The Internet of Things (IoT) has recently advanced from an experimental technology to what will become the backbone of future customer value for both product and service sector businesses. This underscores the cardinal role of IoT on the journey toward the fifth generation of wireless communication systems. IoT technologies augmented with intelligent and big data analytics are expected to rapidly change the landscape of myriads of application domains ranging from health care to smart cities and industrial automations. The emergence of multi-access edge computing (MEC) technology aims at extending cloud computing capabilities to the edge of the radio access network, hence providing real-time, high-bandwidth, low-latency access to radio network resources. IoT is identified as a key use case of MEC, given MEC’s ability to provide cloud platform and gateway services at the network edge. MEC will inspire the development of myriads of applications and services with demand for ultralow latency and high quality of service due to its dense geographical distribution and wide support for mobility. MEC is therefore an important enabler of IoT applications and services which require real-time operations. In this survey, we provide a holistic overview on the exploitation of MEC technology for the realization of IoT applications and their synergies. We further discuss the technical aspects of enabling MEC in IoT and provide some insight into various other integration technologies therein.

/pdf/survey-on-multi-access-edge-computing-for-internet-of-things-54j50846td.pdf

Survey on Multi-Access Edge Computing for Internet of Things Realization

A centralized infrastructure system carries out existing data analytics and decision-making processes from our current highly virtualized platform of wireless networks and the Internet of Things (IoT) applications. There is a high possibility that these existing methods will encounter more challenges and issues in relation to network dynamics, resulting in a high overhead in the network response time, leading to latency and traffic. In order to avoid these problems in the network and achieve an optimum level of resource utilization, a new paradigm called edge computing (EC) is proposed to pave the way for the evolution of new age applications and services. With the integration of EC, the processing capabilities are pushed to the edge of network devices such as smart phones, sensor nodes, wearables, and on-board units, where data analytics and knowledge generation are performed which removes the necessity for a centralized system. Many IoT applications, such as smart cities, the smart grid, smart traffic lights, and smart vehicles, are rapidly upgrading their applications with EC, significantly improving response time as well as conserving network resources. Irrespective of the fact that EC shifts the workload from a centralized cloud to the edge, the analogy between EC and the cloud pertaining to factors such as resource management and computation optimization are still open to research studies. Hence, this paper aims to validate the efficiency and resourcefulness of EC. We extensively survey the edge systems and present a comparative study of cloud computing systems. After analyzing the different network properties in the system, the results show that EC systems perform better than cloud computing systems. Finally, the research challenges in implementing an EC system and future research directions are discussed.

/pdf/edge-of-things-the-big-picture-on-the-integration-of-edge-1eibcu9nos.pdf

Edge of Things: The Big Picture on the Integration of Edge, IoT and the Cloud in a Distributed Computing Environment

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

The Internet of Things (IoT) has recently advanced from an experimental technology to what will become the backbone of future customer value for both product and service sector businesses. This underscores the cardinal role of IoT on the journey towards the fifth generation (5G) of wireless communication systems. IoT technologies augmented with intelligent and big data analytics are expected to rapidly change the landscape of myriads of application domains ranging from health care to smart cities and industrial automations. The emergence of Multi-Access Edge Computing (MEC) technology aims at extending cloud computing capabilities to the edge of the radio access network, hence providing real-time, high-bandwidth, low-latency access to radio network resources. IoT is identified as a key use case of MEC, given MEC's ability to provide cloud platform and gateway services at the network edge. MEC will inspire the development of myriads of applications and services with demand for ultra low latency and high Quality of Service (QoS) due to its dense geographical distribution and wide support for mobility. MEC is therefore an important enabler of IoT applications and services which require real-time operations. In this survey, we provide a holistic overview on the exploitation of MEC technology for the realization of IoT applications and their synergies. We further discuss the technical aspects of enabling MEC in IoT and provide some insight into various other integration technologies therein.

/pdf/survey-on-multi-access-edge-computing-for-internet-of-things-abe329bbwk.pdf

The very last wireless network technology, created to increase the speed and the connections responsiveness, the Fifth-Generation Network (5G) can transmit a great volume of data It uses wireless broadband connections to support specific end-users and businesses services It is specifically useful for the Internet of Vehicles (IoV), guaranteeing fast connections and security The 5G network technology can be used to support Vehicle-to-Everything (V2X) communications and applications on autonomous vehicles It can enable information exchanges between vehicles and other infrastructures and people It can also provide a more comfortable and safer environment and accurate traffic knowledge The traffic ?ow can be improved, reducing pollution and accident rates The cellular network can be associated with V2X as a communicating base to offer enhanced road safety and autonomous driving, and also to offer the IoV connections This survey presents the 5G technology evolution, standards, and infrastructure associated with V2X ecosystem by IoV In other words, it presents the IoV supported by 5G V2X communications, considering its architecture, applications and also the V2X features and protocols, as well as the modes, the evaluation and the technological support in such combination The contribution of this paper is a systematized study about the interaction among these three contents: IoV, 5G, and V2X Eighty four works were selected to present concepts, standards and to identify the ways to overcome challenges This survey aims to guide the development of new 5G-V2X services and technologies dedicated to vehicle communications, and also to indicate future directions

/pdf/a-survey-of-5g-technology-evolution-standards-and-3vw1b5bn73.pdf

A Survey of 5G Technology Evolution, Standards, and Infrastructure Associated With Vehicle-to-Everything Communications by Internet of Vehicles

The evolving cellular network with centric-deployed Cloud Centers has greatly improved the mobile user experience. However, the advent of vehicular networks with a wide variety of new services and devices changes the existing cellular network landscape, and the cellular-based vehicular networks are confronted with serious challenge in terms of latency reduction and flexible service delivery. In this paper, we propose to deploy the specific server, called Mobile-Edge Computing (MEC) server, within the Radio Access Network, and allow them to connect with a set of base stations alongside roads, so as to provide flexible vehicle-related service and efficiently control the radio network. Then, the vision of MEC- assisted slicing network and a traffic scheduling policy are presented to promote network customization. As an instance of the MEC-based tailored network service, a time-predicted handover mechanism for vehicles is further developed by leveraging available road information and the enhanced capacity of the MEC server to satisfy the demand for high mobility and reliability.

A Novel Mobile Edge Computing-Based Architecture for Future Cellular Vehicular Networks

The coupling of federated learning (FL) and multi-access edge computing (MEC) has the potential to foster numerous applications. However, it poses great challenges to train FL fast enough with limited communication and computing resources of mobile edge devices. Motivated by recent development in ultra fast wireless transmissions and promising advances in artificial intelligence (AI) computing hardware of mobile devices, in this paper, we propose a time efficient FL over future mobile edge devices, called dynamic batch sizes assisted federated learning (DBFL) with convergence guarantee. The DBFL allows batch sizes to increase dynamically during training, which can unleash the computing potential of GPU’s parallelism for on- device training and effectively leverage the fast wireless transmissions (WiFi-6, 5G, 6G, etc.) of mobile edge devices. Furthermore, based on the derived DBFL’s convergence bound, we develop a batch size control scheme to minimize the total time consumption of FL over mobile edge devices, which trade-offs the “talking”, i.e., communication time, and “working”, i.e., computing time, by adjusting the incremental factor appropriately. Extensive simulations are conducted to validate the effectiveness of our proposed DBFL algorithm and demonstrate that our scheme outperforms existing time efficient FL approaches in terms of the total time consumption in various settings.

To Talk or to Work: Dynamic Batch Sizes Assisted Time Efficient Federated Learning Over Future Mobile Edge Devices

Preventing COVID-19 disease from spreading in communities will require proactive and effective healthcare resource allocations, such as vaccinations. A fine-grained COVID-19 vulnerability map will be essential to detect the high-risk communities and guild the effective vaccine policy. A mobile-crowdsourcing-based self-reporting approach is a promising solution. However, an accurate mobile-crowdsourcing-based map construction requests participants to report their actual locations, raising serious privacy concerns. To address this issue, we propose a novel approach to effectively construct a reliable community-level COVID-19 vulnerability map based on mobile crowdsourced COVID-19 self-reports without compromising participants’ location privacy. We design a geo-perturbation scheme where participants can locally obfuscate their locations with the geo-indistinguishability guarantee to protect their location privacy against any adversaries’ prior knowledge. To minimize the data utility loss caused by location perturbation, we first design an unbiased vulnerability estimator and formulate the location perturbation probability generation into a convex optimization. Its objective is to minimize the estimation error of the direct vulnerability estimator under the constraints of geo-indistinguishability. Given the perturbed locations, we integrate the perturbation probabilities with the spatial smoothing method to obtain reliable community-level vulnerability estimations that are robust to a small-sampling-size problem incurred by location perturbation. Considering the fast-spreading nature of coronavirus, we integrate the vulnerability estimates into the modified susceptible-infected-removed (SIR) model with vaccination for building a future trend map. It helps to provide a guideline for vaccine allocation when supply is limited. Extensive simulations based on real-world data demonstrate the proposed scheme superiority over the peer designs satisfying geo-indistinguishability in terms of estimation accuracy and reliability.

Constructing Mobile Crowdsourced COVID-19 Vulnerability Map With Geo-Indistinguishability

Federated learning (FL) enables mobile devices to collaboratively learn a shared prediction model while keeping data locally. However, there are two major research challenges to practically deploy FL over mobile devices: (i) frequent wireless updates of huge size gradients v.s. limited spectrum resources, and (ii) energy-hungry FL communication and local computing during training v.s. battery-constrained mobile devices. To address those challenges, in this paper, we propose a novel multi-bit over-the-air computation (M-AirComp) approach for spectrum-efficient aggregation of local model updates in FL and further present an energy-efficient FL design for mobile devices. Specifically, a high-precision digital modulation scheme is designed and incorporated in the M-AirComp, allowing mobile devices to upload model updates at the selected positions simultaneously in the multi-access channel. Moreover, we theoretically analyze the convergence property of our FL algorithm. Guided by FL convergence analysis, we formulate a joint transmission probability and local computing control optimization, aiming to minimize the overall energy consumption (i.e., iterative local computing + multi-round communications) of mobile devices in FL. Extensive simulation results show that our proposed scheme outperforms existing ones in terms of spectrum utilization, energy efficiency, and learning accuracy.

Liang Li

Papers

A Novel Mobile Edge Computing-Based Architecture for Future Cellular Vehicular Networks

To Talk or to Work: Dynamic Batch Sizes Assisted Time Efficient Federated Learning Over Future Mobile Edge Devices

Constructing Mobile Crowdsourced COVID-19 Vulnerability Map With Geo-Indistinguishability

Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation