Medical cyber-physical systems (MCPS) are healthcare critical integration of a network of medical devices. These systems are progressively used in hospitals to achieve a continuous high-quality healthcare. The MCPS design faces numerous challenges, including inoperability, security/privacy, and high assurance in the system software. In the current work, the infrastructure of the cyber-physical systems (CPS) are reviewed and discussed. This article enriched the researches of the networked Medical Device (MD) systems to increase the efficiency and safety of the healthcare. It also can assist the specialists of medical device to overcome crucial issues related to medical devices, and the challenges facing the design of the medical device's network. The concept of the social networking and its security along with the concept of the wireless sensor networks (WSNs) are addressed. Afterward, the CPS systems and platforms have been established, where more focus was directed toward CPS-based healthcare. The big data framework of CPSs is also included.

/pdf/medical-cyber-physical-systems-a-survey-44o0y3ph99.pdf

Medical cyber-physical systems: A survey.

Industry 4.0 can be considered the 21st century's industrial revolution and will soon be the new form of manufacturing delight. The definitive customer would experience manufacturing requests determined by artificial intelligence, machine learning, and automated technologies linked with data science support for gauging customer necessities. Phenomenally, Industry 4.0 is rapidly changing the firm's management and organizational systems, and competencies, as well as making its environment much more explored, even if more complexed than in the past. This new industrial revolution would possess systems with transformative technologies for managing interconnected systems between its physical assets and computational capabilities. Such enterprises would require skilled workforce to improve and operate advanced manufacturing tools and systems, and investigate the machine data, clients, and global capitals, resulting in an escalating need for trained employees proficient in cross-functional capacities and with competencies to cope new processes and IT systems.

Industry 4.0: The New Industrial Revolution

In the present era of Information Technology, almost all big and small scale companies are moving towards cloud to store and manage the data. Cloud computing is a routine of deploying a structure of distant servers speed up on the Internet to store, oversee, and manage information, rather than a neighbourhood server or a PC. The purpose for cloud procurement is reduced cost, adaptability, regular access and refreshed programming. Nowadays, healthcare frameworks are adjusting computerized stages and ending up being more patient-centered and data driven. In this paper, we present a planning empowered intermediary re-encryption method to defeat the security issues. This Technique will allow only limited access rights to an authorized agent to access the records for a specific time period. This technique will use a searchable encryption and proxy Re-encryption technique.

E-Health Cloud Security Using Timing Enabled Proxy Re-Encryption

Named data networking (NDN) is a novel network architecture which adopts a receiver-driven transport approach. However, NDN is the name-based routing and source uncontrollability, and network congestion is inevitable. In this paper, we propose an adaptive congestion control protocol (ACCP) which is divided into two phase to control network congestion before affecting network performance. In the first phase, we employ the time series prediction model based on deep learning to predict the source of congestion for each node. In the second phase, we estimate the level of network congestion by the average queue length based on the outcomes of first phase in each router and explicitly return it back to receiver, and then the receiver adjusts sending rate of Interest packets to realize congestion control. Simulation experiment results show that our proposed ACCP scheme has better performance than ICP and CHoPCoP in terms of the high utilization and minimal packet drop in a multi-source/multi-path environment.

ACCP: adaptive congestion control protocol in named data networking based on deep learning

Measuring the spread of disease during a pandemic is critically important for accurately and promptly applying various lockdown strategies, so to prevent the collapse of the medical system. The latest pandemic of COVID-19 that hits the world death tolls and economy loss very hard, is more complex and contagious than its precedent diseases. The complexity comes mostly from the emergence of asymptomatic patients and relapse of the recovered patients which were not commonly seen during SARS outbreaks. These new characteristics pertaining to COVID-19 were only discovered lately, adding a level of uncertainty to the traditional SEIR models. The contribution of this paper is that for the COVID-19 epidemic, which is infectious in both the incubation period and the onset period, we use neural networks to learn from the actual data of the epidemic to obtain optimal parameters, thereby establishing a nonlinear, self-adaptive dynamic coefficient infectious disease prediction model. On the basis of prediction, we considered control measures and simulated the effects of different control measures and different strengths of the control measures. The epidemic control is predicted as a continuous change process, and the epidemic development and control are integrated to simulate and forecast. Decision-making departments make optimal choices. The improved model is applied to simulate the COVID-19 epidemic in the United States, and by comparing the prediction results with the traditional SEIR model, SEAIRD model and adaptive SEAIRD model, it is found that the adaptive SEAIRD model’s prediction results of the U.S. COVID-19 epidemic data are in good agreement with the actual epidemic curve. For example, from the prediction effect of these 3 different models on accumulative confirmed cases, in terms of goodness of fit, adaptive SEAIRD model (0.99997) ≈ SEAIRD model (0.98548) > Classical SEIR model (0.66837); in terms of error value: adaptive SEAIRD model (198.6563) < < SEAIRD model(4739.8577) < < Classical SEIR model (22,652.796); The objective of this contribution is mainly on extending the current spread prediction model. It incorporates extra compartments accounting for the new features of COVID-19, and fine-tunes the new model with neural network, in a bid of achieving a higher level of prediction accuracy. Based on the SEIR model of disease transmission, an adaptive model called SEAIRD with internal source and isolation intervention is proposed. It simulates the effects of the changing behaviour of the SARS-CoV-2 in U.S. Neural network is applied to achieve a better fit in SEAIRD. Unlike the SEIR model, the adaptive SEAIRD model embraces multi-group dynamics which lead to different evolutionary trends during the epidemic. Through the risk assessment indicators of the adaptive SEAIRD model, it is convenient to measure the severity of the epidemic situation for consideration of different preventive measures. Future scenarios are projected from the trends of various indicators by running the adaptive SEAIRD model.

/pdf/a-new-seaird-pandemic-prediction-model-with-clinical-and-g6dfje1abm.pdf

A new SEAIRD pandemic prediction model with clinical and epidemiological data analysis on COVID-19 outbreak

This paper is targeted at issues including traditional stovepipe data center, low utilization of IT equipment and data resources as a result of rigid IT structure, high maintenance costs and high energy consumption in system operation. By taking Beijing Municipal Committee of Transport BMCT's data center as an example, a way to establish distributed traffic cloud data center based on SOA Service-Oriented Architecture fused with cloud computing is introduced in this paper; in addition, network-aware energy conservation scheduling DENS Data-center Energy-efficient Network-aware Scheduling algorithm applied in cloud data center is put forward to realize the full utilization of all kinds of resources in the cloud data center. Experimental results also show the effectiveness of the proposed algorithm by comparing with traditional DENS algorithms.

Jing Deng

Papers

Building Intelligent Transportation Cloud Data Center Based on SOA