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Intelligent Agent Communication, Computing and Sensing
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Intelligent Agent Communication, Computing and Sensing

A Special Issue of Sensors (ISSN 1424-8220) belonging to the section "Internet of Things".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 1239

Editors

Prof. Dr. Changqiao Xu

E-Mail Website
Guest Editor
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: emerging networking and telecommunications; multimedia communications; artificial intelligence; network security
Special Issues, Collections and Topics in MDPI journals
Dr. Han Xiao

E-Mail Website
Guest Editor
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: multimedia communications; edge intelligence; edge computing; computing power network
Special Issues, Collections and Topics in MDPI journals
Dr. Hao Hao

E-Mail Website
Guest Editor
Shandong Computer Science Center (National Supercomputing Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
Interests: multimedia communication; edge caching; edge computing; reinforcement learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hongtao Zhang

E-Mail Website
Guest Editor
Key Lab of Universal Wireless Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: next-generation wireless networks; 6G mobile communications; reconfigurable intelligent surfaces
Special Issues, Collections and Topics in MDPI journals
Dr. Xingyan Chen

E-Mail Website
Guest Editor
Intelligent Engineering and Automation School, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: industrial internet of things; computing; edge intelligence

Special Issue Information

Dear Colleagues,

The convergence of intelligent agent communication, computing, and sensing represents a transformative advancement in artificial intelligence and distributed systems. Intelligent agents, which are autonomous entities capable of perceiving environments, reasoning, and acting to achieve goals, are increasingly deployed in complex scenarios such as smart cities, industrial automation, and healthcare. This Special Issue aims to explore the interplay of communication protocols, distributed computing frameworks, and sensing technologies that enable agents to collaborate, adapt, and thrive in dynamic, resource-constrained environments. By addressing challenges in interoperability, scalability, and real-time decision-making, this collection will advance the state of the art in agent-based systems and foster interdisciplinary innovation.

The scope of this Special Issue includes the following topics:

  • Decentralized agent communication frameworks;
  • Secure and scalable agent-to-agent interaction models;
  • Edge AI driven communication optimization;
  • Federated learning and collaborative model training;
  • Task allocation and resource scheduling in edge-cloud systems;
  • Fault-tolerant distributed algorithms for dynamic environments;
  • Multi-modal sensing fusion;
  • Energy-efficient sensing architectures for IoT devices.

Prof. Dr. Changqiao Xu
Dr. Han Xiao
Dr. Hao Hao
Prof. Dr. Hongtao Zhang
Dr. Xingyan Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com 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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly 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 2600 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.

Keywords

  • agent communication
  • distributed computing frameworks
  • sensing technologies
  • IoT devices

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

28 pages, 13032 KB  
Article
SEELE: Sense-Driven Edge-Cloud Foreground–Background Split Rendering for Immersive Media Services
by Yuxuan Xiao, Han Xiao, Chuxing Fang, Shaoyun Wu, Mingyu Zhao, Enbo Wang and Changqiao Xu
Sensors 2026, 26(17), 5561; https://doi.org/10.3390/s26175561 - 1 Sep 2026
Viewed by 208
Abstract
Immersive media services increasingly rely on edge-cloud rendering to deliver interactive visual content under dynamic network, computing, and mobility conditions. Rendering an entire scene as a single service couples interaction-sensitive foreground content with context-oriented background content, making it difficult to jointly control latency, [...] Read more.
Immersive media services increasingly rely on edge-cloud rendering to deliver interactive visual content under dynamic network, computing, and mobility conditions. Rendering an entire scene as a single service couples interaction-sensitive foreground content with context-oriented background content, making it difficult to jointly control latency, quality, synchronization, and migration overhead. This paper studies sense-driven edge-cloud foreground–background split rendering for immersive media services. We formulate an online decision problem in which foreground and background rendering layers can be independently controlled under long-term system and migration cost budgets. The formulation turns structural scene separation into a coupled layer-state control problem by preserving asymmetric QoE roles and a common composition requirement. We propose SEELE, a Lyapunov-guided online control algorithm that represents accumulated budget pressure with two virtual queues and converts the long-term constrained problem into lightweight per-slot decisions. The resulting per-slot rule balances immediate QoE loss against queue-weighted system and migration costs. Under sustained resource and network stress, SEELE provides steady-state QoE statistically comparable to a pretrained PPO policy while significantly reducing synchronization violations and improving composition stability. It also improves steady-state QoE and system debt over deterministic and QoE-prioritized baselines. A prototype implementation and controlled characterization further validate split-stream deployment, runtime observability, practical control hooks, and the latency–capacity tradeoff of layered rendering. Full article
(This article belongs to the Special Issue Intelligent Agent Communication, Computing and Sensing)
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23 pages, 8488 KB  
Article
Stage-Aware Swin-Enhanced nnU-Net v2 for Robust Polyp Segmentation in Collaborative Endoscopic Visual Sensing
by Yang Bai and Huan Liu
Sensors 2026, 26(16), 5206; https://doi.org/10.3390/s26165206 - 17 Aug 2026
Viewed by 381
Abstract
Automatic colon polyp segmentation is important for reliable endoscopic visual sensing. However, segmentation performance can be degraded by ambiguous lesion boundaries, heterogeneous appearance, and image quality variations during acquisition or transmission. This study proposes a stage-aware Swin-enhanced nnU-Net v2 framework, where Swin Transformer [...] Read more.
Automatic colon polyp segmentation is important for reliable endoscopic visual sensing. However, segmentation performance can be degraded by ambiguous lesion boundaries, heterogeneous appearance, and image quality variations during acquisition or transmission. This study proposes a stage-aware Swin-enhanced nnU-Net v2 framework, where Swin Transformer blocks are inserted into selected encoder stages while preserving the original nnU-Net v2 pipeline. Different insertion strategies were evaluated on an independent Kvasir-SEG test set, and robustness was further assessed under six synthetic corruption types with three severity levels. The results show that the insertion stage strongly influences the effectiveness of Swin enhancement. Among the evaluated variants, Stage5-Swin achieved the best overall trade-off between segmentation performance, robustness, and computational cost. It achieved the highest Dice scores across all corruption–severity combinations while maintaining comparable external-domain performance on CVC-ClinicDB. Additional FedAvg experiments demonstrated the compatibility of the proposed architecture with collaborative training workflows. Resource analysis further quantified the computational and communication overhead. The findings indicate that middle-to-deep encoder insertion provides a favorable balance between contextual modeling, spatial representation, and efficiency for robust endoscopic segmentation. However, improvements were metric- and corruption-dependent, and moderate overexposure revealed a Precision–Recall trade-off; practical deployment also remains to be validated. Full article
(This article belongs to the Special Issue Intelligent Agent Communication, Computing and Sensing)
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