Smart Home: IoT enables home automation, ambient assisted living, video surveillance, and real-time monitoring; connected appliances such as air conditioners, refrigerators, and microwave act as things; with home automation, IoT monitors and controls lighting, climate, entertainment systems, and appliances; in ambient assisted living, elderly people and people with disabilities can be monitored continuously for medical emergencies such as falls or seizures; real-time monitoring includes home security such as access control and video surveillance.

 Smart City: IoT case studies in smart city focus on connected utilities (meters that automatically collect water, electricity, and gas usage for auto-billing), smart traffic control systems (real-time monitoring of traffic and auto-changing of traffic lights for better management, safety, and reliability), smart public transportation, electronic toll collection systems, and smart parking; smart traffic systems can communicate with smart vehicles, ultimately leading toward autonomous vehicles.

Smart Healthcare - Internet of Medical Things (IoMT): IoMT is aimed at realizing digitized healthcare systems by connecting medical resources and healthcare services; IoMT devices enable remote health monitoring using devices such as heart rate and blood pressure monitors, electronic wristbands, and smart beds monitoring patient sleep patterns; the data can be analyzed on the cloud and devices can be integrated with emergency notification systems; these home-hospital systems were shown to be effective in monitoring COVID-19 patients during the pandemic.

Smart Factory and Industrial IoT (IIoT): IIoT deals with safety in human-robot shared workspace, connected production line, connected warehouse, connected shipment, and logistics; smart factories use sensors such as Frequency-Modulated Continuous Wave (FMCW) radars for environment sensing and distance measurement; human position detection is crucial for workers' safety; connected production lines enable smart manufacturing through swift response to product demands and supplies, and predictive production.

Smart Agriculture: IoT things deployed for collecting sensor data on temperature, humidity, soil moisture, rainfall, pest infestation, and soil content can be used to automate irrigation management and farming techniques; for example, farmers can monitor soil moisture and temperature in deciding when to apply more fertilizers and for precision irrigation; drones can monitor pest infestation based on captured videos and spray pesticides on demand; further IoT applications deal with connected animals, plants, agriculture equipment, packing, and shipping.

Environmental Protection: IoT applications in this domain deal with sensors for monitoring air or water quality, atmospheric or soil conditions; for example, buoys equipped with sensors are deployed at the Great Barrier Reef in Australia to collect biological, physical, and chemical data and join it with maritime traffic to provide suggestions to minimize and prevent coral reef damage; similar IoT applications are possible for earthquake or tsunami early-warning systems; IoT is also demonstrated for monitoring wildlife movements and habitats.

Smartphone-Based IoT Applications: Today's advanced smartphones have inbuilt sensors such as accelerometer and gyroscope; these sensors can analyze user activities such as walking, running, sitting, and reading; combined with smartphone-connected electronic wristbands measuring heart rate and oxygen saturation levels, they can be used in designing and developing different IoT-based fitness and healthcare applications.

IoT Platforms: In large-scale IoT environments, managing a huge number of devices and automating their configurations is challenging; several IoT platforms have been developed providing solutions for device discovery, managing connectivity and configuration, software patch updates, and data management for billions of devices; IoT platforms mainly provide middleware and infrastructure that enables end-users to interact with smart objects seamlessly.

The Cloud Layer in IoT predominantly uses message brokers and big data frameworks; for analyzing collected IoT data, different statistical analysis tools, MapReduce-based distributed data processing (Apache Hadoop, Aneka MapReduce, Apache Spark), and streaming solutions (Apache Kafka buffer feeding Apache Storm and Apache Spark streaming) are employed; machine learning tasks such as collision prediction in connected vehicles or medical emergency prediction in remote patient monitoring can be performed on sensor data; once emergencies are predicted, control signals are sent back to actuators via gateway devices.

Fig.5.5.3 - Experimental Setup of EdgeLens Using Aneka

the framework tested software performance in terms of accuracy, response time, jitter, network bandwidth, and power consumption; the user's mobile phone acts as an edge gateway capturing images, Aneka Master resides at fog node in Melbourne Australia, and Aneka Workers reside on Azure cloud in Victoria (Australia) and Virginia (US) - sample output shown in (Fig-5.5.4- Sample Input and Output Showing in the End User Mobile Phone).

Fig-5.6.1 - Phases of a DevOps Pipeline

(5) Release - point at which a build, after thorough testing, is ready for deployment into production; organizations can control when and how builds are released (manual approval, regular release schedule, or milestone);

(6) Deploy - build is released into production; IaC used to build test environment can also be configured to prepare the production environment; new and existing production environments run simultaneously with new requests routed to new environment, eventually replacing existing one

 (7) Operate - operations team ensures everything runs smoothly, environment automatically scales up and down based on configuration, and feedback from customers is acquired;

(8) Monitor - periodically collects data and performs analytics on customer behavior, performance, and errors, completing the loop.

• Continuous Integration (CI) and Continuous Delivery (CD) are DevOps tactics that make use of multiple automated tools to realize agile software development; with CI, developers make incremental code changes regularly and reliably - CI triggers automated build and test phases, which ensure reliable merging of code changes into the repository; the code is then quickly delivered and effortlessly deployed on the production environment as a part of the CD process, again using automated tools and processes such as IaC.

• DevOps emerged because agile methodology, while a good model for software development, still left scope for configuration mismatch between test and production environments; the agile model proposes that development and testing take place in the developer's computer with continuous customer involvement, but when performed in the developer's premises, there can be configuration mismatch between test and production environments resulting in deployment issues; DevOps teams avoid this by working in silos no longer and using shared environments managed by IaC.

• key benefits of DevOps: faster time-to-market through CI/CD automation, improved collaboration between development and operations teams, consistent and repeatable deployment processes through IaC, early detection of bugs through continuous testing, rapid customer feedback through monitoring, improved reliability and scalability through automated operations management, and reduced manual errors in infrastructure provisioning and configuration.

5.7  Infrastructure-as-Code (IaC)

Infrastructure-as-Code (IaC) is a crucial part of realizing DevOps practices and CI/CD; traditionally, developers manually provision and manage servers, operating systems, storage nodes, and supplementary infrastructure components every time they develop or deploy a release; with cloud computing, the number of infrastructure components in an application has grown significantly and infrastructure management has moved from physical hardware in private data centers to virtual machines and containers in remote cloud data centers, making manual deployment very complex.

• IaC proposes infrastructure provisioning and management through code instead of through manual processes; templates are prepared with configuration parameters for the required infrastructure specifications; these templates make it simpler to edit and distribute configurations and ensure that identical environment is provisioned every time; orchestrators utilize the templates in realizing the expected deployment setup with both hardware and software specifications.

• The templates in IaC are managed through the same version control that the other source code of the application utilizes; templates can be modularized, and thus IaC also implies that the deployment process can be divided into modular components which through automation can then be combined in different ways; IaC enables Infrastructure to be treated like software - it can be versioned, tested, reviewed, and collaborated on just like application code.

o   Chef is a popular configuration management tool written in Ruby and Erlang; with Chef, system configuration is defined using a domain-specific language (DSL) using pure-Ruby language; the configurations are called 'recipes' which can be used for configuring and maintaining local servers as well as integrating with cloud-based platforms such as Amazon EC2, Microsoft Azure, Google Cloud Platform, and OpenStack, by automatically provisioning and configuring new machines; recipes also describe how Chef manages server applications and software such as Apache HTTP Server and MySQL database.

o   Puppet is another software configuration management tool that uses its own declarative language to accomplish stages of the IT infrastructure lifecycle; it supports provisioning, configuring, and managing infrastructure in data centers and cloud infrastructures, as well as installing and managing operating system and application components; system resources and their state can be described using Ruby DSL or Puppet's declarative language in files called 'Puppet manifests'; Puppet follows Model Driven Architecture (MDA), a software design approach for development of software systems.

o   Ansible is a software suite with tools supporting software provisioning, configuration management, and application deployment, enabling IaC; Ansible is designed to configure both Unix-like systems and Microsoft Windows; unlike Puppet or Chef, Ansible is agentless and uses temporary remote connections to the machines via SSH or Windows Remote Management (WinRM) for configuration management; system configuration is defined using its own declarative language in simple plain text files using YAML format; the complete system configuration is called Ansible's inventory which includes modules specific to each machine.

o   Terraform is an IaC tool from HashiCorp that supports several cloud infrastructure providers such as Amazon Web Services, Microsoft Azure, IBM Cloud, Google Cloud Platform, and OpenStack; users define the data center infrastructure using HashiCorp Configuration Language (HCL), a declarative configuration language, or optionally by using JSON; Terraform manages external cloud resources - both public and private - with 'providers'; HashiCorp maintains several official providers, and custom providers can also be integrated for providing declarative configuration to describe the desired final state of the cloud application.

• Proprietary IaC Solutions: In addition to open-source tools, public cloud providers also provide several proprietary IaC solutions such as Amazon Web Services (AWS) CloudFormation, Google Cloud Platform (GCP) Cloud Deployment Manager, and Microsoft Azure Resource Manager; these native IaC tools are tightly integrated with their respective cloud platforms but may introduce vendor lock-in since the templates are cloud-provider-specific.

• Key advantages of IaC:

o   Consistency - identical environments are provisioned every time, eliminating manual configuration errors;

o   Speed - infrastructure can be provisioned in minutes rather than hours or days;

o   Version Control - infrastructure changes can be tracked, reviewed, and rolled back just like code;

o   Reusability - modular templates can be reused across different projects and environments;

o   Documentation - the IaC templates themselves serve as documentation of the infrastructure;

o   Cost Efficiency - automated provisioning reduces the need for dedicated system administration expertise

o   Scalability - infrastructure can be scaled up or down easily by modifying templates.

5.8  Quantum Cloud Computing

(a) Quantum computing is a new computing paradigm involving multiple fields such as computer science, physics, and mathematics which leverages quantum mechanics theory to solve complex problems faster than classical computers; it can solve complex problems in machine learning (ML), optimization problems in finance, chemical systems, drug discovery, and simulation of physical systems faster than classical supercomputers; researchers discovered that quantum computing can solve problems faster than classical computers in recent decades.

·       The fundamental reason for faster computation with quantum computing is the use of qubit (quantum bit) - a basic unit of computation that holds more than one distinct state (both 0 and 1) as shown in (Fig-5.8.1- Notion of Bit vs. Qubit); a qubit is a unit of information encoded in quantum particles; the processing of qubits by control devices at the core of a quantum computer determines the speed of the processor; quantum computers are encompassed with subatomic particles like electrons and photons as a basic unit of computation.

Quantum computers use three principles of quantum mechanics - Superposition, Entanglement, and Decoherence - shown in (Fig-5.8.2- Notion of Superposition and Entanglement):

o   Superposition means two or more quantum states are added and the corresponding result is another valid quantum state; a qubit is represented as α|1> + β|0> where α, β are mixing coefficients - this allows quantum computers to process millions of operations simultaneously;

o   Entanglement is linking of multiple qubits where one determines what happens to others, performing calculations nearly instantly;

o   Decoherence is the loss of quantum state in the qubit (qubit decay) due to radiation or temperature changes - reducing decoherence is the key challenge in designing quantum computers.

• The key components of a quantum computer consist of quantum hardware with three entities: quantum data plane, control and measurement plane, and control processor plane with host processor;

• quantum software is used for quantum algorithms using quantum circuits; historical milestones:

o   1981 - Feynman urged designing quantum computers;

o   1992 - Deutsch-Jozsa deterministic algorithm;

o   1994 - Shor's algorithm (factor large integers);

o   1996 - Grover's algorithm (substantially reduces search time).

o   1998 - first demonstration using 2-qubit NMR quantum computer;

o   2000 - IBM proposed minimal requirements;

o   2011 - D-Wave launched first commercial quantum computer;

o   2016 - IBM Quantum lab enabled cloud-based services;

o   2017 - Qiskit software released; algorithm milestones:

(b) Quantum Cloud Computing enables quantum services to be leased for organizations, companies, academia, and other users as cloud services; quantum cloud essentially employs quantum principles in a distributed computing environment; physical quantum computers are complex - according to IBM, a typical quantum computer could be the approximate size of a car along with cooling systems to cool the superconducting processor; therefore, it is ideal to lease quantum resources from cloud service providers and execute quantum algorithms for various applications.

• IBM Quantum Cloud Services: IBM started offering quantum cloud services in 2016 and offers the utility scale of greater than 100 qubit quantum system over the cloud along with scalable and feasible quantum software; IBM quantum platform offers quantum systems at several geographical locations including Mumbai, Brisbane, Kyiv, with 127 qubits; use of quantum cloud services is based on pay-as-you-go mode, premium plan, or free for trying out; developers can build quantum circuits and operators using Qiskit, and run and deploy them on quantum systems using Qiskit runtime; services include advanced runtime compilation, error suppression, and error mitigation.

• AWS Quantum Cloud Services: AWS started offering quantum services in 2020 with Amazon Braket, where developers and researchers can explore potential applications of quantum systems; Amazon Braket provides a development environment to design quantum algorithms using circuits and test them on simulated quantum systems and further run them on real quantum hardware; Amazon offers services at US-East (N. Virginia), US-West (Oregon), and US-West (N. California) AWS regions; pricing of AWS quantum services uses quantum processing units (QPU) with two versions: per-shot fee and per-task fee (e.g., IonQ quantum hardware with Harmony QPU family has $0.030000 per task and $0.01000 per shot price).

• Azure Quantum Cloud Services: Azure Quantum is the cloud quantum computing service offered in the Azure cloud; Azure provides a Copilot chatbot for writing quantum algorithm code; Azure Quantum Workspace is an ecosystem of quantum services for running optimization applications; Azure also provides hybrid quantum computing where developers utilize classical and quantum architecture to solve problems, mixing classical and quantum instructions; Azure Quantum has SDKs such as Q#, Qiskit, and Cirq; developers write code once and run it on multiple quantum hardware architectures; it is also free and accessible through Jupyter notebooks with various simulators and emulators.

Important Questions (2Marks):

1.     What is serverless computing? Compare open-source and public cloud serverless computing.

2.     Define IoT. List any five domains of IoT applications.

3.     What is CIoT? Compare CIoT, Edge and Fog IoT based cloud computing techniques.

4.     Compare Hierarchical Fog computing and Aneka based Fog/Cloud computing.

5.     Why is DevOps an important methodology in cloud/software computing/engineering? (definition, concept of agility, CI/CD relation)

6.     What is IaC? Explain its significance.

Infrastructure as Code (IaC) is a fundamental way of applying DevOps principles to cloud computing

7.     In reference to Quantum Computing, explain the below terms/notations:

(a)   Qubit, |1⟩ and |0⟩

(b)   Superposition

(c)   Entanglement

(d)   Decoherence

Important Questions (Essay): (1, 3, 6, 7, 8)

1.     Explain “Serverless Computing”.  Discuss about any two computing service providers for each mentioned below:

(a)   public-serverless computing

(b)   open-source serverless computing

2.     Define IoT. Discuss various domains of IoT applications.

3.     Explain clearly the three layers of Cloud-Centric-IoT.

4.     Explain about Hierarchical Fog/Cloud Computing architecture.

5.     Explain Aneka based Fog /Cloud computing architecture.

6.     Explain the eight-phases of DevOps.

7.     Define IaC and explain its significance in modern DevOps practices.

8.     Give inference to Quantum based Cloud Computing of below service providers:

(a)   IBM

(b)   AWS

(c)   Azure

References:

1. Mastering Cloud Computing-Powering AI, Big Data and IoT Applications - 2e- Rajkumar Buyya, Christian Vecchiola, Thamarai Selvi, Sivananda Poojara, Satish N. Srirama