Radio Data Transmission Reduction in Power-Constrained WSN

2018 ◽  
Author(s):  
Koffi V. C. Kevin de Souza ◽  
Jalal Almhana ◽  
Philippe Fournier-Viger
Keyword(s):  
Data Transmission ◽  
Radio Data ◽  
Transmission Reduction

Data Transmission Reduction Model for cloud-based IoT Systems

2021 ◽  
Author(s):  
Aya Elouali ◽  
Higinio Mora ◽  
Francisco J. Mora Gimeno
Keyword(s):  
Data Transmission ◽  
Reduction Model ◽  
Transmission Reduction

Data Transmission Reduction Schemes in WSNs for Efficient IoT Systems

2019 ◽  
Vol 37 (6) ◽  
pp. 1307-1324 ◽  
Author(s):  
Abdallah Jarwan ◽  
Ayman Sabbah ◽  
Mohamed Ibnkahla
Keyword(s):  
Data Transmission ◽  
Transmission Reduction

scholarly journals Classifier-Based Data Transmission Reduction in Wearable Sensor Network for Human Activity Monitoring

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 85
Author(s):  
Marcin Lewandowski ◽  
Bartłomiej Płaczek ◽  
Marcin Bernas
Keyword(s):  
Sensor Networks ◽  
Sensor Network ◽  
Activity Recognition ◽  
Human Activity ◽  
Network Lifetime ◽  
Data Transmission ◽  
Activity Monitoring ◽  
Sensor Nodes ◽  
Wearable Sensor ◽  
Transmission Reduction

The recent development of wireless wearable sensor networks offers a spectrum of new applications in fields of healthcare, medicine, activity monitoring, sport, safety, human-machine interfacing, and beyond. Successful use of this technology depends on lifetime of the battery-powered sensor nodes. This paper presents a new method for extending the lifetime of the wearable sensor networks by avoiding unnecessary data transmissions. The introduced method is based on embedded classifiers that allow sensor nodes to decide if current sensor readings have to be transmitted to cluster head or not. In order to train the classifiers, a procedure was elaborated, which takes into account the impact of data selection on accuracy of a recognition system. This approach was implemented in a prototype of wearable sensor network for human activity monitoring. Real-world experiments were conducted to evaluate the new method in terms of network lifetime, energy consumption, and accuracy of human activity recognition. Results of the experimental evaluation have confirmed that, the proposed method enables significant prolongation of the network lifetime, while preserving high accuracy of the activity recognition. The experiments have also revealed advantages of the method in comparison with state-of-the-art algorithms for data transmission reduction.

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