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Scenario-driven Device-to-Device Access Control in Smart Home IoT

Scenario-Driven Device-to-Device Access Control in Smart Hom...

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IEEE international conference on Trust, Privacy and Security in intelligent systems and Applications (TPS-ISA)

作者： Mehrnoosh Shakarami James Benson Ravi Sandhu Department of Computer Science Institute for Cyber Security (ICS) & NSF Center for Security and Privacy Enhanced Cloud Computing (C-SPECC) University of Texas at San Antonio San Antonio US

The Internet of Things (IoT) has been widely integrated in people's everyday lives. As an infrastructure of connected heterogeneous devices, IoT has not yet achieved the seamless integration of device-to-device collaboration which is necessary for real-life home automation. Smart home IoT devices expect to exchange their collected data or status in certain circumstances, in spite of their heterogeneity, viz. working with different communication protocols, IoT platforms, middleware, data and semantics. Deploying appropriate access control models and mechanisms is of utmost importance as any unauthorized access to data could have a cascading violation of privacy, safety and security of users. In this work, we propose a novel device-to-device access control paradigm in the smart home IoT. Our approach relies on message passing as the paradigm for device-to-device interactions. We further introduce actions and scenarios reflecting the chain of events in the smart home context, which facilitates scenario-driven attribute-based access control. Each scenario is triggered by triggering events, based on previously set administrative definitions. We define totally ordered sets of triggering events using priorities to enable conflict resolution for devices which may run into conflicting commands delivered though messages in different ongoing scenarios. The viability of the proposed approach is substantiated via a formal model and an enforcement architecture, backed up by a proof-of-concept implementation which affirms a trade-off between required authorization and efficacy. Potential future challenges are explored in the context of smart home IoT platforms.

关键词： Authorization Privacy Protocols Message passing Computational modeling Semantics Smart homes

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Artificial Intelligence And Machine Learning

Artificial Intelligence And Machine Learning

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2022 IEEE Future Networks World Forum, FNWF 2022

作者： Kataria, Deepak Walid, Anwar Daneshmand, Mahmoud Dutta, Ashutosh Enright, Michael A. Gu, Rentao Lackpour, Alex Ramachandran, Prakash Wang, Honggang Chen, Chi-Ming Chng, Baw Darema, Frederica Ip Junction United States Nokia Bell Labs United States Stevens Institute of Technology United States Johns Hopkins University Applied Physics Lab United States Quantum Dimension Inc. United States Bupt China Drexel University United States Dell Technologies United States EOTF India UMass Dartmouth United States United States Bawman Llc United States Info Symbiotic Systems Society United States

ISBN: (纸本)9781665462501

In the evolution of artificial Intelligence (AI) and machine learning (ML), reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate objects have been widely used. These features enable the creation of intelligent mechanisms for decision support to overcome the limits of human knowledge processing. In addition, ML algorithms enable applications to draw conclusions and make predictions based on existing data without human supervision, leading to quick, near-optimal solutions even in problems with high dimensionality. Hence, autonomy is a key aspect of current and future AI/ML algorithms. This chapter focuses on the development and implementation of AI/ML technologies for 5G and future networks. The objective is to illustrate how these technologies can be smoothly migrated into 5G systems to increase their performance and to decrease their cost. To that end, this chapter presents the Drivers, Needs, Challenges, Enablers, and Potential Solutions identified for the AI/ML field as applicable to future networks over three-, five-, and ten-year horizons. AI/ML applications for 5G are wide and diverse. In this document, some of the key areas are described which includes networking, securing, cloud computing and others. Over time, this white paper will evolve to encompass even more areas where AI/ML technologies can improve future network performance objectives. © 2022 IEEE.

关键词： Cloud computing

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Influence of Big data Capabilities in Knowledge Management—MSMEs 5th

Influence of Big Data Capabilities in Knowledge Management—...

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5th international conference on ICT for Sustainable Development, ICT4SD 2020

作者： Jha, Ravi Shankar Sahoo, Priti Ranjan Bhubaneswar India

ISBN: (纸本)9789811582882

In recent time innovation of open source technologies, commodity hardware, cloud computing, data-driven application, visualization techniques have paved their way for revolutionary changes in business models and data technology space. This has resulted in creating the enormous opportunities for human creativity to produce novel insights. In the "Era of Information" where huge amount of complexity and varied of data being generated every day from multiple sources or point of sales making business requirements/decisions more composite and time intensive. In information dominated Era, Big data (emerging frontier) plays an instrumental role in offering, insight and prediction is at the heart of almost every scientific/business discipline. The discovery of insights and value from data is the central focus of the industry to create new products and services to meet the changing demands. In digital driven economy, Big data capabilities and offering have been relentlessly empowering organizations knowledge management platforms to ease knowledge management practices including acquisition, creation, sharing storage and transfer to meet organizations near term and future goals and accelerate toward success path. Moreover, Big data capabilities helped MSME firms to leap forward in customer-centric view analyzing transactional data across myriad sources and forming new insight and model not only retain existing customers but also attract new customers or opportunities by offering tailor-made products and services. Most of those data centric insights and models translated into organizational knowledge and being captured, groomed and refined using knowledge management platforms and practices. According to Wikibon (Technology Company), Big data market revenues are projected to increase from $42B in 2018 to $103B in 2027 with a CAGR of approx. 10.48%. Therefore, among all the trends, buzz words, noises and quest for answers, it would be interesting to see how Big data is going to sh

关键词： Big data

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RGB Images driven Recognition of Grapevine Varieties 15th

RGB Images Driven Recognition of Grapevine Varieties

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15th international conference on Soft computing Models in Industrial and Environmental Applications, SOCO 2020

作者： Škrabánek, Pavel Doležel, Petr Matoušek, Radomil Junek, Petr Brno University of Technology Brno Czech Republic University of Pardubice Pardubice Czech Republic

ISBN: (纸本)9783030578015

We present a grapevine variety recognition system based on a densely connected convolutional network. The proposed solution is aimed as a data processing part of an affordable sensor for selective harvesters. The system classifies size normalized RGB images according to varieties of grapes captured in the images. We train and evaluate the system on in-field images of ripe grapes captured without any artificial lighting, in a direction of sunshine likewise in the opposite direction. A dataset created for this purpose consists of 7200 images classified into 8 categories. The system distinguishes among seven grapevine varieties and background, where four and three varieties have red and green grapes, respectively. Its average per-class classification accuracy is at 98.10% and 97.47% for red and green grapes, respectively. The system also well differentiates grapes from background. Its overall average per-class accuracy is over 98%. The evaluation results show that conventional cameras in combination with the proposed system allow construction of affordable automatic selective harvesters. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data handling

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Supervising Plant Growth in a Greenhouse

Supervising Plant Growth in a Greenhouse

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international conference on advances in ICT for Emerging Regions

作者： D.L Alexander G.A.G. Hathnapitiya Aruna I. Gamage P.M.P.C Bandara G.W.M.N.U.I.B Giragama N.H.P. Ravi Supunya Swamakantha Faculty of Computing Sri Lanka Institute of Information technology Malabe Sri Lanka

Since the beginning of civilization, agriculture has played a significant part in the economy of a nation. Currently, as the population continues to increase at a rapid rate, arable lands are dwindling alongside urbanization. Even though farmers devote a substantial amount of time and effort to farming, environmental factors such as seasonal shifts can have a significant impact on the crop. Smart agriculture is implemented to boost the production of high-quality goods and address the lack of control over the farming process. The intelligent greenhouse technology proposed here is called “GSense,” and it could boost plant productivity by managing the greenhouse’s climate. In addition, this solution is useful for novices who are just beginning out in agriculture because it can make recommendations to its user. The execution of the solution is complemented by a mobile application and a desktop application via which the user may submit inputs and examine real-time sensor data

关键词： Smart agriculture Productivity Green products Area measurement Sociology Prototypes Robot sensing systems

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A data driven Platform for Improving Performance Assessment of Software Defined Storage Solutions 8th

A Data Driven Platform for Improving Performance Assessment ...

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international conference on Software Process Improvement (CIMPS)

作者： Avila, Silvana De Gyves Cano, Patricia Ortegon Mejia, Ayrton Mondragon Moreno, Ismael Solis Lepe, Arianne Navarro IBM Corp Mexico Software Lab Carretera Castillo 2200 El Salto 45680 Jalisco Mexico

ISBN: (纸本)9783030335472;9783030335465

Performance is one of the most important dimensions to consider for software quality. It is normally used as the principal competitive advantage offered between similar solutions. Ensuring certain performance levels is not trivial for software companies, since it requires exhaustive test runs and massive data analysis. Normally, companies rely on commercial analytics packages to produce performance insights. However, these are for general purposes and require significant effort to be adapted to particular data requirements use cases. In this paper we introduce DDP, a highly scalable data driven performance platform to analyze and exploit performance data for software defined storage solutions. DDP employs big data and analytics technology to collect, store and process performance data in an efficient and integrated way. We have demonstrated the successful application of DDP for Spectrum Scale, a software defined storage solution, where we have been able to implement performance regression data analysis to validate the performance consistency of new produced builds.

关键词： Software quality Performance Big data data science Software process

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Cyber Security With Emerging Technologies & Challenges

Cyber Security With Emerging Technologies & Challenges

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Ashish Choudhary Adarsh Chaudhary Suman Devi School of Computer Science & Engineering Galgotias University Greater Noida India

In order to save the online information systems of private, cooperative, and government organisations and prevent them from attacks that enters into organizations system must use flawless and smart cybersecurity technologies. Some industries technologies which are now emerging are machine learning, blockchain, forecast security, cloud computing and Artificial intelligence (AI). Likewise, security operations centers (SOCs) should increment the development of safety techniques to conquer defensive cases. For every user the advanced cyber security future protocols should be develop with the help of emerging security *** Security has a key aspect in the plot of IT. data storage has been one of the biggest jobs of our time. Cyber crimes is the first thing which occurs in our mind when we start talking about cyber security that is increasing more & more with time. Different governments and organization took several steps to stop these cases. Exclusive of many covers cyber security is still a major challenge for many people. Challenges & difficulties faced by cyber security on the latest developed technologies and emerging cyber security techniques upgrading the world of cyber security will be discussed in this paper. Increasing of cybersecurity threat is overbearing and very unacceptable. A cyber-attack is visualized with the damage done by cyber-threats, but they are impossible to held – except for a digger situation between nations or *** which successfully created protective techniques should show them and provide it to the world. Fastly developing era increases the demand of safe and intelligent cyber defense techniques, including private cybersecurity groups, research centers of universities and government, corporations, public institutions, start-ups, etc.

关键词： Industries Cloud computing Protocols Government Memory Machine learning Safety

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data-driven sustainable smart manufacturing: A conceptual framework

Data-driven sustainable smart manufacturing: A conceptual fr...

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作者： Mahiri, Fadwa Najoua, Aouatif Souda, Souad Ben Signals Distributed Systems and Artificial Intelligence Laboratory Hassan Ii University of Casablanca Casablanca Morocco

ISBN: (纸本)9781728180410

The IoT technologies, big data analytics, cloud computing, and artificial intelligence advances fundamentally impacted today's manufacturing systems and increased the growth of data generated from manufacturing. Big data as a driver key of intelligent manufacturing empowered companies to adopt data driven approaches to enhance competitiveness, efficiency and sustainability. In this paper a historical evolution of data in manufacturing is overviewed, the smart manufacturing its key technologies and sustainability are related, and a conceptual framework from lifecycle product perspective was proposed to show potential applications of big data analytics in sustainable smart manufacturing. © 2020 IEEE.

关键词： Big data

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Digital Twin driven Blockchain Based Reliable and Efficient 6G Edge Network

Digital Twin Driven Blockchain Based Reliable and Efficient ...

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international conference on Distributed computing in Sensor systems (DCOSS)

作者： Mehmet Ozgen Ozdogan Levent Carkacioglu Berk Canberk Department of Computer Engineering Istanbul Technical University Istanbul Turkey Department of Artificial Intelligence and Data Engineering Istanbul Technical University Istanbul Turkey Aselsan Inc. Ankara Turkey

ISBN: (数字)9781665495127

ISBN: (纸本)9781665495134

With the rapid development of intelligent devices in wireless communication, the fifth generation (5G) mobile networks have limited high data rates, low latency, high avail-ability demands. The sixth-generation (6G) mobile network can use Digital-twin (DT) techniques to meet these demands. DT is the virtual representation of physical aspects such as 6G edge nodes. DT optimize the 6G edge nodes parameters using artificial intelligence (AI) and especially machine learning (ML) algorithms. However, AI and ML bring along privacy and security concerns. Therefore, user data must be protected from unauthorized persons during the 6G edge network recovery and expansion phases. In this paper, we proposed a new reliable Digital Twin-based 6G edge network recovery framework using Blockchain technology. We applied the Transfer Learning (TL) technique to improve our proposed framework’s performance. We ensured data privacy and security using TL and Blockchain.

关键词： 6G mobile communication Wireless communication Privacy Machine learning algorithms Transfer learning Sensor systems Blockchains

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Resource Discovery Mechanisms in Shared computing Infrastructure: A Survey 4th

Resource Discovery Mechanisms in Shared Computing Infrastruc...

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4th international conference of Reliable Information and Communication Technology (IRICT)

作者： Alzboon, Mowafaq Salem Mahmuddin, M. Arif, Suki Jadara Univ POB 150459 Irbid 13115 Jordan Univ Utara Malaysia Sch Comp InterNetWorks Res Lab Sintok 06010 Kedah Malaysia

ISBN: (纸本)9783030335823;9783030335816

The increased number of computers, enlarged network bandwidth, more powerful computers, consumed resources and the acceptance of the Internet has driven the ongoing demand for new and better ways to execute huge problems in shared computing infrastructure such as grid and P2P computing. Resource discovery is extremely significant and challenging issues in Grid and P2P. It is complex because resources are heterogeneous, dynamic, geographically distributed, manageability and autonomous. A well-organized resource discovery mechanism is the fundamental requirements for resource sharing infrastructure, as it supports and facilities the resource administration and scheduling of applications. In computing systems like Grid, provide many resources across multiple administrative domains for the achievement of the goal. The major challenge faced by the system is to find the right resources on the network. In this paper, we study the resource discovery mechanisms that have been used in distributed computing systems so far. The mechanism for discovery of resource is divided into different types: centralized, decentralized, and hierarchical. We review the main developments in these categories and outline the new challenges. This paper also provides a discussion of the differences between the mechanisms considered for scalability, dynamism, reliability and real consultations as well as directions for future research.

关键词： Grid computing Resource discovery Reliability

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