Internet of Things, cognitive systems, and blockchain technology are three fields which have created numerous revolutions in software development. It seems that a combination among these fields may results in emerging a high potential and interesting field. Therefore, in this paper, we propose a framework for Internet of Things based on cognitive systems and blockchain technology. To the best of our knowledge, there is no framework for Internet of Things based on cognitive systems and blockchain. In order to study the applicability of the proposed framework, a recommender system based on the proposed framework is suggested. Since the proposed framework is novel, the suggested recommender system is novel. The suggested recommender system is compared with the existing recommender systems. The results show that the suggested recommender system has several benefits which are not available in the existing recommender systems.
The paper titled “A Self-organized Framework for Insurance Based on Internet of Things and Blockchain” has been accepted for oral presentation at and publication in the proceeding of the IEEE 6th International Conference on the Future Internet of Things and Cloud (FiCloud 2018) which will be held in Barcelona, Spain during 06-08 August 2018.
Authors: Monireh Vahdati, Kamran Gholizadeh HamlAbadi, Ali Mohammad Saghiri, Hassan Rashidi
Blockchain and ai__architectures__challenges_and_future_directions_for_enabli...Hani Sami
Nowadays, nobody neglects the fact that #autonomous_vehicles are the future. Nevertheless, many problems stem from letting machines take control of the streets without embedding a sophisticated decision-making process within. This column spotlights the importance of #security in the smart #Internet_of_Vehicles paradigm, and the integration of #Blockchain and #Artificial_Intelligence for acquiring safety on the road by investigating in an #Edge-based architecture that benefits from the decentralized authority and network topology of a hybrid Blockchain in order to leverage highly accurate decision models. Challenges and future directions of such a combination are listed and discussed in the following article.
The full paper is available from:
https://www.researchgate.net/publication/340999407_AI_Blockchain_and_Vehicular_Edge_Computing_for_Smart_and_Secure_IoV_Challenges_and_Directions
An Event-based Middleware for Syntactical Interoperability in Internet of Th...IJECEIAES
Internet of Things (IoT) connecting sensors or devices that record physical observations of the environment and a variety of applications or other Internet services. Along with the increasing number and diversity of devices connected, there arises a problem called interoperability. One type of interoperability is syntactical interoperability, where the IoT should be able to connect all devices through various data protocols. Based on this problem, we proposed a middleware that capable of supporting interoperability by providing a multi-protocol gateway between COAP, MQTT, and WebSocket. This middleware is developed using event-based architecture by implementing publish-subscribe pattern. We also developed a system to test the performance of middleware in terms of success rate and delay delivery of data. The system consists of temperature and humidity sensors using COAP and MQTT as a publisher and web application using WebSocket as a subscriber. The results for data transmission, either from sensors or MQTT COAP has a success rate above 90%, the average delay delivery of data from sensors COAP and MQTT below 1 second, for packet loss rate varied between 0% - 25%. The interoperability testing has been done using Interoperability assessment methodology and found out that ours is qualified.
IMMERSIVE TECHNOLOGIES IN 5G-ENABLED APPLICATIONS: SOME TECHNICAL CHALLENGES ...ijcsit
5G next-generation networking paradigm with its envisioned capacity, coverage, and data transfer rates
provide a developmental field for novel applications scenarios. Virtual, Mixed, and Augmented Reality will
play a key role as visualization, interaction, and information delivery platforms. The recent hardware and
software developments in immersive technologies including AR, VR and MR in terms of the commercial
availability of advanced headsets equipped with XR-accelerated processing units and Software
Development Kits (SDKs) are significantly increasing the penetration of such devices for entertainment,
corporate and industrial use. This trend creates next-generation usage models which rise serious technical
challenges within all networking and software architecture levels to support the immersive digital
transformation. The focus of this paper is to detect, discuss and propose system development approaches
and architectures for successful integration of the immersive technologies in the future information and
communication concepts like Tactile Internet and Internet of Skills.
Deep learning is not merely an AI technique or a software program, but a new class of smart network information technology that is changing the concept of the modern technology project by offering real-time engagement with reality
Deep learning is a data automation method that replaces hard-coded software with a capacity, in the form of a learning network that is trained to perform a task
Role of artificial intelligence in cloud computing, IoT and SDN: Reliability ...IJECEIAES
Information technology fields are now more dominated by artificial intelligence, as it is playing a key role in terms of providing better services. The inherent strengths of artificial intelligence are driving the companies into a modern, decisive, secure, and insight-driven arena to address the current and future challenges. The key technologies like cloud, internet of things (IoT), and software-defined networking (SDN) are emerging as future applications and rendering benefits to the society. Integrating artificial intelligence with these innovations with scalability brings beneficiaries to the next level of efficiency. Data generated from the heterogeneous devices are received, exchanged, stored, managed, and analyzed to automate and improve the performance of the overall system and be more reliable. Although these new technologies are not free of their limitations, nevertheless, the synthesis of technologies has been challenged and has put forth many challenges in terms of scalability and reliability. Therefore, this paper discusses the role of artificial intelligence (AI) along with issues and opportunities confronting all communities for incorporating the integration of these technologies in terms of reliability and scalability. This paper puts forward the future directions related to scalability and reliability concerns during the integration of the above-mentioned technologies and enable the researchers to address the current research gaps.
The paper titled “A Self-organized Framework for Insurance Based on Internet of Things and Blockchain” has been accepted for oral presentation at and publication in the proceeding of the IEEE 6th International Conference on the Future Internet of Things and Cloud (FiCloud 2018) which will be held in Barcelona, Spain during 06-08 August 2018.
Authors: Monireh Vahdati, Kamran Gholizadeh HamlAbadi, Ali Mohammad Saghiri, Hassan Rashidi
Blockchain and ai__architectures__challenges_and_future_directions_for_enabli...Hani Sami
Nowadays, nobody neglects the fact that #autonomous_vehicles are the future. Nevertheless, many problems stem from letting machines take control of the streets without embedding a sophisticated decision-making process within. This column spotlights the importance of #security in the smart #Internet_of_Vehicles paradigm, and the integration of #Blockchain and #Artificial_Intelligence for acquiring safety on the road by investigating in an #Edge-based architecture that benefits from the decentralized authority and network topology of a hybrid Blockchain in order to leverage highly accurate decision models. Challenges and future directions of such a combination are listed and discussed in the following article.
The full paper is available from:
https://www.researchgate.net/publication/340999407_AI_Blockchain_and_Vehicular_Edge_Computing_for_Smart_and_Secure_IoV_Challenges_and_Directions
An Event-based Middleware for Syntactical Interoperability in Internet of Th...IJECEIAES
Internet of Things (IoT) connecting sensors or devices that record physical observations of the environment and a variety of applications or other Internet services. Along with the increasing number and diversity of devices connected, there arises a problem called interoperability. One type of interoperability is syntactical interoperability, where the IoT should be able to connect all devices through various data protocols. Based on this problem, we proposed a middleware that capable of supporting interoperability by providing a multi-protocol gateway between COAP, MQTT, and WebSocket. This middleware is developed using event-based architecture by implementing publish-subscribe pattern. We also developed a system to test the performance of middleware in terms of success rate and delay delivery of data. The system consists of temperature and humidity sensors using COAP and MQTT as a publisher and web application using WebSocket as a subscriber. The results for data transmission, either from sensors or MQTT COAP has a success rate above 90%, the average delay delivery of data from sensors COAP and MQTT below 1 second, for packet loss rate varied between 0% - 25%. The interoperability testing has been done using Interoperability assessment methodology and found out that ours is qualified.
IMMERSIVE TECHNOLOGIES IN 5G-ENABLED APPLICATIONS: SOME TECHNICAL CHALLENGES ...ijcsit
5G next-generation networking paradigm with its envisioned capacity, coverage, and data transfer rates
provide a developmental field for novel applications scenarios. Virtual, Mixed, and Augmented Reality will
play a key role as visualization, interaction, and information delivery platforms. The recent hardware and
software developments in immersive technologies including AR, VR and MR in terms of the commercial
availability of advanced headsets equipped with XR-accelerated processing units and Software
Development Kits (SDKs) are significantly increasing the penetration of such devices for entertainment,
corporate and industrial use. This trend creates next-generation usage models which rise serious technical
challenges within all networking and software architecture levels to support the immersive digital
transformation. The focus of this paper is to detect, discuss and propose system development approaches
and architectures for successful integration of the immersive technologies in the future information and
communication concepts like Tactile Internet and Internet of Skills.
Deep learning is not merely an AI technique or a software program, but a new class of smart network information technology that is changing the concept of the modern technology project by offering real-time engagement with reality
Deep learning is a data automation method that replaces hard-coded software with a capacity, in the form of a learning network that is trained to perform a task
Role of artificial intelligence in cloud computing, IoT and SDN: Reliability ...IJECEIAES
Information technology fields are now more dominated by artificial intelligence, as it is playing a key role in terms of providing better services. The inherent strengths of artificial intelligence are driving the companies into a modern, decisive, secure, and insight-driven arena to address the current and future challenges. The key technologies like cloud, internet of things (IoT), and software-defined networking (SDN) are emerging as future applications and rendering benefits to the society. Integrating artificial intelligence with these innovations with scalability brings beneficiaries to the next level of efficiency. Data generated from the heterogeneous devices are received, exchanged, stored, managed, and analyzed to automate and improve the performance of the overall system and be more reliable. Although these new technologies are not free of their limitations, nevertheless, the synthesis of technologies has been challenged and has put forth many challenges in terms of scalability and reliability. Therefore, this paper discusses the role of artificial intelligence (AI) along with issues and opportunities confronting all communities for incorporating the integration of these technologies in terms of reliability and scalability. This paper puts forward the future directions related to scalability and reliability concerns during the integration of the above-mentioned technologies and enable the researchers to address the current research gaps.
Named Data Networking (NDN) is a recently designed Internet architecture that benefits data names
instead of locations and creates essential changes in the abstraction of network services from "delivering
packets to specific destinations” to "retrieving data with special names" makes. This fundamental change
creates new opportunities and intellectual challenges in all areas, especially network routing and
communication, communication security, and privacy. The focus of this dissertation is on the forwarding
aircraft feature introduced by NDN. Communication in NDN is done by exchanging interest and data
packets
Speech signal compression and encryption based on sudoku, fuzzy C-means and t...IJECEIAES
Compression and encryption of speech signals are essential multimedia technologies. In the field of speech, these technologies are needed to meet the security and confidentiality of information requirements for transferring huge speech signals via a network, and for decreasing storage space for rapid retrieval. In this paper, we propose an algorithm that includes hybrid transformation in order to analyses the speech signal frequencies. The speech signal is then compressed, after removing low and less intense frequencies, to produce a well compressed speech signal and ensure the quality of the speech. The resulting compressed speech is then used as an input in a scrambling algorithm that was proposed on two levels. One of these is an external scramble that works on mixing up the segments of speech that were divided using Fuzzy C-Means and changing their locations. The internal scramble scatters the values of each block internally based on the pattern of a Sudoku puzzle and quadratic map so that the resulting speech is an input to a proposed encryption algorithm using the threefish algorithm. The proposed algorithm proved to be highly efficient in the compression and encryption of the speech signal based on approved statistical measures.
Novel authentication framework for securing communication in internet-of-things IJECEIAES
Internet-of-Things (IoT) offers a big boon towards a massive network of connected devices and is considered to offer coverage to an exponential number of the smart appliance in the very near future. Owing to the nascent stage of evolution of IoT, it is shrouded by security loopholes because of various reasons. Review of existing research-based solution highlights the usage of conventional cryptographic-based solution over the traditional mechanism of data forwarding process between IoT nodes and gateway. The proposed system presents a novel solution to this problem by a model that is capable of performing a highly secured and cost-effective authentication process. The proposed system introduces Authentication Using Signature (AUS) as well as Security with Complexity Reduction (SCR) for the purpose to resist participation of any form of unknown threats. The outcome of the model shows better security strength with faster response time and energy saving of the IoT nodes.
How to leverage blockchain for making machine learning models more accessible Blockchain Council
Machine learning, an application of artificial intelligence, focuses on developing computer programs that can access data and learn on their own. It deals with providing computers the capability to learn without being explicitly programmed
A blockchain is a decentralised database that is shared across computer network nodes. A blockchain acts as a database, storing information in a digital format. The study primarily aims to explore how in the future, block chain technology will alter several areas of the Indian economy. The current study aims to obtain a deeper understanding of blockchain technology's idea and implementation in India, as well as the technology's potential as a disruptive financial technological innovation.
Secondary sources such as reports, journals, papers, and websites were used to compile all the data. Current and relevant information were utilised to help understand the research goals. All the information is rationally organised to fulfil the objectives. The current research focuses on recommendations for enhancing India's Blockchain ecosystem so that it may become one of the best in the world at utilising this new technology.
There are essential security considerations in the systems used by semiconductor companies like TI. Along
with other semiconductor companies, TI has recognized that IT security is highly crucial during web
application developers' system development life cycle (SDLC). The challenges faced by TI web developers
were consolidated via questionnaires starting with how risk management and secure coding can be
reinforced in SDLC; and how to achieve IT Security, PM and SDLC initiatives by developing a prototype
which was evaluated considering the aforementioned goals. This study aimed to practice NIST strategies
by integrating risk management checkpoints in the SDLC; enforce secure coding using static code analysis
tool by developing a prototype application mapped with IT Security goals, project management and SDLC
initiatives and evaluation of the impact of the proposed solution. This paper discussed how SecureTI was
able to satisfy IT Security requirements in the SDLC and PM phases.
5th Meetup - Ethereum & IoT: examples, opportunities and IBM initiativeAlexander Hirner
IoT must shift from proprietary to decentralized and open but secure for a triple win: hardware manufacturers, consumers and system developers. The reasons why Ethereum is a suitable enabler and testbed for protocol extensions in the IoT space are explained with some examples. Privacy and security provide a common topic to other IoT developments.
Artificial intelligence is part of almost every business today; it facilitates business operations, increases productivity, and offers a variety of ways to speed up communication processes. Artificial intelligence and software (or software applications installed on it), as well as automation through AI systems, perform many of the tasks previously performed by employees and workers. Switching to an automated working environment has resulted in a lot of unnecessary business expenses, substantial time savings and a gradual increase in profits. The automation through AI of various business processes has taken many companies and organizations to the next level in terms of production and management.So, this article explains the role of artificial intelligence, machine learning and cloud computing in business. by Dr. Pawan Whig 2019. Artificial Intelligence and Machine Learning In Business. International Journal on Integrated Education. 2, 2 (Jun. 2019). https://journals.researchparks.org/index.php/IJIE/article/view/516/493 https://journals.researchparks.org/index.php/IJIE/article/view/516
Deep Learning Approaches for Information Centric Network and Internet of Thingsijtsrd
Technologies are rapidly increasing with additions to them every single day. Cloud Computing and the Internet of Things IoT have become two very closely associated with future internet technologies. One provides a platform to the other for success, the benefits of which could be from computing to processing and analyzing the information to reduce latency for real time applications. However, there are a few IoT devices that do not support on device processing. An alternate solution of this is Edge Computing, where the consumers can witness a close call with the computation and services. In this work, we will be to studying and discussing the application of combining Deep Learning with IoT and Information Centric Networking. A Convolutional Neural Network CNN model, a Deep Learning model, can make the most reliable data available from the complex IoT environment. Additionally, some Deep Learning models such as Recurrent Neural Network RNN and Reinforcement Learning have also integrated with IoT, which can also collect the information from real time applications. Aashay Pawar "Deep Learning Approaches for Information - Centric Network and Internet of Things" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33346.pdf Paper Url: https://www.ijtsrd.com/engineering/computer-engineering/33346/deep-learning-approaches-for-information--centric-network-and-internet-of-things/aashay-pawar
Machine learning and ai in a brave new cloud worldUlf Mattsson
Machine learning platforms are one of the fastest growing services of the public cloud. ML, an approach and set of technologies that use Artificial Intelligence (AI) concepts, is directly related to pattern recognition and computational learning. Early adopters of AI have now rolled out cloud-based services that are bringing AI to the masses.
How are AI, deep learning, machine learning, big data, and cloud related? Can machine learning algorithms enable the use of an individual’s comprehensive biological information to predict or diagnose diseases, and to find or develop the best therapy for that individual? How is Quantum Computing in the Cloud related to the use of AI and Cybersecurity?
Join this webinar to learn more about:
- Machine Learning, Data Discovery and Cloud
- Cloud-Based ML Applications and ML services from AWS and Google Cloud
- How to Automate Machine Learning
F2CDM: Internet of Things for Healthcare Network Based Fog-to-Cloud and Data-...Istabraq M. Al-Joboury
Internet of Things (IoT) evolves very rapidly over time, since everything such as sensors/actuators linked together from around the world with use of evolution of ubiquitous computing through the Internet. These devices have a unique IP address in order to communicate with each other and transmit data with features of wireless technologies. Fog computing or so called edge computing brings all Cloud features to embedded devices at edge network and adds more features to servers like pre-store data of Cloud, fast response, and generate overhasty users reporting. Fog mediates between Cloud and IoT devices and thus enables new types of computing and services. The future applications take the advantage of combing the two concepts Fog and Cloud in order to provide low delay Fog-based and high capacity of storage Cloud-based. This paper proposes an IoT architecture for healthcare network based on Fog to Cloud and Data in Motion (F2CDM). The proposed architecture is designed and implemented over three sites: Site 1 contains the embedded devices layer, Site 2 consists of the Fog network layer, while Site 3 consists of the Cloud network. The Fog layer is represented by a middleware server in Al-Nahrain University with temporary storage such that the data lives inside for 30 min. During this time, the selection of up-normality in behavior is send to the Cloud while the rest of the data is wiped out. On the other hand, the Cloud stores all the incoming data from Fog permanently. The F2CDM works using Message Queue Telemetry Transport (MQTT) for fast response. The results show that all data can be monitored from the Fog in real time while the critical data can be monitored from Cloud. In addition, the response time is evaluated using traffic generator called Tsung. It has been found that the proposed architecture reduces traffic on Cloud network and provides better data analysis.
Named Data Networking (NDN) is a recently designed Internet architecture that benefits data names
instead of locations and creates essential changes in the abstraction of network services from "delivering
packets to specific destinations” to "retrieving data with special names" makes. This fundamental change
creates new opportunities and intellectual challenges in all areas, especially network routing and
communication, communication security, and privacy. The focus of this dissertation is on the forwarding
aircraft feature introduced by NDN. Communication in NDN is done by exchanging interest and data
packets
Speech signal compression and encryption based on sudoku, fuzzy C-means and t...IJECEIAES
Compression and encryption of speech signals are essential multimedia technologies. In the field of speech, these technologies are needed to meet the security and confidentiality of information requirements for transferring huge speech signals via a network, and for decreasing storage space for rapid retrieval. In this paper, we propose an algorithm that includes hybrid transformation in order to analyses the speech signal frequencies. The speech signal is then compressed, after removing low and less intense frequencies, to produce a well compressed speech signal and ensure the quality of the speech. The resulting compressed speech is then used as an input in a scrambling algorithm that was proposed on two levels. One of these is an external scramble that works on mixing up the segments of speech that were divided using Fuzzy C-Means and changing their locations. The internal scramble scatters the values of each block internally based on the pattern of a Sudoku puzzle and quadratic map so that the resulting speech is an input to a proposed encryption algorithm using the threefish algorithm. The proposed algorithm proved to be highly efficient in the compression and encryption of the speech signal based on approved statistical measures.
Novel authentication framework for securing communication in internet-of-things IJECEIAES
Internet-of-Things (IoT) offers a big boon towards a massive network of connected devices and is considered to offer coverage to an exponential number of the smart appliance in the very near future. Owing to the nascent stage of evolution of IoT, it is shrouded by security loopholes because of various reasons. Review of existing research-based solution highlights the usage of conventional cryptographic-based solution over the traditional mechanism of data forwarding process between IoT nodes and gateway. The proposed system presents a novel solution to this problem by a model that is capable of performing a highly secured and cost-effective authentication process. The proposed system introduces Authentication Using Signature (AUS) as well as Security with Complexity Reduction (SCR) for the purpose to resist participation of any form of unknown threats. The outcome of the model shows better security strength with faster response time and energy saving of the IoT nodes.
How to leverage blockchain for making machine learning models more accessible Blockchain Council
Machine learning, an application of artificial intelligence, focuses on developing computer programs that can access data and learn on their own. It deals with providing computers the capability to learn without being explicitly programmed
A blockchain is a decentralised database that is shared across computer network nodes. A blockchain acts as a database, storing information in a digital format. The study primarily aims to explore how in the future, block chain technology will alter several areas of the Indian economy. The current study aims to obtain a deeper understanding of blockchain technology's idea and implementation in India, as well as the technology's potential as a disruptive financial technological innovation.
Secondary sources such as reports, journals, papers, and websites were used to compile all the data. Current and relevant information were utilised to help understand the research goals. All the information is rationally organised to fulfil the objectives. The current research focuses on recommendations for enhancing India's Blockchain ecosystem so that it may become one of the best in the world at utilising this new technology.
There are essential security considerations in the systems used by semiconductor companies like TI. Along
with other semiconductor companies, TI has recognized that IT security is highly crucial during web
application developers' system development life cycle (SDLC). The challenges faced by TI web developers
were consolidated via questionnaires starting with how risk management and secure coding can be
reinforced in SDLC; and how to achieve IT Security, PM and SDLC initiatives by developing a prototype
which was evaluated considering the aforementioned goals. This study aimed to practice NIST strategies
by integrating risk management checkpoints in the SDLC; enforce secure coding using static code analysis
tool by developing a prototype application mapped with IT Security goals, project management and SDLC
initiatives and evaluation of the impact of the proposed solution. This paper discussed how SecureTI was
able to satisfy IT Security requirements in the SDLC and PM phases.
5th Meetup - Ethereum & IoT: examples, opportunities and IBM initiativeAlexander Hirner
IoT must shift from proprietary to decentralized and open but secure for a triple win: hardware manufacturers, consumers and system developers. The reasons why Ethereum is a suitable enabler and testbed for protocol extensions in the IoT space are explained with some examples. Privacy and security provide a common topic to other IoT developments.
Artificial intelligence is part of almost every business today; it facilitates business operations, increases productivity, and offers a variety of ways to speed up communication processes. Artificial intelligence and software (or software applications installed on it), as well as automation through AI systems, perform many of the tasks previously performed by employees and workers. Switching to an automated working environment has resulted in a lot of unnecessary business expenses, substantial time savings and a gradual increase in profits. The automation through AI of various business processes has taken many companies and organizations to the next level in terms of production and management.So, this article explains the role of artificial intelligence, machine learning and cloud computing in business. by Dr. Pawan Whig 2019. Artificial Intelligence and Machine Learning In Business. International Journal on Integrated Education. 2, 2 (Jun. 2019). https://journals.researchparks.org/index.php/IJIE/article/view/516/493 https://journals.researchparks.org/index.php/IJIE/article/view/516
Deep Learning Approaches for Information Centric Network and Internet of Thingsijtsrd
Technologies are rapidly increasing with additions to them every single day. Cloud Computing and the Internet of Things IoT have become two very closely associated with future internet technologies. One provides a platform to the other for success, the benefits of which could be from computing to processing and analyzing the information to reduce latency for real time applications. However, there are a few IoT devices that do not support on device processing. An alternate solution of this is Edge Computing, where the consumers can witness a close call with the computation and services. In this work, we will be to studying and discussing the application of combining Deep Learning with IoT and Information Centric Networking. A Convolutional Neural Network CNN model, a Deep Learning model, can make the most reliable data available from the complex IoT environment. Additionally, some Deep Learning models such as Recurrent Neural Network RNN and Reinforcement Learning have also integrated with IoT, which can also collect the information from real time applications. Aashay Pawar "Deep Learning Approaches for Information - Centric Network and Internet of Things" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33346.pdf Paper Url: https://www.ijtsrd.com/engineering/computer-engineering/33346/deep-learning-approaches-for-information--centric-network-and-internet-of-things/aashay-pawar
Machine learning and ai in a brave new cloud worldUlf Mattsson
Machine learning platforms are one of the fastest growing services of the public cloud. ML, an approach and set of technologies that use Artificial Intelligence (AI) concepts, is directly related to pattern recognition and computational learning. Early adopters of AI have now rolled out cloud-based services that are bringing AI to the masses.
How are AI, deep learning, machine learning, big data, and cloud related? Can machine learning algorithms enable the use of an individual’s comprehensive biological information to predict or diagnose diseases, and to find or develop the best therapy for that individual? How is Quantum Computing in the Cloud related to the use of AI and Cybersecurity?
Join this webinar to learn more about:
- Machine Learning, Data Discovery and Cloud
- Cloud-Based ML Applications and ML services from AWS and Google Cloud
- How to Automate Machine Learning
F2CDM: Internet of Things for Healthcare Network Based Fog-to-Cloud and Data-...Istabraq M. Al-Joboury
Internet of Things (IoT) evolves very rapidly over time, since everything such as sensors/actuators linked together from around the world with use of evolution of ubiquitous computing through the Internet. These devices have a unique IP address in order to communicate with each other and transmit data with features of wireless technologies. Fog computing or so called edge computing brings all Cloud features to embedded devices at edge network and adds more features to servers like pre-store data of Cloud, fast response, and generate overhasty users reporting. Fog mediates between Cloud and IoT devices and thus enables new types of computing and services. The future applications take the advantage of combing the two concepts Fog and Cloud in order to provide low delay Fog-based and high capacity of storage Cloud-based. This paper proposes an IoT architecture for healthcare network based on Fog to Cloud and Data in Motion (F2CDM). The proposed architecture is designed and implemented over three sites: Site 1 contains the embedded devices layer, Site 2 consists of the Fog network layer, while Site 3 consists of the Cloud network. The Fog layer is represented by a middleware server in Al-Nahrain University with temporary storage such that the data lives inside for 30 min. During this time, the selection of up-normality in behavior is send to the Cloud while the rest of the data is wiped out. On the other hand, the Cloud stores all the incoming data from Fog permanently. The F2CDM works using Message Queue Telemetry Transport (MQTT) for fast response. The results show that all data can be monitored from the Fog in real time while the critical data can be monitored from Cloud. In addition, the response time is evaluated using traffic generator called Tsung. It has been found that the proposed architecture reduces traffic on Cloud network and provides better data analysis.
The Internet of Things (IoT) is used to improve traditional healthcare systems in different aspects, including monitoring patients’ behaviors. Information gathered by sensors in the IoT plays an essential role in healthcare systems. Because of privacy and security issues, the data must be protected against unauthorized changes. On the other hand, Blockchain technology provides a wide range of mechanisms to protect data against changes. Therefore, IoT-based healthcare monitoring using Blockchain constitutes an exciting technological innovation, which may help mitigate security and privacy concerns related to the gathering of information during patient monitoring. In this chapter, the potential applications of IoT–Blockchain systems are studied, and then monitoring mechanisms in healthcare systems are analyzed. To this end, a novel architecture based on recently reported solutions is proposed. The proposed architecture, with the aid of computational power obtained from the IoT, Blockchain and artificial intelligence, can be used in a wide range of solutions aimed at managing the coronavirus disease 2019 (COVID-19). In order to show the potential of the proposed architecture, three case studies namely path recommendation for pandemic situations, health insurance recommendation, and fighting COVID-19 pandemic algorithm have been suggested. The proposed algorithms will facilitate verification of COVID-19 antibody testing, vaccines, and then will issue a valid certificate for them. These certificates will be registered in the Blockchain in a transparent and immutable manner. A panel on the dashboard is dedicated to showing the certificates. The blockchain-based functionalities of the proposed architecture will be useful for equitable COVID-19 vaccine distribution. At the end of this chapter, other applications of the proposed architecture are summarized, which can be used in pandemic situations.
Towards Interoperable, Cognitive and Autonomic IoT Systems: an Agent-based Ap...Universita della Calabria,
Slides related to the conference paper Towards Interoperable, Cognitive and Autonomic IoT Systems: an Agent-based Approach. Claudio Savaglio, Giancarlo Fortino, Mengchu Zhou. IEEE 3rd Word Forum on IoT (WFIoT)
"Toward Cognitive-IoT Applications -- Integrating AI with Fog Computing" by Dr. Frank C. D. Tsai, Workshop of Mobile IoT with Edge Computing and Artificial Intelligence, sponsored by Ministry of Education, Taiwan
Internet of Things is at the top of the Gartner Hype Cyle and scores of entrepreneurs are out building IoT products and solutions. A key question that requires discussion and clarity is – Who will pay for IoT and why? This talk demystifies the novelty of IoT and explains the roles played by various actors in the IoT ecosystem.
Who will pay for IoT and why? - Atanu Roy Chowdhury, Senior Product Manager a...Lounge47
“WHO WILL PAY FOR IoT AND WHY? " was a thought-provoking session. Here are the Lounge47 key takeaways: 1.IoT is a self-organizing system of Internet connected peripheral systems providing new and improved converged services 2.The value of the digitally charged thing in IoT comes from an extension of the local function with new digital services. Thing(s) + IT=Local function + measurements (historical, instantaneous) leading to new services and supercharged functions 3.The IoT hype is about the opportunity to monetize services from 50b connected devices by 2020 4.IoT devices can be classified by human desires - to know (omniscence), for human connection (telepathy), to protect & be protected (safekeeping), to be healthy & vital ( immortality), to move effortlessly (teleportation), to create, make and play (expression) 5.The IoT difference: Edgeware driving value, the “ecosystem of devices” paradigm and the possibilities for entrepreneurship 6.PC to Mobile industry to IoT is transitioning the traditional “top down” to a more collaborative approach 7.Value to the customer: a.high resolution real-time information b.M2M silos interconnected for greater visibility c.Interoperated and leveraged common infrastructure d.low cost solution solving specific consumer pain points e.improved traceability, resource utilization, health and safety 8.Value to developer: a.can handle multiple business models b.can handle multiple deployment models c.can create new products and services to diversify revenues d. Services in addition to devices can be created by developers 8. The actors in the IoT ecosystem – those that, discover new services, deliver supercharged services, create supercharged services, create Smart Things 9.Technical best practices: a.cost of data acquisition is not homogeneous b.diversity in sensors, devices and vendors is endemic c.business requirements can exceed technology reach d.device failures will happen, plan to handle them e. ensure that products are certified f.security is not an afterthought 10.Business best practices: a.device costs are a function of volumes, functionality and robustness b.there is a creepiness factor to IoT solutions c.new services require training d.market potential is hard to guesstimate e.Know your competition f.understand local regulations and tax regimes g.Process changes will be resisted h.Disgruntled customers seldom return 11. Different Business Models should be considered.
In summary, IoT offers significant opportunities, but the successful players will be those that emphasize and deliver value relative to existing services rather than just offer new functionality with undeterminable value.
IoT Challenges: Technological, Business and Social aspectsRoberto Minerva
Internet of Things is promising to be a set of technologies able to have a high impact on how people live, produce, modify and interact with the environment. Such a transformation is driven by increasing technologies capabilities of sensors/actuators, communications, general-purpose hardware, availability of software and programmability of devices. The integration of so different technologies is a problem in itself and IoT is also trying to solve cogent issues of specific problem domains, such as e-health, transportation, manufacturing, and so on. Large IoT systems (e.g., smart cities) stand on their own because the smartness requires integration of different technologies, processes and different administrative domains creating the needs to deal with a complex system. In addition to technological and problem domain specific challenges, there exist further challenges that fall in business, social and regulation realms. They can greatly impact the deployment and the success of IoT deployment. The speech aims at providing a view on some major technologies challenges of IoT and to cover a few critical business and social issues that could hamper the large deployment of IoT systems by providing some examples of implementation.
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A Framework for Cognitive Internet of Things based on Blockchain
1. 01/18
A.M Saghiri
120
Ali Mohammad Saghiri, AmirKabir University of Technology
Monireh Vahdati, Qazvin Islamic Azad University
Kamran Gholizadeh, Qazvin Islamic Azad University
Mohammad Reza Meybodi, AmirKabir University of Technology
Mehdi Dehghan, AmirKabir University of Technology
Hassan Rashidi, Allameh Tabataba'i University
4. Figure 2. Internet of Things Schematic showing the end users and application areas based
on data[1] (Gubbi et al., 2013)
INTERLOCUTION
04/18
Internet of Things
6. INTRODUCTION
• A cognitive system is a system that can modify its behavior on the basis
of experience.
• Cognitive systems can learn to improve their performance while
operating under their unknown environment.
06/18
Cognitive System
7. RELATED WORKS
They adopt cognitive networking concepts and present a
framework for cognitive peer-to-peer networks and then propose
an approach based on cellular learning automata for designing
cognitive engines for solving network management problems in
peer-to-peer networks.
Cognitive Peer-to-Peer networks [3] (Saghiri and Meybodi, 2016)
Cognitive Networks [2] (Thomas et al., 2007)
A cognitive network, capable of perceiving current network conditions
and then planning, learning, and acting according to end-to-end goals.
Cognitive networks are motivated by the complexity, heterogeneity, and
reliability requirements of tomorrow’s networks, which are increasingly
expected to self-organize to meet user and application objectives.
07/18
8. RELATED WORKS
Cognitive Recommender system in IoT [5] (Gholizadeh et al., 2017)
In previous study, we propose a framework for cognitive recommender
systems in IoT. To the best of our knowledge, this study is first cognitive
recommender system framework the IoT. The proposed algorithm is
compared with the existing recommender systems.
Cognitive Internet of Things (CIoT) [4] (Wu et al., 2014)
Propose an operational framework of CIoT, which mainly characterizes
the interactions among five fundamental cognitive tasks: perceptionaction
cycle, massive data analytics, semantic derivation and knowledge
discovery, intelligent decision-making, and on-demand service
provisioning.
08/18
9. IoT Cognitive Blockchain
A general framework for Cognitive
Internet of Things based on
blockchain
CIoT in
Blockchain
THE PROPOSED MODEL
09/18
10. Observe
Payment Unit
Figure 4. A framework for Cognitive Internet of things based on Blockchain
Act
Goal
Requirement
Layer (RL)
Cognitive Process
Layer (CPL)
THE PROPOSED MODEL
Cognitive Engine
Things Management
Layer (TML)
Smart Contract UnitP2P Communication UnitBlockchain Unit
Payment TypeSmart Contract TypeSensor TypeActuators Type Service Type
10/18
11. • Centralized algorithm
• Semi-centralized algorithm
• Fully-distributed algorithm
(RL)
Algorithm
• Finding the goals of the system
• Managing the complexities related to smart contracts
• Managing the complexities related to payment process
• Managing peer-to-peer communication and blockchain
• Intrusion detection
Engine Types
In (CPL)
THE PROPOSED MODEL
11/18
12. • Selecting the goal of the cognitive engine
• Selecting Blockchain Technology
• Selecting Smart Contract
• Selecting appropriate learning mechanism
• Designing a safe cognitive engine
Decision
THE PROPOSED MODEL
12/18
• Centralized approach
• Semi-centralized approach
• Fully-distributed approach
Approach
13. HYBRID RECOMMENDATION FOR
COGNITIVE RECOMMENDER SYSTEM
Coin Goal
Bitcoin Innovative payment network and a new kind of money
Ethereum Decentralized platform that runs smart contracts
Ripple Provide one frictionless experience to send money globally.
Stellar A platform that connects banks, payments systems, and people. Integrate to move
money quickly, reliably, and at almost no cost.
IoTA Enables companies to explore new business-2-business models by making every
technological resource a potential service to be traded on an open.
MONERO A secure, private, and untraceable cryptocurrency.
ICON Comprised of various institutions ranging from: financial institutions, insurance
companies, hospitals, universities and more.
Golem
Creates a decentralized sharing economy of computing power and supplies software
developers with a flexible, reliable and cheap source of computing power.
Dentcoin The first Blockchain concept designed for the Global Dental Industry.
THE PROPOSED MODEL
TABLE 1: IOT ON THE BLOCKCHAIN COINS 13/18
14. Algorithm cognitive engine
Inputs
User’s commands;
User’s information collected by sensors.
Navigation
Smart-contract1// smart-contract 1 is based on health care
information
Smart-contract2// smart-contract 2 is based on shopping history
X-Service// this service is uses collaborative filtering for
recommendation. X-Service is bought by the uses.
This service uses collaborative filtering for
recommendation.
01 Begin
02 Take the commands of the user;
03 Interpret the commands to the goals of the system;
04 Fetch smart contract considering the goals of the system;
//Smart-contract1 and Smart-contract2 are fetched from
Blockchain
05 Collecting information of the sensors and then storing them
by the blockchain unit;
06 Call the X-Service;
// X-Service provide the list of recommendations.
The information of this service is available in the
blockchain
07 Calculate the discount using Smart-contract1 and Smart-
contract2;
08 Compute the final value of payment;
09 Execute payment process using the user selected wallet;
// the selected wallet is based on Bitcoin.
09 End
CASE STUDYAND COMPARISON
14/18
15. HYBRID RECOMMENDATION FOR
COGNITIVE RECOMMENDER SYSTEMCOMPARE WITH OTHER STUDIES
TABLE II: COMPARE WITH OTHER STUDIES
Reference Blockchain based IoT Based Cognitive Based
Noirie et al., [2017] [6] Not Reported Not Reported
Forestiero, (2018) [7] Not Reported Not Reported
Kaur, (2018) [8] Not Reported Not Reported
Hahn, (2018) [9] Not Reported Not Reported
Bursalioglu et al., (2018) [10] Not Reported Not Reported
Frey et al., (2016) [11] Not Reported
Gholizadeh et al., (2017) [5] Not Reported
Our Study
15/18
16. For future research we can apply the technologies of Web of Things in
the proposed framework.
In this paper, we proposed a framework for Internet of Things based
on cognitive systems and blockchain technology. we studied the
applicability of the proposed framework by designing a cognitive
recommender system algorithm in the shopping center.
To the best of our knowledge, our work is novel and there hasn’t been any
prior study on designing Internet of Things based on cognitive systems
and blockchain technology for recommender systems.
CONCLUSION AND FUTURE RESEARCH
16/18
17. [1] Gubbi, J., Buyya, R., Marusic, S. and Palaniswami, M., Internet of Things (IoT): A vision, architectural elements, and future directions.
Future generation computer systems, 29(7), pp.1645-1660, Elsevier, 2013.
[2] Gupta, M., Blockchain For Dummies, IBM Limited Edition, John Wiley & Sons, Inc., 2017.
[3] R. W. Thomas, D. H. Friend, L. A. Dasilva, and A. B. Mackenzie, "Cognitive Networks," in Cognitive Radio, Software Defined Radio, and
Adaptive Wireless Systems, H. Arslan, Ed., ed Dordrecht, The Netherlands: Springer, 2007.
[4] A. M. Saghiri and M. R. Meybodi, "An approach for designing cognitive engines in cognitive peer-to-peer networks," Journal of Network
and Computer Applications, vol. 70, pp. 17-40, Elsevier, 2016.
[5] Q. Wu, G. Ding, Y. Xu, S. Feng, Z. Du, J. Wang, et al., "Cognitive internet of things: a new paradigm beyond connection," IEEE Internet of
Things Journal, vol. 1, pp. 129-143, IEEE, 2014.
[6] K. Gholizadeh HamlAbadi, A. M. Saghiri, M. Vahdati, M. Dehghan TakhtFooladi, and M. R. Meybodi, "A Framework for Cognitive
Recommender Systems in the Internet of Things (IoT)," presented at the 2017 IEEE 4th International Conference on Knowledge-Based
Engineering and Innovation (KBEI), Tehran, Iran, IEEE, 2017.
[7] L. Noirie, M. Le Pallec, and N. Ammar, "Towards automated IoT service recommendation," in Innovations in Clouds, Internet and
Networks (ICIN), 2017 20th Conference on, Paris, France, pp. 103-106, IEEE, 2017
[8] A. Forestiero, "Multi-agent recommendation system in Internet of Things," in Cluster, Cloud and Grid Computing (CCGRID), 2017 17th
IEEE/ACM International Symposium on, Madrid, Spain, pp. 772-775, IEEE, 2017
[9] P. D. Kaur, "Towards Incorporating Context Awareness to Recommender Systems in Internet of Things," in Proceedings of First
International Conference on Smart System, Innovations and Computing, Jaipur, India, pp. 771-780, Springer, 2018
[10] J. Hahn, "Information & Environment: IoT-Powered Recommender Systems," arXiv preprint arXiv:1801.06552, 2018..
[11] O. Y. Bursalioglu, Z. Li, C. Wang, and H. Papadopoulos, "Efficient C-RAN Random Access for IoT Devices: Learning Links via
Recommendation Systems," presented at the 2018 IEEE International Conference on Communications Workshops, Kansas City, MO, USA,
IEEE, 2018.
[12] R. Frey, D. Wörner, and A. Ilic, "Collaborative Filtering on the Blockchain: A Secure Recommender System for e-Commerce," presented
at the 22nd Americas Conference on Information Systems (AMCIS), San Diego, CA, USA, 2016.
REFERENCES
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