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Post study of Blockchain in smart health environment

Post study of Blockchain in smart health environment

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IEEE conference on Innovative Technologies in intelligent Systems and Industrial Applications (CITISIA)

作者： Mansukhdeep Kaur Mohsin Murtaza Mostafa Habbal Masters of Information Technology Study Group Australia School of Computing and Mathematics Study Group Australia

ISBN: (数字)9781728194370

ISBN: (纸本)9781728194387

Blockchain technology is being popular day by day and also serving in many applications and areas such as Internet of Things, Cloud computing, Big data, Healthcare and many more because of its security strengths and benefits. It has been proven that blockchain applications are being utilized in healthcare to deliver secure data and to manage the medical data safely. Moreover, blockchain is transforming the traditional medical practices in effective ways such as diagnosing the problems effectively and treating through secure information sharing. There is no doubt to say that in future, blockchain will be performing in personalized, valid and secure healthcare by combining the medical information of patient and providing in a secure and updated setup. In this research paper, securities of healthcare applications and traditional and recent security developments are discussed. Due to Covid-19, it has become challenging for healthcare officials and government to protect and record individuals sensitive data safely. Also, spread of misleading information has been also increased during the pandemic and inability of existing platform for information validation leads to public *** of blockchain-based tracking systems is essential for accurate and valid information sharing among people and Government. This paper focus on information security issues raised by Covid-19 pandemic as well as implementation of blockchain-based platform in healthcare to record and protect covid-19 related information and contact tracing.

关键词： COVID-19 Pandemics Government Medical services Blockchain Security Medical diagnostic imaging

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Agent-Based Approach for Decentralized data Analysis in Industrial Cyber-Physical Systems 9th

Agent-Based Approach for Decentralized Data Analysis in Indu...

引用

9th international conference on Industrial Applications of Holonic and Multi-Agent Systems (HoloMAS)

作者： Queiroz, Jonas Leitao, Paulo Barbosa, Jose Oliveira, Eugenio Univ Porto Fac Engn LIACC Porto Portugal Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Campus Santa Apolonia P-5300253 Braganca Portugal

ISBN: (纸本)9783030278786;9783030278779

The 4th industrial revolution is marked by the use of Cyber-Physical Systems (CPSs) to achieve higher levels of flexibility and adaptation in production systems that need to cope with a demanding and ever-changing market, driven by mass customization and high quality products. In this context, data analysis is a key technology enabler in the development of intelligent machines and products. However, in addition to Cloud-based data analysis services, the realization of such CPS requires technologies and approaches capable to effectively support distributed and embedded data analysis capabilities. The advances in Edge computing have promoted the data processing near or at the devices that produce data, which combined with Multi-Agent Systems, allow to develop solutions based on distributed and interacting autonomous entities in open and dynamic environments. In this sense, this paper presents a modular agent-based architecture to design and embed cyber-physical components with data analysis capabilities. The proposed approach defines a set of data processing modules that can be combined to build cyber-physical agents to be deployed at different computational layers. The proposed approach was applied in a smart inspection station for electric motors, where agents embedding data analysis algorithms were distributed among Edge, Fog and Cloud layers. The experimental results illustrated the benefits of distributing the data analysis by different computational layers.

关键词： Cyber-physical systems data analysis Multi-agent systems Edge computing

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Improving the Efficiency of Blockchain Applications with smart Contract based Cyber-insurance

Improving the Efficiency of Blockchain Applications with Sma...

引用

IEEE international conference on Communications (ICC)

作者： Jia Xu Yongqi Wu Xiapu Luo Dejun Yang Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Department of Computing The Hong Kong Polytechnic University Hongkong China Department of Computer Science Colorado School of Mines Golden USA

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Blockchain based applications benefit from decentralization, data privacy, and anonymity. However, they may suffer from inefficiency due to underlying blockchain. In this paper, we aim to address this limitation while still enjoying the privacy and anonymity. Taking the blockchain based crowdsourcing system as an example, we propose a new smart contract based cyber-insurance framework, which can greatly shorten the delay, and enable the workers to obtain the economic compensation for increased security risk caused by a conflict between the need to provide service quickly and delay in payment. We model the process of determining insurance premium and number of confirmations as a Stackelberg Game and prove the existence of Stackelberg Equilibria, at which the utility of the requester is maximized, and none of the workers can improve its utility by unilaterally deviating from its current strategy. The experimental results show that our framework can definitely improve the time efficiency of crowdsourcing. Particularly, it takes on average only 33% of the time required by the naive blockchain based crowdsouring solution for time-sensitive cases.

关键词： Contracts Crowdsourcing Task analysis Insurance Bitcoin

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Vulnerability Analysis of the Urban Transport System in the Context of smart Cities

Vulnerability Analysis of the Urban Transport System in the ...

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IEEE international smart Cities conference (ISC2)

作者： Antônio A. F. G. M. Pertence Raquel A. F. Mini Humberto Torres Marques-Neto Graduate Program in Informatics Pontifical Catholic University of Minas Gerais Belo Horizonte Brazil

ISBN: (数字)9781728182940

ISBN: (纸本)9781728182957

Large cities seek to incorporate intelligent systems into their infrastructure, industrial, educational, and social activities to improve the quality of service provided to citizens and make all processes more efficient. To this end, these cities use technologies that involve the areas of the Internet of Things, Big data, and Governance to make decisions by governments and authorities. In the field of Urban computing, it is possible to use open data in the GTFS format to solve the problems of the urban transport system using complex network metrics that make it possible to model complex interactions between objects in high dimensionality. Stops and routes are modeled as a graph, characterized by complex network metrics. According to the literature, if there is an interruption of the urban transport system, we make an evaluation of the degree of vulnerability of the system in the search for solutions. The main goal of this paper is to propose an approach to analyze the vulnerability of the urban transport system using complex network metrics along with GTFS data in scenarios targeted failures. As a contribution, the proposed approach uses the concept of skewness in the methodology established in the literature. The skewness enables a better understanding and differential the failure conditions from the evaluation of the vulnerability the urban transport system, through a quantitative indication of which local metrics most influence the decay of network metrics. The analysis of the results obtained in the proposed approach serves as a subsidy for studies in the search for solutions to the problems of the urban transport system, providing a tool to the set of technological options for the planning of urban transport systems of smart cities available to governments and authorities. The proposed approach can be applied in different cities, regardless of their size and location, as long as you have access to open geographic data in the GTFS format.

关键词： Measurement smart cities Government Complex networks Tools Search problems Planning

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Recurrent Neural Networks for Colluded Applications Attack Detection in Android OS Devices

Recurrent Neural Networks for Colluded Applications Attack D...

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international Joint conference on Neural Networks (IJCNN)

作者： Igor Khokhlov Ninad Ligade Leon Reznik Golisano College of Computing and Information Sciences Rochester Institute of Technology Rochester NY USA

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

The paper presents a design and an implementation of an intelligent detector of a novel "colluded applications" attack on user's privacy in Android OS devices, which employs recurrent neural network (RNN) models. The paper reports the results of an empirical study that involved the attack research, data collection and pre-processing, the choice of the RNN model for a detector design, multiple detector implementations, their performance evaluation and analysis, and finally, an Android app realization and execution on a real device. We investigate and analyze multiple attack scenarios and the attack influence on such technological signals as memory consumption and a CPU's cores clock speed. For the attack detection, a few detectors exploring multiple RNN models are designed, implemented, and examined. The detectors employ various RNN models, such as a simple recurrent neural network, a long short-term memory, and a gated recurrent unit. Each model's performance in detecting multiple attack scenarios is evaluated and analyzed in order to compare classification models against various criteria.

关键词： smart phones Detectors Recurrent neural networks data models Performance evaluation Handheld computers data collection

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Trust Management in Cyber Physical System using Blockchain

Trust Management in Cyber Physical System using Blockchain

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international conference on computing and Networking Technology (ICCNT)

作者： Bhabendu Kumar Mohanta Utkalika Satapathy Meenu Rani Dey Soumyashree S. Panda Debasish Jena IIIT Bhubaneshwar Odisha India IIT Guwahati Assam

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

Cyber-physical Systems (CPS) are reshaping the way of interaction with the physical world. Moreover, the true potential of CPS will be realized when a decentralized approach will be taken into account. Blockchain is an up-and-coming technology which can establish trust in CPS, where participants do not trust each other. Blockchain applications eliminate the middle man to provide trust and making the processes more efficient & cheaper. It's decentralized nature and cryptographic algorithm make it immune to attack and becomes a secure technology. When the data is stored on the blockchain it becomes immutable, ensuring trust in data. Nevertheless, this does not guarantee the trustworthiness of the device which is generating the data. We propose a blockchain-based signature storage solution to ensure trust among the participants which is further applicable to a diverse range of blockchain-based CPS applications. We have implemented our system using the Ethereum network and Docker Tools. Our proposed solution guarantees security properties i.e. device identification, authentication, integrity, and non-repudiation. Also, it reduces the storage space and storage cost than the traditional usage of blockchain in CPS.

关键词： Blockchain smart contracts Internet of Things Bitcoin Reliability intelligent sensors Authentication

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Distributed Machine Learning for Resilient Operation of Electric Systems

Distributed Machine Learning for Resilient Operation of Elec...

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international conference on smart Energy Systems and Technologies (SEST)

作者： M. Hadi Amini Ahmed Imteaj Javad Mohammadi School of Computing and Information Sciences Florida International University Miami FL USA Sustainability Optimization and Learning for InterDependent networks laboratory (solid lab) FIU Miami FL USA Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9781728147017

ISBN: (纸本)9781728147024

Power system resilience is crucial to ensure secure energy delivery to electricity consumers. Power system outages lead to economical and societal burdens for the society and industries. To mitigate the socio-economical impacts of a power outage, we need to develop efficient algorithms to ensure resilient operation of the power system. In this paper, we first explain the notion of data-driven resilience. Then, we present a pathway of leveraging edge intelligence to improve resilience. To this end, we propose a novel distributed machine learning paradigm. Our proposed structure relies on local Resilience Management Systems (RMS) that serve as intelligent decision-making entities in each area, e.g. an autonomous micro-grid or a smart home can act as RMS. The RMS agents, which are available in different areas, can share their local data (i.e., a microgrid's operational data) with their neighboring RMS to coordinate their decisions in a distributed fashion. This will provide two major advantages: 1) distributed intelligence replaces centralized decision-making leading to robust decision-making and enhanced resilience; 2) since local data are locally shared among all entities within an RMS, if one of the RMS agents fails to communicate with the rest of network, we still can maintain a feasible solution (which is not necessarily optimal). Finally, we presents different scenarios in the simulation results section that showcases the system performance for two buildings under various outage scenarios.

关键词： Resilience Power system reliability Decision making Optimization Load management Collaborative work

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Bus Travel Times Prediction based on Real-Time Traffic data Forecast using Artificial Neural Networks

Bus Travel Times Prediction based on Real-Time Traffic Data ...

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international conference on Electrical, Communication, and Computer Engineering (ICECCE)

作者： Gustavo Henrique Larsen Leopoldo Rideki Yoshioka Claudio Luiz Marte Polytechnic School University of Sao Paulo São Paulo Brazil

ISBN: (数字)9781728171166

ISBN: (纸本)9781728171173

The concept of smart Cities is a trend in large cities. intelligent Transportation Systems) plays an essential role in providing accurate information on bus travel times. It improves the planning of passengers and the agency responsible for public transport. The purpose of this paper is to create a new methodology to predict the travel times of buses based on open data collected in real-time. We constructed a dataset from Sao Paulo City bus fleet location data, real-time traffic data, and traffic forecast from Google Maps. In the following, we trained an Artificial Neural Network (ANN). In the ANN training process, we alternated the dataset and its hyperparameters to find out the combination that provides the lower prediction error. The mean absolute percentage error found was 8.97%, using all data sets except weather data. We showed that our method could provide an accurate bus travel time prediction from web data collected in real-time.

关键词： Real-time systems Artificial neural networks Predictive models Public transportation Kalman filters Cloud computing Support vector machines

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Ocular-Net: Lite-Residual Encoder Decoder Network for Accurate Ocular Regions Segmentation in Various Sensor Images

Ocular-Net: Lite-Residual Encoder Decoder Network for Accura...

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international conference on Big data and smart computing (BIGCOMP)

作者： Rizwan Ali Naqvi Sang-Woong Lee Woong-Kee Loh Department of Intelligent Mechatronics Engineering Sejong University Seoul Republic of Korea Department of Software Gachon University Seongnam Republic of Korea

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160351

Ocular regions such as iris and sclera yield high accuracy in user's biometrics as well as liveness detection systems. The primary purpose for ocular recognition system is the accurate segmentation of the regions of interest that plays the key role in retaining the accuracy and restraining the errors in the whole system. However, accurate ocular regions segmentation in the images in a physical environment is very challenging owing to the images with low resolution, occlusion, blur, ghost effect, unusual glint, and off-angles. Deep learning algorithms with a convolutional neural network (CNN) has achieved promising results for ocular regions segmentation. However, previous CNNbased methods are unable to find the true boundary of ocular regions in non-ideal situations, which results in reduced reliability and accuracy. To overcome these challenges, we present Ocular-Net, a deep learning-based lite-residual encoder-decoder network to determine the accurate ocular regions such as iris and sclera. In this way, the true ocular regions can be segmented with the transfer of high-frequency information using residual skip connections. Additionally, the proposed Ocular-Net does not enhance performance on the cost of increasing depth, complexity or number of parameters, in fact, it has much fewer parameters than the previous state-of-the-art methods. We performed comprehensive experiments and obtained optimum performance on iris and sclera datasets.

关键词： Iris recognition Image segmentation Decoding databases Training Machine learning

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Soft Real Time data Driven IoT for Knee Rehabilitation

Soft Real Time Data Driven IoT for Knee Rehabilitation

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IEEE conference on Innovative Technologies in intelligent Systems and Industrial Applications (CITISIA)

作者： S.M.N. Arosha Senanayake Putri Wulandari Mathematical and Computing Sciences Universiti Brunei Darussalam Brunei Darussalam Electrical Engineering Universitas Al Azhar Indonesia Jakarta Indonesia

ISBN: (数字)9781728194370

ISBN: (纸本)9781728194387

This article presents soft real time data driven Internet of Things (IoT) for knee rehabilitation using cyber physical sensory information system interfaced with cloud storage. Custom made wearable wireless motion capture suit interfaced to smart watch as the IoT are built for biofeedback visualization. Mullti-sensor integration and data fusion mechanisms are employed to obtain input vectors of knowledge base and the output vector is based on patient classification defined using multivariate statistics by the healthcare professionals. Case based reasoning is applied for the established reference standard in order to produce patient centric actual knee rehabilitation status and classification using semi supervised deep learning method. Wearable IoT is automatically updated the actual knee rehabilitation status and classification of a patient using relevant cyber physical sensory information retrieved from the cloud storage connected vis AWS cloud. Hence, a soft real time data drive IoT for knee rehabilitation system is successfully tested and validated using semi supervised deep learning cyber physical sensory information database subject to statistically quantified parameters by health professionals based on principle component analysis and patient centric parameters based on independent component analysis. The data driven IoT built has been validated in rehabilitation clinics by relevant physiotherapists and patients with the average age of ±36.8.

关键词： Deep learning Cloud computing Medical services Real-time systems Internet of Things Biomedical monitoring Information systems

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