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

WANG Xinyi; FEI Zesong; ZHOU Yiqing; HU Jie

doi:10.11999/JEIT240806

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Liu Jianzhuang, Xie Weixin, Gao Xinbo. A NEW METHOD OF DETECTION OF PRIMITIVES IN IMAGES[J]. Journal of Electronics & Information Technology, 1997, 19(2): 183-189.

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WANG Xinyi, FEI Zesong, ZHOU Yiqing, HU Jie. Integrated Sensing, Communication, Computation, and Intelligence Towards IoT: Key Technologies and Future Directions[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240806

Liu Jianzhuang, Xie Weixin, Gao Xinbo. A NEW METHOD OF DETECTION OF PRIMITIVES IN IMAGES[J]. Journal of Electronics & Information Technology, 1997, 19(2): 183-189.

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

doi: 10.11999/JEIT240806

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)

Received Date: 2024-09-19
Rev Recd Date: 2025-02-20

Available Online: 2025-03-01

Abstract

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.

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