Recent Advances in Artificial Intelligence for Wireless Internet of Things and Cyber–Physical Systems: A Comprehensive Survey

Salau, Babajide A.; Rawal, Atul; Rawat, Danda B.

doi:10.1109/jiot.2022.3170449

Cited by 39 publications

(18 citation statements)

References 106 publications

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“…A novel finite memory multi-state sequence learning framework was proposed to reduce latency by reallocating communication resources for critical messages during data scarcity [18]. Minimizing delay in multi-task federated learning was prioritized in multi-access edge computing (MEC) networks, which reduced the time for task execution, and employed matching with incomplete preference lists (UCPL) to manage the colossal data volume produced by IoT devices [19,20] However, to further strengthen data processing capability and computational efficiency, the current bottlenecks in AIoT networks, it is urgent to reduce the data volume in edge computing. Therefore, AIoT networks incorporated pre-processing methods such as data compression, encoding, and feature selection to reduce the data volume [21][22][23][24].…”

Section: Related Workmentioning

confidence: 99%

Two‐step attribute reduction for AIoT networks

Ren,

Lyu,

Wang

et al. 2024

IET Communications

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The evolution of Artificial Intelligence of Things (AIoT) pushes connectivity from human‐to‐things and things‐to‐things, to AI‐to‐things, has resulted in more complex physical networks and logical associations. This has driven the demand for Internet of Things (IoT) devices with powerful edge data processing capabilities, leading to exponential growth in device quantity and data generation. However, conventional data preprocessing methods, such as data compression and encoding, often require edge devices to allocate computational resources for decoding. Additionally, some lossy compression methods, like JPEG, may result in the loss of important information, which has negative impact on the AI training. To address these challenges, this paper proposes a two‐step attribute reduction approach, targeting devices and dimensions, to reduce the massive amount of data in the AIoT network while avoiding unnecessary utilization of edge device resources for decoding. The device‐oriented and dimension‐oriented attribute reductions identify important devices and dimensions, respectively, to mitigate the multimodal interference caused by the large‐scale devices in the AIoT network and the curse of dimensionality associated with high‐dimensional AIoT data. Numerical results and analysis show that this approach effectively eliminates redundant devices and numerous dimensions in the AIoT network while maintaining the basic data correlation.

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Section: Related Workmentioning

confidence: 99%

Two‐step attribute reduction for AIoT networks

Ren,

Lyu,

Wang

et al. 2024

IET Communications

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“…1.2. AI has been widely applied to sensing, control, communication, and data processing in a variety of applications such as biomedical monitoring, robotics systems, and digital twins [48,42,21,15]. In these CPS applications, AI not only serves as a technology for reasoning, planning, learning, and processing, but also enables the manipulation of physical objects.…”

Section: Incorporating Ai Stack Into Mission Stackmentioning

confidence: 99%

An Introduction of System-Scientific Approaches to Cognitive Security

Huang¹,

Zhu²

2023

Preprint

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Human cognitive capacities and the needs of human-centric solutions for "Industry 5.0" make humans an indispensable component in Cyber-Physical Systems (CPSs), referred to as Human-Cyber-Physical Systems (HCPSs), where AI-powered technologies are incorporated to assist and augment humans. The close integration between humans and technologies in Section 1.1 and cognitive attacks in Section 1.2.4 poses emerging security challenges, where attacks can exploit vulnerabilities of human cognitive processes, affect their behaviors, and ultimately damage the HCPS.Defending HCPSs against cognitive attacks requires a new security paradigm, which we refer to as "cognitive security" in Section 1.2.5. The vulnerabilities of human cognitive systems and the associated methods of exploitation distinguish cognitive security from "cognitive reliability" and give rise to a distinctive CIA triad, as shown in Sections 1.2.5.1 and 1.2.5.2, respectively. Section 1.2.5.3 introduces cognitive and technical defense methods that deter the kill chain of cognitive attacks and achieve cognitive security. System scientific perspectives in Section 1.3 offer a promising direction to address the new challenges of cognitive security by developing quantitative, modular, multi-scale, and transferable solutions. Fig. 1.1 illustrates the structure of Chapter 1.

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“…At the same time, the cyber aspect involves computing tools that process this data for instant analysis and decisionmaking [2]. This integration allows real-world objects to translate vast amounts of data into actionable insights, aiming for seamless interaction between the physical and digital realms to foster continuous integration and smart decision-making [3], [4]. This breakthrough is set to revolutionize industries including energy, healthcare, and business, particularly impacting healthcare due to the critical nature of patient data reliant on continuous health monitoring via sensors [2].…”

Section: Introductionmentioning

confidence: 99%

BBAD: Blockchain-Backed Assault Detection for Cyber Physical Systems

Anwar,

Tariq,

Ashraf

et al. 2024

IEEE Access

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Cybersecurity challenges pose a significant threat to Healthcare Cyber Physical Systems (CPS) because they heavily rely on wireless communication. Particularly, jamming attacks can severely disrupt the integrity of these CPS networks. This research introduces a decentralized system to address this issue. Therefore, this paper suggested a system that leverages trust and blockchain technology to detect jamming attacks in healthcare CPS effectively. It proposes a layered model to improve CPS networks' lifetime and performance. In smart healthcare environments, it ensures secure and reliable communication between sensor nodes, wearable sensors, medical devices, and monitoring systems. Results show that the suggested approach outperforms the baseline model in identifying and minimizing jamming assaults, with an average percentage difference of 15.71% more detection rate, 20.21% less packet loss rates, 16.65% less node-level energy consumption, reduced network latency of 8.29%, and 9.63% more network throughput.

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Recent Advances in Artificial Intelligence for Wireless Internet of Things and Cyber–Physical Systems: A Comprehensive Survey

Cited by 39 publications

References 106 publications

Two‐step attribute reduction for AIoT networks

Two‐step attribute reduction for AIoT networks

An Introduction of System-Scientific Approaches to Cognitive Security

BBAD: Blockchain-Backed Assault Detection for Cyber Physical Systems

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