Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability

Internet-of-Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive and Scalable Communication Network

Mobile Edge Computing

This is an open access book. It offers comprehensive, self-contained knowledge on Mobile Edge Computing (MEC), which is a very promising technology for achieving intelligence in the next-generation wireless communications and computing networks. The book starts with the basic concepts, key techniques and network architectures of MEC. Then, we present the wide applications of MEC, including edge caching, 6G networks, Internet of Vehicles, and UAVs. In the last part, we present new opportunities when MEC meets blockchain, Artificial Intelligence, and distributed machine learning (e.g., federated learning). We also identify the emerging applications of MEC in pandemic, industrial Internet of Things and disaster management. The book allows an easy cross-reference owing to the broad coverage on both the principle and applications of MEC. The book is written for people interested in communications and computer networks at all levels. The primary audience includes senior undergraduates, postgraduates, educators, scientists, researchers, developers, engineers, innovators and research strategists

Leveraging Intelligent Computation Offloading with Fog/Edge Computing for Tactile Internet: Advantages and Limitations

[EN] With the recent advancement in wireless communication and networks, we are at the doorstep of the Tactile Internet. The Tactile Internet aims to enable the skill delivery and thereafter democratize the specialized skills for many emerging applications (e.g., remote medical, industrial machinery, remote robotics, autonomous driving). In this article, we start with the motivation of applying intelligent edge computing for computation offloading in the Tactile Internet. Afterward, we outline the main research challenges to leverage edge intelligence at the master, network, and controlled domain of the Tactile Internet. The key research challenges in the Tactile Internet lie in its stringent requirements such as ultra-low latency, ultra-high reliability, and almost zero service outage. We also discuss major entities in intelligent edge computing and their role in the Tactile Internet. Finally, several potential research challenges in edge intelligence for the Tactile Internet are highlighted.This work was supported in part by the National Natural Science Foundation of China under Grant 61901128, and Agile Edge Intelligence for Delay Sensitive IoT (AgilE-IoT) project (Grant No. 9131-00119B) of Independent Research Fund Denmark (DFF).Mukherjee, M.; Guo, M.; Lloret, J.; Zhang, Q. (2020). Leveraging Intelligent Computation Offloading with Fog/Edge Computing for Tactile Internet: Advantages and Limitations. IEEE Network. 34(5):322-329. https://doi.org/10.1109/MNET.001.200000432232934

Resource allocation in mobile edge cloud computing for data-intensive applications

Rapid advancement in the mobile telecommunications industry has motivated the development of mobile applications in a wide range of social and scientific domains. However, mobile computing (MC) platforms still have several constraints, such as limited computation resources, short battery life and high sensitivity to network capabilities. In order to overcome the limitations of mobile computing and benefit from the huge advancement in mobile telecommunications and the rapid revolution of distributed resources, mobile-aware computing models, such as mobile cloud computing (MCC) and mobile edge computing (MEC) have been proposed. The main problem is to decide on an application execution plan while satisfying quality of service (QoS) requirements and the current status of system networking and device energy. However, the role of application data in offloading optimisation has not been studied thoroughly, particularly with respect to how data size and distribution impact application offloading. This problem can be referred to as data-intensive mobile application offloading optimisation. To address this problem, this thesis presents novel optimisation frameworks, techniques and algorithms for mobile application resource allocation in mobile-aware computing environments. These frameworks and techniques are proposed to provide optimised solutions to schedule data intensive mobile applications. Experimental results show the ability of the proposed tools in optimising the scheduling and the execution of data intensive applications on various computing environments to meet application QoS requirements. Furthermore, the results clearly stated the significant contribution of the data size parameter on scheduling the execution of mobile applications. In addition, the thesis provides an analytical investigation of mobile-aware computing environments for a certain mobile application type. The investigation provides performance analysis to help users decide on target computation resources based on application structure, input data, and mobile network status

Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach

With the rapid development of Mobile Edge Computing (MEC), various real-time applications have been deployed to benefit people's daily lives. The performance of these applications relies heavily on the freshness of collected environmental information, which can be quantified by its Age of Information (AoI). In the traditional definition of AoI, it is assumed that the status information can be actively sampled and directly used. However, for many MEC-enabled applications, the desired status information is updated in an event-driven manner and necessitates data processing. To better serve these applications, we propose a new definition of AoI and, based on the redefined AoI, we formulate an online AoI minimization problem for MEC systems. Notably, the problem can be interpreted as a Markov Decision Process (MDP), thus enabling its solution through Reinforcement Learning (RL) algorithms. Nevertheless, the traditional RL algorithms are designed for MDPs with completely unknown system dynamics and hence usually suffer long convergence times. To accelerate the learning process, we introduce Post-Decision States (PDSs) to exploit the partial knowledge of the system's dynamics. We also combine PDSs with deep RL to further improve the algorithm's applicability, scalability, and robustness. Numerical results demonstrate that our algorithm outperforms the benchmarks under various scenarios

VirtFogSim: A parallel toolbox for dynamic energy-delay performance testing and optimization of 5G Mobile-Fog-Cloud virtualized platforms

It is expected that the pervasive deployment of multi-tier 5G-supported Mobile-Fog-Cloudtechnological computing platforms will constitute an effective means to support the real-time execution of future Internet applications by resource- and energy-limited mobile devices. Increasing interest in this emerging networking-computing technology demands the optimization and performance evaluation of several parts of the underlying infrastructures. However, field trials are challenging due to their operational costs, and in every case, the obtained results could be difficult to repeat and customize. These emergingMobile-Fog-Cloud ecosystems still lack, indeed, customizable software tools for the performance simulation of their computing-networking building blocks. Motivated by these considerations, in this contribution, we present VirtFogSim. It is aMATLAB-supported software toolbox that allows the dynamic joint optimization and tracking of the energy and delay performance of Mobile-Fog-Cloud systems for the execution of applications described by general Directed Application Graphs (DAGs). In a nutshell, the main peculiar features of the proposed VirtFogSim toolbox are that: (i) it allows the joint dynamic energy-aware optimization of the placement of the application tasks and the allocation of the needed computing-networking resources under hard constraints on acceptable overall execution times, (ii) it allows the repeatable and customizable simulation of the resulting energy-delay performance of the overall system; (iii) it allows the dynamic tracking of the performed resource allocation under time-varying operational environments, as those typically featuring mobile applications; (iv) it is equipped with a user-friendly Graphic User Interface (GUI) that supports a number of graphic formats for data rendering, and (v) itsMATLAB code is optimized for running atop multi-core parallel execution platforms. To check both the actual optimization and scalability capabilities of the VirtFogSim toolbox, a number of experimental setups featuring different use cases and operational environments are simulated, and their performances are compared

Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control

In recent years, the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks. Such challenges can be potentially overcome by integrating communication, computing, caching, and control (i4C) technologies. In this survey, we first give a snapshot of different aspects of the i4C, comprising background, motivation, leading technological enablers, potential applications, and use cases. Next, we describe different models of communication, computing, caching, and control (4C) to lay the foundation of the integration approach. We review current state-of-the-art research efforts related to the i4C, focusing on recent trends of both conventional and artificial intelligence (AI)-based integration approaches. We also highlight the need for intelligence in resources integration. Then, we discuss integration of sensing and communication (ISAC) and classify the integration approaches into various classes. Finally, we propose open challenges and present future research directions for beyond 5G networks, such as 6G.Comment: This article has been accepted for inclusion in a future issue of China Communications Journal in IEEE Xplor

Computation Offloading and Task Scheduling on Network Edge

The Fifth-Generation (5G) networks facilitate the evolution of communication systems and accelerate a revolution in the Information Technology (IT) field. In the 5G era, wireless networks are anticipated to provide connectivity for billions of Mobile User Devices (MUDs) around the world and to support a variety of innovative use cases, such as autonomous driving, ubiquitous Internet of Things (IoT), and Internet of Vehicles (IoV). The novel use cases, however, usually incorporate compute-intensive applications, which generate enormous computing service demands with diverse and stringent service requirements. In particular, autonomous driving calls for prompt data processing for the safety-related applications, IoT nodes deployed in remote areas need energy-efficient computing given limited on-board energy, and vehicles require low-latency computing for IoV applications in a highly dynamic network. To support the emerging computing service demands, Mobile Edge Computing (MEC), as a cutting-edge technology in 5G, utilizes computing resources on network edge to provide computing services for MUDs within a radio access network. The primary benefits of MEC can be elaborated from two perspectives. From the perspective of MUDs, MEC enables low-latency and energy-efficient computing by allowing MUDs to offload their computation tasks to proximal edge servers, which are installed in access points such as cellular base stations, Road-Side Units (RSUs), and Unmanned Aerial Vehicles (UAVs). On the other hand, from the perspective of network operators, MEC allows a large amount of computing data to be processed on network edge, thereby alleviating backhaul congestion. {MEC is a promising technology to support computing demands for the novel 5G applications within the RAN. The interesting issue is to maximize the computation capability of network edge to meet the diverse service requirements arising from the applications in dynamic network environments. However, the main technical challenges are: 1) how an edge server schedules its limited computing resources to optimize the Quality-of-Experience (QoE) in autonomous driving; 2) how the computation loads are balanced between the edge server and IoT nodes in computation loads to enable energy-efficient computing service provisioning; and 3) how multiple edge servers coordinate their computing resources to enable seamless and reliable computing services for high-mobility vehicles in IoV. In this thesis, we develop efficient computing resource management strategies for MEC, including computation offloading and task scheduling, to address the above three technical challenges. First, we study computation task scheduling to support real-time applications, such as localization and obstacle avoidance, for autonomous driving. In our considered scenario, autonomous vehicles periodically sense the environment, offload sensor data to an edge server for processing, and receive computing results from the edge server. Due to mobility and computing latency, a vehicle travels a certain distance between the instant of offloading its sensor data and the instant of receiving the computing result. Our objective is to design a scheduling scheme for the edge server to minimize the above traveled distance of vehicles. The idea is to determine the processing order according to the individual vehicle mobility and computation capability of the edge server. We formulate a Restless Multi-Armed Bandit (RMAB) problem, design a Whittle index-based stochastic scheduling scheme, and determine the index using a Deep Reinforcement Learning (DRL) method. The proposed scheduling scheme can avoid the time-consuming policy exploration common in DRL scheduling approaches and makes effectual decisions with low complexity. Extensive simulation results demonstrate that, with the proposed index-based scheme, the edge server can deliver computing results to the vehicles promptly while adapting to time-variant vehicle mobility. Second, we study energy-efficient computation offloading and task scheduling for an edge server while provisioning computing services {for IoT nodes in remote areas}. In the considered scenario, a UAV is equipped with computing resources and plays the role of an aerial edge server to collect and process the computation tasks offloaded by ground MUDs. Given the service requirements of MUDs, we aim to maximize UAV energy efficiency by jointly optimizing the UAV trajectory, the user transmit power, and computation task scheduling. The resulting optimization problem corresponds to nonconvex fractional programming, and the Dinkelbach algorithm and the Successive Convex Approximation (SCA) technique are adopted to solve it. Furthermore, we decompose the problem into multiple subproblems for distributed and parallel problem solving. To cope with the case when the knowledge of user mobility is limited, we apply a spatial distribution estimation technique to predict the location of ground users so that the proposed approach can still be valid. Simulation results demonstrate the effectiveness of the proposed approach to maximize the energy efficiency of the UAV. Third, we study collaboration among multiple edge servers in computation offloading and task scheduling to support computing services {in IoV}. In the considered scenario, vehicles traverse the coverage of edge servers and offload their tasks to their proximal edge servers. We develop a collaborative edge computing framework to reduce computing service latency and alleviate computing service interruption due to the high mobility of vehicles: 1) a Task Partition and Scheduling Algorithm (TPSA) is proposed to schedule the execution order of the tasks offloaded to the edge servers given a computation offloading strategy; and 2) an artificial intelligence-based collaborative computing approach is developed to determine the task offloading, computing, and result delivery policy for vehicles. Specifically, the offloading and computing problem is formulated as a Markov decision process. A DRL technique, i.e., deep deterministic policy gradient, is adopted to find the optimal solution in a complex urban transportation network. With the developed framework, the service cost, which includes computing service latency and service failure penalty, can be minimized via the optimal computation task scheduling and edge server selection. Simulation results show that the proposed AI-based collaborative computing approach can adapt to a highly dynamic environment with outstanding performance. In summary, we investigate computing resource management to optimize QoE of MUDs in the coverage of an edge server, to improve energy efficiency for an aerial edge server while provisioning computing services, and to coordinate computing resources among edge servers for supporting MUDs with high mobility. The proposed approaches and theoretical results contribute to computing resource management for MEC in 5G and beyond