The document discusses the integration of cloud computing and internet of things (IoT). It describes how cloud and IoT are complementary technologies that can benefit each other by merging together. The cloud can provide unlimited storage, processing and communication resources to address IoT's constraints, while IoT can extend the cloud's reach to real-world devices and enable new application scenarios. The document reviews literature on this emerging CloudIoT paradigm and analyzes challenges, application scenarios, platforms and open issues in further integrating cloud and IoT technologies.
A premeditated cdm algorithm in cloud computing environment for fpm 2IAEME Publication
This document discusses applying data mining techniques in a cloud computing environment. It begins with an introduction to cloud computing, describing its key characteristics including on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service. The document then provides an overview of common data mining techniques like association, classification, clustering, prediction, and sequential patterns. It reviews recent studies applying these techniques in cloud and distributed systems. The document proposes a new Cloud Data Mining (CDM) algorithm that performs data mining in both cloud and non-cloud environments and compares the costs of each approach.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
A review on orchestration distributed systems for IoT smart services in fog c...IJECEIAES
This paper provides a review of orchestration distributed systems for IoT smart services in fog computing. The cloud infrastructure alone cannot handle the flow of information with the abundance of data, devices and interactions. Thus, fog computing becomes a new paradigm to overcome the problem. One of the first challenges was to build the orchestration systems to activate the clouds and to execute tasks throughout the whole system that has to be considered to the situation in the large scale of geographical distance, heterogeneity and low latency to support the limitation of cloud computing. Some problems exist for orchestration distributed in fog computing are to fulfil with high reliability and low-delay requirements in the IoT applications system and to form a larger computer network like a fog network, at different geographic sites. This paper reviewed approximately 68 articles on orchestration distributed system for fog computing. The result shows the orchestration distribute system and some of the evaluation criteria for fog computing that have been compared in terms of Borg, Kubernetes, Swarm, Mesos, Aurora, heterogeneity, QoS management, scalability, mobility, federation, and interoperability. The significance of this study is to support the researcher in developing orchestration distributed systems for IoT smart services in fog computing focus on IR4.0 national agenda.
Agent based Aggregation of Cloud Services- A Research Agendaidescitation
-Cloud computing has come to the forefront as it
overcomes some of the issues in computing such as storage
space and processing power. It enables ubiquitous accessing
and processing of information without the need of excessive
computing facilities. In this work, we plan to brief some of the
issues in aggregating the cloud services, discovering futuristic
cloud service requests, develop a repository of the same and
propose an agent based Quality of Service (QoS) provisioning
system for cloud clients.
Using of Cloud Technologies in the Process of Preparing Future Specialists fo...ijtsrd
As part of the study, the specifics of the functioning of modern high tech enterprises related to the organization and automation of production processes through the use of various types of information systems are analyzed, and the growing importance of cloud technologies as the optimal means of implementing these processes is determined. Usanov Mehriddin Mustafayevich "Using of Cloud Technologies in the Process of Preparing Future Specialists for Professional Activity" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31715.pdf Paper Url :https://www.ijtsrd.com/humanities-and-the-arts/education/31715/using-of-cloud-technologies-in-the-process-of-preparing-future-specialists-for-professional-activity/usanov-mehriddin-mustafayevich
Group chain is a scalable public blockchain of two-chain structure. Built on the principle of reducing consensus size to achieve high transaction efficiency. Employs the leader group with a small size to collectively commit blocks. Group chain reduce transaction confirmation latency. Achieves a throughput of over 800 TPS with a small leader group size. Performance is close to 600 TPS even in a ledger group size is 100.
The document provides an overview of cloud computing, including its history, key features, deployment models, layers, engineering, storage, interconnection of clouds, and issues. It discusses how cloud computing evolved from earlier concepts and allows users to access computing resources over the internet on an as-needed basis. Some challenges include privacy, legal concerns, use of open standards and security issues.
A Comparison of Cloud Execution Mechanisms Fog, Edge, and Clone Cloud Computing IJECEIAES
Cloud computing is a technology that was developed a decade ago to provide uninterrupted, scalable services to users and organizations. Cloud computing has also become an attractive feature for mobile users due to the limited features of mobile devices. The combination of cloud technologies with mobile technologies resulted in a new area of computing called mobile cloud computing. This combined technology is used to augment the resources existing in Smart devices. In recent times, Fog computing, Edge computing, and Clone Cloud computing techniques have become the latest trends after mobile cloud computing, which have all been developed to address the limitations in cloud computing. This paper reviews these recent technologies in detail and provides a comparative study of them. It also addresses the differences in these technologies and how each of them is effective for organizations and developers.
A premeditated cdm algorithm in cloud computing environment for fpm 2IAEME Publication
This document discusses applying data mining techniques in a cloud computing environment. It begins with an introduction to cloud computing, describing its key characteristics including on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service. The document then provides an overview of common data mining techniques like association, classification, clustering, prediction, and sequential patterns. It reviews recent studies applying these techniques in cloud and distributed systems. The document proposes a new Cloud Data Mining (CDM) algorithm that performs data mining in both cloud and non-cloud environments and compares the costs of each approach.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
A review on orchestration distributed systems for IoT smart services in fog c...IJECEIAES
This paper provides a review of orchestration distributed systems for IoT smart services in fog computing. The cloud infrastructure alone cannot handle the flow of information with the abundance of data, devices and interactions. Thus, fog computing becomes a new paradigm to overcome the problem. One of the first challenges was to build the orchestration systems to activate the clouds and to execute tasks throughout the whole system that has to be considered to the situation in the large scale of geographical distance, heterogeneity and low latency to support the limitation of cloud computing. Some problems exist for orchestration distributed in fog computing are to fulfil with high reliability and low-delay requirements in the IoT applications system and to form a larger computer network like a fog network, at different geographic sites. This paper reviewed approximately 68 articles on orchestration distributed system for fog computing. The result shows the orchestration distribute system and some of the evaluation criteria for fog computing that have been compared in terms of Borg, Kubernetes, Swarm, Mesos, Aurora, heterogeneity, QoS management, scalability, mobility, federation, and interoperability. The significance of this study is to support the researcher in developing orchestration distributed systems for IoT smart services in fog computing focus on IR4.0 national agenda.
Agent based Aggregation of Cloud Services- A Research Agendaidescitation
-Cloud computing has come to the forefront as it
overcomes some of the issues in computing such as storage
space and processing power. It enables ubiquitous accessing
and processing of information without the need of excessive
computing facilities. In this work, we plan to brief some of the
issues in aggregating the cloud services, discovering futuristic
cloud service requests, develop a repository of the same and
propose an agent based Quality of Service (QoS) provisioning
system for cloud clients.
Using of Cloud Technologies in the Process of Preparing Future Specialists fo...ijtsrd
As part of the study, the specifics of the functioning of modern high tech enterprises related to the organization and automation of production processes through the use of various types of information systems are analyzed, and the growing importance of cloud technologies as the optimal means of implementing these processes is determined. Usanov Mehriddin Mustafayevich "Using of Cloud Technologies in the Process of Preparing Future Specialists for Professional Activity" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31715.pdf Paper Url :https://www.ijtsrd.com/humanities-and-the-arts/education/31715/using-of-cloud-technologies-in-the-process-of-preparing-future-specialists-for-professional-activity/usanov-mehriddin-mustafayevich
Group chain is a scalable public blockchain of two-chain structure. Built on the principle of reducing consensus size to achieve high transaction efficiency. Employs the leader group with a small size to collectively commit blocks. Group chain reduce transaction confirmation latency. Achieves a throughput of over 800 TPS with a small leader group size. Performance is close to 600 TPS even in a ledger group size is 100.
The document provides an overview of cloud computing, including its history, key features, deployment models, layers, engineering, storage, interconnection of clouds, and issues. It discusses how cloud computing evolved from earlier concepts and allows users to access computing resources over the internet on an as-needed basis. Some challenges include privacy, legal concerns, use of open standards and security issues.
A Comparison of Cloud Execution Mechanisms Fog, Edge, and Clone Cloud Computing IJECEIAES
Cloud computing is a technology that was developed a decade ago to provide uninterrupted, scalable services to users and organizations. Cloud computing has also become an attractive feature for mobile users due to the limited features of mobile devices. The combination of cloud technologies with mobile technologies resulted in a new area of computing called mobile cloud computing. This combined technology is used to augment the resources existing in Smart devices. In recent times, Fog computing, Edge computing, and Clone Cloud computing techniques have become the latest trends after mobile cloud computing, which have all been developed to address the limitations in cloud computing. This paper reviews these recent technologies in detail and provides a comparative study of them. It also addresses the differences in these technologies and how each of them is effective for organizations and developers.
Cloud computing allows for location-independent computing resources that can be accessed on demand. It has evolved from earlier technologies like utility computing and now commonly uses a client-server model. The key features of cloud computing include agility, cost savings, scalability, and reliability, though privacy and security concerns still need to be addressed.
The document proposes an Inter-Cloud Architecture (ICA) to address integration and interoperability issues in heterogeneous multi-domain cloud environments. The key components of the ICA include: (1) a multi-layer Cloud Services Model for vertical integration of cloud services, (2) an Inter-Cloud Control and Management Plane for application/infrastructure control across clouds, and (3) an Inter-Cloud Federation Framework to federate independently managed cloud infrastructure from different providers. The ICA aims to facilitate on-demand provisioning of complex cloud infrastructure involving multiple providers and integration with legacy services.
Most downloaded article for an year in academia - Advanced Computing: An Inte...acijjournal
Advanced Computing: An International Journal (ACIJ) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the advanced computing. The journal focuses on all technical and practical aspects of high performance computing, green computing, pervasive computing, cloud computing etc. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding advances in computing and establishing new collaborations in these areas.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document provides an overview of cloud computing presented by Rajesh Math. It begins with an introduction to cloud computing, defining it as internet-based computing using shared resources provided on-demand. It then discusses the history, architecture, types (public, private, hybrid), components (SaaS, PaaS, IaaS), advantages like flexibility and cost savings, and disadvantages like security issues and dependency on providers. Recent developments in cloud computing from 2007-2008 are summarized. The document concludes that while cloud computing provides benefits, concerns around security and privacy must still be addressed.
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.
Efficient and reliable hybrid cloud architecture for big databaseijccsa
The objective of our paper is to propose a Cloud computing framework which is feasible and necessary for
handling huge data. In our prototype system we considered national ID database structure of Bangladesh
which is prepared by election commission of Bangladesh. Using this database we propose an interactive
graphical user interface for Bangladeshi People Search (BDPS) that use a hybrid structure of cloud
computing handled by apache Hadoop where database is implemented by HiveQL. The infrastructure
divides into two parts: locally hosted cloud which is based on “Eucalyptus” and the remote cloud which is
implemented on well-known Amazon Web Service (AWS). Some common problems of Bangladesh aspect
which includes data traffic congestion, server time out and server down issue is also discussed.
Cloud Computing Role in Information technologyKHakash
Simply put, cloud computing is the delivery of computing services—including servers, storage, databases, networking, software, analytics, and intelligence—over the Internet (“the cloud”) to offer faster innovation, flexible resources, and economies of scale.
Information technology (IT) is part of the value chain and corporate strategy of companies (PORTER, 1998). This area has been highlighted by its rapid development being responsible for several technological innovations, which affect the strategic positioning of companies. In the 1970s the computing model based on both proprietary technology and high cost large computers, known as mainframes, contributed to the formation of oligopolies in companies providing IT services which provided data processing to consumer companies.
This document compares and contrasts cloud computing and grid computing. Grid computing refers to cooperation between multiple computers and servers to boost computational power, with a focus on high-capacity CPU tasks. Cloud computing delivers on-demand access to shared computing resources like networks, servers, storage and applications via the internet. Key differences include grid computing having a lower level of abstraction and scalability compared to cloud computing. Cloud computing also has stronger fault tolerance, is more widely accessible via the internet, and offers real-time services through its utility-based pricing model.
Cloud computing and security issues in theIJNSA Journal
Cloud computing has formed the conceptual and infrastructural basis for tomorrow’s computing. The
global computing infrastructure is rapidly moving towards cloud based architecture. While it is important
to take advantages of could based computing by means of deploying it in diversified sectors, the security
aspects in a cloud based computing environment remains at the core of interest. Cloud based services and
service providers are being evolved which has resulted in a new business trend based on cloud technology.
With the introduction of numerous cloud based services and geographically dispersed cloud service
providers, sensitive information of different entities are normally stored in remote servers and locations
with the possibilities of being exposed to unwanted parties in situations where the cloud servers storing
those information are compromised. If security is not robust and consistent, the flexibility and advantages
that cloud computing has to offer will have little credibility. This paper presents a review on the cloud
computing concepts as well as security issues inherent within the context of cloud computing and cloud
infrastructure.
A Study on Cloud and Fog Computing Security Issues and SolutionsAM Publications
Cloud computing is the significant part of the data world. The security level in cloud is undefined. Fog computing is the new buzz word added to the technical world. And the term Fog was coined by CISCO. The need for Fog computing is security and gets the data more closely to the end-user. Fog Computing is not going to replace the Cloud computing, it will be acting as the intermediate layer for securing the data which is stored inside the cloud. The principal idea of this paper is to provide data safety measures to the Cloud storage through Fog Computing. Fog Computing will be playing the vital role for the future technology. The Internet of Things (IoT) will be using the Fog computing to implement the smart World concept. So, in the future we have to handle huge amount of data and we need to provide the security for the Data. This study gives the security solutions available for the different issues.
Cloud computing has become the mainstream of the emerging technologies for information interchange and accessibility. With such systems, the information accessed from any geographic location on this planet with some decent kind of internet connection. Applying machine learning together with artificial intelligence in dealing with the problem of energy reduction in cloud data center is an innovative idea. A large combination of Artificial intelligence is playing a significant role in cloud environment. For that matter, the Big organization providers like Amazon have taken steps to ensure that they can continue to expand their fast-growing cloud services to commensurate with the fast growth of population. These companies have built large data centers in remote parts of the world to overcome a shortage of information. These centers consume significant amounts of electrical energy. There is often a lot of energy wastage. According to IDC white paper, data centers have tremendously wasted billions of energy regarding billing and cash. Additionally, researchers have argued that by the year 2020 the energy consumption rate would have doubled. Research in this area is still a hot topic. This paper seeks to address the energy efficiency issue at a Cloud Data Center using machine learning methodologies, principles, and practices. This article also aims to bring out possible future implementation methods for artificially intelligent agents that would help reduce energy wastage at a Cloud data center and thus help ameliorate the great big energy problem at hand.
Cloud computing is Internet based development and use of computer technology. It is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. Users need not have knowledge of, expertise in, or control over the technology infrastructure "in the cloud" that supports them. Cloud computing is a hot topic all over the world nowadays, through which customers can access information and computer power via a web browser. As the adoption and deployment of cloud computing increase, it is critical to evaluate the performance of cloud environments. Currently, modeling and simulation technology has become a useful and powerful tool in cloud computing research community to deal with these issues. Cloud simulators are required for cloud system testing to decrease the complexity and separate quality concerns. Cloud computing means saving and accessing the data over the internet instead of local storage. In this paper, we have provided a short review on the types, models and architecture of the cloud environment.
Fog Computing – Enhancing the Maximum Energy Consumption of Data Servers.dbpublications
Fog Computing and IoT systems make use of end-user premises devices as local servers. Here, we are identifying the scenarios for which running applications from NDCs are more energy-efficient than running the same applications from MDC. With the complete survey and analysis of various energy consumption factors such as different flow-variants and time-variants with respect to the Network Equipment we found two energy consumption use cases and respective results. Parameters such as current Load, Pmax, Cmax, Incremental Energy etc evolved with respect to system structure and various data related parameters leading to the conclusion that the NDC utilizes relatively reduced factor of energy comparative to the MDC. The study reveals that NDC as a part of Fog makeweights the MDCs to accompany respective applications, especially in the scenarios where IoT based applications are used where end users are the source data providers and can maximize the server utilization.
Fog computing has emerged as a new paradigm for architecting IoT applications that require greater scalability, performance and security. This talk will motivate the need to Fog Computing and explain what it is and how it differs from other initiatives in Telco such as Mobile/Multiple-Access Edge Computing.
The Riisk and Challllenges off Clloud ComputtiingIJERA Editor
Cloud computing is a computing technology aiming to share storage, computation, and services transparently
among a massive users. Current cloud computing systems pose serious limitation to protecting the confidentiality
of user data. Since the data share and stored is presented in unencrypted forms to remote machines owned and
operated by third party service providers despite it sensitivity (example contact address, mails), the risks of
disclosing user confidential data by service providers may be quite high and the risk of attacking cloud storage
by third party is also increasing. The purpose of this study is to review researches done on this technology,
identify the security risk and explore some techniques for protecting users‟ data from attackers in the cloud.
Fog computing or fog networking, also known as fogging, is an architecture that uses edge devices to carry out a substantial amount of computation, storage, and communication locally and routed over the internet backbone.
CLOUD COMPUTING IN EDUCATION: POTENTIALS AND CHALLENGES FOR BANGLADESHIJCSEA Journal
Cloud Computing is an emerging technology. It is a growing technology which can change traditional IT systems. It plays a major role in today’s technology sector. People are using it every day through one way or another. Education sector is not out of this phenomenon. At the present time the teaching method is changing and students are becoming much technology based and therefore it is necessary that we think about the most recent technologies to incorporate in the teaching and learning methods. By sharing Information technology related services in the cloud, educational institutions can better concentrate on offering students, teachers, faculty and staff the essential instruments. Bangladesh is a developing country. So applying this technology on education sector is a huge challenge for Bangladesh. In this paper it is discussed that how Bangladesh can be benefited by applying cloud in education and its challenges followed by some case studies and success stories.
Living in the Cloud: Hosting Data & Apps Using the Google Infrastructureguest517f2f
The document discusses Google's cloud computing infrastructure and services, including App Engine and Google data APIs. App Engine allows developers to run web applications on Google's servers and infrastructure, providing automatic scaling. Google data APIs provide a standardized way to access and share data through RESTful protocols like AtomPub, allowing developers to build applications that integrate with Google and other services. Examples are given of using the Google data protocol for CRUD operations and querying through demonstrations of sample applications built with App Engine and the APIs.
I have 74 modules on the CPAN and I haven\'t
yet given a talk about most of them. I\'ll pick ten
useful but less-known modules of mine and give two
minute introductions to each. Léon Brocard
We are from the internet --- we know the value of open source.
Hardware and storage is unfortunately real, but you can outsource it all.
This talk will guide you through how to exploit cloud computing today
to make you happier and more efficient Léon Brocard
Cloud computing allows for location-independent computing resources that can be accessed on demand. It has evolved from earlier technologies like utility computing and now commonly uses a client-server model. The key features of cloud computing include agility, cost savings, scalability, and reliability, though privacy and security concerns still need to be addressed.
The document proposes an Inter-Cloud Architecture (ICA) to address integration and interoperability issues in heterogeneous multi-domain cloud environments. The key components of the ICA include: (1) a multi-layer Cloud Services Model for vertical integration of cloud services, (2) an Inter-Cloud Control and Management Plane for application/infrastructure control across clouds, and (3) an Inter-Cloud Federation Framework to federate independently managed cloud infrastructure from different providers. The ICA aims to facilitate on-demand provisioning of complex cloud infrastructure involving multiple providers and integration with legacy services.
Most downloaded article for an year in academia - Advanced Computing: An Inte...acijjournal
Advanced Computing: An International Journal (ACIJ) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the advanced computing. The journal focuses on all technical and practical aspects of high performance computing, green computing, pervasive computing, cloud computing etc. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding advances in computing and establishing new collaborations in these areas.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document provides an overview of cloud computing presented by Rajesh Math. It begins with an introduction to cloud computing, defining it as internet-based computing using shared resources provided on-demand. It then discusses the history, architecture, types (public, private, hybrid), components (SaaS, PaaS, IaaS), advantages like flexibility and cost savings, and disadvantages like security issues and dependency on providers. Recent developments in cloud computing from 2007-2008 are summarized. The document concludes that while cloud computing provides benefits, concerns around security and privacy must still be addressed.
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.
Efficient and reliable hybrid cloud architecture for big databaseijccsa
The objective of our paper is to propose a Cloud computing framework which is feasible and necessary for
handling huge data. In our prototype system we considered national ID database structure of Bangladesh
which is prepared by election commission of Bangladesh. Using this database we propose an interactive
graphical user interface for Bangladeshi People Search (BDPS) that use a hybrid structure of cloud
computing handled by apache Hadoop where database is implemented by HiveQL. The infrastructure
divides into two parts: locally hosted cloud which is based on “Eucalyptus” and the remote cloud which is
implemented on well-known Amazon Web Service (AWS). Some common problems of Bangladesh aspect
which includes data traffic congestion, server time out and server down issue is also discussed.
Cloud Computing Role in Information technologyKHakash
Simply put, cloud computing is the delivery of computing services—including servers, storage, databases, networking, software, analytics, and intelligence—over the Internet (“the cloud”) to offer faster innovation, flexible resources, and economies of scale.
Information technology (IT) is part of the value chain and corporate strategy of companies (PORTER, 1998). This area has been highlighted by its rapid development being responsible for several technological innovations, which affect the strategic positioning of companies. In the 1970s the computing model based on both proprietary technology and high cost large computers, known as mainframes, contributed to the formation of oligopolies in companies providing IT services which provided data processing to consumer companies.
This document compares and contrasts cloud computing and grid computing. Grid computing refers to cooperation between multiple computers and servers to boost computational power, with a focus on high-capacity CPU tasks. Cloud computing delivers on-demand access to shared computing resources like networks, servers, storage and applications via the internet. Key differences include grid computing having a lower level of abstraction and scalability compared to cloud computing. Cloud computing also has stronger fault tolerance, is more widely accessible via the internet, and offers real-time services through its utility-based pricing model.
Cloud computing and security issues in theIJNSA Journal
Cloud computing has formed the conceptual and infrastructural basis for tomorrow’s computing. The
global computing infrastructure is rapidly moving towards cloud based architecture. While it is important
to take advantages of could based computing by means of deploying it in diversified sectors, the security
aspects in a cloud based computing environment remains at the core of interest. Cloud based services and
service providers are being evolved which has resulted in a new business trend based on cloud technology.
With the introduction of numerous cloud based services and geographically dispersed cloud service
providers, sensitive information of different entities are normally stored in remote servers and locations
with the possibilities of being exposed to unwanted parties in situations where the cloud servers storing
those information are compromised. If security is not robust and consistent, the flexibility and advantages
that cloud computing has to offer will have little credibility. This paper presents a review on the cloud
computing concepts as well as security issues inherent within the context of cloud computing and cloud
infrastructure.
A Study on Cloud and Fog Computing Security Issues and SolutionsAM Publications
Cloud computing is the significant part of the data world. The security level in cloud is undefined. Fog computing is the new buzz word added to the technical world. And the term Fog was coined by CISCO. The need for Fog computing is security and gets the data more closely to the end-user. Fog Computing is not going to replace the Cloud computing, it will be acting as the intermediate layer for securing the data which is stored inside the cloud. The principal idea of this paper is to provide data safety measures to the Cloud storage through Fog Computing. Fog Computing will be playing the vital role for the future technology. The Internet of Things (IoT) will be using the Fog computing to implement the smart World concept. So, in the future we have to handle huge amount of data and we need to provide the security for the Data. This study gives the security solutions available for the different issues.
Cloud computing has become the mainstream of the emerging technologies for information interchange and accessibility. With such systems, the information accessed from any geographic location on this planet with some decent kind of internet connection. Applying machine learning together with artificial intelligence in dealing with the problem of energy reduction in cloud data center is an innovative idea. A large combination of Artificial intelligence is playing a significant role in cloud environment. For that matter, the Big organization providers like Amazon have taken steps to ensure that they can continue to expand their fast-growing cloud services to commensurate with the fast growth of population. These companies have built large data centers in remote parts of the world to overcome a shortage of information. These centers consume significant amounts of electrical energy. There is often a lot of energy wastage. According to IDC white paper, data centers have tremendously wasted billions of energy regarding billing and cash. Additionally, researchers have argued that by the year 2020 the energy consumption rate would have doubled. Research in this area is still a hot topic. This paper seeks to address the energy efficiency issue at a Cloud Data Center using machine learning methodologies, principles, and practices. This article also aims to bring out possible future implementation methods for artificially intelligent agents that would help reduce energy wastage at a Cloud data center and thus help ameliorate the great big energy problem at hand.
Cloud computing is Internet based development and use of computer technology. It is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. Users need not have knowledge of, expertise in, or control over the technology infrastructure "in the cloud" that supports them. Cloud computing is a hot topic all over the world nowadays, through which customers can access information and computer power via a web browser. As the adoption and deployment of cloud computing increase, it is critical to evaluate the performance of cloud environments. Currently, modeling and simulation technology has become a useful and powerful tool in cloud computing research community to deal with these issues. Cloud simulators are required for cloud system testing to decrease the complexity and separate quality concerns. Cloud computing means saving and accessing the data over the internet instead of local storage. In this paper, we have provided a short review on the types, models and architecture of the cloud environment.
Fog Computing – Enhancing the Maximum Energy Consumption of Data Servers.dbpublications
Fog Computing and IoT systems make use of end-user premises devices as local servers. Here, we are identifying the scenarios for which running applications from NDCs are more energy-efficient than running the same applications from MDC. With the complete survey and analysis of various energy consumption factors such as different flow-variants and time-variants with respect to the Network Equipment we found two energy consumption use cases and respective results. Parameters such as current Load, Pmax, Cmax, Incremental Energy etc evolved with respect to system structure and various data related parameters leading to the conclusion that the NDC utilizes relatively reduced factor of energy comparative to the MDC. The study reveals that NDC as a part of Fog makeweights the MDCs to accompany respective applications, especially in the scenarios where IoT based applications are used where end users are the source data providers and can maximize the server utilization.
Fog computing has emerged as a new paradigm for architecting IoT applications that require greater scalability, performance and security. This talk will motivate the need to Fog Computing and explain what it is and how it differs from other initiatives in Telco such as Mobile/Multiple-Access Edge Computing.
The Riisk and Challllenges off Clloud ComputtiingIJERA Editor
Cloud computing is a computing technology aiming to share storage, computation, and services transparently
among a massive users. Current cloud computing systems pose serious limitation to protecting the confidentiality
of user data. Since the data share and stored is presented in unencrypted forms to remote machines owned and
operated by third party service providers despite it sensitivity (example contact address, mails), the risks of
disclosing user confidential data by service providers may be quite high and the risk of attacking cloud storage
by third party is also increasing. The purpose of this study is to review researches done on this technology,
identify the security risk and explore some techniques for protecting users‟ data from attackers in the cloud.
Fog computing or fog networking, also known as fogging, is an architecture that uses edge devices to carry out a substantial amount of computation, storage, and communication locally and routed over the internet backbone.
CLOUD COMPUTING IN EDUCATION: POTENTIALS AND CHALLENGES FOR BANGLADESHIJCSEA Journal
Cloud Computing is an emerging technology. It is a growing technology which can change traditional IT systems. It plays a major role in today’s technology sector. People are using it every day through one way or another. Education sector is not out of this phenomenon. At the present time the teaching method is changing and students are becoming much technology based and therefore it is necessary that we think about the most recent technologies to incorporate in the teaching and learning methods. By sharing Information technology related services in the cloud, educational institutions can better concentrate on offering students, teachers, faculty and staff the essential instruments. Bangladesh is a developing country. So applying this technology on education sector is a huge challenge for Bangladesh. In this paper it is discussed that how Bangladesh can be benefited by applying cloud in education and its challenges followed by some case studies and success stories.
Living in the Cloud: Hosting Data & Apps Using the Google Infrastructureguest517f2f
The document discusses Google's cloud computing infrastructure and services, including App Engine and Google data APIs. App Engine allows developers to run web applications on Google's servers and infrastructure, providing automatic scaling. Google data APIs provide a standardized way to access and share data through RESTful protocols like AtomPub, allowing developers to build applications that integrate with Google and other services. Examples are given of using the Google data protocol for CRUD operations and querying through demonstrations of sample applications built with App Engine and the APIs.
I have 74 modules on the CPAN and I haven\'t
yet given a talk about most of them. I\'ll pick ten
useful but less-known modules of mine and give two
minute introductions to each. Léon Brocard
We are from the internet --- we know the value of open source.
Hardware and storage is unfortunately real, but you can outsource it all.
This talk will guide you through how to exploit cloud computing today
to make you happier and more efficient Léon Brocard
Networks are great in theory, but have some well-known limitations that you should bear in mind when using them. Come find out what they are so that you don't make the same mistakes.
Living in the Cloud: Hosting Data & Apps Using the Google InfrastructurePamela Fox
In the modern web, the user rules. Nearly every successful web app has to worry about scaling to an exponentially growing user base and giving those users multiple ways of interacting with their data. Pamela Fox, Maps API Support Engineer & Developer advocate, provides an overview of two technologies - Google App Engine and the Google Data APIs - that aim to make web development and data portability easier.
Discovering and Understanding The Security Issues In IoT CloudCSCJournals
The rapid growth and adoption of IoT technologies in sectors of life are challenged by the resources constrained IoT devices. However, the growth of IoT technologies can be enhanced by integrating them with cloud computing. Hence, a new area of computing called IoT Cloud or CloudIoT has emerged. That is, the data collected from the IoT technologies are stored and processed in the cloud infrastructure so that IoT technologies are relived from resources constrained issue. As a result, some new classes of security and privacy issues are introduced. This paper presents security issues pertaining to IoT cloud.
The document discusses the integration of Internet of Things (IoT) and cloud computing, referred to as Cloud of Things. It identifies several key issues with this integration, such as protocol support, energy efficiency, resource allocation, identity management, and security/privacy. Potential solutions are provided for some of the issues. The conclusion discusses the need for more study on the impact of these issues based on the specific IoT application and services provided.
Cloud computing is a new technology which refers to an infrastructure where both software and hardware application are operate for the network with the help of internet. Cloud computing provide these services with the help of rule know as you pay as you go on. Internet of things (IoT) is a new technology which is growing rapidly in the field of telecommunications. The aim of IoT devices is to connect all things around us to the internet and thus provide us with smarter cities, intelligent homes and generally more comfortable lives. The combation of cloud computing and IoT devices make rapid development of both technologies. In this paper, we present information about IoT and cloud computing with a focus on the security issues of both technologies. Concluding we present the contribution of cloud computing to the IoT technology. Thus, it shows how the cloud computing technology improves the function of the IoT. Finally present the security challenges of both technologies IoT and cloud computing.
Assignment Of Sensing Tasks To IoT Devices Exploitation Of A Social Network ...Dustin Pytko
The document proposes exploiting a Social Internet of Things (SIoT) paradigm to assign sensing tasks to IoT devices in a mobile crowdsensing (MCS) scenario. A new algorithm is presented to fairly allocate resources and sensing tasks among devices while extending device lifetime. The algorithm creates an energy consumption profile for each device and task that is shared over the SIoT network. Emulations showed the proposed approach extended the time for the first device's battery to deplete by over 40% compared to alternative approaches.
Hardware/Software Interoperability and Single Point Vulnerability Problems of...BRNSS Publication Hub
As reiterated by many authors, internet of things (IoT) is the network of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, actuators, and connectivity which enables these things to connect and exchange data, creating opportunities for more direct integration of the physical world into computer-based systems, resulting in efficiency improvements, economic benefits, and reduced human exertions. This is made possible by the communications models with the enabling technologies which make communications possible among IoT connected devices, although, with drawbacks. These drawbacks are the major reasons for adoption problems of IoT services by the society. This paper carried out an investigative study on previous works on the societal applications and adoption problems of IoT, IoT communications models, and pros and cons of IoT. Through the study, it was revealed that for IoT devices and services to be widely adopted with no or minimal problems, future IoT technology will not only address the known drawbacks but also will require hardware and software components that are highly interoperable, dependable, reconfigurable, and, in many applications, certifiable.
The Role of Cloud-MANET Framework in the Internet of Things (IoT)AlAtfat
In the next generation of computing, Mobile ad-hoc network
(MANET) will play a very important role in the Internet of Things (IoT). The
MANET is a kind of wireless networks that are self-organizing and auto
connected in a decentralized system. Every device in MANET can be moved
freely from one location to another in any direction. They can create a network
with their neighbors’ smart devices and forward data to another device. The IoTCloud-MANET framework of smart devices is composed of IoT, cloud
computing, and MANET. This framework can access and deliver cloud services
to the MANET users through their smart devices in the IoT framework where all
computations, data handling, and resource management are performed. The smart
devices can move from one location to another within the range of the MANET
network. Various MANETs can connect to the same cloud, they can use cloud
service in a real time. For connecting the smart device of MANET to cloud needs
integration with mobile apps. My main contribution in this research links a new
methodology for providing secure communication on the internet of smart
devices using MANET Concept in 5G. The research methodology uses the
correct and efficient simulation of the desired study and can be implemented in a
framework of the Internet of Things in 5G.
This document discusses hardware/software interoperability and single point vulnerability problems with internet of things (IoT) systems. It summarizes previous work that identified key barriers to wider IoT adoption, including a desire for components that are highly interoperable, dependable, reconfigurable, and certifiable. The document then examines IoT communication models like device-to-device, device-to-cloud, and device-to-gateway. While these models provide flexibility, interoperability challenges can arise from proprietary protocols and data formats. Future IoT systems will need to address these issues to gain broad societal acceptance.
The document discusses hardware/software interoperability and single point vulnerability problems with internet of things (IoT) systems. It provides background on IoT, describing how connected devices can communicate through different models including device-to-device, device-to-cloud, and device-to-gateway. The paper reviews literature on societal applications and adoption barriers for IoT. It finds that for widespread adoption, future IoT technologies will need highly interoperable and dependable hardware/software that is reconfigurable and certifiable to address known problems and vulnerabilities.
Performance Analysis of Internet of Things Protocols Based Fog/Cloud over Hig...Istabraq M. Al-Joboury
The Internet of Things (IoT) becomes the future of a global data field in which the embedded devices communicate with each other, exchange data and making decisions through the Internet. IoT could improves the qualityoflife in smart cities, but a massive amount of data from different smart devices could slow down or crash database systems. In addition, IoT data transfer to Cloud for monitoring information and generating feedback thus will lead to highdelay in infrastructure level. Fog Computing can help by offering services closer to edge devices. In this paper, we propose an efficient system architecture to mitigate the problem of delay. We provide performance analysis like responsetime, throughput and packet loss for MQTT (Message Queue Telemetry Transport) and HTTP (Hyper Text Transfer Protocol) protocols based on Cloud or Fog serverswith large volume of data form emulated traffic generator working alongsidewith one real sensor. We implement both protocols in the same architecture, with low cost embedded devices to local and Cloud servers with different platforms. The results show that HTTP response time is 12.1 and 4.76 times higher than MQTT Fog and cloud based located in the same geographical area of the sensors respectively. The worst case in performance is observed when the Cloud is public and outside the country region. The results obtained for throughput shows that MQTT has the capability to carry the data with available bandwidth and lowest percentage of packet loss. We also prove that the proposed Fog architecture is an efficient way to reduce latency and enhance performance in Cloud based IoT.
A Survey on the Security Issues of Software Defined Networking Tool in Cloud ...ijcnes
The Advent of the digital age has led to a rise in different types of data with every passing day. In fact, it is expected that half of the total data used around the world will be on the cloud nowadays. This complex data needs to be stored, processed and analyzed for information gaining that can be used for several organizations. Cloud computing provides an appropriate platform for Software Defined Networking (SDN) in communicating and computing requirements of the latter. It makes cloud-based networking a viable research field in the current scenario. However, several issues addressed and risk needs to be mitigated in the L2 cloud server. Virtual networks and cloud federation being considered in the network virtualization over L3 cloud router. This research work explores the existing research challenges and discusses open issues for the security in cloud computing and its uses in the relevant field by means of a comparative analysis of L2 server L3 router based on SDN tools. Also, an analysis of such issues are discussed and summarized. Finally, the best tool identified for the use cloud security.
Architectural design of IoT-cloud computing integration platformTELKOMNIKA JOURNAL
An integration between the Internet of Things (IoT) and cloud computing can potentially leverage the
utilization of both sides. As the IoT based system is mostly composed by the interconnection of pervasive
and constrained devices, it can take a benefit of virtually unlimited resources of cloud entity i.e storage
and computation services to store and process its sensed data. On the other hand, the cloud computing
system may get benefit from IoT by broadening its reach to real world environment applications. In order
to incarnate this idea, a cloud software platform is needed to provide an integration layer between the IoT
and cloud computing taking into account the heterogenity of network communication protocols as well as the
security and data management issues. In this study, an architectural design of IoT-cloud platform for IoT and
cloud computing integration is presented. The proposed software platform can be decomposed into five main
components namely cloud-to-device interface, authentication, data management, and cloud-to-user interface
component. In general, the cloud-to-device interface acts as a data transmission endpoint between the whole
cloud platform system and its IoT devices counterpart. Before a session of data transmission established,
the communication interface contact the authentication component to make sure that the corresponding IoT
device is legitimate before it allowed for sending the sensor data to cloud environment. Notice that a valid IoT
device can be registered to the cloud system through web console component. The received sensor data
are then collected in data storage component. Any stored data can be further analyzed by data processing
component. User or any developed applications can then retrieve collected data, either raw or processed
data, through API data access and web console.
THE INTERNET OF THINGS: NEW INTEROPERABILITY, MANAGEMENT AND SECURITY CHALLENGESIJNSA Journal
The Internet of Things (IoT) brings connectivity to about every objects found in the physical space. It extends connectivity to everyday objects. From connected fridges, cars and cities, the IoT creates opportunities in numerous domains. However, this increase in connectivity creates many prominent challenges. This paper provides a survey of some of the major issues challenging the widespread adoption of the IoT. Particularly, it focuses on the interoperability, management, security and privacy issues in the IoT. It is concluded that there is a need to develop a multifaceted technology approach to IoT security,
management, and privacy.
THE INTERNET OF THINGS: NEW INTEROPERABILITY, MANAGEMENT AND SECURITY CHALLENGESIJNSA Journal
The Internet of Things (IoT) brings connectivity to about every objects found in the physical space. It
extends connectivity to everyday objects. From connected fridges, cars and cities, the IoT creates
opportunities in numerous domains. However, this increase in connectivity creates many prominent
challenges. This paper provides a survey of some of the major issues challenging the widespread adoption
of the IoT. Particularly, it focuses on the interoperability, management, security and privacy issues in the
IoT. It is concluded that there is a need to develop a multifaceted technology approach to IoT security,
management, and privacy.
THE INTERNET OF THINGS: NEW INTEROPERABILITY, MANAGEMENT AND SECURITY CHALLENGESIJNSA Journal
The Internet of Things (IoT) brings connectivity to about every objects found in the physical space. It extends connectivity to everyday objects. From connected fridges, cars and cities, the IoT creates opportunities in numerous domains. However, this increase in connectivity creates many prominent challenges. This paper provides a survey of some of the major issues challenging the widespread adoption of the IoT. Particularly, it focuses on the interoperability, management, security and privacy issues in the IoT. It is concluded that there is a need to develop a multifaceted technology approach to IoT security, management, and privacy.
IRJET - Cloud Computing and IoT ConvergenceIRJET Journal
This document discusses the convergence of cloud computing and the Internet of Things (IoT). It first provides background on both cloud computing and IoT, noting how cloud computing enables distributed computing resources and how IoT involves billions of interconnected devices. It then argues that the cloud features of on-demand access, scalability, and resource pooling are essential for supporting the IoT world. The document also discusses how cloud computing can offer sharing of resources, location independence, virtualization, and elasticity to benefit IoT. Finally, it outlines some challenges of combining IoT and cloud technologies, such as handling large volumes of real-time and unstructured IoT data from distributed sources.
SECURITY AND PRIVACY AWARE PROGRAMMING MODEL FOR IOT APPLICATIONS IN CLOUD EN...ijccsa
This document summarizes a research paper on privacy-preserving techniques for IoT data in cloud environments. It introduces two differential privacy algorithms: 1) Generic differential privacy (GenDP) which provides generalized privacy protection for homogeneous and heterogeneous IoT metadata through data portioning. 2) Cluster-based differential privacy which groups similar data into clusters before defining classifiers to validate privacy. The paper evaluates these techniques and finds the cluster-based approach offers better security than customized interactive algorithms while maintaining data utility. Overall, the study presents new differential privacy methods for anonymizing IoT metadata stored in the cloud.
A MIDDLEWARE FOR THE INTERNET OF THINGSIJCNCJournal
The Internet of Things (IoT) connects everyday objects including a vast array of sensors, actuators, and smart devices, referred to as “things” to the Internet, in an intelligent and pervasive fashion. This connectivity gives rise to the possibility of using the tracking capabilities of things to impinge on the location privacy of users. Most of the existing management and location privacy protection solutions do not consider the low-cost and low-power requirements of things; or, they do not account for the heterogeneity, scalability, or autonomy of communications supported in the IoT. Moreover, these traditional solutions do not consider the case where a user wishes to control the granularity of the disclosed information based on
the context of their use (e.g. based on the time or the current location of the user). To fill this gap, a middleware, referred to as the Internet of Things Management Platform (IoT-MP) is proposed in this paper.
This document discusses trends in fog computing. It explores how fog computing addresses limitations of cloud computing for IoT applications by processing data closer to the edge. Machine learning is being integrated into fog computing to help manage resources and address challenges. Deep learning integrated with fog computing can improve security by detecting cyberattacks. Fog computing provides benefits over cloud for applications like storage as a service and building smart cities due to lower latency and reduced network usage. The document examines various studies on fog computing architectures, applications, and emerging technologies.
This document summarizes a research paper on the adoption of cloud computing in Nepal. The study found that both government and business organizations in Nepal have similar low levels of cloud computing adoption. Major barriers to adoption identified were security concerns, lack of connectivity, availability issues, vendor location restrictions, and legal uncertainties. The paper reviews the history and definitions of cloud computing. It also profiles existing cloud service providers in Nepal and the types of cloud services they offer. The research methodology used surveys and interviews to assess the current status of cloud adoption among organizations in Nepal.
This review article provides a comprehensive overview of recent advances in evolving computing paradigms including cloud, edge, and fog computing. It discusses the limitations of cloud computing that led to the development of edge and fog computing paradigms. Specifically, it explores how edge and fog computing aim to address issues like latency, bandwidth constraints, and privacy concerns of cloud computing. It also examines the convergence of machine learning with edge/fog computing and its significance. Additionally, it identifies several open challenges and future research directions in these evolving computing paradigms.
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
Mending Clothing to Support Sustainable Fashion_CIMaR 2024.pdfSelcen Ozturkcan
Ozturkcan, S., Berndt, A., & Angelakis, A. (2024). Mending clothing to support sustainable fashion. Presented at the 31st Annual Conference by the Consortium for International Marketing Research (CIMaR), 10-13 Jun 2024, University of Gävle, Sweden.
1. On the Integration of Cloud Computing
and Internet of Things
Alessio Botta, Walter de Donato, Valerio Persico, Antonio Pescap´e
University of Napoli Federico II
fa.botta, walter.dedonato, valerio.persico, pescapeg@unina.it
Abstract—Cloud computing and Internet of Things (IoT), two
very different technologies, are both already part of our life. Their
massive adoption and use is expected to increase further, making
them important components of the Future Internet. A novel
paradigm where Cloud and IoT are merged together is foreseen
as disruptive and an enabler of a large number of application
scenarios. In this paper we focus our attention on the integration
of Cloud and IoT, which we call the CloudIoT paradigm. Many
works in literature have surveyed Cloud and IoT separately:
their main properties, features, underlying technologies, and open
issues. However, to the best of our knowledge, these works
lack a detailed analysis of the CloudIoT paradigm. To bridge
this gap, in this paper we review the literature about the
integration of Cloud and IoT. We start analyzing and discussing
the need for integrating them, the challenges deriving from such
integration, and how these issues have been tackled in literature.
We then describe application scenarios that have been presented
in literature, as well as platforms – both commercial and open
source – and projects implementing the CloudIoT paradigm.
Finally, we identify open issues, main challenges and future
directions in this promising field.
I. INTRODUCTION AND MOTIVATION
The Internet of Things (IoT) paradigm is based on in-telligent
and self configuring nodes (things) interconnected
in a dynamic and global network infrastructure. It represents
one of the most disruptive technologies, enabling ubiquitous
and pervasive computing scenarios. IoT is generally charac-terized
by real world and small things with limited storage
and processing capacity, and consequential issues regarding
reliability, performance, security, and privacy. On the other
hand, Cloud computing has virtually unlimited capabilities in
terms of storage and processing power, is a much more mature
technology, and has most of the IoT issues at least partially
solved. Thus, a novel IT paradigm in which Cloud and IoT are
two complementary technologies merged together is expected
to disrupt both current and future Internet [59], [23]. We call
this new paradigm CloudIoT. This paper reviews the literature
about the integration of Cloud and IoT a really promising
topic for both research and industry. We have reviewed the
rich and articulate state of the art in this field by considering
a large number of papers proposing an integrated usage of
Cloud and IoT, and published between 2008 and 2013 in
different, selected venues. As shown in Fig. 1, both topics
gained popularity in the last few years (Fig. 1a), and the
number of papers dealing with Cloud and IoT separately shows
an increasing trend since 2008 (Fig. 1b)1. On the other hand,
1Data have been obtained from Google Trends (https://www.google.com/
trends/) and Scholar (scholar.google.com/) web facilities.
a more recent and rapidly increasing trend deals with Cloud
and IoT together. Following the indications reported in [40],
we adopt the research methodology schematically depicted in
Fig. 2. We first provide a temporal characterization of the
literature aiming at showing in a qualitative way the temporal
behavior of the research and the common interest about the
CloudIoT paradigm. Second, we provide a detailed discussion
on the CloudIoT paradigm, highlighting the complementarity
and the need for their integration. Third, we detail the new
application scenarios stemming from the adoption of the
CloudIoT paradigm. Fourth, jointly analyzing the CloudIoT
paradigm and the application scenarios, we derive the hot
topics and related issues for research. Fifth, we describe
the main platforms (both commercial and open source) and
research projects in the field of CloudIoT. Finally, thanks to
the previous seven steps, we derive the open issues and future
directions in the field of CloudIoT.
(a) Interest from Google research trends. (b) Interest by content and title.
Fig. 1: Research and interest trends about Cloud and IoT.
II. CLOUD AND IOT: THE NEED FOR THEIR
INTEGRATION
The two worlds of Cloud and IoT have seen an independent
evolution. However, several mutual advantages deriving from
their integration have been identified in literature and are fore-seen
in the future. On the one hand, IoT can benefit from the
virtually unlimited capabilities and resources of Cloud to com-pensate
its technological constraints (e.g., storage, processing,
energy). Specifically, the Cloud can offer an effective solution
to implement IoT service management and composition as well
as applications that exploit the things or the data produced
by them. On the other hand, the Cloud can benefit from
IoT by extending its scope to deal with real world things in
a more distributed and dynamic manner, and for delivering
new services in a large number of real life scenarios. The
complementary characteristics of Cloud and IoT arising from
the different proposals in literature and inspiring the CloudIoT
2. Fig. 2: The research methodology adopted in this work.
paradigm are reported in Tab. I. Essentially, the Cloud acts
as intermediate layer between the things and the applications,
where it hides all the complexity and the functionalities
necessary to implement the latter. This framework will impact
future application development, where information gathering,
processing, and transmission will produce new challenges to
be addressed, also in a multi-cloud environment [30]. In the
following, we summarize the issues solved and the advantages
obtained when adopting the CloudIoT paradigm.
Storage resources. IoT involves by definition a large amount
of information sources (i.e., the things), which produce a huge
amount of non-structured or semi-structured data [30] having
the three characteristics typical of Big Data [60]: volume (i.e.,
data size), variety (i.e., data types), and velocity (i.e., data
generation frequency). Hence it implies collecting, accessing,
processing, visualizing, archiving, sharing, and searching large
amounts of data [50]. Offering virtually unlimited, low-cost,
and on-demand storage capacity, Cloud is the most convenient
and cost effective solution to deal with data produced by
IoT [50]. This integration realizes a new convergence sce-nario
[47], where new opportunities arise for data aggrega-tion
[32], integration [56], and sharing with third parties [56].
Once into the Cloud, data can be treated in a homogeneous
manner through standard APIs [32], can be protected by
applying top-level security [27], and directly accessed and
visualized from any place [50].
Computational resources. IoT devices have limited process-ing
resources that do not allow on-site data processing. Data
collected is usually transmitted to more powerful nodes where
aggregation and processing is possible, but scalability is chal-lenging
to achieve without a proper infrastructure. The unlim-ited
processing capabilities of Cloud and its on-demand model
allow IoT processing needs to be properly satisfied and enable
analyses of unprecedented complexity [27], [47]. Data-driven
decision making and prediction algorithms would be possible
at low cost and would provide increasing revenues and reduced
risks [56]. Other perspectives would be to perform real-time
processing (on-the-fly) [50], [27], to implement scalable, real-time,
collaborative, sensor-centric applications [32], to manage
complex events [50], and to implement task offloading for
energy saving [53].
Communication resources. One of the requirements of IoT
TABLE I: Complementarity and Integration of Cloud and IoT.
IoT Cloud
pervasive ubiquitous (resources usable
(things placed everywhere) from everywhere)
real world things virtual resources
limited virtually unlimited
computational capabilities computational capabilities
limited storage or virtually unlimited
no storage capabilities storage capabilities
Internet as a point of convergence Internet for service delivery
big data source means to manage big data
is to make IP-enabled devices communicate through dedicated
hardware, and the support for such communication can be
very expensive. Cloud offers an effective and cheap solution
to connect, track, and manage any thing from anywhere at
any time using customized portals and built-in apps [50].
Thanks to the availability of high speed networks, it enables
the monitoring and control of remote things [50], [32], [47],
their coordination [32], [47], [52], their communications [32],
and the real-time access to the produced data [50].
New capabilities. IoT is characterized by a very high het-erogeneity
of devices, technologies, and protocols. Therefore,
scalability, interoperability, reliability, efficiency, availability,
and security can be very difficult to obtain. The integration
with the Cloud solves most of these problems [32], [27], [52],
also providing additional features such as ease-of-access, ease-of-
use, and reduced deployment costs [27].
New paradigms. The adoption of the CloudIoT paradigm
enables new scenarios for smart services and applications
based on the extension of Cloud through the things [50], [52]:
SaaS (Sensing as a Service) [50], [56], [27], providing
ubiquitous access to sensor data;
SAaaS (Sensing and Actuation as a Service) [50],
enabling automatic control logics implemented in the
Cloud;
SEaaS (Sensor Event as a Service) [50], [27], dis-patching
messaging services triggered by sensor
events;
SenaaS (Sensor as a Service) [56], enabling ubiquitous
management of remote sensors;
DBaaS (DataBase as a Service) [56], enabling ubiq-uitous
database management;
DaaS (Data as a Service) [56], providing ubiquitous
access to any kind of data;
EaaS (Ethernet as a Service) [56], providing ubiqui-tous
layer-2 connectivity to remote devices;
IPMaaS (Identity and Policy Management as a Ser-vice)
[56], enabling ubiquitous access to policy and
identity management functionalities;
VSaaS (Video Surveillance as a Service) [49], pro-viding
ubiquitous access to recorded video and imple-menting
complex analyses in the Cloud.
III. APPLICATIONS
In this section we describe a wide set of applications that
are made possible or significantly improved thanks to the
3. Fig. 3: Application scenarios driven by the CloudIoT paradigm.
CloudIoT paradigm. For each application we point out the
challenges, which we discuss in detail in Sec. IV.
Healthcare. IoT and multimedia technologies have made their
entrance in the healthcare field thanks to ambient-assisted
living and telemedicine [57]. Smart devices, mobile Internet,
and Cloud services contribute to the continuous and systematic
innovation of Healthcare and enable cost effective, efficient,
timely, and high-quality ubiquitous medical services [41], [33].
Pervasive healthcare applications generate a vast amount of
sensor data that have to be managed properly for further
analysis and processing [29]. The adoption of Cloud in this
scenario leads to the abstraction of technical details, eliminat-ing
the need for expertise in, or control over, the technology
infrastructure [15], [43], and it represents a promising solution
for managing healthcare sensor data efficiently [29]. It further
makes mobile devices suited for health information delivery,
access and communication, also on the go [46], enhancing
medical data security, availability, and redundancy [41], [15].
Moreover, it enables the execution (in the Cloud) of secure
multimedia-based health services, overcoming the issue of run-ning
heavy multimedia security algorithms on devices with
limited computational capacity and small batteries [46]. In this
field, common issues related to management, technology, se-curity,
and law have been investigated: interoperability, system
security, streaming Quality of Service (QoS), and dynamically
increasing storage are commonly considered obstacles [41].
Smart City. IoT can provide a common middleware for
future-oriented Smart-City services [17], [52], acquiring infor-mation
from different heterogeneous sensing infrastructures,
accessing all kinds of geo-location and IoT technologies (e.g.,
3D representations through RFID sensors and geo-tagging),
and exposing information in a uniform way. A number of
recently proposed solutions suggest to use Cloud architectures
to enable the discovery, connection, and integration of sensors
and actuators, thus creating platforms able to provision and
support ubiquitous connectivity and real-time applications for
smart cities [45]. Frameworks can consist of a sensor platform
(with APIs for sensing and actuating) and a Cloud platform
for the automatic management, analysis, and control of big
data from large-scale, real world devices [52]. This type of
advanced service model hides the complexity of the underlying
Cloud infrastructure, whilst at the same time meeting com-plex
public sector requirements for Cloud, such as security,
heterogeneity, interoperability, scalability, extensibility, high
reactivity, and configurability [17], [52]. Moreover, Cloud-based
platforms help to make it easier for third parties to
develop and provide IoT plugins enabling any device to be
connected to the Cloud [17]. While cities share common
concerns – such as the need to effectively share information
within and between cities and the desire for enhanced cross-border
protocols – they lack a common infrastructure and
methodology for collaborating [17], which may result from
the application of a holistic approach [52]. Common issues are
related to security, resilience, and real-time interactions [52].
Smart Home and Smart Metering. IoT has large applica-tion
in home environments, where heterogeneous embedded
devices enable the automation of common in-house activities.
In this scenario, the Cloud is the best candidate for building
flexible applications with only a few lines of code, making
home automation a trivial task [39]. In order to let a variety
of independent single-family smart homes access reusable
services over the Internet, the resulting solution should satisfy
three crucial requirements [54]: internal network intercon-nection
(i.e., every digital appliance in smart home should
be able to interconnect with any other), intelligent remote
control (i.e., appliances and services in the smart home should
be intelligently manageable by any device from anywhere),
and automation (i.e., interconnected appliances within the
home should implement their functions via linking to services
provided by smart-home oriented Cloud). Several smart-home
applications proposed in literature involve (wireless) sensor
networks and implement smart metering solutions to provide
recognition of appliances [24], intelligent management of
energy consumption [24], lighting, heating, and air condi-tioning
[36]. Several issues must be resolved to materialize
this vision [36]. Home devices should be web-enabled, their
discovery and service description should be standardized, and
the interaction with them should be uniform. Administration
and control of devices could be leveraged by deploying more
powerful computing devices, acting as mediators among IoT
devices and Cloud components, for implementing complex
functionalities on top of them, mitigating the volume and the
frequency of communications with the Cloud.
Video Surveillance. Intelligent video surveillance has become
a tool of the greatest importance for several security-related
applications. As an alternative to in-house, self-contained man-agement
systems, complex video analytics require Cloud-based
solutions (VSaaS [49]) to properly satisfy the requirements of
storage (e.g., stored media is centrally secured, fault-tolerant,
on-demand, scalable, and accessible at high-speed) and pro-cessing
(e.g., video processing, computer vision algorithms
and pattern recognition modules to extract knowledge from
scenes). Proposed solutions [34] intelligently store and manage
video content originating from (IP and analog) cameras, and
efficiently deliver it to multiple user devices through the
Internet, by distributing the processing tasks over the physical
server resources on-demand, in a load-balanced and fault-tolerant
fashion.
Automotive and Smart Mobility. As an emerging technology,
4. IoT is expected to offer promising solutions to transform
transportation systems and automobile services (i.e., Intelli-gent
Transportation Systems, ITS). The integration of Cloud
technologies with WSNs, RFID, satellite networks, and other
intelligent transportation technologies represents a promising
opportunity to tackle the main current challenges [38]. A
new generation of IoT-based vehicular data Clouds can be
developed and deployed to bring many business benefits, such
as increasing road safety, reducing road congestion, managing
traffic, and recommending car maintenance or fixing [38]. The
literature proposes several examples of multi-layered, Cloud-based
vehicular data platforms that merge Cloud computing
and IoT technologies. These platforms aim at providing real-time,
cheap, secure, and on-demand services to customers,
through different types of Clouds, which also include tempo-rary
vehicular Clouds (i.e., formed by the vehicles representing
the Cloud datacenters [20]). Vehicular Clouds are designed
to expand the conventional Clouds in order to increase on-demand
the whole Cloud computing, processing, and stor-age
capabilities, by using under-utilized facilities of vehicles.
Thanks to these platforms, innovative, vehicular, data Cloud
services can be deployed (e.g., intelligent parking Cloud
service, or vehicular data-mining Cloud service) [38]. More
in general, ethernet and IP-based routing (being less expen-sive
and more flexible than related technologies) are claimed
to be very important technologies for future communication
networks in electric vehicles, enabling the link between the
vehicle electronics and the Internet, integrating the vehicle
into a typical IoT, and meeting the demand for powerful
communication with Cloud-based services [37]. Several issues
have been identified in literature affecting this application
scenario [38], [20]. The huge number of vehicles and their
dynamically changing number make system scalability difficult
to achieve. Vehicles moving at various speeds frequently cause
intermittent communication impacting performance, reliability,
and QoS. The lack of an established infrastructure makes it dif-ficult
to implement effective authentication and authorization
mechanisms, with impacts on security and privacy provision
[44]. The lack of global standards and experimental studies on
realistic ITS-Clouds affect interoperability.
Smart Energy and Smart Grid. IoT and Cloud can be
effectively merged to provide intelligent management of en-ergy
distribution and consumption in both local and wide area
heterogeneous environments. In the first case, for instance,
lighting could be provided where and when strictly necessary
by exploiting the information collected by different types of
nodes [36]. Such nodes have sensing, processing, and network-ing
capabilities, but limited resources. Hence, computing tasks
should be properly distributed among them or demanded to the
Cloud, where more complex and comprehensive decisions can
be made. In the second case, the problem on energy alternative
and compatible use can be solved by integrating system data in
the Cloud, while providing self-healing, mutual operation and
participation of the users, optimal electricity quality, distributed
generation, and demand response [55]. The integration of
Cloud platforms in this IoT scenario increases the concerns
about security and privacy issues for Smart Grid software
deployment for utilities. These concerns should be adequately
addressed to realize the full potential of such application:
consumers should gain more confidence in sharing data to help
improving and optimizing services offered [51].
IV. HOT TOPICS AND RELATED CHALLENGES
Migrating IoT application specific data into the Cloud
offers great convenience, such as reduction of cost and com-plexity
related to direct hardware management. Accordingly,
the complex scenario of CloudIoT includes many aspects
related to several heterogeneous topics, each of them im-posing
challenges when requiring specific capabilities to be
satisfied. For instance, the following capabilities are required
to guarantee trusted and efficient services: security, privacy,
reliability, availability, portability, and semantic interoperabil-ity
[19], [30]. When critical IoT applications move towards
the Cloud, new concerns arise due to the lack of some
essential properties: trust in the service provider, knowledge
about service level agreements (SLAs), and knowledge about
the physical location of the data storage. Accordingly, new
challenges require specific attention [19]: the distributed nature
of the system exposes several significant attacks (e.g., session
riding, SQL injection, cross site scripting, and side-channel)
and vulnerabilities (e.g., session hijacking and virtual machine
escape); multi-tenancy could compromise security and may
lead to sensitive information leakage; public key cryptography
cannot be applied at all layers due to the computing power
constraints imposed by the things. As summarized in Table II,
in the following we report how providing certain capabilities
has proved challenging in the recent literature when addressing
several main topics pertaining to CloudIoT.
Service delivery. IoT services are typically provided as iso-lated
vertical solutions, in which all system components are
tightly coupled to the specific application context. For each
deployed application service, providers have to survey tar-get
application environments, analyze its requirements, select
hardware devices, integrate heterogeneous subsystems, develop
applications, provide computing infrastructure, and provide
service maintenance. On the other hand, leveraging Cloud ser-vice
delivery models, CloudIoT can enable efficient, scalable,
and easily-extensible IoT service delivery [42]. Although PaaS-like
models would represent a generic solution for facilitating
the deployment of most IoT applications, their adoption moves
some IoT challenges into the Cloud world. For instance, the
interaction with (management of) huge amounts of highly het-erogeneous
things (produced data) has to be properly addressed
into the Cloud at different levels.
Networking and Communication Protocols. CloudIoT in-volves
machine-to-machine (M2M) communications among
many heterogeneous devices with different protocols [19],
which depend on the specific application scenario. Dealing
with this heterogeneity to manage the things in a uniform
fashion while providing required performance [22], [18] is
challenging. The majority of application areas does not in-volve
mobility: in stationary scenarios, IoT often adopts IEEE
802.15.4/6LoWPAN solutions [48]. On the other hand, other
scenarios such as vehicular networks mostly adopt IEEE
802.11p specifications. However, being WiFi and Bluetooth
the most widely used radio technologies for personal networks,
their adoption for IoT applications is increasing: they represent
a cheaper solution, most mobile devices already support them
(e.g., smart phones), and both standards are becoming more
and more low power. In some other cases, when power con-straints
are less critical, GPRS is used for Internet connectivity,
but it results in a very costly solution (e.g., multiple SIM cards
5. are necessary) [48].
Big data. With an estimated number of 50 billion devices
that will be networked by 2020, specific attention must be
paid to transportation, storage, access, and processing of the
huge amount of data they will produce. Thanks to the recent
development in technologies, IoT will be one of the main
sources of big data, and Cloud will enable to store it for long
time and to perform complex analyses on it. The ubiquity
of mobile devices and sensor pervasiveness, indeed call for
scalable computing platforms (every day 2.5 quintillion bytes
of data are created) [28]. Handling this data conveniently is
a critical challenge, as the overall application performance
is highly dependent on the properties of the data manage-ment
service [28]. Hence, following the NoSQL movement,
both commercial and open source solutions adopt alternative
database technologies for big data [25]: time-series, key-value,
document store, wide column stores, and graph databases.
Unfortunately, no perfect data management solution exists for
the Cloud to manage big data [56].
Sensor Networks. Sensor networks have been defined as the
major enabler of IoT [56] and as one of the five technologies
that will shape the world, offering the ability to measure, infer,
and understand environmental indicators, from delicate ecolo-gies
and natural resources to urban environments [35]. Recent
technological advances have made efficient, low-cost, and low-power
miniaturized devices available for use in large-scale,
remote sensing applications [14]. Moreover, smartphones, even
though limited by power consumption and reliability, come
with a variety of sensors (GPS, accelerometer, digital compass,
microphone, and camera), enabling a wide range of mobile
applications in different domains of IoT. In this context, the
timely processing of huge and streaming sensor data, subject
to energy and network constraints and uncertainties, has been
identified as the main challenge [58]. Cloud provides new
opportunities in aggregating sensor data and exploiting the
aggregates for larger coverage and relevancy, but at the same
time affects privacy and security [58]. Furthermore, being
lack of mobility a typical aspect of common IoT devices,
the mobility of sensors introduced by smartphones as well as
wearable electronics represents a new challenge [48].
User Participation. The investment into omnipresent Internet-capable
devices is not reasonable in every scenario. Therefore,
it would be convenient to give the opportunity to users to
participate in submitting data that represent a thing [16].
Users could also be empowered with new building blocks and
tools: accelerators, frameworks, and toolkits will enable the
participation of users in IoT as done in the Internet through
Wikis and Blogs [26]. Such tools and techniques should enable
researchers and design professionals to learn about users work,
giving users an active role in technology design. To achieve
this, these tools should allow users to easily experiment various
design possibilities in a cost-effective way. In this scenario,
one challenge is to provide adequate prototyping tools for
implementing a cooperative prototyping approach, where users
and designers explore together applications and their relations.
Monitoring. As largely documented in the literature, mon-itoring
is an essential activity in Cloud environments for
capacity planning, for managing resources, SLAs, performance
and security, and for troubleshooting [13]. As a consequence,
CloudIoT inherits the same monitoring requirements from
Cloud, but the related challenges are further affected by
volume, variety, and velocity characteristics of IoT.
V. PLATFORMS, SERVICES, AND RESEARCH
PROJECTS
In this section we describe open source and commercial
platforms for CloudIoT and three research projects focused on
this topic, which we believe are an example of the research
efforts funded in this important area.
A. Platforms
There are several open source and commercial platforms
for Cloud and IoT integration. Most of them are aimed at
solving one of the main issues in this field that is related to
the heterogeneity of things and Clouds. These platforms try to
bridge this gap implementing a middleware towards the things
and another towards the Cloud, and they typically provide
an API towards the applications. Other platforms are instead
bound to specific hardware devices or Clouds. Tab. III reports
the main characteristics of these platforms and services. In the
following we review both types of platforms, starting with the
ones belonging to the former group.
IoTCloud [1] is an open source project aimed at integrating
the things (smart phones, tablets, robots, Web pages, etc.)
with backends for managing sensors and their messages and
for providing an API to applications interested in these data.
The platform has been showcased with video sensors (i.e., IP
cameras) on the FutureGrid Cloud testbed [31]. The software is
available online through github. OpenIoT [2] is another open
source effort fostered by a research project financed by the
EU. The project aims at providing a middleware to configure
and deploy algorithms for collection and filtering messages
by things, while at the same time generating and processing
events for interested applications. Among the main focuses of
OpenIoT are the mobility aspects of IoT for energy-efficient
orchestration of data collecting and transmission to the Cloud.
The project web site [2] contains different videos showing
possible applications in several scenarios. Also this software
is available online through github. There are also projects
specifically aimed at creating toolkits for the interaction of IoT
and Cloud. For example, IoT Toolkit (run by a Silicon Valley
based organization called OSIOT) [3] aims at developing a
toolkit that allows to glue the several protocols available both
for the things, for the Cloud, and for the applications.
As for the commercial solutions (e.g. platforms typically
bound to specific things or Clouds), Postscapes publishes a
catalog of projects, events, interviews, and company/job list-ings
within the industry [4]. Interesting examples include open
source projects run by private companies. For example, the
open source project of NimBits [5] provides a set of software
to be installed on private or public Clouds (mainly Google
App Engine) to create a PaaS that collects data from things
and triggers computations or alerts when specific conditions
are verified. The company provides also a Cloud service for
running this software that is free of charge but has some
usage limitations. On the other side, there are companies that
provide things ready to be integrated in Clouds. For example,
openPicus [6] is an Italian company which builds things (e.g.
6. TABLE II: Topics and challenges pertaining CloudIoT.
Efficiency
Scalability
Extensibility
Reliability
Security
Privacy
Interoperability
Availability
Mobility
Pervasiveness
Energy saving
Uniformity
Maintainability
Cost effectiveness
Large scale
Service delivery X X X X X X X
Monitoring X X X X X X
Big Data X X X X X X X X X
Sensor Networks X X X X X X X X X
User Participation X X X X X X X
Networking and Communication Protocols X X X X
small sensors equipped with WiFi or GPRS connectivity) using
an open hardware approach. The idea is to build very cheap
products that have full TCP/IP stack implemented and HTTP
server on-board, which allows to interact with them using
simple RESTful APIs.
Finally, there are also several services (es. Xively [7],
Open.Sen.se [8], ThingSpeak [9]) that allow to collect data
from things and to store these data on the Cloud offered by the
service provider. These services typically provide an API and
different example applications to use the data collected by the
things. Starting from these services, companies have created
toolkits for their integration in CloudIoT frameworks. For
example, NetLab [10] is a toolkit for interaction among phys-ical
and digital objects (e.g. controlling video movies through
arduino). NetLab has created two widgets called CouldIn and
CloudOut that allow to interact with several CloudIoT services.
In particular, they allow to periodically send data from things
to these services or to periodically retrieve data from these
services. Compliant services include Xively (formerly COSM
and Pachube), Open.Sen.se, and ThingSpeak.
TABLE III: Platforms, services and research projects.
Proprietary things
Private cloud
IoTCloud X X Open things
n/a n/a Public cloud
X Free
X Open source
X May13 X
Ready to use Application API
Last update
OpenIoT X X X X X X X Apr14 X
IoT Toolkit X X n/a n/a X X n/a Dec 13
NimBits X X X partly X X n/a Mar 14 X
openPicus X n/a n/a n/a n/a X n/a n/a
Xively X X X X X n/a X
Open.Sen.se X X X X X n/a X
ThingSpeak X X X X X n/a X
NetLab X X X X X n/a Dec 13 X
B. Research Projects
ClouT [11], which stands for “Cloud of things”, is a
research project run by industrial and research partners as
well as city administrations from Europe and Japan. The
partners aim at developing infrastructure, services, tools and
applications for municipalities and their stakeholders (citizens,
service developers, etc.) to create, deploy, and manage user-centric
applications based on IoT and Cloud integration. Target
applications include enhanced public transportation, increased
citizen participation through mobile devices (e.g. to photograph
and record situations of interest to city administrators), safety
management, city event monitoring, and emergency manage-ment.
IoT6 is a European research project on the future Internet
of Things. It aims at exploiting IPv6 and related standards (e.g.,
6LoWPAN, CORE, COAP) to overcome current shortcomings
(e.g. in terms of fragmentation) in the area of IoT research and
development. Its main objectives are to research, design, and
develop a highly scalable IPv6-based Service-Oriented Archi-tecture
to achieve interoperability, mobility, Cloud computing
integration, and intelligence distribution among heterogeneous
things, applications, and services.
The OpenIoT project, cited before for its open source
platform, aims at creating an open source middleware for
getting information from heterogeneous things, hiding the
differences among these objects. The project explores efficient
ways to use and manage Cloud environments for things and
resources (such as sensors, actuators, and smart devices) and
offering utility-based (i.e., pay-as-you-go) IoT services.
VI. OPEN ISSUES AND FUTURE DIRECTIONS
Thanks to the analyses we have done in Sec. II, in Sec.
III, and in Sec. IV, here we resume the main issues related to
CloudIoT still requiring research efforts and point out some
future directions.
The need for Standards. Even though the scientific commu-nity
gave multiple contributions to the deployment and stan-dardization
of IoT and Cloud paradigms, a clear necessity of
standard protocols, architectures and APIs is being demanded
in order to facilitate the interconnection among heterogeneous
smart objects and the creation of enhanced services, which
realize the CloudIoT paradigm [52]. In particular Mobile-to-
Mobile (M2M) is a leading paradigm, but there is a very little
standardization for it. Hence the several existing solutions use
standard Internet, cellular, and Web technologies. Moreover,
the state of the art lacks domain specific environments for rapid
development and efficient CloudIoT service delivery. Indeed,
most architectures proposed at the initial stage of IoT either
have come from the WSN perspective or are based on Cloud
at the center.
Energy Efficient Sensing. WSN typically consists of low-cost,
low-power, and energy-constrained sensors. Each opera-tion,
calculation, and inter-communication consumes the node
energy. Also when specific sensors are replaced by smart
devices, energy saving is a major issue and energy efficient
management is more than desirable. Several techniques can
be adopted to achieve energy saving: for instance, compres-sive
sensing enables reduced signal measurements without
impacting accurate reconstruction of the signal and utilizes
synchronous communication to reduce the transmitting power
7. of each sensor. On the other side, CloudIoT, involving Cloud
paradigm is not affected by energy issues. Indeed, it is possible
to lease on-demand computing and storage resources through
Cloud in an optimal manner from the energetic point of view.
In addition, local computations can be shifted to the Cloud, in
order to save computational resources and device energy.
New Protocols. MAC and routing protocols are critical for
the system to work efficiently. Even though several protocols
have been proposed for various domains (with TDMA, CSMA,
and FDMA schemes), none of them has been accepted as
a standard, and with the growing number of things, further
research is required. Energy is the main consideration for rout-ing
protocols. Although the literature reports existing routing
protocols can work with minor modifications in IoT scenarios
(considering that the number of hops in multi-hop scenario is
limited and that a backbone is typically available) [35], IETF
ROLL workgroup (which deals with routing over low-power
lossy networks) claims that existing routing protocols such as
OSPF, IS-IS, AODV, and OLSR have been found to not satisfy
Low Power and Lossy Networks (LLNs) specific routing
requirements in their current form (e.g. optimization for energy
saving, typical point-to-multipoint traffic patterns, restricted
frame-sizes, limits in trading efficiency for generality). Hence
RPL (ripple) protocol draft has been proposed [12].
Participative Sensing. The problem of missing samples is
critical in people-centric sensing. Indeed, relying on users
volunteering data is a severe limitation to the ability to
produce meaningful data. Moreover data ownership and pri-vacy
issues must be addressed and appropriate participation
incentives must be identified to achieve genuine end-user
engagement [35].
Complex Data Mining. Current technologies are not able to
completely solve all issues related to the complexity of big
data. The percentage of data an organization can analyse is on
the decline: due to the high number of big data producers and
the high frequency of data generation, the gap between data
available to organizations and data they can process is getting
wider. Approximate results are typically orders of magnitude
faster compared to traditional query execution [56]. Research
efforts are required to meet the challenge of big data. New
technologies and query optimizations are required to analyze
large volumes of data faster with efficient resource and power
consumption [56]. Big data consists of high-valued data mixed
with dirty and erroneous data. Extracting useful information
at different spatial and temporal resolutions is a challenging
problem in artificial intelligence research. While state-of-art
methods use shallow learning, an emerging focus is deep
learning, which aims at learning multiple levels of abstraction,
which can be used to interpret given data. Heterogeneous
spatio-temporal (geo-related and sparsely distributed) data
coming from IoT elaborations are not always ready for direct
consumption using visualization platforms. New visualization
schemes have to be developed (e.g. 3D, GIS), in order to create
attractive and easy to understand visualizations [35].
Cloud Capabilities. Security, which concerns every networked
environment, is a major issue for CloudIoT. Indeed both its IoT
side (i.e., RFID, WSN) and its Cloud side are vulnerable to a
number of attacks. In IoT context, encryption can ensure data
confidentiality, integrity, and authenticity. However it cannot
address insider attacks (e.g. during WSN reprogramming)
and it is difficult to implement on computation-constrained
devices. RFID is the most vulnerable component, since no
higher level of intelligence can be enabled on it. Also, security
aspects related to Cloud require attention since Cloud handles
the economics, along with data and tools. Moreover, Cloud
platforms need to be enhanced to support the rapid creation
of applications, by providing domain specific programming
tools and environments and seamless execution of applications,
harnessing capabilities of multiple dynamic and heterogeneous
resources, to meet QoS requirements of diverse users. Cloud
scheduling algorithms need to manage task duplication for
failure management, in order to deliver services in a reliable
manner. Moreover, they should be able to deal with QoS
parameters.
Fog Computing. Fog computing is an extension of classic
Cloud computing to the edge of the network (as fog is a cloud
close to the ground). It has been designed to support IoT appli-cations,
characterized by latency constraints and requirement
for mobility and geo-distribution [21]. Even though computing,
storage and networking are resources of both the Cloud and
the Fog, the latter has specific characteristics: edge location
and location awareness implying low latency; geographical
distribution and a very large number of nodes in contrast
to centralized Cloud; support for mobility (through wireless
access) and real-time interaction (instead of batch processes);
support for interplay with the Cloud.
ACKNOWLEDGEMENTS
This work is partially funded by the MIUR projects: PLATINO
(PON01 01007), SMART HEALTH (PON04a2 C) and SIRIO
(PON01 02425).
REFERENCES
[1] https://sites.google.com/site/opensourceiotcloud/.
[2] http://www.openiot.eu/.
[3] http://iot-toolkit.com/.
[4] http://postscapes.com/internet-of-things-cloud.
[5] http://www.nimbits.com/.
[6] http://www.iot-a.eu/.
[7] https://xively.com/.
[8] http://open.sen.se/.
[9] https://thingspeak.com/.
[10] http://www.netlabtoolkit.org/learning/tutorials/iot-cloud-services/.
[11] http://clout-project.eu/home2/.
[12] https://datatracker.ietf.org/wg/roll/charter/.
[13] G. Aceto, A. Botta, W. De Donato, and A. Pescap`e. Cloud monitoring:
A survey. Computer Networks, 57(9):2093–2115, 2013.
[14] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci. Wireless
sensor networks: a survey. Computer networks, 2002.
[15] F. Alagoz et al. From cloud computing to mobile Internet, from user
focus to culture and hedonism: the crucible of mobile health care and
wellness applications. In ICPCA 2010. IEEE, 2010.
[16] C. Atkins et al. A Cloud Service for End-User Participation Concerning
the Internet of Things. In Signal-Image Technology Internet-Based
Systems (SITIS), 2013 International Conference on. IEEE, 2013.
[17] P. Ballon, J. Glidden, P. Kranas, A. Menychtas, S. Ruston, and S. Van
Der Graaf. Is there a need for a cloud platform for european
smart cities? In eChallenges e-2011 Conference Proceedings, IIMC
International Information Management Corporation, 2011.
8. [18] M. Bernaschi, F. Cacace, A. Pescape, and S. Za. Analysis and
experimentation over heterogeneous wireless networks. In Tridentcom.
IEEE, 2005.
[19] T. Bhattasali, R. Chaki, and N. Chaki. Department of Computer
Science Engineering, University of Calcutta, Kolkata, India. In India
Conference (INDICON), 2013 Annual IEEE, pages 1–6. IEEE, 2013.
[20] S. Bitam and A. Mellouk. ITS-cloud: Cloud computing for Intelligent
transportation system. In Global Communications Conference (GLOBE-COM),
2012 IEEE, pages 2054–2059. IEEE, 2012.
[21] F. Bonomi, R. Milito, J. Zhu, and S. Addepalli. Fog computing and its
role in the internet of things. In Proceedings of the first edition of the
MCC workshop on Mobile cloud computing, pages 13–16. ACM, 2012.
[22] A. Botta, A. Pescap´e, and G. Ventre. Quality of service statistics over
heterogeneous networks: Analysis and applications. European Journal
of Operational Research, 191(3):1075–1088, 2008.
[23] H.-C. Chao. Internet of things and cloud computing for future internet.
In Ubiquitous Intelligence and Computing, Lecture Notes in Computer
Science. 2011.
[24] S.-Y. Chen, C.-F. Lai, Y.-M. Huang, and Y.-L. Jeng. Intelligent
home-appliance recognition over IoT cloud network. In Wireless
Communications and Mobile Computing Conference (IWCMC), 2013
9th International, pages 639–643. IEEE, 2013.
[25] A. Copie, T.-F. Fortis, and V. I. Munteanu. Benchmarking Cloud
Databases for the Requirements of the Internet of Things. In 34th
International Conference on Information Technology Interfaces, ITI
2013, pages 77–82, 2013.
[26] I. P. Cvijikj and F. Michahelles. The Toolkit Approach for End-user
Participation in the Internet of Things. In Architecting the Internet of
Things, pages 65–96. Springer, 2011.
[27] S. K. Dash, S. Mohapatra, and P. K. Pattnaik. A Survey on Application
of Wireless Sensor Network Using Cloud Computing. International
Journal of Computer science Engineering Technologies (E-ISSN:
2044-6004), 1(4):50–55, 2010.
[28] C. Dobre and F. Xhafa. Intelligent services for Big Data science. Future
Generation Computer Systems, 2013.
[29] C. Doukas and I. Maglogiannis. Bringing iot and cloud computing
towards pervasive healthcare. In Innovative Mobile and Internet
Services in Ubiquitous Computing (IMIS), 2012 Sixth International
Conference on, pages 922–926. IEEE, 2012.
[30] European Commission. Definition of a research and innovation policy
leveraging Cloud Computing and IoT combination. Tender specifica-tions,
SMART 2013/0037, 2013.
[31] G. Fox, G. von Laszewski, J. Diaz, K. Keahey, J. Fortes, R. Figueiredo,
S. Smallen, W. Smith, and A. Grimshaw. Futuregrid – A reconfigurable
testbed for cloud, hpc and grid computing. Contemporary High Per-formance
Computing: From Petascale toward Exascale, Computational
Science. Chapman and Hall/CRC, 2013.
[32] G. C. Fox, S. Kamburugamuve, and R. D. Hartman. Architecture
and measured characteristics of a cloud based internet of things. In
Collaboration Technologies and Systems (CTS), 2012 International
Conference on, pages 6–12. IEEE, 2012.
[33] D. Gachet, M. de Buenaga, F. Aparicio, and V. Padr´on. Integrating
internet of things and cloud computing for health services provisioning:
The virtual cloud carer project. In Innovative Mobile and Internet
Services in Ubiquitous Computing (IMIS), 2012 Sixth International
Conference on, pages 918–921. IEEE, 2012.
[34] F. Gao. VSaaS Model on DRAGON-Lab. International Journal of
Multimedia Ubiquitous Engineering, 8(4), 2013.
[35] J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami. Internet of Things
(IoT): A vision, architectural elements, and future directions. Future
Generation Computer Systems, 29(7):1645–1660, 2013.
[36] D.-M. Han and J.-H. Lim. Smart home energy management system
using IEEE 802.15. 4 and zigbee. Consumer Electronics, IEEE
Transactions on, 56(3):1403–1410, 2010.
[37] P. Hank, S. M¨uller, O. Vermesan, and J. Van Den Keybus. Automotive
ethernet: in-vehicle networking and smart mobility. In Proceedings
of the Conference on Design, Automation and Test in Europe, pages
1735–1739. EDA Consortium, 2013.
[38] W. He, G. Yan, and L. Xu. Developing Vehicular Data Cloud Services
in the IoT Environment. 2009.
[39] A. Kamilaris et al. The smart home meets the web of things.
International Journal of Ad Hoc and Ubiquitous Computing, 2011.
[40] B. Kitchenham. Procedures for performing systematic reviews. Keele,
UK, Keele University, 33:2004, 2004.
[41] A. M.-H. Kuo. Opportunities and challenges of cloud computing to
improve health care services. Journal of medical Internet research,
13(3), 2011.
[42] F. Li, M. V¨ogler, M. Claeßens, and S. Dustdar. Efficient and scalable
IoT service delivery on Cloud. In Cloud Computing (CLOUD), 2013
IEEE Sixth International Conference on, pages 740–747. IEEE, 2013.
[43] H. L¨ohr, A.-R. Sadeghi, and M. Winandy. Securing the e-health
cloud. In Proceedings of the 1st ACM International Health Informatics
Symposium, pages 220–229. ACM, 2010.
[44] P. Marchetta, E. Natale, A. Salvi, A. Tirri, M. Tufo, and D. De Pasquale.
Trusted information and security in smart mobility scenarios: The case
of s2-move project. In Algorithms and Architectures for Parallel
Processing, pages 185–192. Springer, 2013.
[45] N. Mitton, S. Papavassiliou, A. Puliafito, and K. S. Trivedi. Combining
Cloud and sensors in a smart city environment. EURASIP Journal on
Wireless Communications and Networking, 2012(1):1–10, 2012.
[46] M. Nkosi and F. Mekuria. Cloud computing for enhanced mobile health
applications. In Cloud Computing Technology and Science (CloudCom),
2010 IEEE Second International Conference on. IEEE, 2010.
[47] P. Parwekar. From Internet of Things towards cloud of things. In Com-puter
and Communication Technology (ICCCT), 2011 2nd International
Conference on, pages 329–333. IEEE, 2011.
[48] P. P. Pereira, J. Eliasson, R. Kyusakov, J. Delsing, A. Raayatinezhad,
and M. Johansson. Enabling cloud connectivity for mobile internet of
things applications. In Service Oriented System Engineering (SOSE),
2013 IEEE 7th International Symposium on. IEEE, 2013.
[49] A. Prati, R. Vezzani, M. Fornaciari, and R. Cucchiara. Intelligent Video
Surveillance as a Service. In Intelligent Multimedia Surveillance, pages
1–16. Springer, 2013.
[50] B. P. Rao, P. Saluia, N. Sharma, A. Mittal, and S. V. Sharma. Cloud
computing for Internet of Things sensing based applications. In
Sensing Technology (ICST), 2012 Sixth International Conference on,
pages 374–380. IEEE, 2012.
[51] Y. Simmhan, A. G. Kumbhare, B. Cao, and V. Prasanna. An analysis
of security and privacy issues in smart grid software architectures
on clouds. In Cloud Computing (CLOUD), 2011 IEEE International
Conference on, pages 582–589. IEEE, 2011.
[52] G. Suciu, A. Vulpe, S. Halunga, O. Fratu, G. Todoran, and V. Suciu.
Smart Cities Built on Resilient Cloud Computing and Secure Internet
of Things. In Control Systems and Computer Science (CSCS), 2013
19th International Conference on, pages 513–518. IEEE, 2013.
[53] D. Yao, C. Yu, H. Jin, and J. Zhou. Energy Efficient Task Scheduling in
Mobile Cloud Computing. In Network and Parallel Computing, pages
344–355. Springer, 2013.
[54] X. Ye and J. Huang. A framework for Cloud-based Smart Home. In
Computer Science and Network Technology (ICCSNT), 2011 Interna-tional
Conference on, volume 2, pages 894–897. IEEE, 2011.
[55] M. Yun and B. Yuxin. Research on the architecture and key technology
of Internet of Things (IoT) applied on smart grid. In Advances in Energy
Engineering (ICAEE), 2010 International Conference on, pages 69–72.
IEEE, 2010.
[56] A. Zaslavsky, C. Perera, and D. Georgakopoulos. Sensing as a service
and big data. arXiv preprint arXiv:1301.0159, 2013.
[57] Q. Zhang, L. Cheng, and R. Boutaba. Cloud computing: state-of-the-art
and research challenges. Journal of internet services and applications,
1(1):7–18, 2010.
[58] F. Zhao. Sensors meet the cloud: Planetary-scale distributed sensing
and decision making. In Cognitive Informatics (ICCI), 2010 9th IEEE
International Conference on, pages 998–998. IEEE, 2010.
[59] J. Zhou, T. Leppanen, E. Harjula, M. Ylianttila, T. Ojala, C. Yu, and
H. Jin. Cloudthings: A common architecture for integrating the internet
of things with cloud computing. In CSCWD, 2013. IEEE.
[60] P. Zikopoulos, C. Eaton, et al. Understanding big data: Analytics for
enterprise class hadoop and streaming data. McGraw-Hill Osborne
Media, 2011.