Semantic Communication Networks Empowered Artificial Intelligence of Things
- URL: http://arxiv.org/abs/2407.06082v1
- Date: Thu, 4 Jul 2024 14:39:28 GMT
- Title: Semantic Communication Networks Empowered Artificial Intelligence of Things
- Authors: Yuntao Wang,
- Abstract summary: This paper presents a comprehensive survey of security and privacy threats across various layers of semantic communication systems.
We identify critical open issues in this burgeoning field warranting further investigation.
- Score: 2.590720801978138
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Semantic communication aims to facilitate purposeful information exchange among diverse intelligent entities, including humans, machines, and organisms. It emphasizes precise semantic transmission over data fidelity, striving for meaningful expression while optimizing communication resources for efficient information transfer. Nevertheless, extant semantic communication systems face security, privacy, and trust challenges in integrating AI technologies for intelligent communication applications. This paper presents a comprehensive survey of security and privacy threats across various layers of semantic communication systems and discusses state-of-the-art countermeasures within both academic and industry contexts. Finally, we identify critical open issues in this burgeoning field warranting further investigation.
Related papers
- Generative AI for Secure Physical Layer Communications: A Survey [80.0638227807621]
Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content.
In this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks.
We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity.
arXiv Detail & Related papers (2024-02-21T06:22:41Z) - Pragmatic Goal-Oriented Communications under Semantic-Effectiveness Channel Errors [3.266331042379877]
In forthcoming AI-assisted 6G networks, integrating semantic, pragmatic, and goal-oriented communication strategies becomes imperative.
This paper proposes and details a novel mathematical modeling of errors stemming from language mismatches at both semantic and effectiveness levels.
Our numerical results show the potential of the proposed mechanism to compensate for language mismatches, thereby enhancing the attainability of reliable communication under noisy communication environments.
arXiv Detail & Related papers (2024-01-19T16:43:47Z) - Will 6G be Semantic Communications? Opportunities and Challenges from
Task Oriented and Secure Communications to Integrated Sensing [49.83882366499547]
This paper explores opportunities and challenges of task (goal)-oriented and semantic communications for next-generation (NextG) networks through the integration of multi-task learning.
We employ deep neural networks representing a dedicated encoder at the transmitter and multiple task-specific decoders at the receiver.
We scrutinize potential vulnerabilities stemming from adversarial attacks during both training and testing phases.
arXiv Detail & Related papers (2024-01-03T04:01:20Z) - Generative AI-aided Joint Training-free Secure Semantic Communications
via Multi-modal Prompts [89.04751776308656]
This paper proposes a GAI-aided SemCom system with multi-model prompts for accurate content decoding.
In response to security concerns, we introduce the application of covert communications aided by a friendly jammer.
arXiv Detail & Related papers (2023-09-05T23:24:56Z) - Learning Multi-Agent Communication with Contrastive Learning [3.816854668079928]
We introduce an alternative perspective where communicative messages are considered as different incomplete views of the environment state.
By examining the relationship between messages sent and received, we propose to learn to communicate using contrastive learning.
In communication-essential environments, our method outperforms previous work in both performance and learning speed.
arXiv Detail & Related papers (2023-07-03T23:51:05Z) - Cognitive Semantic Communication Systems Driven by Knowledge Graph:
Principle, Implementation, and Performance Evaluation [74.38561925376996]
Two cognitive semantic communication frameworks are proposed for the single-user and multiple-user communication scenarios.
An effective semantic correction algorithm is proposed by mining the inference rule from the knowledge graph.
For the multi-user cognitive semantic communication system, a message recovery algorithm is proposed to distinguish messages of different users.
arXiv Detail & Related papers (2023-03-15T12:01:43Z) - On the Role of Emergent Communication for Social Learning in Multi-Agent
Reinforcement Learning [0.0]
Social learning uses cues from experts to align heterogeneous policies, reduce sample complexity, and solve partially observable tasks.
This paper proposes an unsupervised method based on the information bottleneck to capture both referential complexity and task-specific utility.
arXiv Detail & Related papers (2023-02-28T03:23:27Z) - 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) - Beyond Transmitting Bits: Context, Semantics, and Task-Oriented
Communications [88.68461721069433]
Next generation systems can be potentially enriched by folding message semantics and goals of communication into their design.
This tutorial summarizes the efforts to date, starting from its early adaptations, semantic-aware and task-oriented communications.
The focus is on approaches that utilize information theory to provide the foundations, as well as the significant role of learning in semantics and task-aware communications.
arXiv Detail & Related papers (2022-07-19T16:00:57Z) - Towards Human-Agent Communication via the Information Bottleneck
Principle [19.121541894577298]
We study how trading off these three factors -- utility, informativeness, and complexity -- shapes emergent communication.
We propose Vector-Quantized Variational Information Bottleneck (VQ-VIB), a method for training neural agents to compress inputs into discrete signals embedded in a continuous space.
arXiv Detail & Related papers (2022-06-30T20:10:20Z)
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.