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 coming of the fifth-generation (5G) mobile communications, in mobile edge computing (MEC), the growth of user services and the personalization of QoS requirements have posed great challenges for heterogeneous wireless networks (HWNs) access selection. Based on the multiattribute decision theory and the fuzzy logic theory, we propose a novel network selection scheme for multiservice QoS requirements in MEC. The main procedures of the scheme include dynamic adaptive process, fuzzy process, hierarchical analysis, and integrated attributes assessment. The scheme proposed contributes to efficiently reduce the ping-pong effect and effectively select accurate network in a dynamic environment. Simulation results show that our scheme can select network access according to the type of user services and whether to switch networks. In addition, compared with commonly used simple additive weighting (SAW), random access selection (RAS), and price-based and QoS-based network selection scheme, our scheme has better performance in improving average user satisfaction and reducing access failures.

Adaptive Multiservice Heterogeneous Network Selection Scheme in Mobile Edge Computing

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.

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

Mobile edge computing (MEC) as a new type of computing model can expand the computing power of cloud computing to the edge of radio access network (RAN), which brings a large number of applications close for end user Compared to traditional cloud computing, computation tasks being offloaded to edge clouds nearby to execute can reduce transmission delay and energy consumption However, how to select the best edge cloud in a dense cell to execute tasks remains challenging To address this challenge, in this paper we propose joint user selection and resource allocation algorithm in MEC to maximize the user’s energy efficiency, defined as the ratio of user throughput to its energy consumption We formulate the energy efficiency maximization problem as a mixed integer fractional nonlinear optimization problem, which involves both users’ offloading selection and uplink transmission power To solve this non-convex optimization problem, we transform it into an equivalent subtractive convex optimization problem by relaxation transformation method, and furthermore provide the corresponding optimal solution of user selection and power allocation Numerical results show that compared with other selection schemes, the proposed optimal scheme has a significant improvement in energy efficiency

Energy-efficient user selection and resource allocation in mobile edge computing

In the 5G ultra-dense wireless heterogeneous network system, it is a crucial issue to implement an effective network selection strategy to satisfy the demands of massive edge users and novel 5G services. In this paper, we model the network selection problem of edge users requesting different services as a bipartite graph, and propose a network selection algorithm based on weighted bipartite graph matching for 5G ultra-dense heterogeneous networks, named BGMNS. The proposed algorithm combines Analytic Hierarchy Process (AHP) and Grey Relation Analysis (GRA) to analyze the preferences of multiple services for different network attributes and obtain the Quality of Experience (QoE) of different edge users for each network. Moreover, in order to realize the fair allocation of network resources, we comprehensively consider the importance of the requested services and the obtained QoE by edge users to construct system fairness index. The BGMNS algorithm can optimize the overall QoE of users under the premise of ensuring the system fairness. Simulation results show that compared to the existing network selection algorithms, the proposed BGMNS algorithm can not only provide stable access to users when network status fluctuates randomly, but also effectively reduce user blocking probability as well as total packet loss rate, and significantly improve user average energy efficiency.

Adaptive Multi-Access Algorithm for Multi-Service Edge Users in 5G Ultra-Dense Heterogeneous Networks

5G heterogeneous network system incorporates multiple radio access technologies (RATs), which enables the massive connection of Internet of things (IoT) devices and popularity of diverse IoT services. However, with the tremendous growth of IoT connections, personalization of service requirements and deepening of network heterogeneity, how to adaptively optimize the quality of experience (QoE) of IoT users in any motion state while balancing network load is still a major challenge in 5G system. Therefore, this paper proposes an innovative access selection mechanism named REMNS that allows users requesting IoT services to obtain optimal QoE in 5G heterogeneous networks. In particular, a network pre-assessment mechanism based on fuzzy logic is exploited to filter available networks by user devices. Furthermore, REMNS devises a comprehensive preference evaluation framework based on the subjective-oriented Analytic Hierarchy Process (AHP) and objective-oriented Entropy Weight Method (EWM) to measure the preference degrees of each IoT service for network attributes. Subsequently, we put forward a relative entropy based multi-service access selection algorithm to make servers rapidly sort optimal network from the filtered available networks, so as to enhance QoE for users under the constraint of limited network capacities. The evaluation results demonstrate that the REMNS can effectively maintain stable service connections and significantly improve user QoE.

Anqi Zhu

Papers

Adaptive Multiservice Heterogeneous Network Selection Scheme in Mobile Edge Computing

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

Energy-efficient user selection and resource allocation in mobile edge computing

Adaptive Multi-Access Algorithm for Multi-Service Edge Users in 5G Ultra-Dense Heterogeneous Networks

Adaptive Access Selection Algorithm for Multi-Service in 5G Heterogeneous Internet of Things