With the advent of the Internet of Things (IoT) era, a wide array of wireless sensors supporting the IoT have proliferated. As key elements for enabling the IoT, wireless sensor nodes require minimal energy consumption and low device complexity. In particular, energy-efficient resource scheduling is critical in maintaining a network of wireless sensor nodes, since the energy-intensive processing of wireless sensor nodes and their interactions is too complicated to control. In this study, we present a practical deep Q-network (DQN)-based packet scheduling algorithm that coordinates the transmissions of multiple IoT devices. The scheduling algorithm dynamically adjusts the connection interval (CI) and the number of packets transmitted by each node within the interval. Through various experiments, we verify how effectively the proposed scheduler improves energy efficiency and handles the time-varying nature of the network environment. Moreover, we attempt to gain insight into the optimized packet scheduler by analyzing the policy of the DQN scheduler. The experimental results show that the proposed scheduling algorithm can further prolong a network’s lifetime in a dynamic network environment in comparison with that in other alternative schemes while ensuring the quality of service (QoS).

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.