Abstract
Bluetooth low energy (BLE) is a promising candidate technology for use in the Internet of Things (IoT) because of its ultra-low-power communication. Although BLE devices are designed to run on a small battery for a few years, several attempts have been made to extend BLE lifetime through various techniques. In particular, emerging approaches such as artificial intelligence (AI) can be utilized to further improve the BLE energy efficiency. For this purpose, this article proposes a Q-learning-based scheduling algorithm for BLE. The proposed scheduling algorithm dynamically adjusts the key parameters that govern the operation of the BLE transmission scheme. These key parameters, namely, the length of connection interval and the number of packets to transmit during the interval, have a profound effect on energy efficiency and the quality of service (QoS) specified in terms of maximum latency. According to the framework of reinforcement learning, our Q-learning-based scheduling algorithm is appropriately constructed to simultaneously provide a longer network lifetime and satisfy the QoS requirement. The numerical results show that the proposed Q-learning-based approach significantly increases the network lifetime compared to alternative methods while meeting QoS requirements.
Highlights
Internet of Things (IoT) is a key technology driving toward a new perspective of the world where almost all conceivable things are connected to a network for remote sensing and control [1]–[3]
Our system model captures the essential dynamics of the Bluetooth low energy (BLE) connection mode in which the master controls the key transmission parameters, including the connection interval (CI) length and the number of packets to transmit per CI
The battery level is initially set to 1000.0 units for the slaves and infinite for the master, and communication continues until the battery drains out
Summary
Internet of Things (IoT) is a key technology driving toward a new perspective of the world where almost all conceivable things are connected to a network for remote sensing and control [1]–[3]. The realization of the IoT is enabled by the integration of various technologies, including smart devices, wireless networking, cloud computing, and data analytics [1], [2]. Among these elements, this article focuses on the wireless networking responsible for delivering the data collected from the sensor nodes to remote sites. Several wireless networking technologies have been proposed for the IoT such as IEEE 802.15.4, ZigBee, BLE, LoRaWAN. BLE devices can operate with a common coin-cell battery for several years [8], [9], there have been numerous attempts to extend its lifetime through
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