AI-Empowered Data Offloading in MEC-Enabled IoV Networks
- URL: http://arxiv.org/abs/2204.10282v1
- Date: Thu, 31 Mar 2022 09:31:53 GMT
- Title: AI-Empowered Data Offloading in MEC-Enabled IoV Networks
- Authors: Afonso Fontes, Igor de L. Ribeiro, Khan Muhammad, Amir H. Gandomi,
Gregory Gay, Victor Hugo C. de Albuquerque
- Abstract summary: This article surveys research studies that use AI as part of the data offloading process, categorized based on four main issues: reliability, security, energy management, and service seller profit.
Various challenges to the process of offloading data in a MEC-enabled IoV network have emerged, such as offloading reliability in highly mobile environments, security for users within the same network, and energy management to keep users from being disincentivized to participate in the network.
- Score: 40.75165195026413
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Advancements in smart vehicle design have enabled the creation of Internet of
Vehicle (IoV) technologies that can utilize the information provided by various
sensors and wireless communication to perform complex functionality. Many of
these functionalities rely on high computational power and low latency. Mobile
Edge Computing (MEC) technologies have been proposed as a way to meet these
requirements, as their proximity and decentralization provide unique benefits
for networks like real-time communication, higher throughput, and flexibility.
Diverse challenges to the process of offloading data in a MEC-enabled IoV
network have emerged, such as offloading reliability in highly mobile
environments, security for users within the same network, and energy management
to keep users from being disincentivized to participate in the network. This
article surveys research studies that use AI as part of the data offloading
process, categorized based on four main issues: reliability, security, energy
management, and service seller profit. Afterward, this article discusses
challenges and future perspectives for IoV technologies.
Related papers
- The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective [64.36680481458868]
Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge.
This paper provides a survey of security and privacy in MEC from the perspective of Artificial Intelligence (AI)
We focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI.
arXiv Detail & Related papers (2024-01-03T07:47:22Z) - Multi-Tier Hierarchical Federated Learning-assisted NTN for Intelligent
IoT Services [24.10349383347469]
This study explores MT-HFL's role in fostering a decentralized, collaborative learning environment.
It enables IoT devices to not only contribute but also make informed decisions in network management.
This setup ensures efficient data handling, advanced privacy and security measures, and responsive to fluctuating network conditions.
arXiv Detail & Related papers (2023-05-09T14:03:22Z) - The Internet of Senses: Building on Semantic Communications and Edge
Intelligence [67.75406096878321]
The Internet of Senses (IoS) holds the promise of flawless telepresence-style communication for all human receptors'
We elaborate on how the emerging semantic communications and Artificial Intelligence (AI)/Machine Learning (ML) paradigms may satisfy the requirements of IoS use cases.
arXiv Detail & Related papers (2022-12-21T03:37:38Z) - Learning, Computing, and Trustworthiness in Intelligent IoT
Environments: Performance-Energy Tradeoffs [62.91362897985057]
An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous devices that can collaboratively execute semi-autonomous IoT applications.
This paper provides a state-of-the-art overview of these technologies and illustrates their functionality and performance, with special attention to the tradeoff among resources, latency, privacy and energy consumption.
arXiv Detail & Related papers (2021-10-04T19:41:42Z) - Making a Case for Federated Learning in the Internet of Vehicles and
Intelligent Transportation Systems [6.699060157800401]
Internet of Vehicles (IoV) is transformed into an Intelligent Transportation System (ITS)
To address these challenges, Federated Learning, a collaborative and distributed intelligence technique, is suggested.
With a multitude of use cases and benefits, Federated Learning is a key enabler for ITS and is poised to achieve widespread implementation in 5G and beyond networks and applications.
arXiv Detail & Related papers (2021-02-19T20:07:17Z) - Autonomous Maintenance in IoT Networks via AoI-driven Deep Reinforcement
Learning [73.85267769520715]
Internet of Things (IoT) with its growing number of deployed devices and applications raises significant challenges for network maintenance procedures.
We formulate a problem of autonomous maintenance in IoT networks as a Partially Observable Markov Decision Process.
We utilize Deep Reinforcement Learning algorithms (DRL) to train agents that decide if a maintenance procedure is in order or not and, in the former case, the proper type of maintenance needed.
arXiv Detail & Related papers (2020-12-31T11:19:51Z) - Challenges of AI in Wireless Networks for IoT [4.415110372506057]
The Internet of Things (IoT) will require ubiquitous connectivity, context-aware and dynamic service mobility, and extreme security through the wireless network infrastructure.
The main challenges in using AI in the wireless network infrastructure that facilitate end-to-end IoT communication are highlighted.
arXiv Detail & Related papers (2020-07-09T11:00:56Z) - Artificial Intelligence Aided Next-Generation Networks Relying on UAVs [140.42435857856455]
Artificial intelligence (AI) assisted unmanned aerial vehicle (UAV) aided next-generation networking is proposed for dynamic environments.
In the AI-enabled UAV-aided wireless networks (UAWN), multiple UAVs are employed as aerial base stations, which are capable of rapidly adapting to the dynamic environment.
As a benefit of the AI framework, several challenges of conventional UAWN may be circumvented, leading to enhanced network performance, improved reliability and agile adaptivity.
arXiv Detail & Related papers (2020-01-28T15:10:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.