Simulation and Resultsmentioning

confidence: 99%

Semi-Supervised Clustering-Based DANA Algorithm for Data Gathering and Disease Detection in Healthcare Wireless Sensor Networks (WSN)

Sinha,

Aljrees,

Pandey

et al. 2023

Sensors

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Wireless sensor networks (WSNs) have emerged as a promising technology in healthcare, enabling continuous patient monitoring and early disease detection. This study introduces an innovative approach to WSN data collection tailored for disease detection through signal processing in healthcare scenarios. The proposed strategy leverages the DANA (data aggregation using neighborhood analysis) algorithm and a semi-supervised clustering-based model to enhance the precision and effectiveness of data collection in healthcare WSNs. The DANA algorithm optimizes energy consumption and prolongs sensor node lifetimes by dynamically adjusting communication routes based on the network’s real-time conditions. Additionally, the semi-supervised clustering model utilizes both labeled and unlabeled data to create a more robust and adaptable clustering technique. Through extensive simulations and practical deployments, our experimental assessments demonstrate the remarkable efficacy of the proposed method and model. We conducted a comparative analysis of data collection efficiency, energy utilization, and disease detection accuracy against conventional techniques, revealing significant improvements in data quality, energy efficiency, and rapid disease diagnosis. This combined approach of the DANA algorithm and the semi-supervised clustering-based model offers healthcare WSNs a compelling solution to enhance responsiveness and reliability in disease diagnosis through signal processing. This research contributes to the advancement of healthcare monitoring systems by offering a promising avenue for early diagnosis and improved patient care, ultimately transforming the landscape of healthcare through enhanced signal processing capabilities.

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Section: Simulation and Resultsmentioning

confidence: 99%

Semi-Supervised Clustering-Based DANA Algorithm for Data Gathering and Disease Detection in Healthcare Wireless Sensor Networks (WSN)

Sinha,

Aljrees,

Pandey

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The suggested optimal bit allocation approach [14] takes into account both node topology and sensor-to-target range. Furthermore, authors in [15] proposed switched quantizer-based event triggered-Kalman consensus filtering algorithm to overcome the limited bandwidth conditions in WSNs. It is evident from all the above literature, data fusion plays a significant role in combining the quantized measurements at the fusion center, coming from several sensors.…”

Section: A Related Workmentioning

confidence: 99%

Distributed Fusion of Optimally Quantized Local Tracker Estimates for Underwater Wireless Sensor Networks

et al. 2022

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Multi-sensor underwater surveillance has been a significant research problem for civilian and naval applications. Due to limited bandwidth considerations, the underwater wireless sensor networks (UWSNs) use measurement quantization to transmit information from individual sensors to the fusion center to perform centralized tracking/fusion. However, at the measurement level, quantization of azimuth information is complex due to its non-linear behavior. To address this problem, this paper proposes to perform the distributed tracking and quantizing the local estimates (state and covariance) to provide improved bandwidth and reduce computational load. The local tracker estimates the updated state and covariance of a target's time-varying dynamics in the given surveillance from the obtained measurements using extended Kalman filter (EKF) and global nearest neighbor (GNN) data association. The measurement model contains both detections of target and false alarms. This paper uses optimal quantization rather than linear quantization owing to its minimal bandwidth requirement. Once the quantized local tracks are obtained at the fusion center, these tracks are quantified using track-to-track association (T2TA) in the S-D assignment framework. The associated tracks are fused using correlation-free fusion algorithms like covariance intersect (CI), sampling covariance intersects (SCI), ellipsoidal intersect (EI), and arithmetic average (AA) algorithms to achieve the global track. The position root mean square error (PRMSE), bandwidth, and error ellipses are used to quantify the performance of the proposed framework. The simulation results show that the PRMSE of the optimally quantized fusion estimates yields good agreement with the unquantized method. Simulation results further reveals that, optimal quantization utilizes lower bandwidth compared to linear quantization. In addition, optimally quantized local estimates accomplishes promising covariance regions at the fusion center.INDEX TERMS Correlation-free fusion, extended Kalman filter, state quantization, target tracking, trackto-track association.

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Underwater Wireless Sensor Networks Using Distributed Fusion of Optimally Quantized Estimations from Local Trackers

Pal,

Mohan,

Rao

et al. 2023

2023 3rd International Conference on Pervasive Computing and Social Networking (ICPCSN)

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Analysis of Distributed Wireless Sensor Systems with a Switched Quantizer

Cited by 3 publications

References 32 publications

Semi-Supervised Clustering-Based DANA Algorithm for Data Gathering and Disease Detection in Healthcare Wireless Sensor Networks (WSN)

Semi-Supervised Clustering-Based DANA Algorithm for Data Gathering and Disease Detection in Healthcare Wireless Sensor Networks (WSN)

Distributed Fusion of Optimally Quantized Local Tracker Estimates for Underwater Wireless Sensor Networks

Underwater Wireless Sensor Networks Using Distributed Fusion of Optimally Quantized Estimations from Local Trackers

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