面向物联网的通感算智融合：关键技术与未来展望

王新奕; 费泽松; 周一青; 胡杰

doi:10.11999/JEIT240806

面向物联网的通感算智融合：关键技术与未来展望

doi: 10.11999/JEIT240806

王新奕¹,
费泽松^1, ,,
周一青^{2, 3},
胡杰⁴

1.
北京理工大学信息与电子学院北京 100081
2.
中国科学院计算技术研究所处理器国家重点实验室北京 100190
3.
中国科学院大学北京 100049
4.
电子科技大学信息与通信工程学院成都 611731

基金项目: 国家重点研发计划(2021YFB2900200)，国家自然科学基金(U20B2039, 62301032)，中国博士后科学基金(2023TQ0028, 2023M730267)

详细信息

作者简介:
王新奕：男，副教授，博士生导师，研究方向为通信感知计算融合、无人机通信等

费泽松：男，教授，博士生导师，研究方向为移动通信、通信感知计算融合、星地融合通信、智能通信等

周一青：女，研究员，博士生导师，研究方向为宽带无线通信技术

胡杰：男，教授，博士生导师，研究方向为6G中的无线通信与资源管理技术以及通信、计算与感知一体化融合技术

通讯作者:
费泽松　feizesong@bit.edu.cn

中图分类号: TN929.5
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- 被引次数: 0
出版历程
- 收稿日期: 2024-09-19
- 修回日期: 2025-02-20
- 网络出版日期: 2025-03-01
- 刊出日期: 2025-04-01

Integrated Sensing, Communication, Computation, and Intelligence Towards IoT: Key Technologies and Future Directions

WANG Xinyi¹,
FEI Zesong^{1
, ,},
ZHOU Yiqing^{2, 3},
HU Jie⁴

1.
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
2.
State Key Laboratory of Processors, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
3.
University of Chinese Academy of Sciences, Beijing 100049, China
4.
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds: The National Key R&D Program of China (2021YFB2900200), The National Natural Science Foundation of China (U20B2039, 62301032), China Postdoctoral Science Foundation (2023TQ0028, 2023M730267)

摘要

摘要: 智慧城市、智能工厂等物联网新兴业务对通信速率、感知精度、计算效率提出更高要求，算力网络的发展与通信网络内生感知与内生智能能力的挖掘为构建通信-感知-计算-智能融合的物联网奠定扎实基础。该文首先结合未来业务概述了物联网对通信、感知、计算、智能4个功能的需求；然后基于对物联网典型特征分析，阐述4项关键技术的技术原理、方法，提出通感算智一体物联网新范式，总结了相关技术的研究进展；最后探讨了技术挑战与未来研究方向。
- 通信-感知-计算-智能融合 /
- 第6代移动通信系统 /
- 物联网 /
- 网络架构
Abstract: Significance The Internet of Things (IoT) has become a transformative technology in intelligent systems across diverse domains, including smart cities, industrial automation, and healthcare. However, traditional IoT systems, which isolate sensing, communication, computation, and intelligence, often face inefficiencies in resource utilization, increased latency, and scalability issues. These challenges are further intensified by the dynamic and resource-constrained requirements of future 6G IoT applications. Integrated Sensing, Communication, Computation, and Intelligence (ISCCI) offers a novel paradigm that unifies these functionalities, aiming to meet the needs of low-power transmission, multimodal sensing, low-latency computation, and distributed intelligence. By overcoming the fragmentation inherent in existing architectures, ISCCI can optimize resource allocation, enhance system adaptability, and support the development of next-generation IoT systems. Progress The development of 6G IoT depends on significant advancements across four key areas: communication, sensing, computation, and intelligence. These advancements aim to address the challenges of low-power transmission, multimodal sensing, low-latency computation, and distributed intelligence, enabling efficient resource utilization and robust system operation for future IoT applications. Specifically: (1) Communication: Low-power communication is essential for sustainable operation in large-scale IoT systems. Backscatter communication plays a critical role by allowing devices to transmit data without generating their own signals, thereby significantly reducing energy consumption. Simultaneously, Wireless Power Transfer (WPT) technology provides an energy-efficient solution for powering passive IoT devices, ensuring long-term operation even in resource-constrained environments. The integration of backscatter communication and WPT enhances passive IoT systems’ functionality and reduces maintenance requirements. (2) Sensing: Multimodal sensing integrates data from various sensor types, enabling accurate perception of complex environments. This approach supports real-time monitoring and adaptive decision-making in dynamic IoT scenarios. For instance, environmental sensing technologies can extract valuable insights from ambient signals, enhancing system awareness and facilitating efficient resource allocation. (3) Computation: Low-latency computation frameworks, such as edge computing, are essential for reducing reliance on centralized cloud servers. By offloading computational tasks to edge nodes, these frameworks improve system responsiveness and enable real-time processing. Additionally, joint communication and computation optimization techniques allow efficient task allocation, balancing resource constraints and application demands in heterogeneous IoT environments. (4) Intelligence: Distributed intelligence is achieved through collaborative learning frameworks like federated learning. By enabling IoT devices to train machine learning models collaboratively without sharing raw data, this approach ensures data privacy while promoting scalable intelligence across the network. Furthermore, real-time decision-making algorithms enable IoT systems to dynamically adapt to varying conditions, ensuring robust and efficient operation. Conclusions This paper proposes a future ISCCI IoT architecture comprising terminal nodes, fusion network elements, and service centers, and highlights four key enabling technologies that support the ISCCI IoT paradigm: (1) Environmental backscatter communication and sensing coexistence technology: At the terminal level, this technology enables IoT devices to use ambient signals for both communication and sensing, reducing the need for dedicated resources and improving energy efficiency. (2) Cloud-edge-device collaborative sensing task processing: Building on terminal-level capabilities, this technology orchestrates hierarchical processing architectures to optimize the allocation of sensing tasks across cloud, edge, and device layers. It ensures real-time performance and scalability by dynamically allocating tasks based on computational demands and latency requirements. (3) Intelligent computing for enhanced sensing prediction: To further enhance system adaptability, this technology integrates intelligent computing capabilities into the IoT network architecture. It improves sensing accuracy and enables proactive resource allocation, ensuring efficient and adaptive system operation in dynamic and unpredictable environments. (4) Sensing-communication-energy unified waveform design: At the core of the ISCCI paradigm, this technology develops multifunctional waveforms that simultaneously support sensing, communication, and energy transfer functionalities. IoT nodes can use the harvested wireless energy to power their sensing and communication functions, extending the network’s operational lifespan. Additionally, environmental sensing results can aid in channel reconstruction, reducing signaling overhead associated with channel information acquisition. By leveraging efficient sensing-communication-energy unified waveforms, the ISCCI paradigm is expected to significantly enhance system adaptability, scalability, and resource efficiency in resource-constrained environments. Prospects Despite these advancements, ISCCI systems still face several challenges. Future research will focus on the following areas to enable the practical deployment of ISCCI systems: (1) Theoretical Foundations: The absence of comprehensive theoretical models for ISCCI integration hinders the development of unified performance metrics. Future research should prioritize formulating mathematical models that account for the complex interactions between sensing, communication, computation, and intelligence. Additionally, there is a need for tools to evaluate system performance across diverse real-world scenarios. Research should also aim to integrate communication, sensing, and computation metrics, which are currently assessed using incompatible indicators. (2) Network Architecture: Designing flexible and adaptive network architectures is essential to support ISCCI functionalities. In large-scale IoT scenarios, such as smart cities, multi-base cooperative sensing networks with adaptive switching strategies are crucial for overcoming challenges like weak echoes and interference. Moreover, synchronizing massive node information presents significant challenges, as even small timing errors can severely impact accuracy. Research should focus on methods to address synchronization and improve network performance by leveraging cooperative edge nodes, as well as multi-node collaborative transmission and interaction mechanisms. (3) Interference Management: Managing interference is critical in dense IoT environments. Advanced algorithms for resource allocation, interference cancellation, and multi-user coordination are required to mitigate interference effects. AI-driven techniques can optimize terminal scheduling and resource allocation to avoid frequency interference. Additionally, edge computing capabilities will be essential for identifying and suppressing external interference, ensuring reliable communication, and enhancing ISCCI system performance. Addressing these challenges will be key to unlocking the full potential of ISCCI and ensuring its successful deployment in 6G IoT systems.
- Integrated Sensing, Communication, Computation, and Intelligence (ISCCI) /
- 6G communication system /
- Internet of Things (IoT) /
- Network architecture

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图 1 “通、感、算、智”一体化的智慧城市物联示意图

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图 2 背向散射系统硬件架构与工作流程^[8]

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图 3 无源背向散射通信系统架构

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图 4 无线信能同传系统接收架构

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图 5 移动边缘计算架构示意图

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图 6 联邦学习架构和流程示意图

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图 7 通感算智一体物联网网络架构

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图 8 RIS辅助的背向散射通信与感知融合系统

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图 9 云边端协同计算辅助感知任务处理

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表 1 地理区域特征对轨迹预测模型性能影响^[76]

性能指标 MIA数据集 PIT数据集

最小平均误差(m) 0.872 1.076
最小终点误差(m) 1.511 1.960
错误概率 0.213 0.286

下载: 导出CSV

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