Bluetooth low energy (BLE) beacon network is commonly adopted to provide many emerging and smart IoT services. A BLE beacon network is usually formed by either battery-powered beacon, or energy harvesting beacon recently. The on-going monitoring of the energy status in these beacon devices is critical for the timely battery replacement and the reliable application operation. Some earlier works utilize the user smartphones to collect the energy status of BLE beacons bearing the users and report the data to the central monitoring platform. However, this approach can induce a lot of reporting updates that causes heavy loading on the network and server for continuous monitoring. It may suffer from the poor monitoring performance and severe data loss of energy status when some beacon devices...[ Read more ]

Bluetooth low energy (BLE) beacon network is commonly adopted to provide many emerging and smart IoT services. A BLE beacon network is usually formed by either battery-powered beacon, or energy harvesting beacon recently. The on-going monitoring of the energy status in these beacon devices is critical for the timely battery replacement and the reliable application operation. Some earlier works utilize the user smartphones to collect the energy status of BLE beacons bearing the users and report the data to the central monitoring platform. However, this approach can induce a lot of reporting updates that causes heavy loading on the network and server for continuous monitoring. It may suffer from the poor monitoring performance and severe data loss of energy status when some beacon devices do not have enough chances of user passing by. This kind of data loss is even more severe for the energy-harvesting beacons due to its property of rapid fluctuations of energy status. Hence, the thesis first introduced an efficient reporting framework of energy status for battery-powered beacons that can significantly reduce the amount of reporting updates. This framework can remove unnecessary reporting by smartly extending the report intervals required on every user smartphone without compromising the monitoring performance. In addition, another data recovery framework of energy status for energy-harvesting beacons is proposed, which can deal with the severe data loss even for energy-harvesting beacon. A recurrent architecture of support vector regression is adopted to learn the rapid changes of the energy status in energy-harvesting beacons. Both frameworks are validated with the dataset collected from the real beacon networks. Our proposed reporting framework can reduce the amount of reporting traffic up to 70% for the 99% of estimation accuracy. Whereas, our recovery framework is also proved to achieve the 90% of estimation accuracy even under a severe loss rate of data. Beside the publications, the contributions of this thesis also include the prototypes of our proposed frameworks, which are possible to support effective and reliable monitoring of energy status for future BLE beacon networks.