Unit 4 -IOT3_IoT_platform_Design_methodology.pptx
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Unit 4 -IOT1.pptx
The document discusses an IoT platform design methodology that is independent of specific products or services. The methodology includes specifying the purpose and requirements, defining processes, domain models, information models, services, functional and operational views, and integrating devices and components. It then discusses developing applications. As an example, it provides the purpose, behavior and system management requirements for a home automation system that allows controlling lights in a home remotely using a web application.
IOT Platform Design Methodology
The document outlines the key steps in an IoT design methodology:
1. Define the purpose, requirements, and use cases of the system.
2. Specify the domain model, information model, services, and IoT level.
3. Develop functional and operational views describing the system components and how they will communicate and operate.
4. Integrate the physical devices and components and draw schematics.
5. Develop the IoT application to implement the designed system.
Chapter 5 IoT Design methodologies
The document outlines a 10-step IoT design methodology that includes purpose and requirements specification, process specification, domain modeling, information modeling, service specifications, IoT level specification, functional view specification, operational view specification, device and component integration, and application development. It then provides an example application of this methodology to design a smart home automation system for controlling lights remotely. The example walks through each step for specifying the purpose, domain model, information model, services, functional views, and developing the application and native controller components.
[PPT] _ Unit 2 _ 9.0 _ Domain Specific IoT _Home Automation.pdf
The document outlines a 10-step IoT design methodology that includes requirements specification, process specification, domain modeling, information modeling, service specifications, level specification, functional and operational views, device integration, and application development. It then applies this methodology to design a smart home automation system case study, outlining the purpose, behavior, and requirements in step 1. Steps 2-10 are then summarized for the home automation example, covering process specification through application development using RESTful web services, a native Python controller service, and a Django application frontend. Finally, it notes how the full system would be integrated using these components on a Raspberry Pi device.
Chapter-5.pdf
The document outlines a 10-step IoT design methodology that includes requirements specification, process specification, domain modeling, information modeling, service specifications, level specification, functional and operational views, device integration, and application development. It then applies this methodology to design a smart home automation system case study that controls lights remotely. Key steps include defining the system purpose and requirements, modeling concepts and entities, specifying services, developing a native controller service, and building a web application to control lights.
IoT Methodology.pptx
IoT methodology, IoT Level Specification
Functional View Specification
Operational View Specification
Device and Component Integration
Application Development
Posecco cluster meeting
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- The modeling covers business, IT, and infrastructure layers. At the business layer, it models institutions, business processes, services, and information. At the IT layer, it models IT services and their components, interfaces, and relationships. At the infrastructure layer, it models physical and virtual resources, nodes, and the overall landscape topology.
- The goal is to support service providers by allowing reuse of services via standardized models and tools, while ensuring security policies are appropriately applied across customer instantiations.
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This document discusses testing uncertainties in cyber-physical systems (CPS) that span Internet of Things (IoT) and cloud infrastructures. It proposes combining model-driven engineering and elastic execution techniques to dynamically provision both the CPS under test and testing utilities across various IoT and cloud infrastructures. Specifically, it suggests using software-defined IoT units and cloud-based elastic services that can be composed, controlled via APIs, and provisioned elastically to enable testing CPS configurations and behaviors across heterogeneous environments.
Recommended
Unit 4 -IOT1.pptx
The document discusses an IoT platform design methodology that is independent of specific products or services. The methodology includes specifying the purpose and requirements, defining processes, domain models, information models, services, functional and operational views, and integrating devices and components. It then discusses developing applications. As an example, it provides the purpose, behavior and system management requirements for a home automation system that allows controlling lights in a home remotely using a web application.
IOT Platform Design Methodology
The document outlines the key steps in an IoT design methodology:
1. Define the purpose, requirements, and use cases of the system.
2. Specify the domain model, information model, services, and IoT level.
3. Develop functional and operational views describing the system components and how they will communicate and operate.
4. Integrate the physical devices and components and draw schematics.
5. Develop the IoT application to implement the designed system.
Chapter 5 IoT Design methodologies
The document outlines a 10-step IoT design methodology that includes purpose and requirements specification, process specification, domain modeling, information modeling, service specifications, IoT level specification, functional view specification, operational view specification, device and component integration, and application development. It then provides an example application of this methodology to design a smart home automation system for controlling lights remotely. The example walks through each step for specifying the purpose, domain model, information model, services, functional views, and developing the application and native controller components.
[PPT] _ Unit 2 _ 9.0 _ Domain Specific IoT _Home Automation.pdf
The document outlines a 10-step IoT design methodology that includes requirements specification, process specification, domain modeling, information modeling, service specifications, level specification, functional and operational views, device integration, and application development. It then applies this methodology to design a smart home automation system case study, outlining the purpose, behavior, and requirements in step 1. Steps 2-10 are then summarized for the home automation example, covering process specification through application development using RESTful web services, a native Python controller service, and a Django application frontend. Finally, it notes how the full system would be integrated using these components on a Raspberry Pi device.
Chapter-5.pdf
The document outlines a 10-step IoT design methodology that includes requirements specification, process specification, domain modeling, information modeling, service specifications, level specification, functional and operational views, device integration, and application development. It then applies this methodology to design a smart home automation system case study that controls lights remotely. Key steps include defining the system purpose and requirements, modeling concepts and entities, specifying services, developing a native controller service, and building a web application to control lights.
IoT Methodology.pptx
IoT methodology, IoT Level Specification
Functional View Specification
Operational View Specification
Device and Component Integration
Application Development
Posecco cluster meeting
- PoSecCo aims to model services for future internet security requirements from a service provider perspective. It takes into account security requirements from customers, laws/regulations, and suppliers.
- The modeling covers business, IT, and infrastructure layers. At the business layer, it models institutions, business processes, services, and information. At the IT layer, it models IT services and their components, interfaces, and relationships. At the infrastructure layer, it models physical and virtual resources, nodes, and the overall landscape topology.
- The goal is to support service providers by allowing reuse of services via standardized models and tools, while ensuring security policies are appropriately applied across customer instantiations.
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This document discusses testing uncertainties in cyber-physical systems (CPS) that span Internet of Things (IoT) and cloud infrastructures. It proposes combining model-driven engineering and elastic execution techniques to dynamically provision both the CPS under test and testing utilities across various IoT and cloud infrastructures. Specifically, it suggests using software-defined IoT units and cloud-based elastic services that can be composed, controlled via APIs, and provisioned elastically to enable testing CPS configurations and behaviors across heterogeneous environments.
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The recent trends in the computer industry are the one and only thing i.e., web services. Because of the common availability and open technologies web services are relevant to all. Service-oriented architecture (SOA) helps organizations to transform their business processes for high performance by simplifying the underlying information systems. The most challenging aspect of building successful software applications is clearly understanding and specifying the requirements that an application must satisfy. Use case modeling is an increasingly popular approach for identifying and defining requirements for software applications of all types. Use cases describe the behavior of the system as its users interact with it. This approach helps to place the software requirements in the framework of a user doing some useful work with the system. This type of approach helps to map software requirements to the relevant enduser business processes, a very powerful concept. This paper presents how the relationship between use case model and Service oriented architecture
Study on Use Case Model for Service Oriented Architecture Development
The recent trends in the computer industry are the one
and only thing i.e., web services. Because of the common
availability and open technologies web services are relevant to
all. Service-oriented architecture (SOA) helps organizations to
transform their business processes for high performance by
simplifying the underlying information systems. The most
challenging aspect of building successful software applications is
clearly understanding and specifying the requirements that an
application must satisfy. Use case modeling is an increasingly
popular approach for identifying and defining requirements for
software applications of all types. Use cases describe the
behavior of the system as its users interact with it. This approach
helps to place the software requirements in the framework of a
user doing some useful work with the system. This type of
approach helps to map software requirements to the relevant enduser business processes, a very powerful concept. This paper
presents how the relationship between use case model and
Service oriented architecture.
Unit 4 -IOT2.pptx
This document discusses the methodology for designing an IOT platform. It includes specifying requirements, processes, domain models, information models, services, and integrating devices and components. As an example, it outlines the process specification for a home automation system that allows remote control of lights using a web application. Key steps in the process specification include defining the purpose, behavior, system management requirements, data analysis requirements, and security requirements. It also describes defining the domain model, which abstractly represents concepts, objects and relationships in the IOT domain, such as physical entities, virtual entities, devices, resources, and services.
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Study on Use Case Model for Service Oriented Architecture Development
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1. JPEG uses discrete cosine transform (DCT) to convert pixel values into frequency coefficients, followed by quantization which reduces the number of bits for each coefficient.
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Архитектура промышленного интернета
This document summarizes an IoT project deliverable that discusses concepts and solutions for discovering IoT resources and managing their dynamic associations. It presents approaches for looking up and discovering IoT services and virtual entities that represent physical things, as well as methods for finding new associations between services and entities as their relationships change over time. The deliverable includes details on relevant data models, functional components, and interfaces in the IoT architecture, and examines how location, semantics, federations, peer-to-peer systems and domain-based approaches could be used to implement the discovery and lookup capabilities.
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The project Security for Future Networks (SecFuNet)1 proposes the design of a framework
providing secure identification and authentication, secure data transfer and secure
virtualized infrastructure.
In this paper, we present some of the most important ones currently available and we
present a comparative study should examine some models and frameworks of Identity
Management. Initially, we had identified OpenID, Higgins and Shibboleth frameworks as
those providing facilities that are the closest to our proposals and our requirements.
However, with the literature prospection more frameworks have being included in our
study, which has allowed to expand our state of the art on IdM. In our study, some features
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Bhadale group of companies systems engineering services catalogue
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Simha_MSTR & BO
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Huffman coding is a popular data compression technique that assigns variable-length binary codes to symbols based on their frequency of occurrence, with more common symbols getting shorter codes. It works by building a binary tree from the frequency of each symbol and assigning codes based on paths from the root to each leaf node. This results in more efficient transmission of data compared to fixed-length codes like ASCII by using fewer total bits. While it does not achieve optimal compression like entropy encoding since it uses an integral number of bits per code, Huffman coding is simple to implement and provides good compression for many types of data.
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This document discusses image compression techniques. It defines compression as storing data in a format that requires less space, which is useful for communications by allowing more data to be transmitted in fewer bits. There are two main types of compression: lossy compression, where the original message cannot be recovered exactly, and lossless compression, where the original message can be decoded precisely. Huffman coding is described as a popular lossless compression technique that assigns variable length codes to symbols based on frequency, with more common symbols getting shorter codes.
ModuleII092.pdf
Homomorphic filtering is used to simultaneously normalize brightness across an image and increase contrast by removing multiplicative noise. It treats illumination and reflectance components of an image as additive in the logarithmic domain rather than multiplicative in the spatial domain. High-pass filtering is applied to suppress low frequencies representing illumination and amplify high frequencies representing reflectance. This reduces intensity variation while highlighting detail. Histogram modification techniques can be applied to color images but care must be taken to avoid color shifts, such as by applying the modification to one color band and using original ratios or transforming to brightness/lightness.
ModuleII091.pdf
Digital image processing techniques can be used to enhance images in the spatial domain. Spatial averaging reduces noise by replacing each pixel value with the average value of neighboring pixels. As more images are averaged, the output image approaches the true noise-free image. Median filtering removes salt and pepper noise by replacing each pixel with the median value in the local neighborhood. Homomorphic filtering can remove multiplicative noise by separating illumination and reflectance components in the frequency domain and applying a high-pass filter. Histogram equalization can improve contrast in color images by applying the technique to one color band and using original ratios to determine values for other bands.
ModuleII090.pdf
This document discusses techniques for digital image enhancement through histogram modification, including histogram stretching, shrinking, sliding, equalization, and specification. Histogram modification performs gray level mapping to modify image contrast by considering a histogram's shape and spread. Histogram stretching increases contrast by mapping values across the full range, while shrinking decreases contrast by compressing values. Sliding makes images lighter or darker by adding or subtracting from values. Equalization makes histograms as flat as possible to improve contrast, and specification allows interactively defining a target histogram to remap an image's values. These techniques are useful for improving low-contrast or unbalanced images.
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