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An Integrated Blockchain Approach to Model Secure Internet of Things based Systems

An Integrated Blockchain Approach to Model Secure Internet o...

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data analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), international conference on

作者： Pramod Mathew Jacob Prasanna Mani Santhosh Simon Renju Rachel Varghese Kuruvilla John Nisha Aniyan Providence College of Engineering Chengannur Kerala India SITE Vellore Institute of Technology Vellore TN India

ISBN: (数字)9781728196756

ISBN: (纸本)9781728196763

Internet of Things (IoT) made a tremendous change in the field of automation technologies. Objects or Things in the real world can be converted into a `smart object' by attaching some sensors or actuators. These devices are capable of communicating each other using internet. Light weight protocols make the IoT system more user friendly. But one of the critical challenges faced by any IoT designer is to provide the data security for IoT applications. Though the `things' are connected to a network, there is a high chance of data stealing or data manipulation. Block chain can be used to ensure data security, scalability and reliability of IoT applications. We propose a secure blockchain based IoT architecture to assure data security. Blockchain is utilized for tracking and coordinating millions of connected components. Blockchain uses decentralized approach which can prevent from single points of failures thereby creates a stable interoperable secure system. Cryptographic algorithms can be used for securing the sensor data. Thus, our model enhances not only security but also the privacy concerns of an end user.

关键词： Blockchain Internet of Things Security Computer architecture Peer-to-peer computing data models Business

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Hybrid Workflow Provisioning and Scheduling on Edge Cloud computing Using a Gradient Descent Search Approach

Hybrid Workflow Provisioning and Scheduling on Edge Cloud Co...

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international Symposium on Parallel and Distributed computing

作者： Raed Alsurdeh Rodrigo N. Calheiros Kenan M. Matawie Bahman Javadi School of Computer Data and Mathematical Sciences Western Sydney University Sydney Australia

ISBN: (数字)9781728189468

ISBN: (纸本)9781728189475

The dramatic growth of the Internet of Things (IoT) technology in many application domains, ranging from intelligent video surveillance, smart retail to the Internet-of-Vehicles brings new computation challenges for rationalized utilization of computing resources. IoT application execution refers to hybrid processing model of stream and batch to achieve data analytics objectives. Hybrid workflow execution combines the challenges of latency-sensitive and resource-intensive processing. To resolve these challenges, we proposed a two stages hybrid workflow scheduling framework on edge cloud computing. In the first stage, we proposed a resource estimation algorithm based on a linear optimization approach, the gradient descent search (GDS) and in the second stage, we adopted a cluster-based provisioning and scheduling technique on heterogeneous edge cloud resources. This work provides a multi-objective optimization model for execution time and monetary cost under constraints of deadline and throughput. Results demonstrated the framework performance in controlling the execution of hybrid workflows by an efficient tuning for stream processing parameters, such as arrival rate and processing throughput. Under working constraints, the proposed scheduler provides significant improvement for large hybrid workflows in terms of execution time and monetary cost with an average of 8% and 35%, respectively.

关键词： Cloud computing Task analysis Processor scheduling Estimation Computational modeling data models Optimization

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intelligent Communication for Internet of Things (IoRT) 5th

Intelligent Communication for Internet of Things (IoRT)

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5th international conference on Recent Innovations in computing , ICRIC 2022

作者： Bhat, Kaleem Ullah Kumar, Neerendra Koul, Neha Verma, Chaman Enescu, Florentina Magda Raboaca, Maria Simona Department of Computer Science and IT Central University of Jammu Jammu & Kashmir Jammu India Department of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest Hungary Department of Electronics Communications and Computers University of Pitesti Pitești Romania National Research and Development Institute for Cryogenic and Isotopic Technologies-ICSI Rm. Valcea Romania

ISBN: (纸本)9789819906000

To enable Machine-to-Machine (M2M) communication and data transmission using fundamental network protocols like TCP/IP, the Internet of Things (IoT) provides a sturdy framework for attaching things to the internet. Its cutting-edge technology is having an impact on numerous fields of study and development of smart solutions. IoRT is the integration of diverse technologies which encompasses cloud computing, machine learning, artificial intelligence, and Internet of Things (IoT). With so many devices and so few resources, security issues like device authorization and authentication and maintaining the integrity and privacy of data arise. IoT was forced to apply conventional security techniques because of a variety of new circumstances, including restricted computational power, RAM size, ROM size, register width, different operating environments, and others. The consequences of the several cryptanalyses performed on these ciphers by various cryptanalysts are also examined in this study. This paper provides background information on lightweight block ciphers in order to protect transmitted data within the network. For more secure communication, we have also implemented the PRESENT block cipher due to its lightweight behavior. A future dimension is also proposed to develop a good lightweight cipher. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cryptography

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A Protein Embedding Model for Drug Molecular Screening

A Protein Embedding Model for Drug Molecular Screening

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international conference on Big data and smart computing (BIGCOMP)

作者： Chao Che Min Zhu Yongjun Zhu Qiang Zhang Dongsheng Zhou Bin Wang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China Department of Library and Information Science Sungkyunkwan University Seoul Republic of Korea

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160351

Application of machine learning method in new drug development can greatly shorten the process of experimental discovery and reduce the risk of clinical failure. However, the feature extraction of proteins sequence is very difficult due to the large dimension. To this end, we propose a Protein Embedding Model(PEM) for drug molecular screening to predict the interaction between proteins and small molecules. Specifically, PEM first classifies 20 kinds of amino acids into 6 categories to reduce the dimension and learns the representation of protein borrowing the idea of word embedding. Then the model uses multiple imputation to fill the physical and chemical properties of small molecule compounds. Finally, the model uses LightGBM model to predict the affinity value Ki between proteins and small molecules. Experiments show that the model can effectively extract the features of proteins and small molecules and outperforms other traditional methods on the data provided by a drug discovery and development company.

关键词： Amino acids Feature extraction Drugs Protein sequence Chemicals Compounds

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Novel Workload Balancing Method for UAV-based Edge Cloud computing Systems with Handover

Novel Workload Balancing Method for UAV-based Edge Cloud Com...

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IEEE international conference on Communications (ICC)

作者： Hayato Shimaday Yuichi Kawamotoy Nei Katoy Graduate School of Information Sciences Tohoku University Sendai Japan

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Internet of Things (IoT) has become an integral and pervasive part of everyday life, e.g. the infrastructure of intelligent traffic systems and smart cities. Such next-generation IoT applications require intelligent data processing that is performed via edge cloud computing (ECC) within a short period. In ECC, the edge server executes data processing; this is expected to reduce service delay. However, in the existing ECC research, infrastructure such as radio towers for communication and base stations for installing edge servers is indispensable; thus, it cannot respond to the demand for computing resources in the event of large-scale disasters and in infrastructureless areas. Therefore, researchers are studying the provision of computing resources using unmanned aerial vehicles (UAVs). On account of these studies, computation resources may be provided in several situations. However, in the face of this technology, to provide services with the delay time that the application allows, it is necessary to consider the workload balance and communication range of the UAV. In this paper, we propose a handover solution method considering workload balance and create a mathematical model with a transparent system. Moreover, numerical analyses show the effectiveness of the proposed method. With this contribution, it is possible to provide communication and computational resources in infrastructureless areas.

关键词： Delays Servers Relays Task analysis Handover Cloud computing

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Predicting the stock market index using GRU for the year 2020

Predicting the stock market index using GRU for the year 202...

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Emerging Systems and intelligent computing (ESIC), international conference on

作者： S. Narayana Siddabathula Naga Divya Sri Seelam Rohith Kumar Tappiti Ajay Shaik Sapa Vasiq Dept. of CSE SR Gudlavalleru Engineering College Gudlavalleru Andhra Pradesh India

Predicting the behavior of the stock exchange is crucial, since it ranks as one of the foremost frequently studied fields nowadays. It is challenging to forecast the financial sector that requires a careful analysis of historical trends. The precision of conventional methods for predicting changes in stock prices was insufficient. In order to anticipate the values of stocks, deep learning becomes particularly popular technique. Deep Learning is a particularly popular method for anticipating stock prices because of the efficiency in patterns that are not linear. Recurrent neural networks were used to increase prediction accuracy since share price movements are dependent on sequence of time. Within Recurrent Neural Networks (RNNs), their pivotal strength lies in their adeptness at managing sequential data and grasping offered a distinct challenge as well as a chance to investigate sophisticated forecasting algorithms. Nonlinear stock price movements cannot be accurately predicted by traditional data analytics approaches. Owing to its effectiveness in capturing intricate, nonlinear patterns within time-series data, deep learning stands out as the predominant method employed for predicting stock prices. A 25% drop in global stock prices and a 30% reduction in G20 countries market prices were recorded in March 2020. Many businesses experience significant losses, and it may be very challenging for them to recover. While many stockholders suffered losses, several businesses managed to continue making money throughout this period. whether the Gated Recurrent Unit (GRU) model, exhibits superior predictive capabilities for significant fluctuations in stock prices during the year 2020 compared to the LSTM model. This research involves a comparative analysis between GRU and LSTM using diverse stock indices, with historical data sourced from the Yahoo Finance API. Notably, the Mean Absolute Percentage Error (MAPE) values demonstrate that GRU outperforms LSTM in forecasting accur

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Cutting Edge IoT Technology for smart Indian Pharma

Cutting Edge IoT Technology for Smart Indian Pharma

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Advance computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Pushpendu Rakshit Pramod Kumar Srivastava Amity Business School Mumbai Amity University Maharashtra India

ISBN: (纸本)9781728177427

Indian pharmaceutical industry has been anticipated best over the years across world. It's a larger supplier of medical facilities to entire world and a leading nation in pharma manufacturing. Internet of things provides the pharma industry with a selection of new personalized market opportunities, better controlled environment of pharma manufacturing, prevent maintenance of equipment and improve diversified supply chain management system. Owing to the deficiencies in transparency, the pharmaceutical manufacturing may be unable to warrant innocuous and safe medicinal production and circulation. Adoption of internet of things could represent a new competitive advantage for pharma companies exhibiting pharma IoT. This is a conceptual explorative research demonstrates future of Indian pharma, which is in a verge to become self-reliant nation. The use of internet of things, artificial intelligence, big data analytics and tracking of customers carrying internet connected digital devices (TCCICDD) and sensors implanted in wearables would help to locate the customer medical history with health monitoring. This would also alert both the clinical manufacturers and users resulting in designing personalized based better future of Indian pharma industry. Future Pharma would see the business link paraphernalia across development and dispersal by using better, more reliable larger volumes of data to revolutionize manufacturing.

关键词： Wearable computers Medical services Companies Manufacturing Internet of Things Medical diagnostic imaging intelligent sensors

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Harnessing Deep Learning for Fault Detection in Industry 4.0: A Multimodal Approach

Harnessing Deep Learning for Fault Detection in Industry 4.0...

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IEEE international conference on Cognitive Machine Intelligence (CogMI)

作者： Jialie Shen Marie Morrison Haiyan Miao Fengshou Gu Department of Computer Science City St Georgia’s University of London London United Kingdom School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (数字)9798350386721

ISBN: (纸本)9798350386738

The advent of Industry 4.0 has reshaped the modern industries (e.g., Manufacturing, Automotive, Aerospace and Defense), driven by the rapid development of artificial intelligence, smart sensing technologies, and interconnected cyber-physical systems. One of the most important goal for Industry 4.0 is the improvement and enhancement in productivity, reduction of production losses, and operational efficiency. High quality fault detection within different environments is the foundation to achieve these goals. Industry 4.0 emphasizes the use of interconnected systems, smart sensors, and advanced analytics, which generate large volumes of data from production environments. The data complexity and scale, product by the widespread deployment of sensors and IoT devices, demands sophisticated Machine Learning (ML) and data-driven methods. In this paper, we argue that the future of accurate and reliable fault detection lies in the seamless fusion of edge-cloud computing, explainable AI, and adaptive machine learning algorithms capable of processing high-frequency, multimodal data streams in real time. Different kinds of advantages for multimondal learning and core enabling techniques are also discussed and revise. This vision paper outlines the future trajectory of multimodal learning based fault detection, via identifying critical research gaps and promising directions for future study.

关键词： Productivity Machine learning algorithms Fault detection Real-time systems Fourth Industrial Revolution Trajectory Internet of Things intelligent sensors Streams Machine intelligence

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Gaussian Privacy Protector for Online data Communication in a Public World

Gaussian Privacy Protector for Online Data Communication in ...

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IEEE international conference on Big data Security on Cloud (BigdataSecurity), High Performance and smart computing (HPSC) and intelligent data and Security (IDS)

作者： Zahir Alsulaimawi School of Electrical Engineering and Computer Science Oregon State University Corvallis OR

ISBN: (数字)9781728168739

ISBN: (纸本)9781728168746

Capturing privacy requirements for the Internet of Things (IoT) devices is essential to create sufficient public confidence to adopt and promote such devices. However, data privacy-preserving has a particular issue in the era of IoT because of a wide variety of facets and aspects that makes it difficult to formulate. The objective of the proposed research is to design a novel continuous high-dimensional data release mechanism called Gaussian Privacy Protector (GPP). In such cases, GPP can prevent adversaries from mining private information from the released data while maximizing the amount of information revealed about the utility data. We utilize variational lower bounds of mutual information approximation that is implemented as supervised learning using an adversarial training algorithm. We use two datasets: MNIST (hand-written digits) and HAPT-Recognition (Human Activities and Postural Transitions' Recognition using smartphone data) to evaluate our algorithm. Our experimental results on these datasets show that our approach is highly effective in removing private information from the datasets while allowing non-private data to be mined effectively.

关键词： IoT privacy-preserving Bayesian belief nets variational mutual information deep learning dimension reduction big data security dataset Machine Learning Protector Device Component Privacy Research Proposal data communication Gauss Semi-supervised Learning GENERAL PLANT PROJECTS information

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Modified AI-based learning Approach to Secure data using Deep learning algorithms

Modified AI-based learning Approach to Secure Data using Dee...

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intelligent computing and Emerging Communication Technologies (ICEC), international conference on

作者： Venkata Sai Swaroop Reddy Nallapareddy Department of Software Engineering Sony Interactive Entertainment Austin

ISBN: (数字)9798331508432

ISBN: (纸本)9798331508449

intelligent cyber security systems and policies of today rely on deep learning, a technology derived from artificial neural networks (ANNs). Cyber risk analytics that use artificial intelligence (AI) to boost business resilience and understand cyber security risk: pros and cons. Make use of deep learning tools like generative adversarial networks, deep belief networks, deep transfer learning, and deep reinforcement learning to intelligently combat cyber threats. There is also a set of tools that includes generic adversarial networks, auto-encoders, multilayer perceptrons, convolutional neural networks, recurrent neural networks (also known as long short-term memory), and so on. It is also possible to use these networks in ensembles or hybrid techniques. The goal of the backpropagation method is to convert inputs into outputs by making the most of the network weights. When training, a number of optimisation methods are employed, including Adam, Stochastic Gradient Descent, and Limited Memory BFGS. A wide range of cybersecurity issues could be addressed by these neural networks. Building reliable Internet of Things (IoT) systems, detecting harmful botnet traffic, analyzing malware, and conducting security threat analyses are all possible with MLP-based networks. Finding a computationally expensive solution to a complex security model may be difficult with MLP since the model is hyperparameter sensitive and needs adjustment of many different parameters, including the number of hidden layers, neurons, and iterations.

关键词： Deep learning Training Computational modeling smart contracts Transfer learning data models Internet of Things Computer crime Biological neural networks Long short term memory

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