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Smart City Healthcare Cyber Physical System: Characteristics, Technologies and Challenges

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Abstract and Figures

The recent pandemic has demanded a strong and smart healthcare system which can monitor the patients efficiently and handle the situation that arises from the outbreak of the disease. Smart healthcare cyber physical systems are the future systems as they integrate the physical and cyber world for efficient functioning of medical processes and treatment through external monitoring and control of patients, medical devices and equipment for continuous communication and information exchange of physiological data. Technologies like Internet of Things, Machine learning and Artificial Intelligence have given birth to smart cyber physical systems like Smart Healthcare Systems, Smart Homes, Smart Vehicular Systems and Smart Grid. Such systems are interdisciplinary in nature with multitude of technologies contributing to its effective working. This paper presents a case study on healthcare cyber physical systems presenting its characteristics, role of various technologies in its growth and major challenges in successful implementation of cyber physical medication systems.
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Wireless Personal Communications (2022) 122:1413–1433
1 3
Smart City Healthcare Cyber Physical System: Characteristics,
Technologies andChallenges
Accepted: 9 August 2021 / Published online: 26 August 2021
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
The recent pandemic has demanded a strong and smart healthcare system which can mon-
itor the patients efficiently and handle the situation that arises from the outbreak of the
disease. Smart healthcare cyber physical systems are the future systems as they integrate
the physical and cyber world for efficient functioning of medical processes and treatment
through external monitoring and control of patients, medical devices and equipment for
continuous communication and information exchange of physiological data. Technologies
like Internet of Things, Machine learning and Artificial Intelligence have given birth to
smart cyber physical systems like Smart Healthcare Systems, Smart Homes, Smart Vehicu-
lar Systems and Smart Grid. Such systems are interdisciplinary in nature with multitude
of technologies contributing to its effective working. This paper presents a case study on
healthcare cyber physical systems presenting its characteristics, role of various technolo-
gies in its growth and major challenges in successful implementation of cyber physical
medication systems.
Keywords Artificial intelligence· Healthcare cyber physical systems· Symbiotic cyber
physical systems· Security
1 Introduction
In the current pandemic, a strong healthcare system is a backbone for any smart city.
Effective and efficient monitoring of patients together with regular supply of necessary
medicines and treatment with aid of medical devices is possible when a system can meet
the demand and supply situation. Such a scenario is possible with a predictive mecha-
nism which forecasts the healthcare situation and works smartly for handling the medical
Smart Healthcare Cyber Physical Systems (SHCPS) are the future systems capable
of supporting the medical fraternity in handling the pandemic situation effectively. Such
systems comprise of physical world of patients, medical devices and equipment; exter-
nally controlled and monitored medical treatment, connected with cyber world through
* Rupali Verma
1 Computer Science andEngineering, Punjab Engineering College, Chandigarh, India
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... However, there are also growing criticisms where smart urban forests are being considered as socialpolitical technologies. Some digital technologies are prone to digital harm and categorization (Verma, 2022). Digital technologies are discouraged in some social settings in view of their largely Western neoliberal digital design and their seemingly neoliberal capitalist paradigm of design which appears to promote the commodification of nature. ...
... Even though this study did not examine the causes of differential application of technologies, reviewed literature indicates there is no universal model for urban forest governance and that differences in technology application could be attributed to various enablers such as good governance where urban residents are involved in technology creation, public private partnerships and the growing body of scientific knowledge on technology applications in forestry (Barns, 2019;Gabrys, 2020;Konijnendijk et al., 2018). Barriers to technology adoption in the two cities may be attributed to criticisms pointed out in reviewed literature which indicate that digital technologies may be prone to digital harm and categorization (Verma, 2022). ...
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Digital technologies are increasingly being incorporated in the management and governance of urban forests to provide the information needed for sustainable and more livable cities. However, there is scarce information on the documented lessons from applying these digital technologies for urban forestry management in many developing countries. This study addressed this challenge using a literature review in the case of Nairobi, Kenya, and Kampala, Uganda. The results substantiate that urban forests are important city assets enhancing ecological stability and sustainable development. As such, the digital technologies of urban forest management practices are rapidly expanding in the two cities to enhance urban forestry and create new opportunities for sustainable development. Both cities have differentiated integration of digital technologies in the vegetative, community support, and resource management components of urban forest governance and management, with important information and lessons being generated for city authorities and policymakers. In general, the technology implementation level in Kampala city is higher than in Nairobi City. This differentiation could be attributed to differences in the socio-political contexts of the two cities, which present different enablers and barriers to technology application in urban forestry. Nevertheless, more location-specific practices and experiences with a focus on how to diversify opinions and actors in digital technologies should be pursued.
... They are represented as an atmosphere ground equipment. This functions automatically through the program initiated through artificial intelligence [14]- [16]. ...
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Recent developments in the Internet of Things have led to the notion of the Internet of Medical Things (IoMT) designed to enhance the standard of healthcare for user-specific services in e-healthcare and e-Medical environments. Sensitive data exposure via wireless media has been attributed to a lack of effective authentication. Due to increased mobility, user and device authentication becomes more laborious and necessitates shared keys. Henceforth, an end-to-end lightweight mutual authentication scheme is required to preserve the privacy of the user and the integrity of medical data. Within the framework of these criteria, the present work introduces a novel Remote Authentication Method (RAM) using Autonomous Shared Keys (ASK) for robust service security. Based on user validity and service distributions, this technique produces remote keys. However, information frequency and service length are used to modify the key-sharing process with federated learning that emphasizes the autonomous key decoupling post-service closure. It relies on the device and service span and its average session duration. The authentication is processed using volatile session keys from the healthcare center to the user application, preventing false user injections. Overall, the performance assessment and multivariate analysis with state-of-the-art protocols prove that the proposed ASK-RAM-IMOT is more reliable and suited for IoMT applications, demonstrating its security strength. There is a significant positive impact on authentication time with a reduction of 10.18% on average. The most intriguing correlation is with the session outage and false rate by 10.63% and 12.19% respectively which contributes to the improved service span of 8.02%.
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Infrastructure for the Internet of Things is being created in smart cities with long-term viability for a range of manufacturing uses, including smart manufacturing as well as smart industries. However in a smart city environment, security measures may not be effective and the existing system also have several drawbacks such as latency, privacy, scalability and security. A block chain-based IoT framework is being developed to address these problems. Initially, the raw data’s are collected from the smart cities through various IoT devices. Then the data’s are pre-processed using Adaptive Data Cleaning, it also contain a prediction method called denoising auto encoder which is used to converting the data from low into high quality form. Then the pre-processed data is given to the block chain based distributed network. Block construction, Request + transaction, transmit block to other nodes in the network, and verification are the four block chain functionalities in this network. For verifying the transaction Modified Deep Neural Network is used. The verifier must select between two possibilities after the transaction has been verified: yes or no. If the state is correct, the transaction is performed and a block is generated in the blockchain. Or else, the process will be halted if any attack is identified in the transaction. The simulation analysis shows that the proposed method obtain 96% accuracy, 0.04% error, precision is 95% so on. This demonstrates that the proposed strategy outperforms other methods currently being used. Based on this proposed method the transaction is executed securely to provide secure smart city environment.
Digitalization of healthcare is not a new concept; however, nowadays the need for change has become obvious. The rapid increases in technological advancements in health and information technologies enable them to converge and develop in harmony with each other. Digital technologies are ubiquitous and aim to improve healthcare systems and allow new business models. From this point of view, selection and prioritization of them in order to keep up with the technological developments become vital. However, the allocation of resources to meet increasing demands is a multifaceted problem. In this framework, this study aims to analyze the way to smart healthcare. Therefore, initially, current technological implementations in health are reviewed. Afterward, the structure of health technology assessment models used in the selection of technology is detailed. Finally, the managerial strategies for administrative levels are presented in order to form the basis for further studies in this field.
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The Internet of things emerges in the world’s smart evolution through Artificial Intelligence (AI) and Blockchain Technology in Cyber-Physical Systems, which increases the exponential rate of smart manufacturing devices. As more industrial devices are getting connected day by day. Obtaining that massive amount of data transfer easy efficiency and data centralization is much more difficult to process by any human. Blockchain technology has proposed AI-based Cyber-Physical Systems (CPS) for the industry, conferring a secure and efficient financial transaction interoperability, communication security, and processing of a large amount of data. However, there is a wide deployment of industrial compatibility through the Internet of Things (IoT), but this technology will benefit the industrial devices to maintain, predict and awareness on their own. On the one hand, Blockchain Technology advances the device’s authentication security enhancement; on the other hand, AI will provide adaptive learning toward cyber attacks during low batteries. Blockchain will also provide load balancing of Edge devices. We explained Blockchain implementation to fabricate a particular AI-based hybrid medical device and Smart manufacturing based on the proposed design. AI authentication will leverage the features of the security systems, and Blockchain Technology will validate the data privacy and Secure data transfer. With Smart monitoring and supervision sub-systems, the CPS can decrease expert technicians’ needs and diminish manufacturing limits during implantation. Therefore, CPS integration could also lower the cost of fabrication. This chapter discussed the integration of Blockchain Technology and AI in a cyber-physical system. The integration of CPS in Smart Manufacturing and Medical Devices is well explained. The challenges for CPS are also described in this chapter.KeywordsInternet of Things (IoT)Cyber-Physical System (CPS)Artificial Intelligence (AI)Blockchain Technology
The Internet of Things (IoT) allows city officials to monitor the city in real time and communicate smoothly with the community. Smart cities need to provide the best possible service to their citizens on the most basic infrastructure. A successful smart city should make any service available to all in general and equally. In this paper, an artificial intelligence (AI) based smart sustainable IoT model was proposed to enhance the different services in the smart city environment. Where the list of services has begun to play a vital role in the construction of smart cities. The service tracks route priority and non-priority services to various locations. The proposed model provides a prominent place for both services. At a cut-off level, the proposed model achieved 94.97% of service recognition, 3.3% of service rejection, 94.06% of service accuracy, 95.86% of service precision, 94.34% of service recall, and 95.68% of F1-Score while compared with the existing models.KeywordsSmart citiesElectronic methodsSensorsInternet of thingsArtificial intelligencePriority routing
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In this era of wireless COVID-19 telehealth, visiting hospital for regular follow-ups could invite coronavirus in someone’s body. Opting for proactive E-health services is the best thing. It helps the remote patients to share their confidential data through secured encryption. Telehealth services are emerging element in these proactive medical sciences. It helps the remote patients to share their confidential data through secured transmission. In this paper, amino acid guided matrix encoding scheme has been proposed. White blood cell count or Leukocute count is a dominant indicator of patients’ health condition, even amid COVID-19. An abnormal growth in leukocyte count is mainly caused due to an infection, cancer, or any other severe symptoms. It initiates internal haematological inflammations, cardiovascular diseases, Type II diabetes, etc. Therefore, tracking leukocyte count may for disease diagnosis and further treatments. The leukocyte count is generally done in different pathologies, and the data evaluation needs the expertise of a pathologist. In this paper, a technique involving security measures to transmit the result of the histological test with the help of cryptography has been proposed. The data to be transferred to the concerned physician for further diagnosis with the help of proposed way of encryption using amino acids, which ensures no data loss, no data modification, no data theft in the middle of transmission. The proposed encryption method using the amino acid codes has produced results showing satisfactory performances such as p-values found to be 7.215544e−04 and 8.48904e−03 for the key stream and cipher key matrix monobit test respectively, and 8.10245e−04 and 8.10245e−04 for the key stream and cipher key matrix frequency test respectively. It may be used as a transmission module in any wireless COVID-19 Telehealth Systems.
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In the wake of COVID-19 disease, caused by the SARS-CoV-2 virus, we designed and developed a predictive model based on Artificial Intelligence (AI) and Machine Learning algorithms to determine the health risk and predict the mortality risk of patients with COVID-19. In this study, we used a dataset of more than 2,670,000 laboratory-confirmed COVID-19 patients from 146 countries around the world including 307,382 labeled samples. This study proposes an AI model to help hospitals and medical facilities decide who needs to get attention first, who has higher priority to be hospitalized, triage patients when the system is overwhelmed by overcrowding, and eliminate delays in providing the necessary care. The results demonstrate 89.98% overall accuracy in predicting the mortality rate. We used several machine learning algorithms including Support Vector Machine (SVM), Artificial Neural Networks, Random Forest, Decision Tree, Logistic Regression, and K-Nearest Neighbor (KNN) to predict the mortality rate in patients with COVID-19. In this study, the most alarming symptoms and features were also identified. Finally, we used a separate dataset of COVID-19 patients to evaluate our developed model accuracy, and used confusion matrix to make an in-depth analysis of our classifiers and calculate the sensitivity and specificity of our model.
Conference Paper
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Simulation-based analysis is essential in the model-based design process of Cyber-Physical Systems (CPS). Since heterogeneity is inherent to CPS, virtual prototyping of CPS designs and the simulation of their behavior in various environments typically involve a number of physical and computation/communication domains interacting with each other. Affordability of the model-based design process makes the use of existing domain-specific modeling and simulation tools all but mandatory. However, this pressure establishes the requirement for integrating the domain-specific models and simulators into a semantically consistent and efficient system-of-system simulation. The focus of the paper is the interoperability of popular integration platforms supporting heterogeneous multi-model simulations. We examine the relationship among three existing platforms: the High-Level Architecture (HLA)-based CPS Wind Tunnel (CPSWT), MOSAIK, and the Functional Mockup Unit (FMU). We discuss approaches to establish interoperability and present results of ongoing work in the context of an example.
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In recent years, the Internet of Things (IoT) has drawn convincing research ground as a new research topic in a wide variety of academic and industrial disciplines, especially in healthcare. The IoT revolution is reshaping modern healthcare systems incorporating technological, economic, and social prospects. It is evolving healthcare systems from conventional to more personalized healthcare system where patients can be diagnosed, treated, and monitored more easily. The current global challenge of the COVID-19 pandemic caused by the novel severe contagious respiratory syndrome coronavirus 2 presents the greatest global public health crisis since the pandemic influenza outbreak of 1918. By the moment this paper was written, the number of diagnosed COVID-19 cases around the world reaches more than 14 million. Since then there has been a rapid rise in the different research communities to combating this worldwide threat and IoT technology is one of the pioneers in this area. In the context of COVID-19, IoT enabled /linked devices/applications are utilized to lower the possible spread of COVID-19 to others by early diagnosis, monitoring patients, and practicing defined protocols after patients recovery. This paper surveys the role of IoT-based technologies in facing with COVID-19 and reviews the state-of-the-art architectures, platforms, applications, and industrial IoT-based solutions combating COVID-19 in three main phases including early diagnosis, quarantine time, and after recovery.
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Context As Industrial Cyber-Physical Systems (ICPS) become more connected and widely-distributed, often operating in safety-critical environments, we require innovative approaches to detect and diagnose the faults that occur in them. Objective We profile fault identification and diagnosis techniques employed in the aerospace, automotive, and industrial control domains. Each of these sectors has adopted particular methods to meet their differing diagnostic needs. By examining both theoretical presentations as well as case studies from production environments, we present a profile of the current approaches being employed and identify gaps. Methodology A scoping study was used to identify and compare fault detection and diagnosis methodologies that are presented in the current literature. We created categories for the different diagnostic approaches via a pilot study and present an analysis of the trends that emerged. We then compared the maturity of these approaches by adapting and using the NASA Technology Readiness Level (TRL) scale. Results Fault identification and analysis studies from 127 papers published from 2004 to 2019 reveal a wide diversity of promising techniques, both emerging and in-use. These range from traditional Physics-based Models to Data-Driven Artificial Intelligence (AI) and Knowledge-Based approaches. Hybrid techniques that blend aspects of these three broad categories were also encountered. Predictive diagnostics or prognostics featured prominently across all sectors, along with discussions of techniques including Fault trees, Petri nets and Markov approaches. We also profile some of the techniques that have reached the highest Technology Readiness Levels, showing how those methods are being applied in real-world environments beyond the laboratory. Conclusions Our results suggest that the continuing wide use of both Model-Based and Data-Driven AI techniques across all domains, especially when they are used together in hybrid configuration, reflects the complexity of the current ICPS application space. While creating sufficiently-complete models is labour intensive, Model-free AI techniques were evidenced as a viable way of addressing aspects of this challenge, demonstrating the increasing sophistication of current machine learning systems. Connecting ICPS together to share sufficient telemetry to diagnose and manage faults is difficult when the physical environment places demands on ICPS. Despite these challenges, the most mature papers present robust fault diagnosis and analysis techniques which have moved beyond the laboratory and are proving valuable in real-world environments.
Bronchial asthma is one of the most common chronic diseases of childhood and considered as a major health problem globally. The irregularity in meteorological factors has become a primary cause of health severity for the individuals suffering from asthma. In the presented research, a dew-cloud assisted cyber-physical system (CPS) is proposed to analyze the correlation between the meteorological and health parameters of the individuals. The work is primarily focused on determining the health adversity caused by the irregular scale of meteorological factors in real-time. IoT-assisted smart sensors are utilized to capture ubiquitous information from indoor environment that make a vital impact on the health of the individual directly or indirectly. The data is analyzed over the cyber-space to quantify the probable irregular health events by utilizing the data classification efficiency of Weighted-Naïve Bayes modeling technique. Moreover, the relationship between meteorological and health parameters is estimated by utilizing the Adaptive Neuro-Fuzzy Inference System (ANFIS) and calculate a unifying factor over the temporal scale. To validate the monitoring performance, the proposed model is implemented in the four schools of Jalandhar, India. The experimental evaluation of the proposed model acknowledges the performance efficiency through several statistical approaches. Furthermore, the comparative analysis is evaluated with state-of-the-art decision-making algorithms that demonstrate the effectiveness of the proposed solution for the targeted application.
Wearable electronic devices, which allow physiological signals to be continuously monitored, can be used in the early detection of asymptomatic and pre-symptomatic cases of COVID-19.
Recently, an infectious disease, coronavirus disease 2019 (COVID-19), has been reported in Wuhan, China, and spread worldwide within a couple of months. There have been seen an outbreak of COVID-19 in many countries, where the infected patients' rate overwhelmed the inadequate medical services. The push of patient-centered interoperability (PCI) from medical institution-centered interoperability may defeat the current and post resultant disease of the COVID-19 pandemic. This paper proposes a state-of-the-art privacy-preserving medical data sharing system based on Hyperledger Fabric (MedHypChain), where each transaction is secured via an Identity-based broadcast group signcryption scheme. We proved that MedHypChain achieves confidentiality, anonymity, traceability, and unforgeability. Besides, we regularize the MedHypChain to implement the PCI healthcare system, where the patient manages its health-related information in the blockchain that can be accessible to the authorized entity. We also use the Hyperledger caliber as a benchmark tool to analyze the performance of MedHypChain in three metrics (latency time, execution time, and throughput) for up to 20 permissioned nodes. Finally, we compare MedHypChain with related blockchain-based healthcare systems and found that the proposed scheme needs the least computation cost and communication cost and achieves all security features, such as authenticity, scalability, and access control.
In this paper we make the case for the new class of Self-aware Cyber-physical Systems. By bringing together the two established fields of cyber-physical systems and self-aware computing, we aim at creating systems with strongly increased yet managed autonomy, which is a main requirement for many emerging and future applications and technologies. Self-aware cyber-physical systems are situated in a physical environment and constrained in their resources, they understand their own state and environment and, based on that understanding, are able to make decisions autonomously at runtime in a self-explanatory way. In an attempt to lay out a research agenda, we bring up and elaborate on five key challenges for future self-aware cyber-physical systems: (i) How can we build resourcesensitive yet self-aware systems? (ii) How to acknowledge situatedness and subjectivity? (iii) What are effective infrastructures for implementing self-awareness processes? (iv) How can we verify self-aware cyber-physical systems and, in particular, which guarantees can we give? (v) What novel development processes will be required to engineer self-aware cyber-physical systems?We review each of these challenges in some detail and emphasize that addressing all of them requires the system to make a comprehensive assessment of the situation and a continual introspection of its own state, in order to sensibly balance diverse requirements, constraints, short-term and long-term objectives. Throughout, we draw on three examples of cyber-physical systems that may benefit from self-awareness: a multi-processor system-on-chip, a Mars rover, and an implanted insulin pump. These three very different systems nevertheless have similar characteristics: limited resources, complex unforeseeable environmental dynamics, high expectations on their reliability, and substantial levels of risk associated with malfunctioning. Using these examples, we discuss the potential role of self-awareness in both highly complex and rather more simple systems, and as a main conclusion we highlight the need for research on above listed topics.
Context: Cyber-Physical Systems (CPSs) are gradually and widely introducing autonomous capabilities into everything. However, human participation is required to accomplish tasks that are better performed with humans (often called human-in-the-loop). In this way, human-in-the-loop solutions have the potential to handle complex tasks in unstructured environments, by combining the cognitive skills of humans with autonomous systems behaviors. Objective: The objective of this paper is to provide appropriate techniques and methods to help designers analyze and design human-in-the-loop solutions. These solutions require interactions that engage the human, provide natural and understandable collaboration, and avoid disturbing the human in order to improve human experience. Method: We have analyzed several works that identified different requirements and critical factors that are relevant to the design of human-in-the-loop solutions. Based on these works, we have defined a set of design principles that are used to build our proposal. Fast-prototyping techniques have been applied to simulate the designed human-in-the-loop solutions and validate them. Results: We have identified the technological challenges of designing human-in-the-loop CPSs and have provided a method that helps designers to identify and specify how the human and the system should work together, focusing on the control strategies and interactions required. Conclusions: The use of our approach facilitates the design of human-in-the-loop solutions. Our method is practical at earlier stages of the software life cycle since it allows domain experts to focus on the problem and not on the solution.