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The functionality of the Internet is continually changing from the Internet of Computers (IoC) to the "Internet of Things (IoT)". Most connected systems, called Cyber-Physical Systems (CPS), are formed from the integration of numerous features such as humans and the physical environment , smart objects, and embedded devices and infrastructure. Ther...
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Wearable computing has garnered a lot of attention due to its various advantages, including automatic recognition and categorization of human actions from sensor data. However, wearable computing environments can be fragile to cyber security attacks since adversaries attempt to block, delete, or intercept the exchanged information via insecure comm...
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... The findings indicate that when it comes to accurately categorising the arrhythmias under investigation-premature ventricular contraction (94%), a fusion of ventricular beat (81%), and supraventricular premature beat (82%), the k-nearest neighbour has the highest accuracy percentage. AI-enabled IoT and CPS algorithms for tracking patients were developed by Ramasamy et al. [11]. This algorithm has two components: algorithms for classifying diseases and ones that forecast them. ...
The rapid advancement in Cyber-Physical Systems (CPS) and wireless networking technologies has ushered in a new era for healthcare, particularly in the domain of smart health monitoring for elderly populations. This paper explores the development and implementation of a state-of-the-art cyber-physical wireless network system designed to enhance the health monitoring of elderly individuals, thereby improving their quality of life and enabling independent living. By leveraging the interconnectivity of CPS within a 6G wireless network framework, our proposed system facilitates real-time health monitoring and emergency response through the seamless integration of wearable health sensors, mobile devices, and cloud computing services. The core of our system architecture is built on robust wireless communication protocols that ensure reliable data transmission from wearable sensors to the central monitoring system. This paper introduces a digital twin (DT)-based model designed to enhance healthcare monitoring within smart homes, aiming at graphical monitoring, healthcare prediction, and intelligent control. A high-fidelity DT of the home and its equipment is developed for visual monitoring purposes, and two sets of devices are utilised to continually gather users’ electrocardiograph (ECG) waves and the WiFi signals within the home. Furthermore, two advanced deep learning algorithms are proposed for detecting falls using WiFi signals and identifying atrial fibrillation from ECG waves recorded by wearable devices. The effectiveness of the proposed model for smart home monitoring is confirmed through experimental results, showcasing the superiority of the developed intelligent algorithms in healthcare prediction compared to existing solutions.
... Without adequate safeguards, highly personal information can end up in the wrong hands, for example, those of insurers, employers, and other commercial parties. 87 Further, the opaque nature of many AI applications also poses a challenge to patients' access to, use of, and control over personal data. 88 The output generated by AI can also create new challenges for privacy and confidentiality. ...
Europe is ageing and the health workforce crisis puts the quality and availability of healthcare at risk, especially in elderly care. Artificial Intelligence (AI) solutions may alleviate the health workforce crisis and contribute to the highest attainable standard of physical and mental health for all in an ageing Europe. At the same time, however, these solutions may create new-and unforeseen-human rights challenges. Given the fragmented regulatory framework governing medical AI and the lack of sector-specific standards and implementation guidelines, it is difficult for healthcare institutions, health professionals, and informal caretakers to determine how AI can be deployed in elderly care in a manner that respects, protects, and realises the human rights of older patients. In this light, this chapter introduces the outlines of a patients' rights impact assessment (PRIA) tool for AI in healthcare. The proposed model is sector-specific and takes the form of a self-assessment questionnaire to inform decision-making.
... CPS IoT [1] AI [2] [4] 2023 3 8 * 1 * 2 * 1 Yamagata University * 2 Kobe University ■ Fig. 1 Piezoelectric-sensing end-effector for non-destructive tactile identification to be developed in this study ...
A computational framework that is extremely fast to learn without a labyrinth of coding is known as physical reservoir computing. This computation inputs some action into a real physical system and obtains an output from the observed nonlinear dynamics of the system. It has been suggested that this output can be applied to machine learning to achieve high accuracy in time series data prediction and pattern recognition. Therefore, verification of the materials and structural factors required for highly practical physical reservoir computing is underway from a materials engineering perspective. In particular, it is important to clarify how the different physical properties of materials affect the time series data extracted from the reservoir, which responds to physical actions, and how this positively impacts learning efficiency. In this study, we developed two piezoelectric sensing end-effectors, Gel Biter and Gel Toucher, which consist of two groups of 3D structured polymeric materials inspired by the human mouth and fingers. Then, we discuss the accuracy of physical reservoir computing and the necessary elemental verification in non-destructive tactile discrimination between biting and touching. As a result, we found that both achieved a tactile discrimination accuracy of over 80%, and that the integration of different nonlinear responses, in other words, a multidimensional capture, is useful for accurate object recognition by selecting and combining the reservoirs to be used to improve accuracy.
... In conclusion, previous studies [31][32][33][34][35] have used traditional algorithms for traffic anomaly detection in smart Cities, which have limitations in handling large datasets and are prone to overfitting. Our work fills this knowledge gap by evaluating the effectiveness of machine learning algorithms for smart City security under different scenarios and employing a Logistic Boosted Algorithm for detecting traffic anomalies. ...
With the increasing deployment of Internet of Things (IoT) devices in Smart Cities, ensuring the security and integrity of these systems is of paramount importance. This study focuses on the detection of anomalies and prevention of security attacks in Smart City environments, specifically in the context of traffic management. We compare the performance of three machine learning algorithms, namely logistic boosted algorithms, random forest, and support vector machines, using a dataset collected from various Smart City devices. Through comprehensive evaluation metrics such as accuracy, precision, recall, and F1 score, we analyze the efficacy of each algorithm. Our results indicate that the logistic boosted algorithm outperforms the other approaches in accurately identifying data outliers and achieving high performance across all evaluation metrics. Notably, it excels in handling imbalanced datasets and leveraging ensemble methods to enhance overall performance. While the random forest algorithm demonstrates exceptional performance in all metrics, the support vector machines approach exhibits superior recall but comparatively lower accuracy, precision, and F1 score. Moreover, the logistic boosted method showcases fewer misclassifications in the confusion matrices, making it highly effective for anomaly detection. The area under the ROC curve is highest for the logistic boosted algorithm (0.98), further emphasizing its effectiveness in filtering out anomalies. The random forest algorithm achieves the highest area under the curve (0.982), while the support vector machines method shows the lowest (0.968). Based on our findings, we conclude that the logistic boosted algorithm is the most effective for monitoring Smart City systems, followed by the random forest approach. However, the support vector machines method can be a viable alternative if recall is the primary performance metric of concern. Future research can focus on comparing the efficacy of different machine learning approaches for anomaly detection in Smart City environments and exploring the impact of unique dataset properties on algorithm performance. Additionally, exploring the potential of deep learning algorithms for traffic anomaly detection in Smart City systems could be a promising area of study. This study contributes to the advancement of state-of-the-art anomaly detection algorithms for ensuring the security and efficiency of Smart City applications.
... Cyber-physical systems (CPSs) are systems that integrate sensing, computation, control, and networking capabilities to perform specific tasks [1]. They are gaining increasing importance in various sectors which include but are not limited to control and robotics systems [2], smart grids [3], healthcare [4], and intelligent transportation [5]. CPS are highly exposed to cyber-attacks which cause harm/damage to their infrastructure, data, and operation. ...
The increasing prevalence of cyber threats has created a critical need for robust defense against such incidents. Many Cyber Intrusion Detection Systems (CIDSs), utilizing machine learning have been developed for this purpose. Although, these recent CIDSs have provided the capability to analyze vast amounts of data and identify malicious activities, there are still challenges to be tackled to enhance their effectiveness. The exponential growth of the search space is one of these challenges which makes finding an optimal solution computationally infeasible for large datasets. Furthermore, the weight space while searching for optimal weight is highly nonlinear. Motivated by the observed characteristics, complexities, and challenges in the field, this paper presents an innovative (CIDS) named ANWO-MLP (Adaptive Nonlinear Whale Optimization Multi-layer Perceptron). A novel feature selection method called ANWO-FS (Adaptive Nonlinear Whale Optimization-Feature Selection) is employed in the proposed CIDS to identify the most predictive features enabling robust MLP training even in the highly nonlinear weight spaces. The insider threat detection process is improved by investigating vital aspects of CIDS, including data processing, initiation, and output handling. We adopt ANWOA (previously proposed by us) to mitigate local stagnation, enable rapid convergence, optimize control parameters, and handle multiple objectives by initializing the weight vector in the ANWO-MLP training with minimal mean square error. Experiments conducted on three highly imbalanced datasets demonstrate an average efficacy rate of 98.33%. The details of the results below show the robustness, stability, and efficiency of the proposed ANWO-MLP compared to existing approaches.
... Eqn. (7) to (9) are used to mimic the selection of prey and attack behaviour. ...
The Internet of Medical Things (IoMT) has paved the way for innovative approaches to collecting and managing medical data. With the large and sensitive medical data being processed hence, the need for a strong identity and privacy become necessary. The present paper suggests a comprehensive method of PriMedGuard which aims at protection of the personal medical information. The first stage will be data collection from devices and sensors, then data cleaning to transform the data into the required format. There is also a safety system in the system that registers and authenticates authorized entities as well as ETDO (Enhanced Tasmanian Devil Optimization algorithm) is used for generating asymmetric cryptographic keys. The data is encrypted using the Secure Bit-Count Transmutation (SBCT) Data Encryption Algorithm and then put in the locations provided by the InterPlanetary File System (IPFS), a decentralized and distributed storage system. A safe smart contract on the blockchain is created so that the data retrieval is secure and MedSecEnsemble Detection is proposed as an intrusion detection technique in the IoMT network. By using this method, data will stay available while at the same time integrity, confidentiality and protection against vulnerabilities are ensured. Hence, the Internet of Medical Things ecosystem will be secured from unauthorized access and possible security threats.
... In complex systems like the Internet of Things (IoT), cyber-physical-systems (CPS), and others, computational intelligence (CI)-based solutions help fight cybersecurity issues [12,13]. Due to the exponential rise of IoT devices, security challenges, including system vulnerabilities, malware detection, data security concerns, personal and public physical safety risks, privacy difficulties, and data storage management, are emerging. ...
Users of computer networks may benefit from cloud computing, which is a fairly new abstraction that offers features like processing as well as the sharing and storing of data. As a result of the services it provides, cloud computing is drawing significant investments from across the world. Despite this, Cloud Computing Security continues to be one of the most important issues for businesses and consumers that use cloud computing systems. A few of the security flaws that are associated with cloud computing were passed down from earlier computer systems. In contrast, the other flaws were brought about by the distinctive qualities and design of cloud computing. The newly developed platform has measures that restrict data access to just those users who are authorized to do so. Using the user’s identification and authentication/authorization information, a third-party service is responsible for managing access to the data. This service checks on all requests. Sensitive information and facts pertaining to users are encrypted both while in transit and while being stored. The platform was put into operation, analysed, and compared to other cloud platforms that were already in existence in terms of how effective it was in comparison to other platforms. When compared to the other security platforms, the findings demonstrated that this platform performed as anticipated in a relatively short amount of time and offered robust protection against the acts of an intruder.
... Moreover, there have been novel proposals for integrated technology's security interfaces, such as a framework for secure e-health services that lessens security setbacks in independent ones [26]. Additionally, secure smart wearable computing through AI-enabled IoT and cyber-physical systems has shown promising results for health monitoring [58]. The potential impact of these advancements has been further highlighted in a comprehensive study that discusses the integration of Blockchain, AI, and IoT technologies and their benefits, including increased security, transparency, and automation [7,41]. ...
... Moreover, Ikharo et al. [26] propose a novel framework for the security of e-health data in the AI, IoT, and Blockchain ecosystem, emphasizing the significance of ensuring robust security in integrated IoT technologies [26]. Additionally, the study by Ramasamy et al. demonstrates the efficient use of AI-enabled IoT-CPS algorithms for health monitoring, indicating the practical application of AI in IoT for significant societal impact [58,65]. ...
... This work sheds light on the critical aspect of security in AI-IoT integration, particularly in the context of healthcare applications. Ramasamy et al. demonstrated the efficiency of an AI-enabled IoT-CPS algorithm in detecting patient diseases and fall events, showcasing the potential advancements in health monitoring enabled by AI-integrated IoT systems [58]. ...
This paper explores the dynamic intersection of Artificial Intelligence (AI) and the Internet of Things (IoT), focusing on emerging trends in intelligent data analysis and privacy protection. Employing a systematic literature review, we analyze recent advancements and challenges in AI-IoT integration. The paper highlights key developments in sensor data anomaly detection, AI-driven big-data analytics, and IoT-based communication techniques. It delves into the com-plexities of privacy protection in IoT, examining innovative solutions like federated learning and blockchain technologies. The study also addresses the challenges in AI for IoT, prioritizing the most pressing issues such as data security, ethical implementation, and compliance with regula-tions like GDPR. The research underscores the need for adaptable solutions that balance techno-logical advancements with privacy and security considerations, setting a path for future explora-tion in AI-empowered IoT applications.
... These advances could potentially enable a more seamless connection between rehabilitation training sessions, leading to more accurate health monitoring, prognosis, and improved decision-making for optimal rehabilitation recovery. Likewise, in [121][122][123] the authors focused on enhancing patient diagnosis and treatment using AI techniques. ...
Cyber-physical systems (CPS) are recognized for their intelligence as they seamlessly interact with humans, enhancing the physical world through computation, communication, and control. Over the last few years, the evolution of CPS, including IoT components, has significantly impacted many facets of people’s lifestyles. It has been immersed in a wide range of services and applications in various areas, including manufacturing, healthcare, and energy. However, the interrelationship between the cyber and physical worlds gives rise to a multitude of research problems and challenges. In the healthcare field, for instance, CPS introduces complexities related to interoperability, privacy, data security, and real-time data processing with critical implications for the reliability and safety of medical processes. To address these challenges and harness the full potential of CPS in healthcare, this paper, through a comprehensive literature review, aims to discuss cutting-edge CPS technologies and solutions that hold promise for healthcare applications. To this end, we propose a comprehensive architectural model that can serve as a benchmark for implementing CPS in healthcare applications. This model thoroughly details how services, components, and technologies can be integrated to transform massive raw data collected from the physical world into valuable information for an enhanced decision-making process. Finally, a use case on healthcare CPS is presented, outlining its characteristics, the role that different technologies have played in its development, and the major challenges in implementing such systems successfully. This study provides a cohesive understanding of the role CPS can play in empowering healthcare while offering insights into the challenges, and future research trends.
... This integration has appeared in several forms, such as the Internet of things (IoT), industrial 4.0, the Industrial Internet, machine-to-machine (M2M), Internet of Everything, the Smarter Planet, TSensors (Trillion Sensors), and The Fog computing environment. All previous systems are Cyber-Physical Systems (CPS) which are an integration of computation with physical processes [1,2]. CPS systems face challenges like complex structure, poorly defined safety properties, interaction with complex and stochastic environments that are difficult to model, and the interactions between the system and environment exposed to failure [3,4]. ...
... In [30], the authors proposed an Adaptive Stochastic Gradient Descent Algorithm to evaluate the risk of fetal abnormality, the findings of this work suggest that proposed innovative method can successfully classify the anomalies linked with nuchal translucency thickening. In [2], the authors proposed an AI-enabled IoT-CPS Algorithm which doctors can utilise to discover diseases in patients based on AI. The experimental results demonstrate that the proposed algorithm is more efficiently compared with existing algorithms. ...
Sensors are the main components in Cyber-Physical Systems (CPS), which transmit large amounts of physical values and big data to computing platforms for processing. On the other hand, the embedded processors (as edge devices in fog computing) spend most of their time reading the sensor signals as compared with computing time. The impact of sensors on the performance of fog computing is very great, thus, the enhancement of the reading time of sensors will positively affect the performance of fog computing, and solves the CPS challenges such as delay, timed precision, temporal behavior, energy, and cost. In this paper, we propose an algorithm based on the 1st derivative of the sensor signal to generate an adaptive sampling frequency. The proposed algorithm uses an adaptive frequency to capture the sudden and rapid change in sensor signal in the steady state. Finally, we realize and tested it using the Ptolemy II Modeling Environment.
