Cognitive Adaptive Systems for Industrial Internet of Things Using Reinforcement Algorithm

Rajawat, Anand Singh; Goyal, S. B.; Chauhan, Chetan Singh; Bedi, Pradeep; Prasad, Mukesh; Jan, Tony

doi:10.3390/electronics12010217

Cited by 9 publications

(4 citation statements)

References 24 publications

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“…The authors have proposed a comprehensive combination of traditional and Quantum Neural Network/ML (QNN) techniques as part of AIML in the IOMT vulnerability assessment. Specifically, the protection of sensitive and private data at multiple levels can start from storing data in distributed cloud nodes and then fuse heterogeneous IOMT data by using ML classification algorithms such as QNN to predict which vulnerability in IOMT-based network traffic is a malicious attack [109,110]. In [111], the authors highlight types of cybersecurity attacks related to the IOT, especially those using RFID and WSN technologies, which are the basic technologies used in IOT applications.…”

Section: Artificial Intelligence and Machine Learning For Strengtheni...mentioning

confidence: 99%

Cybersecurity in Internet of Medical Vehicles: State-of-the-Art Analysis, Research Challenges and Future Perspectives

Bhukya,

Thakur,

Mudhivarthi

et al. 2023

Sensors

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The “Internet-of-Medical-Vehicles (IOMV)” is one of the special applications of the Internet of Things resulting from combining connected healthcare and connected vehicles. As the IOMV communicates with a variety of networks along its travel path, it incurs various security risks due to sophisticated cyber-attacks. This can endanger the onboard patient’s life. So, it is critical to understand subjects related to “cybersecurity” in the IOMV to develop robust cybersecurity measures. In this paper, the goal is to evaluate recent trends and state-of-the-art publications, gaps, and future outlooks related to this research area. With this aim, a variety of publications between 2016 and 2023 from “Web-of-Science” and “Scopus” databases were analysed. Our analysis revealed that the IOMV is a niche and unexplored research area with few defined standards and frameworks, and there is a great need to implement robust cybersecurity measures. This paper will help researchers to gain a comprehensive idea of this niche research topic, as it presents an analysis of top journals and highly cited papers, their challenges and limitations, the system model and architecture of the IOMV, related applicable standards, potential cyber-attacks, factors causing cybersecurity risks, various artificial intelligence techniques for developing potential countermeasures, the assessment and parameterisation of cybersecurity risks, constraints and challenges, and future outlooks for implementing cybersecurity measures in the IOMV.

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Section: Artificial Intelligence and Machine Learning For Strengtheni...mentioning

confidence: 99%

Cybersecurity in Internet of Medical Vehicles: State-of-the-Art Analysis, Research Challenges and Future Perspectives

Bhukya,

Thakur,

Mudhivarthi

et al. 2023

Sensors

View full text Add to dashboard Cite

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“…Recent years have witnessed the rapid growth of the Internet of Things (IoT), where different types of real-world elements such as wearable sensors are connected and allowed to autonomously interact with each other, promoting the development of fields such as Healthcare IoT [1], smart cities [2], industrial IoT [3], etc. Continuous-Flow Microfluidic Biochips (CFMBs) have received widespread attention due to their miniaturization and integration.…”

Section: Introductionmentioning

confidence: 99%

Three-Stage Rapid Physical Design Algorithm for Continuous-Flow Microfluidic Biochips Considering Actual Fluid Manipulations

Liu,

Pan

et al. 2024

Electronics

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With the continuous development of microfluidic technology, continuous-flow microfluidic biochips (CFMBs) are being increasingly used in the Internet of Things. The automation design of CFMBs has also received widespread attention. The architecture design of CFMBs is divided into a high-level synthesis stage and a physical design stage. Among them, the problem of the physical design stage is very complex. At this stage, the chip architecture is generated based on the device library and a set of flow paths, taking into account the actual fluid manipulations, while minimizing the cost of the chip, such as the number of ports, total length of flow channels, number of flow channel intersections. As fabrication technology advances, the number of devices integrated into CFMBs is increasing. The existing physical design algorithms can no longer meet the design requirements of CFMBs in terms of time. Therefore, we propose a three-stage rapid physical design algorithm for CFMBs considering the actual fluid manipulations. The proposed algorithm includes a port-driven preprocessing stage, a force-directed quadratic placement stage, and a negotiation-based routing stage. In the port-driven preprocessing stage, a port-driven preprocessing algorithm is proposed to generate connection matrices between ports and devices to reduce the number of ports introduced. In the force-directed quadratic placement stage, we model the placement problem as an extremum problem of a quadratic cost function, which mathematically reduces the search space significantly and shortens the running time of the algorithm significantly. In the negotiation-based routing stage, a heuristic negotiation-based routing algorithm and a flow channel strategy that prioritizes the construction of parallel execution are proposed to reduce the running time of the algorithm while ensuring that the number of crossings in the routing solution is close to the optimal solution. Experimental results confirm that our proposed method is able to generate the high-quality solutions quickly. Under general scale problems, compared to the existing method based on ILP, our proposed method achieves a speedup ratio of 23,171 in terms of CPU time and optimizations in terms of number of ports and port reuse of 3.18% and 6.52%, respectively. These optimizations come at the cost of only a slight increase in the number of intersections, the flow length, and the number of flow valves. In addition, our proposed method can effectively solve large-scale problems that cannot be solved by existing method based on ILP.

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“…In this context, the decentralized machine learning (ML) paradigm, particularly reinforcement learning (RL), emerges as a transformative approach for addressing the challenges posed by dynamic wireless environments [8]. Unlike traditional centralized methods, decentralized ML empowers individual devices to autonomously adapt and optimize resource allocations based on real-time interactions and experiences [9]. The following sections delve into the intricacies of decentralized ML with RL, highlighting its benefits and introducing a novel decentralized RL model tailored for dynamic resource optimization in wireless networks.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Machine Learning for Dynamic Resource Optimization in Wireless Networks using Reinforcement Learning

K.Shantha Shalini

2024

jes

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Efficient allocation of resources is crucial for optimizing wireless networks that face constraints in bandwidth, power, and spectrum. This paper proposes a decentralized reinforcement learning (RL) model that departs from traditional centralized paradigms to revolutionize resource optimization. The proposed model empowers individual wireless devices with autonomous decision-making capabilities, enhancing adaptability and scalability by leveraging Deep Q-Network (DQN) and Proximal Policy Optimization (PPO). The innovative integration of memory mechanisms facilitates learning from past experiences, addressing the dynamic nature of wireless environments. This decentralized RL model offers practical implications for improved efficiency, adaptability, and reliability in wireless network resource optimization. By transforming individual devices into collaborative decision-makers, our proposed model contributes to a resilient and responsive wireless communication infrastructure. The specific contributions of this paper include the pioneering use of DQN and PPO algorithms within a multi-agent system, offering a groundbreaking solution for dynamic resource optimization in wireless networks.

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Cognitive Adaptive Systems for Industrial Internet of Things Using Reinforcement Algorithm

Cited by 9 publications

References 24 publications

Cybersecurity in Internet of Medical Vehicles: State-of-the-Art Analysis, Research Challenges and Future Perspectives

Cybersecurity in Internet of Medical Vehicles: State-of-the-Art Analysis, Research Challenges and Future Perspectives

Three-Stage Rapid Physical Design Algorithm for Continuous-Flow Microfluidic Biochips Considering Actual Fluid Manipulations

Decentralized Machine Learning for Dynamic Resource Optimization in Wireless Networks using Reinforcement Learning

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