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RSS FeedsSensors, Vol. 20, Pages 107: Experimental Evaluation of Advertisement-Based Bluetooth Low Energy Communication (Sensors)

 
 

24 december 2019 08:00:12

 
Sensors, Vol. 20, Pages 107: Experimental Evaluation of Advertisement-Based Bluetooth Low Energy Communication (Sensors)
 


This paper addresses the efficiency of Bluetooth Low Energy (BLE) communication in a network composed of a large number of tags that transmit information to a single hub using advertisement mode. Theoretical results show that the use of advertisements enables hundreds and thousands of BLE devices to coexist in the same area and at the same time effectively transmit messages. Together with other properties (low power consumption, medium communication range, capability to detect a signal’s angle-of-arrival, etc.), this makes BLE a competing technology for the Internet of Things (IoT) applications. However, as the number of communicating devices increases, the advertisement collision intensifies and the communication performance of BLE drops. This phenomena was so far analyzed theoretically, in simulations and in small-scale experiments, but large-scale experiments are not presented in the literature. This paper complements previous results and presents an experimental evaluation of a real IoT-use case, which is the deployment of over 200 tags communicating using advertisements. We evaluate the impact of the number of advertisements on the effective data reception rate and throughput. Despite the advertisement collision rate in our experiment varying between 0.22 and 0.33, we show that BLE, thanks to the multiple transmission of advertisements, can still ensure acceptable data reception rates and fulfill the requirements of a wide range of IoT applications.


 
197 viewsCategory: Chemistry, Physics
 
Sensors, Vol. 20, Pages 109: Tackling Faults in the Industry 4.0 Era--A Survey of Machine-Learning Solutions and Key Aspects (Sensors)
Sensors, Vol. 20, Pages 105: Applying Mobile Augmented Reality (AR) to Teach Interior Design Students in Layout Plans: Evaluation of Learning Effectiveness Based on the ARCS Model of Learning Motivation Theory (Sensors)
 
 
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