Intelligent Edge: When AI Meets Edge Computing

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "Internet of Things (IoT) and Industrial IoT".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 515

Special Issue Editor

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Guest Editor
TSYS School of Computer Science, Columbus State University, Columbus, GA 31907, USA
Interests: cloud and edge computing; big data analytics; genomics; advanced metering infrastructure (AMI); architectures for smart grids; energy consumption prediction for smart buildings

Special Issue Information

Dear Colleagues,

This Special Issue investigates the dynamic interplay between artificial intelligence (AI) and edge computing, two revolutionary technologies that are redefining the landscape of modern computing. AI brings the power of both machine learning and intelligent decision making. In contrast, edge computing offers the advantage of localized data processing. This reduces latency by reducing the load on cloud data centers and enhancing data security. The data are processed locally and in a distributed manner, which spreads out the attack surface and reduces the need to transmit sensitive information.

The convergence of these technologies leads to groundbreaking applications across various sectors, including healthcare, smart cities, industrial automation, and the Internet of Things (IoT).

Contributions in this Special Issue explore how AI algorithms can be optimized for edge devices, considering their limited resources. It also delves into how edge computing can facilitate real-time AI applications by processing data closer to its source. Additionally, the Special Issue examines security and privacy concerns in the intelligent edge network, addressing how to safeguard sensitive information in decentralized environments.

This Special Issue aims to provide insights into the current state and future prospects of intelligent edge systems, offering a comprehensive understanding for researchers, practitioners, and enthusiasts in the field.

These edits enhance readability and ensure that the text flows smoothly, maintaining the original meaning and emphasis on the importance of the Intelligent Edge.

Selected Topics (but not limited to):

  1. Optimizing AI Algorithms for Edge Computing Environments

Exploring how AI algorithms can be tailored to operate efficiently on edge devices with limited computational resources.

  1. Security and Privacy in Intelligent Edge Systems

Investigating the security challenges and privacy implications inherent in deploying AI on edge computing platforms.

  1. Edge AI in IoT Applications

Discussing the role of edge computing in enhancing AI-driven applications in the Internet of Things (IoT), particularly in smart homes, cities, and industries.

  1. Real-Time Data Processing and Decision Making

Examining how edge computing enables real-time data analysis and immediate decision-making in critical applications like autonomous vehicles and healthcare monitoring.

  1. Energy Efficiency in Edge AI Systems

Addressing the challenges and solutions for energy-efficient AI processing at the edge, crucial for battery-operated and remote devices.

  1. Edge Computing in 5G Networks

Exploring the synergy between 5G technologies and edge computing in facilitating faster and more reliable AI applications.

  1. AI-Driven Edge Computing in Healthcare

Discussing the impact of edge AI in medical diagnostics, patient monitoring, and telemedicine, with a focus on privacy and real-time data analysis.

  1. Scalability and Management of Edge AI Networks

Investigating the architectural and management challenges in scaling edge AI systems, including deployment strategies and maintenance.

  1. Edge AI for Industrial Automation

Analyzing the application of AI and edge computing in industrial settings, focusing on predictive maintenance, quality control, and supply chain optimization.

  1. Ethical and Regulatory Considerations in Intelligent Edge

Delving into the ethical implications and regulatory challenges of deploying AI at the edge, including data governance and compliance issues.

Dr. Riduan Abid
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computers is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • edge computing
  • intelligent edge
  • edge security
  • edge applications

Published Papers (1 paper)

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23 pages, 516 KiB  
Exploiting Anytime Algorithms for Collaborative Service Execution in Edge Computing
by Luís Nogueira, Jorge Coelho and David Pereira
Computers 2024, 13(6), 130; - 23 May 2024
Viewed by 320
The diversity and scarcity of resources across devices in heterogeneous computing environments can impact their ability to meet users’ quality-of-service (QoS) requirements, especially in open real-time environments where computational loads are unpredictable. Despite this uncertainty, timely responses to events remain essential to ensure [...] Read more.
The diversity and scarcity of resources across devices in heterogeneous computing environments can impact their ability to meet users’ quality-of-service (QoS) requirements, especially in open real-time environments where computational loads are unpredictable. Despite this uncertainty, timely responses to events remain essential to ensure desired performance levels. To address this challenge, this paper introduces collaborative service execution, enabling resource-constrained IoT devices to collaboratively execute services with more powerful neighbors at the edge, thus meeting non-functional requirements that might be unattainable through individual execution. Nodes dynamically form clusters, allocating resources to each service and establishing initial configurations that maximize QoS satisfaction while minimizing global QoS impact. However, the complexity of open real-time environments may hinder the computation of optimal local and global resource allocations within reasonable timeframes. Thus, we reformulate the QoS optimization problem as a heuristic-based anytime optimization problem, capable of interrupting and quickly adapting to environmental changes. Extensive simulations demonstrate that our anytime algorithms rapidly yield satisfactory initial service solutions and effectively optimize the solution quality over iterations, with negligible overhead compared to the benefits gained. Full article
(This article belongs to the Special Issue Intelligent Edge: When AI Meets Edge Computing)
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