Q‐learning based flexible task scheduling in a global view for the Internet of Things

Abstract

Summary 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%.

Adaptive offloading in mobile-edge computing for ultra-dense cellular networks based on genetic algorithm

“…In recent years, optimization methods based on reinforcement learning [17][18][19][20] and artificial intelligence [21,22] have emerged. A reinforcement learning-based online computation offloading approach for block chainempowered mobile edge computing was proposed in [17].…”

“…Gao et al investigated a DNN based MEC scheme considering multiple mobile devices and one MEC server in [19]. 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 in [20]. Miao et al [21] put forward a new intelligent computation offloading based MEC architecture in combination with artificial intelligence (AI) technology.…”

Energy-aware MAC protocol for data differentiated services in sensor-cloud computing

“…With the development of the Internet of Things (IoT) [15], more and more sensor devices are deployed in various applications, such as vehicles network [18,42], traffic monitoring, healthcare [43], security monitoring [44][45][46], and industrial networks [47][48][49]). Numerous sensor devices are used to sense and collect data, providing rich data for various applications [50].…”

Fast Multicast With Adjusting Transmission Power and Active Slots in Software Define IoT

“…Since users accessing network services are all located at the edge of the network, users can get services nearby [3], [6], thus reducing the delay, jitter, and high energy consumption caused by sending service requests to the cloud through long routing [12], [14]. In particular, the combination of current edge computing and artificial intelligence has made distributed deep data analysis and processing more common [32], [33], which has further promoted the widespread application and deployment of IoT devices [34]- [36].…”