Details of Research Outputs

With billions of sensor-based devices connected to the Internet of Things (IoT), it is a pivotal issue to design an effective task scheduling scheme when the resource of sensor nodes is limited. In the past, Q-learning based task scheduling scheme which only focuses on the node angle led to poor performance of the whole network. Thus, a Q-learning based flexible task scheduling with global view (QFTS-GV) scheme is proposed to improve task scheduling success rate, reduce delay, and extend lifetime for the IoT. First, the Q-learning framework, including state set, action set, and rewards function is defined in a global view so as to forms the basis of the QFTS-GV scheme. Then, a task scheduling policy is established with distinguishing rewards for nodes in different areas of the network, so the energy-strained nodes can be protected to ensure a high lifetime, and the energy-relaxed nodes can increase their transmission power to promote the benefits of the whole network. Finally, experimental results demonstrate that the QFTS-GV scheme can achieve a higher task scheduling success rate, lower delay, and less energy consumption. Compared with the Q-learning based task scheduling scheme, the QFTS-GV improves the task scheduling success rate by 1.42% to 7.13%, reduces the delay by 24.60% to 42.56%, and saves energy by 21.18% to 36.60%.