Fog computing is a system level architecture that distributes computing, storage, control and networking functions closer to users along the continuum between IoT devices and the cloud. It aims to address issues like high latency and network congestion that result from processing and analyzing IoT data solely in the cloud. Key characteristics include supporting real-time interactions, mobility, low latency applications and an extremely large number of heterogeneous devices.
IoT: Ongoing challenges and opportunities in Mobile TechnologyAI Publications
Mobile technology opens the door for a new kind of learning called here and now learning that occurs when learners have access to information anytime and anywhere to perform authentic activities in the context of their learning. Mobile devices, applications and services have become integrated into people's daily lives on a personal and professional level. The purpose of this study was to investigate challenges &opportunities of IoT in mobile technology. The paper is divided in 5 sections and the content of the paper covers the history, elements, challenges and opportunities salong with future of IoT specific to Indian Mobile arena.
Open Source Edge Computing Platforms - OverviewKrishna-Kumar
IEEE 11th International Conference - COMSNETS 2019 - Last MilesTalk - Jan 2019. This talk is for Beginner or intermediate levels only. Kubernetes and related edge platforms are discussed.
IoT: Ongoing challenges and opportunities in Mobile TechnologyAI Publications
Mobile technology opens the door for a new kind of learning called here and now learning that occurs when learners have access to information anytime and anywhere to perform authentic activities in the context of their learning. Mobile devices, applications and services have become integrated into people's daily lives on a personal and professional level. The purpose of this study was to investigate challenges &opportunities of IoT in mobile technology. The paper is divided in 5 sections and the content of the paper covers the history, elements, challenges and opportunities salong with future of IoT specific to Indian Mobile arena.
Open Source Edge Computing Platforms - OverviewKrishna-Kumar
IEEE 11th International Conference - COMSNETS 2019 - Last MilesTalk - Jan 2019. This talk is for Beginner or intermediate levels only. Kubernetes and related edge platforms are discussed.
In the Field of Internet of Things IOT the devices by themselves can recognize the environment and conduct a certain functions by itself. IOT Devices majorly consist with sensors. Cloud Computing which is based in sensor networks manages huge amount of data which includes transferring and processing which takes delayed in service response time. As the growth of sensor network is increased, the demand to control and process the data on IOT devices is also increasing. Vikas Vashisth | Harshit Gupta | Dr. Deepak Chahal "Fog Computing: An Empirical Study" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30675.pdf Paper Url :https://www.ijtsrd.com/computer-science/realtime-computing/30675/fog-computing-an-empirical-study/vikas-vashisth
Deep Learning Approaches for Information Centric Network and Internet of Thingsijtsrd
Technologies are rapidly increasing with additions to them every single day. Cloud Computing and the Internet of Things IoT have become two very closely associated with future internet technologies. One provides a platform to the other for success, the benefits of which could be from computing to processing and analyzing the information to reduce latency for real time applications. However, there are a few IoT devices that do not support on device processing. An alternate solution of this is Edge Computing, where the consumers can witness a close call with the computation and services. In this work, we will be to studying and discussing the application of combining Deep Learning with IoT and Information Centric Networking. A Convolutional Neural Network CNN model, a Deep Learning model, can make the most reliable data available from the complex IoT environment. Additionally, some Deep Learning models such as Recurrent Neural Network RNN and Reinforcement Learning have also integrated with IoT, which can also collect the information from real time applications. Aashay Pawar "Deep Learning Approaches for Information - Centric Network and Internet of Things" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33346.pdf Paper Url: https://www.ijtsrd.com/engineering/computer-engineering/33346/deep-learning-approaches-for-information--centric-network-and-internet-of-things/aashay-pawar
STEAM++ AN EXTENSIBLE END-TO-END FRAMEWORK FOR DEVELOPING IOT DATA PROCESSING...ijcsit
IoT applications usually rely on cloud computing services to perform data analysis such as filtering, aggregation, classification, pattern detection, and prediction. When applied to specific domains, the IoT needs to deal with unique constraints. Besides the hostile environment such as vibration and electricmagnetic interference, resulting in malfunction, noise, and data loss, industrial plants often have Internet access restricted or unavailable, forcing us to design stand-alone fog and edge computing solutions. In this context, we present STEAM++, a lightweight and extensible framework for real-time data stream processing and decision-making in the network edge, targeting hardware-limited devices, besides proposing a micro-benchmark methodology for assessing embedded IoT applications. In real-case experiments in a semiconductor industry, we processed an entire data flow, from values sensing, processing and analysing data, detecting relevant events, and finally, publishing results to a dashboard. On average, the application consumed less than 500kb RAM and 1.0% of CPU usage, processing up to 239 data packets per second and reducing the output data size to 14% of the input raw data size when notifying events.
oT applications usually rely on cloud computing services to perform data analysis such as filtering,
aggregation, classification, pattern detection, and prediction. When applied to specific domains, the IoT
needs to deal with unique constraints. Besides the hostile environment such as vibration and electric-
magnetic interference, resulting in malfunction, noise, and data loss, industrial plants often have Internet
access restricted or unavailable, forcing us to design stand-alone fog and edge computing solutions.
Understand about the fundamentals of Internet of Things and its building blocks along with their
characteristics.
Understand the recent application domains of IoT in everyday life.
Gain insights about the current trends of Associated IOT technologoes and IOT Anlaytics.
Understand about the fundamentals of Internet of Things and its building blocks along with their
characteristics.
Understand the recent application domains of IoT in everyday life.
Gain insights about the current trends of Associated IOT technologoes and IOT Anlaytics.
SECURITY AND PRIVACY AWARE PROGRAMMING MODEL FOR IOT APPLICATIONS IN CLOUD EN...ijccsa
The introduction of Internet of Things (IoT) applications into daily life has raised serious privacy concerns
among consumers, network service providers, device manufacturers, and other parties involved. This paper
gives a high-level overview of the three phases of data collecting, transmission, and storage in IoT systems
as well as current privacy-preserving technologies. The following elements were investigated during these
three phases:(1) Physical and data connection layer security mechanisms(2) Network remedies(3)
Techniques for distributing and storing data. Real-world systems frequently have multiple phases and
incorporate a variety of methods to guarantee privacy. Therefore, for IoT research, design, development,
and operation, having a thorough understanding of all phases and their technologies can be beneficial. In
this Study introduced two independent methodologies namely generic differential privacy (GenDP) and
Cluster-Based Differential privacy ( Cluster-based DP) algorithms for handling metadata as intents and
intent scope to maintain privacy and security of IoT data in cloud environments. With its help, we can
virtual and connect enormous numbers of devices, get a clearer understanding of the IoT architecture, and
store data eternally. However, due of the dynamic nature of the environment, the diversity of devices, the
ad hoc requirements of multiple stakeholders, and hardware or network failures, it is a very challenging
task to create security-, privacy-, safety-, and quality-aware Internet of Things apps. It is becoming more
and more important to improve data privacy and security through appropriate data acquisition. The
proposed approach resulted in reduced loss performance as compared to Support Vector Machine (SVM) ,
Random Forest (RF) .
This includes short description about modern computer network technologies like: 5G Technology, Artificial intelligence (AI), Augmented Reality and Virtual Reality, IoT, Edge/cloud computing, WIFI-6, SDN, SD-WAN, DevOps etc.
In recent years, the number of end users connected to the internet of things (IoT) has increased, and we have witnessed the emergence of the cloud computing paradigm. These users utilize network resources to meet their quality of service (QoS) requirements, but traditional networks are not configured to backing maximum of scalability, real-time data transfer, and dynamism, resulting in numerous challenges. This research presents a new platform of IoT architecture that adds the benefits of two new technologies: software-defined networking and fog paradigm. Software-defined networking (SDN) refers to a centralized control layer of the network that enables sophisticated methods for traffic control and resource allocation. So, fog paradigm allows for data to be analyzed and managed at the edge of the network, making it suitable for tasks that require low and predictable delay. Thus, this research provides an in-depth view of the platform organize and performance of its base ingredients, as well as the potential uses of the suggested platform in various applications.
Walking through the fog (computing) - Keynote talk at Italian Networking Work...FBK CREATE-NET
"Walking through the fog (computing): trends, use-cases and open issues"
Despite its huge success in many IT-enabled application scenarios, cloud computing has demonstrated some intrinsic limitations that may severely limit its adoption in several contexts where constraints like e.g. preserving data locally, ensuring real-time reactivity or guaranteeing operation continuity despite lack of Internet connectivity (or a combination of them) are mandatory. These distinguishing requirements fostered an increased interest toward computing approaches that inherit the flexibility and adaptability of the cloud paradigm, while acting in proximity of a specific scenario. As a consequence, the emergence of this “proximity computing” approach has exploded into a plethora of architectural solutions (and novel terms) like fog computing, edge computing, dew computing, mist computing but also cloudlets, mobile cloud computing, mobile edge computing (and probably few others I may not be aware of…). The talk will initially make an attempt to introduce some clarity among these “foggy” definitions by proposing a taxonomy whose aim is to help identifying their peculiarities as well as their overlaps. Afterwards, the most important components of a generalized proximity computing architecture will be explained, followed by the description of few research works and use cases investigated within our Center and based on this emerging paradigm. An overview of open issues and interesting research directions will conclude the talk.
Efficient ECC-Based Authentication Scheme for Fog-Based IoT EnvironmentIJCNCJournal
The rapid growth of cloud computing and Internet of Things (IoT) applications faces several threats, such as latency, security, network failure, and performance. These issues are solved with the development of fog computing, which brings storage and computation closer to IoT-devices. However, there are several challenges faced by security designers, engineers, and researchers to secure this environment. To ensure the confidentiality of data that passes between the connected devices, digital signature protocols have been applied to the authentication of identities and messages. However, in the traditional method, a user's private key is directly stored on IoTs, so the private key may be disclosed under various malicious attacks. Furthermore, these methods require a lot of energy, which drains the resources of IoT-devices. A signature scheme based on the elliptic curve digital signature algorithm (ECDSA) is proposed in this paper to improve the security of the private key and the time taken for key-pair generation. ECDSA security is based on the intractability of the Elliptic Curve Discrete Logarithm Problem (ECDLP), which allows one to use much smaller groups. Smaller group sizes directly translate into shorter signatures, which is a crucial feature in settings where communication bandwidth is limited, or data transfer consumes a large amount of energy. In this paper, we have chosen the safe curve types of elliptic-curve cryptography (ECC) such as M221, SECP256r1, curve 25519, Brainpool P256t1, and M-551. These types of curves are the most secure curves of other curves of ECC as their security is based on the complexity of the ECDLP of the curve. And these types of curves exceed the complexity of the ECDLP. A valid signature can be generated without reestablishing the whole private key. ECDSA ensures data security and successfully reduces intermediate attacks. The efficiency and effectiveness of ECDSA in the IoT environment are validated by experimental evaluation and comparison analysis. The results indicate that, in comparison to the two-party ECDSA and RSA, the proposed ECDSA decreases computation time by 65% and 87%, respectively. Additionally, as compared to two-party ECDSA and RSA, respectively, it reduces energy consumption by 77% and 82%.
Efficient ECC-Based Authentication Scheme for Fog-Based IoT EnvironmentIJCNCJournal
The rapid growth of cloud computing and Internet of Things (IoT) applications faces several threats, such as latency, security, network failure, and performance. These issues are solved with the development of fog computing, which brings storage and computation closer to IoT-devices. However, there are several challenges faced by security designers, engineers, and researchers to secure this environment. To ensure the confidentiality of data that passes between the connected devices, digital signature protocols have been applied to the authentication of identities and messages. However, in the traditional method, a user's private key is directly stored on IoTs, so the private key may be disclosed under various malicious attacks. Furthermore, these methods require a lot of energy, which drains the resources of IoT-devices. A signature scheme based on the elliptic curve digital signature algorithm (ECDSA) is proposed in this paper to improve the security of the private key and the time taken for key-pair generation. ECDSA security is based on the intractability of the Elliptic Curve Discrete Logarithm Problem (ECDLP), which allows one to use much smaller groups. Smaller group sizes directly translate into shorter signatures, which is a crucial feature in settings where communication bandwidth is limited, or data transfer consumes a large amount of energy. In this paper, we have chosen the safe curve types of elliptic-curve cryptography (ECC) such as M221, SECP256r1, curve 25519, Brainpool P256t1, and M-551. These types of curves are the most secure curves of other curves of ECC as their security is based on the complexity of the ECDLP of the curve. And these types of curves exceed the complexity of the ECDLP. A valid signature can be generated without reestablishing the whole private key. ECDSA ensures data security and successfully reduces intermediate attacks. The efficiency and effectiveness of ECDSA in the IoT environment are validated by experimental evaluation and comparison analysis. The results indicate that, in comparison to the two-party ECDSA and RSA, the proposed ECDSA decreases computation time by 65% and 87%, respectively. Additionally, as compared to two-party ECDSA and RSA, respectively, it reduces energy consumption by 77% and 82%.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
In the Field of Internet of Things IOT the devices by themselves can recognize the environment and conduct a certain functions by itself. IOT Devices majorly consist with sensors. Cloud Computing which is based in sensor networks manages huge amount of data which includes transferring and processing which takes delayed in service response time. As the growth of sensor network is increased, the demand to control and process the data on IOT devices is also increasing. Vikas Vashisth | Harshit Gupta | Dr. Deepak Chahal "Fog Computing: An Empirical Study" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30675.pdf Paper Url :https://www.ijtsrd.com/computer-science/realtime-computing/30675/fog-computing-an-empirical-study/vikas-vashisth
Deep Learning Approaches for Information Centric Network and Internet of Thingsijtsrd
Technologies are rapidly increasing with additions to them every single day. Cloud Computing and the Internet of Things IoT have become two very closely associated with future internet technologies. One provides a platform to the other for success, the benefits of which could be from computing to processing and analyzing the information to reduce latency for real time applications. However, there are a few IoT devices that do not support on device processing. An alternate solution of this is Edge Computing, where the consumers can witness a close call with the computation and services. In this work, we will be to studying and discussing the application of combining Deep Learning with IoT and Information Centric Networking. A Convolutional Neural Network CNN model, a Deep Learning model, can make the most reliable data available from the complex IoT environment. Additionally, some Deep Learning models such as Recurrent Neural Network RNN and Reinforcement Learning have also integrated with IoT, which can also collect the information from real time applications. Aashay Pawar "Deep Learning Approaches for Information - Centric Network and Internet of Things" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33346.pdf Paper Url: https://www.ijtsrd.com/engineering/computer-engineering/33346/deep-learning-approaches-for-information--centric-network-and-internet-of-things/aashay-pawar
STEAM++ AN EXTENSIBLE END-TO-END FRAMEWORK FOR DEVELOPING IOT DATA PROCESSING...ijcsit
IoT applications usually rely on cloud computing services to perform data analysis such as filtering, aggregation, classification, pattern detection, and prediction. When applied to specific domains, the IoT needs to deal with unique constraints. Besides the hostile environment such as vibration and electricmagnetic interference, resulting in malfunction, noise, and data loss, industrial plants often have Internet access restricted or unavailable, forcing us to design stand-alone fog and edge computing solutions. In this context, we present STEAM++, a lightweight and extensible framework for real-time data stream processing and decision-making in the network edge, targeting hardware-limited devices, besides proposing a micro-benchmark methodology for assessing embedded IoT applications. In real-case experiments in a semiconductor industry, we processed an entire data flow, from values sensing, processing and analysing data, detecting relevant events, and finally, publishing results to a dashboard. On average, the application consumed less than 500kb RAM and 1.0% of CPU usage, processing up to 239 data packets per second and reducing the output data size to 14% of the input raw data size when notifying events.
oT applications usually rely on cloud computing services to perform data analysis such as filtering,
aggregation, classification, pattern detection, and prediction. When applied to specific domains, the IoT
needs to deal with unique constraints. Besides the hostile environment such as vibration and electric-
magnetic interference, resulting in malfunction, noise, and data loss, industrial plants often have Internet
access restricted or unavailable, forcing us to design stand-alone fog and edge computing solutions.
Understand about the fundamentals of Internet of Things and its building blocks along with their
characteristics.
Understand the recent application domains of IoT in everyday life.
Gain insights about the current trends of Associated IOT technologoes and IOT Anlaytics.
Understand about the fundamentals of Internet of Things and its building blocks along with their
characteristics.
Understand the recent application domains of IoT in everyday life.
Gain insights about the current trends of Associated IOT technologoes and IOT Anlaytics.
SECURITY AND PRIVACY AWARE PROGRAMMING MODEL FOR IOT APPLICATIONS IN CLOUD EN...ijccsa
The introduction of Internet of Things (IoT) applications into daily life has raised serious privacy concerns
among consumers, network service providers, device manufacturers, and other parties involved. This paper
gives a high-level overview of the three phases of data collecting, transmission, and storage in IoT systems
as well as current privacy-preserving technologies. The following elements were investigated during these
three phases:(1) Physical and data connection layer security mechanisms(2) Network remedies(3)
Techniques for distributing and storing data. Real-world systems frequently have multiple phases and
incorporate a variety of methods to guarantee privacy. Therefore, for IoT research, design, development,
and operation, having a thorough understanding of all phases and their technologies can be beneficial. In
this Study introduced two independent methodologies namely generic differential privacy (GenDP) and
Cluster-Based Differential privacy ( Cluster-based DP) algorithms for handling metadata as intents and
intent scope to maintain privacy and security of IoT data in cloud environments. With its help, we can
virtual and connect enormous numbers of devices, get a clearer understanding of the IoT architecture, and
store data eternally. However, due of the dynamic nature of the environment, the diversity of devices, the
ad hoc requirements of multiple stakeholders, and hardware or network failures, it is a very challenging
task to create security-, privacy-, safety-, and quality-aware Internet of Things apps. It is becoming more
and more important to improve data privacy and security through appropriate data acquisition. The
proposed approach resulted in reduced loss performance as compared to Support Vector Machine (SVM) ,
Random Forest (RF) .
This includes short description about modern computer network technologies like: 5G Technology, Artificial intelligence (AI), Augmented Reality and Virtual Reality, IoT, Edge/cloud computing, WIFI-6, SDN, SD-WAN, DevOps etc.
In recent years, the number of end users connected to the internet of things (IoT) has increased, and we have witnessed the emergence of the cloud computing paradigm. These users utilize network resources to meet their quality of service (QoS) requirements, but traditional networks are not configured to backing maximum of scalability, real-time data transfer, and dynamism, resulting in numerous challenges. This research presents a new platform of IoT architecture that adds the benefits of two new technologies: software-defined networking and fog paradigm. Software-defined networking (SDN) refers to a centralized control layer of the network that enables sophisticated methods for traffic control and resource allocation. So, fog paradigm allows for data to be analyzed and managed at the edge of the network, making it suitable for tasks that require low and predictable delay. Thus, this research provides an in-depth view of the platform organize and performance of its base ingredients, as well as the potential uses of the suggested platform in various applications.
Walking through the fog (computing) - Keynote talk at Italian Networking Work...FBK CREATE-NET
"Walking through the fog (computing): trends, use-cases and open issues"
Despite its huge success in many IT-enabled application scenarios, cloud computing has demonstrated some intrinsic limitations that may severely limit its adoption in several contexts where constraints like e.g. preserving data locally, ensuring real-time reactivity or guaranteeing operation continuity despite lack of Internet connectivity (or a combination of them) are mandatory. These distinguishing requirements fostered an increased interest toward computing approaches that inherit the flexibility and adaptability of the cloud paradigm, while acting in proximity of a specific scenario. As a consequence, the emergence of this “proximity computing” approach has exploded into a plethora of architectural solutions (and novel terms) like fog computing, edge computing, dew computing, mist computing but also cloudlets, mobile cloud computing, mobile edge computing (and probably few others I may not be aware of…). The talk will initially make an attempt to introduce some clarity among these “foggy” definitions by proposing a taxonomy whose aim is to help identifying their peculiarities as well as their overlaps. Afterwards, the most important components of a generalized proximity computing architecture will be explained, followed by the description of few research works and use cases investigated within our Center and based on this emerging paradigm. An overview of open issues and interesting research directions will conclude the talk.
Efficient ECC-Based Authentication Scheme for Fog-Based IoT EnvironmentIJCNCJournal
The rapid growth of cloud computing and Internet of Things (IoT) applications faces several threats, such as latency, security, network failure, and performance. These issues are solved with the development of fog computing, which brings storage and computation closer to IoT-devices. However, there are several challenges faced by security designers, engineers, and researchers to secure this environment. To ensure the confidentiality of data that passes between the connected devices, digital signature protocols have been applied to the authentication of identities and messages. However, in the traditional method, a user's private key is directly stored on IoTs, so the private key may be disclosed under various malicious attacks. Furthermore, these methods require a lot of energy, which drains the resources of IoT-devices. A signature scheme based on the elliptic curve digital signature algorithm (ECDSA) is proposed in this paper to improve the security of the private key and the time taken for key-pair generation. ECDSA security is based on the intractability of the Elliptic Curve Discrete Logarithm Problem (ECDLP), which allows one to use much smaller groups. Smaller group sizes directly translate into shorter signatures, which is a crucial feature in settings where communication bandwidth is limited, or data transfer consumes a large amount of energy. In this paper, we have chosen the safe curve types of elliptic-curve cryptography (ECC) such as M221, SECP256r1, curve 25519, Brainpool P256t1, and M-551. These types of curves are the most secure curves of other curves of ECC as their security is based on the complexity of the ECDLP of the curve. And these types of curves exceed the complexity of the ECDLP. A valid signature can be generated without reestablishing the whole private key. ECDSA ensures data security and successfully reduces intermediate attacks. The efficiency and effectiveness of ECDSA in the IoT environment are validated by experimental evaluation and comparison analysis. The results indicate that, in comparison to the two-party ECDSA and RSA, the proposed ECDSA decreases computation time by 65% and 87%, respectively. Additionally, as compared to two-party ECDSA and RSA, respectively, it reduces energy consumption by 77% and 82%.
Efficient ECC-Based Authentication Scheme for Fog-Based IoT EnvironmentIJCNCJournal
The rapid growth of cloud computing and Internet of Things (IoT) applications faces several threats, such as latency, security, network failure, and performance. These issues are solved with the development of fog computing, which brings storage and computation closer to IoT-devices. However, there are several challenges faced by security designers, engineers, and researchers to secure this environment. To ensure the confidentiality of data that passes between the connected devices, digital signature protocols have been applied to the authentication of identities and messages. However, in the traditional method, a user's private key is directly stored on IoTs, so the private key may be disclosed under various malicious attacks. Furthermore, these methods require a lot of energy, which drains the resources of IoT-devices. A signature scheme based on the elliptic curve digital signature algorithm (ECDSA) is proposed in this paper to improve the security of the private key and the time taken for key-pair generation. ECDSA security is based on the intractability of the Elliptic Curve Discrete Logarithm Problem (ECDLP), which allows one to use much smaller groups. Smaller group sizes directly translate into shorter signatures, which is a crucial feature in settings where communication bandwidth is limited, or data transfer consumes a large amount of energy. In this paper, we have chosen the safe curve types of elliptic-curve cryptography (ECC) such as M221, SECP256r1, curve 25519, Brainpool P256t1, and M-551. These types of curves are the most secure curves of other curves of ECC as their security is based on the complexity of the ECDLP of the curve. And these types of curves exceed the complexity of the ECDLP. A valid signature can be generated without reestablishing the whole private key. ECDSA ensures data security and successfully reduces intermediate attacks. The efficiency and effectiveness of ECDSA in the IoT environment are validated by experimental evaluation and comparison analysis. The results indicate that, in comparison to the two-party ECDSA and RSA, the proposed ECDSA decreases computation time by 65% and 87%, respectively. Additionally, as compared to two-party ECDSA and RSA, respectively, it reduces energy consumption by 77% and 82%.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
2. Disclaimer
All information presented in this document and all supplementary
materials and programming code represent only my personal opinion
and current understanding and has not received any endorsement or
approval by IPT - Intellectual Products and Technologies or any third
party. It should not be taken as any kind of advice, and should not be
used for making any kind of decisions with potential commercial impact.
The information and code presented may be incorrect or incomplete. It is
provided "as is", without warranty of any kind, express or implied,
including but not limited to the warranties of merchantability, fitness for a
particular purpose and non-infringement. In no event shall the author or
copyright holders be liable for any claim, damages or other liability,
whether in an action of contract, tort or otherwise, arising from, out of or
in connection with the information, materials or code presented or the
use or other dealings with this information or programming code.
2
3. Trademarks
OpenFog™ is trademark or registered trademark of OPEN FOG
CONSORTIUM, INC.
Oracle®, Java™ and JavaScript™ are trademarks or registered
trademarks of Oracle and/or its affiliates.
LEGO® is a registered trademark of LEGO® Group. Programs are not
affiliated, sponsored or endorsed by LEGO® Education or LEGO®
Group.
Raspberry Pi™ is a trademark of Raspberry Pi Foundation.
Other names may be trademarks of their respective owners.
3
4. About me
4
Trayan Iliev
–CEO of IPT – Intellectual Products &
Technologies
– Oracle®
certified programmer 15+ Y
–End-to-end reactive fullstack apps with Java,
ES6/7, TypeScript, Angular, React and Vue.js
– 12+ years IT trainer
–Voxxed Days, jPrime, jProfessionals,
BGOUG, BGJUG, DEV.BG speaker
–Organizer RoboLearn hackathons and IoT
enthusiast (http://robolearn.org)
–Lecturer @ Sofia University – courses:
Internet of Things (with SAP), Multiagent
Systems and Social Robotics
6. Fog Computing – between IoT Devices
and The Cloud
Edge, Fog, Mist & Cloud Computing
Fog domains and fog federation, wireless sensor
networks, multi-layer IoT architecture
Fog computing standards and specifications
Practical use-case scenarios & advantages of fog
Fog analytics and intelligence on the edge
Technologies for distributed asynchronous event
processing and analytics in real time
Lambda architecture – Spark, Storm, Kafka, Apex,
Beam, Spring Reactor & WebFlux
Eclipse IoT platform
6
7. We Live in Exponential Time!
7
“The future is widely misunderstood. Our forebears
expected it to be pretty much like their present, which
had been pretty much like their past.”
–Ray Kurzweil, The Singularity Is Near
8. We Live in Exponential Time
8
“intuitive linear” view of technological progress vs. the
“historical exponential” view.
The Law of Accelerating Returns: Evolution applies
positive feedback in that the more capable methods
resulting from one stage of evolutionary progress are
used to create the next stage. As a result, the rate of
progress of an evolutionary process increases
exponentially over time.
A correlate of the above observation is that the
“returns” of an evolutionary process (e.g., the speed,
cost-effectiveness, or overall “power” of a process)
increase exponentially over time.
http://www.kurzweilai.net/the-law-of-accelerating-returns, By – Ray Kurzweil, The Singularity Is Near
9. IoT, IoE, WoT – What It Means?
9
The Internet-of-Things (IoT) is a self-configuring and
adaptive network which connects real-world things to
the Internet enabling them to communicate with other
connected objects leading to the realization of a new
range of ubiquitous services.
R. Minerva et al. - Towards a definition of IoT
Technical report, IEEE, 2015
10. Example: Internet of Things (IoT)
CC BY 2.0, Source:
https://www.flickr.com/photos/wilgengebroed/8249565455/
Radar, GPS, lidar for navigation and obstacle
avoidance ( 2007 DARPA Urban Challenge )
10
11. Engineering, Science & Art
Source: https://commons.wikimedia.org/w/index.php?curid=551256, CC BY-SA 3.0
11
12. Key Elements of IoT
Internet of Things (IoT)
Identification Sensors Connectivity Computation Services Semantic
12
14. Cloud Computing - Definition
14
Cloud computing is a model for enabling ubiquitous,
convenient, on-demand network access to a shared
pool of configurable computing resources (e.g.,
networks, servers, storage, applications, and
services) that can be rapidly provisioned and released
with minimal management effort or service provider
interaction. This cloud model is composed of five
essential characteristics, three service models, and
four deployment models.
National Institute of Standards and Technology
18. Edge Computing (Mesh Computing)
18
“… places applications, data and processing at the
logical extremes of a network rather than centralizing
them. Placing data and data-intensive applications at
the Edge reduces the volume and distance that data
must be moved.”
IoT Guide
http://internetofthingsguide.com/d/edge_computing.htm
19. Fog Computing: Definition
19
A horizontal, system-level architecture that distributes
computing, storage, control and networking functions
closer to he users along a cloud-to-thing continuum.
OpenFog™ Consortium
20. Vertical vs. Horizontal IoT
20
http://www.iot-week.eu/overview_2013/iot-week-2012/programme-1/tuesday-1/presentations/semantic-
interoperability/K1_ETSI-M2M-oneM2M-and-the-need-for-semantics-IoTWeek2012.pdf, By Joerg Swetina (NEC)-
ETSI M2M / oneM2M and the need for semantics
21. Fog vs. Edge Computing - Diff
21
Fog computing Edge computing
Works with the Cloud Defined separately without Cloud
Multi-layered architecture, N-tier
deployments, multiple IoT verticals
Limited number of local layers,
not aware of IoT verticals
Independent of the devices and
aware of the entire fog domain
Device and few local services
aware, no domain awareness
Addresses networking, storage,
control, processing acceleration
Addresses computation only,
little interoperability
Analytics: collection, analysis, ML,
anomalies, optim., multiple devices
Analytics scoped to a single device
– printer, camera, machine, etc.
End-to-end (E2E) security – data
protection, session, hardware level
Custom security, partial point
solution, VPN, Firewall
Virtualization, containerization,
App/ Soft PLC hosting, RT control,
modularity, High Availability (HA)
No virtualization, Hard PLC/ Soft
PLC stack integrated on device
hardware, not modular, not HA
22. Cloud, Fog and Mist Computing
Cloud Computing
(Data-centers)
Fog Computing
(Fog Nodes, Edge
Gateways, Cloudlets)
Hundreds
Thousands
Millions
Mist Computing
(Smart IoT Devices,
Sensor Networks) SN1 SN2
...
23. Mist Computing - Definition
23
Mist computing is a lightweight and rudimentary form
of computing power that resides directly within the
network fabric at the edge of the network, the fog
layer closest to the smart end-devices, using
microcomputers and microcontrollers to feed into fog
computing nodes and potentially onward towards the
cloud computing services.
National Institute of Standards and Technology
24. Why Fogging?
24
Exponential Data in Realtime – data generated by IoT
devices is growing exponentially – especially high
bandwidth devices and apps like: LIDARs, 3D cameras,
US arrays, gaming, streaming, augmented reality, etc.
If all data should go to the cloud for analysis and
speech/ image/ 3D scene recognition → a recipe for
network congestion, high-latency, and self-made DoS.
Many metrics like performance, efficiency, scalability,
latency, security, bandwidth, reliability, privacy could be
greatly improved if the processing, analysis, and
partially decision making are done locally – close to the
source of data.
25. Fog Nodes, Domains & Federation
expansion of functionality over disperse geolocations.
25
Fog Node – The physical and logical network element
that implements fog computing services. It is somewhat
analogous to a server in cloud computing.
Fog Domain – seamlessly extends cloud computing to
the edge for secure control and management of domain
specific hardware, software, and standard compute,
storage and network functions.
Fog Federation – secure control and management of
multiple fog domain instances, including edge
devices, computes, networking, storage & services in a
distributed and consistent manner, providing horizontal
26. Fog Comuting – a Cloud-Based Ecosystem
for Smart End-Devices
26
https://csrc.nist.gov/csrc/media/publications/sp/800-191/draft/documents/sp800-191-draft.pdf, National Institute
of Standards and Technology (NIST) Definition of Fog Computing – Special Publication Draft 800-191
28. IoT Object Identification Standards
28
ISO/IEC 20248 Automatic Identification and Data
Capture Techniques – QR Code, RFID, NFC – secure
identification of Things.
GS1's EPC Tag Data Standard (TDS) and EPCglobal
Architecture Framework (EPC Network) – allows
storing and querying of data related to objects
identified with Electronic Product Code numbers.
IPv6 – allows identification of network interaces and IP
package routing, not permanent
Mapping EPC to IPv6
29. Fog Standards and Specifications
29
National Institute of Standards and Technology (NIST)
Definition of Fog Computing – Special Publication Draft
800-191
OpenFog Consortium – OpenFog Reference
Architecture for Fog Computing – medium to high level
description of system architectures for fog nodes and
networks, including of multiple viewpoints (Functional,
Deployment), views (Software, System, Node), and
Perspectives (cross-cutting concerns). Based on:
ISO/IEC/IEEE 42010:2011 – Systems and software
engineering — Architecture description
30. NIST Definition of Fog Computing
30
Fog computing is a horizontal, physical or virtual
resource paradigm that resides between smart end-
devices and traditional cloud or data centers. This
paradigm supports vertically-isolated, latency-sensitive
applications by providing ubiquitous, scalable, layered,
federated, and distributed computing, storage, and
network connectivity.
National Institute of Standards and Technology
31. Fog Computing Characteristics - I
31
Contextual location awareness, and low latency
Geographical distribution & distributed deployment
Large-scale sensor networks – environment monitoring,
Smart Grid → distributed computing and storage
Very large number of nodes – geo-distributed
Support for mobility – mobile devices: smartphones, etc.
Real-time interactions – streaming, lambda architecture
Predominance of wireless IoT access: analytics, compute
Heterogeneity – deployed in wide variety of environments
32. Fog Computing Characteristics - II
32
Interoperability and federation - seamless support of
certain services (e.g. real-time streaming) requires the
cooperation of different providers.
Support for real-time analytics and interplay with the
Cloud -while Fog nodes provide localization, therefore
enabling low latency and context awareness, the Cloud
provides global centralization. Many applications require
both Fog localization and Cloud globalization, particularly
for analytics and Big Data.
Fog is particularly well suited to real-time streaming
analytics as opposed to historical, Big Data batch
analytics that is normally carried out in a data center.
33. Fog as a Service (FaaS)
33
Pay-as-you-go model
Includes:
• Software as a Service (SaaS)
• Platform as a Service (PaaS)
• Infrastructure as a Service (IaaS)
34. OpenFog Consortium
34
Founded in November 2015 by ARM, Cisco, Dell, Intel,
Microsoft and Princeton University
Open participation from across industry, academia and
non-profit organizations that have an interest in the
emerging IoT landscape
Defines open architectural framework enabling IoT
industry convergence and game-changing innovation
through fog computing
Efforts complementary to other initiatives like: Industrial
Internet Consortium (IIC), ETSI-MEC (Mobile Edge
Computing), OPC-UA, Open Connectivity Foundation
(OCF), OpenNFV, etc.
35. Example Use Cases
35
Traffic Control and Smart Cars – Vehicle-to-X –
including: Vehicle-to-Vehicle (V2V), Vehicle-to-
Infrastructure (V2I), Vehicle-to-Manufacturer (V2M),
multiple public and private fog and cloud networks.
Security Cameras and Surveillance – in smart cities /
homes, retail stores, factories, public transportation,
airports – terabytes per day by single camera → local
processing, anomaly detection, and decision making.
Smart cities- parking, shopping, healthcare, infrastructure
Smart Buildings – HVAC, lighting, doors, parking,
security, elevtors, support for smartphones, tablets, etc.
36. Core Principles
36
Scalable
Agile and open
Secure
Autonomous
Flexible & programmable
Highly available
Reliable
Remotely serviceable
Hierarchically structured based on business needs
39. Using Context, Higher-Order Sofia
Components and Observables with May 15, 2017
Slide 39
Sources:ReactJS [https://github.com/facebook/react/ ]
Licensed under the
Creative Commons Attribution 4.0 International Public License
R
e
Пa
c
оt дход на интелигентните агенти при
моделиране на знания и системи
41. 41
Message Driven – asynchronous message-passing allows
to establish a boundary between components that ensures
loose coupling, isolation, location transparency, and
provides the means to delegate errors as messages
[Reactive Manifesto].
The main idea is to separate concurrent producer and
consumer workers by using message queues.
Message queues can be unbounded or bounded (limited
max number of messages)
Unbounded message queues can present memory
allocation problem in case the producers outrun the
consumers for a long period → OutOfMemoryError
Scalable, Massively Concurrent
42. Data / Event / Message Streams
42
“Conceptually, a stream is a (potentially never-ending)
flow of data records, and a transformation is an
operation that takes one or more streams as input,
and produces one or more output streams as a
result.”
Apache Flink: Dataflow Programming Model
43. Data Stream Programming
43
The idea of abstracting logic from execution is hardly
new -- it was the dream of SOA. And the recent
emergence of microservices and containers shows that
the dream still lives on.
For developers, the question is whether they want to
learn yet one more layer of abstraction to their coding.
On one hand, there's the elusive promise of a common
API to streaming engines that in theory should let you
mix and match, or swap in and swap out.
Tony Baer (Ovum) @ ZDNet - Apache Beam and Spark:
New coopetition for squashing the Lambda Architecture?
44. Lambda Architecture - I
https://commons.wikimedia.org/w/index.php?curid=34963986, By Textractor - Own work, CC BY-SA 4
44
45. Lambda Architecture - II
https://commons.wikimedia.org/w/index.php?curid=34963987, By Textractor - Own work, CC BY-SA 4
45
46. Lambda Architecture - III
46
Data-processing architecture designed to handle
massive quantities of data by using both batch- and
stream-processing methods
Balances latency, throughput, fault-tolerance, big
data, real-time analytics, mitigates the latencies of
map-reduce
Data model with an append-only, immutable data
source that serves as a system of record
Ingesting and processing timestamped events that are
appended to existing events. State is determined from
the natural time-based ordering of the data.
47. Druid Distributed Data Store (Java)
https://commons.wikimedia.org/w/index.php?curid=33899448 By Fangjin Yang - sent to me personally, GFDL
Apache
ZooKeeper
MySQL /
PostgreSQL
HDFS /
Amazon S3
47
48. Lambda Architecture: Projects - I
Apache Spark is an open-source
cluster-computing framework. Spark
Streaming, Spark Mllib
Apache Storm is a distributed
stream processing – streams DAG
Apache Apex™ unified stream and
batch processing engine.
48
53. Reactive Streams Spec.
53
Reactive Streams – provides standard for
asynchronous stream processing with non-blocking
back pressure.
Minimal set of interfaces, methods and protocols for
asynchronous data streams
April 30, 2015: has been released version 1.0.0 of
Reactive Streams for the JVM (Java API,
Specification, TCK and implementation examples)
Java 9: java.util.concurrent.Flow
54. Reactive Streams Spec.
54
Publisher – provider of potentially unbounded number
of sequenced elements, according to Subscriber(s)
demand.
Publisher.subscribe(Subscriber) => onSubscribe onNext*
(onError | onComplete)?
Subscriber – calls Subscription.request(long) to
receive notifications
Subscription – one-to-one Subscriber ↔ Publisher,
request data and cancel demand (allow cleanup).
Processor = Subscriber + Publisher
55. FRP = Async Data Streams
55
FRP is asynchronous data-flow programming using the
building blocks of functional programming (e.g. map,
reduce, filter) and explicitly modeling time
Used for GUIs, robotics, and music. Example (RxJava):
Observable.from(
new String[]{"Reactive", "Extensions", "Java"})
.take(2).map(s -> s + " : on " + new Date())
.subscribe(s -> System.out.println(s));
Result:
Reactive : on Wed Jun 17 21:54:02 GMT+02:00 2015
Extensions : on Wed Jun 17 21:54:02 GMT+02:00 2015
56. Project Reactor
56
Reactor project allows building high-performance (low
latency high throughput) non-blocking asynchronous
applications on JVM.
Reactor is designed to be extraordinarily fast and can
sustain throughput rates on order of 10's of millions of
operations per second.
Reactor has powerful API for declaring data
transformations and functional composition.
Makes use of the concept of Mechanical Sympathy
built on top of Disruptor / RingBuffer.
62. Hot and Cold Event Streams
62
PULL-based (Cold Event Streams) – Cold streams (e.g.
RxJava Observable / Flowable or Reactor Flow / Mono)
are streams that run their sequence when and if they
are subscribed to. They present the sequence from the
start to each subscriber.
PUSH-based (Hot Event Streams) – emit values
independent of individual subscriptions. They have their
own timeline and events occur whether someone is
listening or not. When subscription is made observer
receives current events as they happen.
Example: mouse events
65. Top New Features in Spring 5
65
Reactive Programming Model
Spring Web Flux
Reactive DB repositories & integrations + hot event
streaming: MongoDB, CouchDB, Redis, Cassandra,
Kafka
JDK 8+ and Java EE 7+ baseline
Testing improvements – WebTestClient (based on
reactive WebFlux WebClient)
Kotlin functional DSL
66. Spring 5 Main Building Blocks
66
Source: https://spring.io
Spring Boot 2.0
Project Reactor
Spring Security Reactive
Servlet Stack
(one request per thread)
Every JEE Servlet Container
(tomact, jetty, undertow, ...)
Reactive Stack
(async IO)
Nonblocking NIO Runtimes
(Netty, Servlet 3.1 Containers)
Spring Security
Spring MVC Spring WebFlux
Spring Data Repositories
JDBC, JPA, NoSQL
Spring Data Reactive Repositories
Mongo, Cassandra, Redis, Couchbase
67. WebFlux: Best Expalined in Code
67
Lets see REST service Spring 5 WebFlux demos.
Available @GitHub:
https://github.com/iproduct/spring-5-webflux
68. Eclipse IoT Platform
68
Based on: https://www.researchgate.net/publication/279177017_Internet_of_Things_A_Survey_on_Enabling_
Technologies_Protocols_and_Applications, By Ala Al-Fuqaha et al. - Internet of Things: A Survey on Enabling
Technologies, Protocols and Applications
69. IPTPI: RPi2 + Ardunio Robot
Raspberry Pi 2 (quad-core ARMv7
@ 900MHz) + Arduino Leonardo
cloneA-Star 32U4 Micro
Optical encoders (custom), IR
optical array, 3D accelerometers,
gyros, and compass MinIMU-9 v2
IPTPI is programmed in Java
using Pi4J, Reactor, RxJava, Akka
More information about IPTPI:
http://robolearn.org/iptpi-robot/
69
70. IPTPI: RPi2 + Ardunio Robot
3D accelerometers, gyros,
and compass MinIMU-9 v2
Pololu DRV8835
Dual Motor Driver
for Raspberry Pi
Arduino Leonardo clone
A-Star 32U4 Micro
USB Stereo
Speakers - 5V
LiPo Powebank
15000 mAh
70
73. LeJaRo: Lego®
Java Robot
73
Modular – 3 motors (with encoders) – one driving each
track, and third for robot clamp.
Three sensors: touch sensor (obstacle avoidance), light
color sensor (follow line), IR sensor (remote).
LeJaRo is programmed in Java using LeJOS library.
More information about LeJaRo:
http://robolearn.org/lejaro/
Programming examples available @GitHub:
https://github.com/iproduct/course-social-robotics/tre
e/master/motors_demo
LEGO® is a registered trademark of LEGO® Group. Programs of IPT are not
affiliated, sponsored or endorsed by LEGO® Education or LEGO® Group.
76. Demo Code
76
Reactive Java Robotics and IoT with Reactor,
RxJS, Angular 2 Demos are available @ GitHub:
https://github.com/iproduct/course-social-robotics
77. Thank’s for Your Attention!
Trayan Iliev
CEO of IPT – Intellectual Products
& Technologies
http://iproduct.org/
http://robolearn.org/
https://github.com/iproduct
https://twitter.com/trayaniliev
https://www.facebook.com/IPT.EACAD
https://plus.google.com/+IproductOrg
77