Browse ORBilu by Open Access
- What it is and what it isn't
- Green Road / Gold Road?
- Ready to Publish. Now What?
- How can I support the OA movement?
- Where can I learn more?
ORBilu is a project developed by
   Intelligent Blockchain-based Edge Computing via Deep Reinforcement Learning: Solutions and Challenges; Nguyen, van Dinh  ; et alin IEEE Network (in press) The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in wireless Internet-of-Things networks, by enabling task offloading with security enhancement ... [more ▼] The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in wireless Internet-of-Things networks, by enabling task offloading with security enhancement based on blockchain mining. Yet the existing approaches for these enabling technologies are isolated, providing only tailored solutions for specific services and scenarios. To fill this gap, we propose a novel cooperative task offloading and blockchain mining (TOBM) scheme for a blockchain-based MEC system, where each edge device not only handles computation tasks but also deals with block mining for improving system utility. To address the latency issues caused by the blockchain operation in MEC, we develop a new Proof-of-Reputation consensus mechanism based on a lightweight block verification strategy. To accommodate the highly dynamic environment and high-dimensional system state space, we apply a novel distributed deep reinforcement learning-based approach by using a multi-agent deep deterministic policy gradient algorithm. Experimental results demonstrate the superior performance of the proposed TOBM scheme in terms of enhanced system reward, improved offloading utility with lower blockchain mining latency, and better system utility, compared to the existing cooperative and non-cooperative schemes. The paper concludes with key technical challenges and possible directions for future blockchain-based MEC research. [less ▲] Detailed reference viewed: 67 (10 UL)Guest Editorial: Space Information Networks: Technological Challenges, Design Issues, and Solutions; ; et al in IEEE Network (2021), 35(4), 16-18 It has been expected that the space information networks (SIN), as an extension of the terrestrial network, would provide high-speed, high-capacity, global continuous communication, and data transmission ... [more ▼] It has been expected that the space information networks (SIN), as an extension of the terrestrial network, would provide high-speed, high-capacity, global continuous communication, and data transmission services anywhere for anyone at any time. With rapid advances in relevant technologies (e.g., satellite miniaturization technology, reusable rocket launch technology, and semiconductor technology), low-orbit satellites, drones, and airships can be integrated into the SIN to supply more comprehensive network connectivity. The standard development organizations including 3GPP, ITU, and ETSI already starts corresponding standardization activities to support nonterrestrial networks in SIN. It can be foreseen that SIN will be expanded to provide not only telephone services but also various kinds of Internet services, and it is thus able to serve many more users with different demands. [less ▲] Detailed reference viewed: 35 (1 UL)Ready Player One: UAV Clustering based Multi-Task Offloading for Vehicular VR/AR Gaming; ; et al in IEEE Network (2019) With rapid development of unmanned aerial vehicle (UAV) technology, application of UAVs for task offloading has received increasing interest in academia. However, real-time interaction between one UAV and ... [more ▼] With rapid development of unmanned aerial vehicle (UAV) technology, application of UAVs for task offloading has received increasing interest in academia. However, real-time interaction between one UAV and the mobile edge computing node is required for processing the tasks of mobile end users, which significantly increases the system overhead and is unable to meet the demands of large-scale artificial intelligence (AI)-based applications. To tackle this problem, in this article, we propose a new architecture for UAV clustering to enable efficient multi-modal multi-task offloading. With the proposed architecture, the computing, caching, and communication resources are collaboratively optimized using AI-based decision making. This not only increases the efficiency of UAV clusters, but also provides insight into the fusion of computation and communication. [less ▲] Detailed reference viewed: 167 (33 UL)Emerging Edge Computing Technologies for Distributed IoT Systems; Sharma, Shree Krishna ; et alin IEEE Network (2019) The ever-increasing growth of connected smart devices and Internet of Things (IoT) verticals is leading to the crucial challenges of handling the massive amount of raw data generated by distributed IoT ... [more ▼] The ever-increasing growth of connected smart devices and Internet of Things (IoT) verticals is leading to the crucial challenges of handling the massive amount of raw data generated by distributed IoT systems and providing timely feedback to the end-users. Although existing cloud computing paradigm has an enormous amount of virtual computing power and storage capacity, it might not be able to satisfy delaysensitive applications since computing tasks are usually processed at the distant cloud-servers. To this end, edge/fog computing has recently emerged as a new computing paradigm that helps to extend cloud functionalities to the network edge. Despite several benefits of edge computing including geo-distribution, mobility support and location awareness, various communication and computing related challenges need to be addressed for future IoT systems. In this regard, this paper provides a comprehensive view on the current issues encountered in distributed IoT systems and effective solutions by classifying them into three main categories, namely, radio and computing resource management, intelligent edge-IoT systems, and flexible infrastructure management. Furthermore, an optimization framework for edge-IoT systems is proposed by considering the key performance metrics including throughput, delay, resource utilization and energy consumption. Finally, a Machine Learning (ML) based case study is presented along with some numerical results to illustrate the significance of ML in edge-IoT computing. [less ▲] Detailed reference viewed: 308 (5 UL)Shared Access Satellite-Terrestrial Reconfigurable Backhaul Network Enabled by Smart Antennas at MmWave Band; ; et al in IEEE Network (2018) Detailed reference viewed: 126 (2 UL) |
||
