An Efficient Framework of Energy Status Reporting for BLE Beacon Networks

被引:1
|
作者
Wong, Simon [1 ]
She, James [2 ]
Jeon, Kang Eun [1 ,3 ]
机构
[1] Hong Kong Univ Sci & Technol, Dept Elect & Comp Engn, HKUST NIE Social Media Lab, Hong Kong, Peoples R China
[2] Hamad Bin Khalifa Univ, Coll Sci & Engn, Doha, Qatar
[3] Sungkyunkwan Univ, Convergence Res Inst, Suwon 2066, South Korea
关键词
Monitoring; Batteries; Internet of Things; Estimation; Reliability; Servers; Logic gates; Battery reporting; BLE beacon; energy status; Internet of Things (IoT); reduction on network requests; SYSTEM;
D O I
10.1109/JIOT.2023.3237858
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With growing demands for Internet of Things (IoT) applications, BLE beacon networks are rapidly being adopted. Periodic battery replacement operations and onsite maintenance are required to ensure continuous and reliable service. These operations are labor intensive and resource exhaustive. Therefore, Bluetooth gateways/mobile devices are often employed to monitor/collect the energy status. However, the gateways consume a considerable amount of network requests, and the user existence influences the data collection, thus the data accuracy, based on mobile devices. Reducing the number of energy status reports and maintaining the high accuracy of the energy status monitoring service is essential to catalyze a generic adoption of beacon networks and IoT infrastructure of similar nature in more businesses and real-life applications. In this article, we proposed a novel energy status monitoring framework that will dynamically change the energy status report interval based on the discharging rate of the battery, thereby reducing the total number of network requests and maintaining the required accuracy of energy status. The proposed framework identifies the BLE beacons with similar battery discharging rates, suggests a dynamic report interval, and leverages this information to reduce the number of energy status reports. We have experimented with real-life BLE beacon energy status data for 50 days to demonstrate that we could reduce the total number of network requests up to 70% while retaining 99% estimation accuracy.
引用
收藏
页码:10426 / 10437
页数:12
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