The key to the explosion of the Internet of Things and the ability to collect, analyze, and provide big data in the cloud is edge computing, which is a new computing paradigm in which data is processed from edges. Edge Computing has been attracting attention as one of the top 10 strategic technology trends in the past two years and has innovative potential. It provides shorter response times, lower bandwidth costs, and more robust data safety and privacy protection than cloud computing. In particular, artificial intelligence technologies are rapidly incorporating edge computing. In this paper, we introduce the concepts, backgrounds, and pros and cons of edge computing, explain how it operates and its structure hierarchically with artificial intelligence concepts, list examples of its applications in various fields, and finally suggest some improvements and discuss the challenges of its application in three representative technological fields. We intend to clarify various analyses and opinions regarding edge computing and artificial intelligence.

Understanding Edge Computing: Engineering Evolution With Artificial Intelligence

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

Mobile edge computing (MEC) can use wireless access network (RAN) to provide the services required by user's information technology (IT) and cloud computing functions nearby, which can create a high-performance and low latency service environment. Performing task offloading and data caching at access points (APs) in a cooperative manner can reduce the heavy backhaul load and the retransmission of content downloading. However, in edge networks (ENs), how to maximize storage utilization while reducing service latency and energy consumption is still a key issue, because the heterogeneity of ENs and the uneven distribution of users make it difficult to determine which MEC server and what data should be cached. In this paper, we study a two-tier MEC system, which enables data caching and computing offloading policy to minimize the network cost at the user equipment (UE) side, while satisfying the constraints of task offloading deadline, the cache capacity at APs and the computing capability of MEC servers. The optimization problem is formulated as a mixed integer nonlinear program (MINLP) problem. In order to solve the problem, we transform it into an equivalent task offloading convex optimization problem by fixing an optimization variable. Furthermore, we solve a cache placement problem by dynamic programming (DP) algorithm. Then we propose a distributed collaborative data caching and computing offloading (CDCCO) iterative algorithm. Simulation results demonstrate that our proposed CDCCO algorithm can significantly reduce the network cost and achieve better performance than other existing schemes.

Collaborative Data Caching and Computation Offloading for Multi-Service Mobile Edge Computing

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

In this article, the problem of vertical handover in software-defined network (SDN) based heterogeneous networks (HetNets) is studied. In the studied model, HetNets are required to offer diverse services for mobile users. Using an SDN controller, HetNets have the capability of managing users’ access and mobility issues but still have the problems of ping-pong effect and service interruption during vertical handover. To solve these problems, a mobility-aware seamless handover method based on multipath transmission control protocol (MPTCP) is proposed. The proposed handover method is executed in the controller of the software-defined HetNets (SDHetNets) and consists of three steps: location prediction, network selection, and handover execution. In particular, the method first predicts the user’s location in the next moment with an echo state network (ESN). Given the predicted location, the SDHetNet controller can determine the candidate network set for the handover to pre-allocate network wireless resources. Second, the target network is selected through fuzzy analytic hierarchical process (FAHP) algorithm, jointly considering user preferences, service requirements, network attributes, and user mobility patterns. Then, seamless handover is realized through the proposed MPTCP-based handover mechanism. Simulations using real-world user trajectory data from Korea Advanced Institute of Science & Technology show that the proposed method can reduce the handover times by 10.85% to 29.12% compared with traditional methods. The proposed method also maintains at least one MPTCP subflow connected during the handover process and achieves a seamless handover.

Mobility-Aware Seamless Handover With MPTCP in Software-Defined HetNets

With the continuous innovation of Internet of Things (IoT) technology, multi-network convergence has become the mainstream of the IoT. In order to ensure that the IoT users can normally perform information exchange in different services, how to optimize network resource allocation in multiple networks is a key issue. In this paper, we propose a resource allocation algorithm for heterogeneous IoT based on overall user satisfaction. On the basis of considering user satisfaction based on energy efficiency and transmission rate, an efficient online algorithm is obtained through calculating the optimal bandwidth of users in different access networks. The simulation results show that comparing to transmission rate based algorithm and load-balancing algorithm, the network resource allocation algorithm proposed can significantly improve the overall user satisfaction.

A Resource Allocation Algorithm Based on Overall User Satisfaction of Internet of Things

In the heterogeneous wireless networks, it remains a significant challenge to achieve an efficient network selection strategy to satisfy the demands of a massive number of edge users and novel 5G services. In this paper, we formulate the network selection problem for edge users as a discrete-time Markov model, and propose a Nash Q-learning based intelligent network access algorithm for multi-agent system, named MAQNS. We consider the joint optimization of network selection strategies among different types of networks, aiming at maximizing the long-term performance of multi-agent system. Meanwhile, we use Analytic Hierarchy Process (AHP) and Grey Relation Analysis (GRA) to characterize the user preferences for networks. Experimental results show that comparing to the existing network selection algorithms, the proposed MAQNS has better performance in terms of system throughput, user blocking probability, average energy efficiency and average delay.

Anqi Zhu

Papers

A Resource Allocation Algorithm Based on Overall User Satisfaction of Internet of Things

i5GAccess: Nash Q-learning Based Multi-Service Edge Users Access in 5G Heterogeneous Networks

Game-theoretic robotic offloading via multi-agent learning for agricultural applications in heterogeneous networks