The proliferation of Internet of Things (IoT) and the success of rich cloud services have pushed the horizon of a new computing paradigm, edge computing, which calls for processing the data at the edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing. Finally, we present several challenges and opportunities in the field of edge computing, and hope this paper will gain attention from the community and inspire more research in this direction.

Edge Computing: Vision and Challenges

Fog/edge computing has been proposed to be integrated with Internet of Things (IoT) to enable computing services devices deployed at network edge, aiming to improve the user’s experience and resilience of the services in case of failures. With the advantage of distributed architecture and close to end-users, fog/edge computing can provide faster response and greater quality of service for IoT applications. Thus, fog/edge computing-based IoT becomes future infrastructure on IoT development. To develop fog/edge computing-based IoT infrastructure, the architecture, enabling techniques, and issues related to IoT should be investigated first, and then the integration of fog/edge computing and IoT should be explored. To this end, this paper conducts a comprehensive overview of IoT with respect to system architecture, enabling technologies, security and privacy issues, and present the integration of fog/edge computing and IoT, and applications. Particularly, this paper first explores the relationship between cyber-physical systems and IoT, both of which play important roles in realizing an intelligent cyber-physical world. Then, existing architectures, enabling technologies, and security and privacy issues in IoT are presented to enhance the understanding of the state of the art IoT development. To investigate the fog/edge computing-based IoT, this paper also investigate the relationship between IoT and fog/edge computing, and discuss issues in fog/edge computing-based IoT. Finally, several applications, including the smart grid, smart transportation, and smart cities, are presented to demonstrate how fog/edge computing-based IoT to be implemented in real-world applications.

A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications

In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications, e.g., for medical purposes and in vehicular networks. Traditional cloud-based Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL.

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Federated Learning in Mobile Edge Networks: A Comprehensive Survey

Multi-access edge computing (MEC) is an emerging ecosystem, which aims at converging telecommunication and IT services, providing a cloud computing platform at the edge of the radio access network MEC offers storage and computational resources at the edge, reducing latency for mobile end users and utilizing more efficiently the mobile backhaul and core networks This paper introduces a survey on MEC and focuses on the fundamental key enabling technologies It elaborates MEC orchestration considering both individual services and a network of MEC platforms supporting mobility, bringing light into the different orchestration deployment options In addition, this paper analyzes the MEC reference architecture and main deployment scenarios, which offer multi-tenancy support for application developers, content providers, and third parties Finally, this paper overviews the current standardization activities and elaborates further on open research challenges

/pdf/on-multi-access-edge-computing-a-survey-of-the-emerging-5g-q09cwte3qi.pdf

On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration

pdf/recent-advances-in-physical-reservoir-computing-a-review-13cbj2805o.pdf

Recent advances in physical reservoir computing: A review

Edge Computing: A Primer

In this chapter, we will summarize the challenges in Edge Computing and bring forward some potential solutions and opportunities worth further research, including programmability, naming, data abstraction, service management, privacy and security and optimization metrics.

Challenges and Opportunities in Edge Computing

Since threshold-based fall detection has been widely studied by many research groups, accuracy is still a main limitation affected by personal factors. To this end, a personalized threshold extraction approach being adapted for the fall detection usage for different individual is proposed to increase the fall detection accuracy. Moreover, we also implement a fall detection algorithm called ALARM to verify the feasibility of the proposed threshold extraction approach. Results of comprehensive evaluation show it has high accuracy of 96.76% for fall detection, while the sensitivity and the specificity are 92.01% and 99.13%, respectively, based on the data collected from 8 volunteers.

ALARM: A novel fall detection algorithm based on personalized threshold

A system includes an infrared camera, an infrared light source, and a processor. The processor is programmed to receive, from the infrared camera, an image of a hand illuminated using the infrared light source, and send the image to a remote server to identify a user corresponding to the hand according to a vein pattern of the hand. An image of a hand illuminated using an infrared light source is received from an infrared camera. Region-of-interest segmentation is performed on the image to generate a segmented image of consistent hand location and orientation. Feature extraction is performed on the segmented image to generate a feature-extracted vein image. Matching of the feature-extracted vein image is performed against a database of feature-extracted vein images to identify a user identity corresponding to the hand.

Palm vein-based low-cost mobile identification system for a wide age range

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