Industrial Internet of Things (IIoT) has emerged with the developments of various communication technologies. In order to guarantee the security and privacy of massive IIoT data, blockchain is widely considered as a promising technology and applied into IIoT. However, there are still several issues in the existing blockchain-enabled IIoT: 1) unbearable energy consumption for computation tasks; 2) poor efficiency of consensus mechanism in blockchain; and 3) serious computation overhead of network systems. To handle the above issues and challenges, in this article, we integrate mobile-edge computing (MEC) into blockchain-enabled IIoT systems to promote the computation capability of IIoT devices and improve the efficiency of the consensus process. Meanwhile, the weighted system cost, including the energy consumption and the computation overhead, are jointly considered. Moreover, we propose an optimization framework for blockchain-enabled IIoT systems to decrease consumption, and formulate the proposed problem as a Markov decision process (MDP). The master controller, offloading decision, block size, and computing server can be dynamically selected and adjusted to optimize the devices energy allocation and reduce the weighted system cost. Accordingly, due to the high-dynamic and large-dimensional characteristics, deep reinforcement learning (DRL) is introduced to solve the formulated problem. Simulation results demonstrate that our proposed scheme can improve system performance significantly compared to other existing schemes.

Energy-Efficient Resource Allocation for Blockchain-Enabled Industrial Internet of Things With Deep Reinforcement Learning

Edge computing and its role in Industrial Internet: Methodologies, applications, and future directions

Offloading dependent tasks in multi-access edge computing: A multi-objective reinforcement learning approach

Mobile users typically have a series of complex tasks consisting of time-constrained workflows and concurrent workflows that need to be processed. However, these tasks cannot be performed directly locally due to resource limitations of the mobile terminal, especially for battery life. Fortunately, mobile edge computing (MEC) has been recognized as a promising technology which brings abundant resource at the edge of mobile network enabling mobile devices to overcome the resource and capacity constraints. However, edge servers, such as cloudlets, are heterogeneous and have limited resources. Thus, it is important to make an appropriate offloading strategy to maximize the utility of each cloudlet. In view of this, the time consumption and energy consumption of mobile devices and resource utilization of cloudlets have been taken into consideration in this study. Firstly, a multiconstraint workflow mode has been established, and then a multiobjective optimization mode is formulated. Technically, an improved optimization algorithm is proposed to address this mode based on Nondominated Sorting Genetic Algorithm II. Both extensive experimental evaluations and detailed theoretical analysis are conducted to show that the proposed method is effective and efficiency.

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Energy- and Resource-Aware Computation Offloading for Complex Tasks in Edge Environment

Mobile edge computing (MEC) can significantly enhance device computing power by offloading service workflows from mobile device computing to mobile network edges. Thus how to implement an efficient computation offloading mechanism is a major challenge nowadays. For the purpose of addressing this problem, this paper aims to reduce application completion time and energy consumption of user device (UD) in offloading. The algorithm proposed formalizes the computation offloading problem into an energy and time optimization problem according to user experience, and obtains the optimal cost strategy on the basis of deep Q-learning (DQN). The simulation results show that comparing to the known local execution algorithm and random offloading algorithm, the computation offloading algorithm proposed in this paper can significantly reduce the energy consumption and shorten the completion time of service workflow execution.

Minghong Guo

Papers

Computation Offloading for Workflow in Mobile Edge Computing Based on Deep Q-Learning