2.2.1 Cloud Evolution

In the last two decades the cloud and accommodating models have experienced major transformation from cloud hosted to micro services and serverless architecture. The deep insight about these transformations is as follows:

2.2.2 Monolithic Cloud Hosting Architecture

The services of cloud computing hosting started by the first decade of the 21st century. These three tier application use cases require abilities like distributed computing, network, storage, and computation, etc. The consumers were taking advantage of elasticity of the resources to scale up and down based on the demand. The adaptable functionality introduced in Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) allow for a single instance for the sake of scalability. However, the architecture means that it’s all in one competences cannot pay for the replication of instances for other purposes, such as having multiple versions, or as a by-product of deployments. It offered three different functional areas illustrated in Figure 2.1 .

Infrastructure as a Sservice (IaaS): This one is most malleable from all three flavors of services, but act as a pay-as-per model; allows to outsource their IT infrastructure such as hardware, OS, and networking.

Platform as a Service (PaaS): This service of cloud computing offers platform to their clients for offering to run different business applications; thus the cloud developers does not need to worry about all the server infrastructure, network, and monitoring tasks.

Software as a Service (SaaS): This is a software distribution model; allows users to connect and use software applications as a whole without responsibility even for the application code. This offers a variety of benefit subscription options that provide the software services out of the box but it’s inflexible if the client needs to have any custom business functions outside of what’s offered by the provider.

2.2.3 Micro Service Cloud Architecture

It is an architectural concept with cluster of micro services that differentiate the monolithic paradigm into smaller independent units. They are powerful, and enable smaller teams to own the full-cycle development of specific business and technical capabilities. Developers can deploy or upgrade code at any time without adversely impacting the other parts of the systems (client applications or other services). The services can also be scaled up or down based on demand, at the individual service level. A client application that needs to use a specific business function calls the appropriate micro service without requiring the developers to code the solution from scratch or to package the solution as library in the application. The micro services approach encouraged a contract-driven development between service providers and service consumers. This sped up the overall time of development and reduced dependency among teams. In other words, micro services made the teams more loosely coupled and accelerated the development of solutions, which are critical for organizations, especially the business start-ups.

2.2.4 Serverless Cloud Architecture

A hot trend that revolutionized the world and gained a lot of attention in the last few years is serverless architecture, also recognized as serverless computing. Serverless computing is a step further than the PaaS model in that it scales automatically and fully abstracts from the application developers. In this computing platform, an illusion has been created, originally for the benefits of developers whose business services hosted at cloud; and extended the way people use it.

These backend services provide clients a platform to write and deploy codes without the worrying of underlying infrastructure; and to pay on the basis of computation done instead of fixed charge. The backbone of this computing arena is lower cost, simplified scalability and quicker turnaround time; while the cold start of same requested functions becomes necessary trade-off of Function-as-a-Service (FaaS).

2.3.1 How Does It Work?

Cloud computing and IoT are two enabling technologies often paired together, but it is also necessary to know that how exactly do the two entities interact with each other? While the IoT connectivity could exist without the cloud, it’s safe to say that cloud computing enables many IoT devices to function with much greater power and efficiency. In an IoT network, many IoT sensors (dozens, hundreds, or thousands) collect data and send it to a central location for analysis. This data is important to be analyzed in real time with proper analytics tools so that the faults and failures can be resolved in minimal time, which is the core purpose of this integration. Cloud computing helps by storing all the data from thousands of sensors (IoT) and applying the needed rule engines and analytics algorithms to provide the expected outcomes of those data points. The cloud providers also allow smart companies functionality to store and process copious amounts of data at minimal costs, opening the door to big data analytics. Without the cloud, aggregating IoT data across large areas and different devices is far more complicated. Cloud IoT includes the underlying infrastructure, servers, and storage, needed for real-time operations and processing with services and standards necessary for connecting, managing, and securing different IoT devices and business applications. The integrated cloud IoT architecture is depicted in Figure 2.2.

For example, a smart city can benefit from the cloud-based deployment of its IoT systems and applications. A city is likely to deploy many IoT applications, such as applications for smart energy management, smart water management, smart transport management, urban mobility of the citizens, and more. These applications comprise multiple sensors and devices, along with computational components. Furthermore, they are likely to produce very large data volumes. Cloud integration enables the city to host these data and applications in a cost-effective way. Furthermore, the elasticity of the cloud can directly support expansions to these applications, but also the rapid deployment of new ones without major concerns about the provisioning of the required cloud computing resources creates some issues and challenges for smart platform.

2.4.1 Edge Computing

The three main gears of the IoT are physical things, communication networks and the cloud. In countless current IoT systems, the actual things have limited computing power and its main purpose is to transmit sensor data over communication networks to a cloud backend. With minimum local data processing involved and the basic approach of performing all processing in the cloud is not scalable and finds its limits as the number of devices sending data grows, or data volume from a device exceeds a certain amount. So that a distributed computing framework called edge computing introduces that brings applications closer to data sources as IoT devices or local edge servers. This belonging of data at edge servers deliver many enterprise benefits such as faster accesses, improved response time, and improved bandwidth availability. This intelligent integration enabled IoT to store, process, and analyze data on the edge of the network, with increasing edge intelligence and connectivity that drives intelligent systems. It states that edge intelligence will be the key value that delivers exceptional growth for IoT implementation. This integrated computing trend by IoT sensor nodes, edge computing, software, and cloud services for a wide array of IoT applications. Edge computing in the context of IoT means that data is processed at the place where it is generated. Pre-processing, filtering, analytics, and even artificial intelligence (AI) are done on the edge device instead of central point. This also helps to reduce latency, data volumes, and loads on cloud infrastructure, while at the same time improving responsiveness, robustness, and resiliency of applications. Edge computing can be done on advanced Programmable Logic Controllers (PLCs), Programmable Automation Controllers (PACs), Industrial PCs, or IoT gateway devices in industrial IoT applications. Futuristic advancement in networking technologies, such as 5G wireless, are allowing for edge computing systems to accelerate the creation or support of real-time applications, such as video processing and analytics, self-driving cars, AI, and robotics.

2.4.2 Fog Computing

Another advanced cloud IoT integration paradigm is fog computing; it can be seen as an extension of edge computing where computing, storage, control, and networking functions are distributed locally and routed to the cloud using Internet backbone. Fog computing means additional computing and networking layers between edge and the cloud. In nature, fog exists between ground and clouds, same fog computing sits between physical things and cloud computing. An interesting and important aspect of fog computing is that it takes the concepts from cloud computing and brings them closer to the things network. The fog extends the cloud services to be closer to the things network that produce and act on IoT data. These special devices, called fog nodes, can be deployed anywhere with a network connection: for example on a factory floor, on top of a power pole, alongside a railway track, in a vehicle, or on an oil rig. Any device with computing, storage, and network connectivity capability can be a fog node. Examples include industrial controllers, switches, routers, embedded servers, and video surveillance cameras. Fog nodes considered by analyzing the most time-sensitive data at the network edge, close to where it is generated instead of sending vast amounts of IoT data to the cloud, perform action on IoT data in milliseconds, based on policy and sends selected data to the cloud for historical analysis and longer-term storage. Fog computing is not a replacement of cloud computing by any measure, it works in conjunction with cloud computing, optimizing the use of available resources creating data-path hierarchy and transmitting only summary and exception data to the cloud that leads to increased business agility, improved security, and higher quality of service levels.

2.4.3 Mist Computing

While fog computing is the answer to many challenges in the cloud IoT domain, such as high bandwidth requirements and manageability of applications, this concept can be further extended by pushing the computation to the end devices. In fog computing the gateway bears the responsibility for IoT application execution, regardless whether the application is just simple data collection or building automation with many actuation tasks. However, this approach can also have some drawbacks, such as increased delays in applications involving control, unnecessary high bandwidth requirements as all data must move through the gateway. The gateway is a single point of failure for applications that must be executed on the network and the operation of the entire network is dependent on the gateway; then a new concept of mist computing introduces.

Mist computing takes fog computing concepts even further by pushing appropriate computation to the very edge of the network, to the sensor and actuator devices that make up the network. With mist computing the computation is performed at the edge of the network in the microcontrollers in the embedded nodes. The mist computing paradigm decreases latency and further increases the autonomy of a solution. By applying the principles of service-based architecture among the end devices the application can be described as a combination of services, which are dependent on each other. Any device that has access to the network can subscribe to a service that is offered by any of the devices on the network. It is utilized at the extreme edge of a network that consists of microcontrollers and sensors. By working at the extreme edge, mist computing can harvest resources with the help of computation and communication capabilities available on the sensor. Mist computing infrastructure uses microcontrollers and microcomputers to transfer data to fog computing nodes and eventually to the cloud. Using this network infrastructure, arbitrary computations can be processed and managed on the sensor itself. Each device in the network must be aware of its location as most applications tend to be location dependent. The necessary “location awareness” can be created at installation time (by “telling” the device, what its location), or the devices can determine their location autonomously by determining their location relative to some existing beacons, with known locations. Applications will be able to support this relative location functionality and incorporate this into mist. The services provided by end devices may also be requested by mobile devices or servers, in which the service request reaching a specific network is routed to the device, that is able to provide the specific service. This means that in one network end devices and a gateway may be both providing services to the same server. As an example in a building automation scenario we may be interested in room occupancy information for every single room, so all the occupancy sensors in the individual rooms must report information directly to the server, while the operation times of standalone air conditioning (AC) units may be aggregated (in the gateway) to estimate the total power usage of AC units in the building.

2.4.4 Cloudlets

The mobile as well as other IT devices nowadays are being developed embedded with a number of advanced features such as augmented reality, face recognition, natural language processing, gaming, video processing, 3D modeling software, etc. These applications usually are resource-hungry, requiring intensive computation and high energy usage. But the mobile devices are resource constrained in terms of processing power and battery life. So, in order to execute these types of applications, the resource intensive applications are uploaded to the cloud using a mechanism called offloading where all these processing can be carried out in cloud using the resources there, and the results are send back to the IT devices in our hand. Based on the type of tasks and the needed resources, the whole process or a part of the process get offloaded to the cloud for processing. But as I mentioned above in the edge computing section, sending data from data resources to clouds that are miles away have latency and bandwidth issues. And, if there is a situation where the Internet service provider failed to conserve the connection between the device and the cloud server, there will be delays, packet loss, and interrupt user experience. So, in order to avoid and reduce these problems, the cloudlet concept was introduced in a cloud computing paradigm.

A standard definition for cloudlet is “cloudlets are mobility-enhanced small-scale cloud data centers that are located at the edge of the Internet.” So, by using cloudlets, the resource intensive tasks can be offloaded to it for processing hence will reduce latency, bandwidth, and save a lot of time. Cloudlets’ latency and bandwidth advantages are especially relevant in the context of automobiles, to complement vehicle-to-vehicle approaches being explored for real-time control and accident avoidance. During failures, a cloudlet can serve as a proxy for the cloud and perform its critical services. Upon repair of the failure, actions that were tentatively committed to the cloudlet might need to be propagated to the cloud for reconciliation. Including these privacy and security conservation are other benefits of cloudlets. While using cloud for processing, our secure data have to travel to cloud servers’ miles away, hence security of the data will be in sometimes breached. Hence, by using cloudlets, all the private data will be processed at the edge of devices and help in the conservation of the security and privacy of data [8–10]. Enterprise users must understand that the edge, fog, mist, and cloudlets computing paradigm have their own set of strengths and weakness their conceptual architecture referenced in Figure 2.3.

Understanding and using these paradigms correctly will help in ensuring that the growing number of IoT devices can work efficiently. Also, businesses can utilize fog, mist, edge, and cloudlets computing together to utilize their strengths and minimize their limitations. As these networking architectures complement each other, businesses can use them to design secure, reliable, and highly functional IoT solutions for smart world yet their comparative analysis also requires to choose any one of them or in the form of integration as based upon IoT application use case. The summarization details of these presented in Table 2.2.

2.5.1 Monolithic Cloud-IoT Architecture

The oldest and traditional way of integrating applications using monolithic approach comprises a client-side user interface, a server-side application, and a database. It is unified and all the functions are managed and served in one place. It is one large code base and lack of modularity leads to issues that if developers want to update or change something, they access the same code base. So, they make changes in the whole stack at once. In spite of having functionality such as less crosscutting concerns, easier debugging and testing, simple to deploy, and development becomes cumbersome with growing IoT devices and real-time versioning of updates with hard management, not scalable, and sometimes act as a barrier for new technologies. It was designed for application development before the proliferation of public cloud and mobile applications and has difficulty to adapting an application that become too large and complex to make frequent changes. Not only that, but it also requires the maintenance of at least three layers of hardware and software, that can make it inefficient (Figure 2.4).

2.5.2 Micro Service Cloud-IoT Architecture

Independent components, easier understanding, better scalability, flexibility, and higher level of agility advances the concept of micro service cloud-IoT integration. These independent units carry out every application process as a separate service. So all the services have their own logic and the database as well as perform the specific functions. As each service covers its own scope and can be updated, deployed, and scaled independently the extra complexity, system distribution, cross-cutting concerns, and multitude of independently deployable components testing becomes much harder in this model. While the migrating to micro services cloud-IoT integration allows smooth adding of new SaaS services: anomaly detection, delivery prediction, route recommendations, object detection in logistics, natural language processing for document verification, data mining, and sensor data processing. One of the best choices for creating and running micro services application architectures for IoT is by using containers; that encapsulate a lightweight virtualization runtime environment for your IoT application and allow you to move the application from the developer’s desktop all the way to production deployment. You can run containers on virtual or physical machines in the majority of available operating systems. Containers present a consistent software environment, and you can encapsulate all dependencies of your application as a deployable unit. Containers can run on a laptop, bare metal server, or in a public cloud (see Figure 2.5).

2.5.3 Serverless Cloud Integration

An architectural integration with IoT is handling your cloud environment’s underlying servers so that you don’t have to. It allows developers to focus on writing code without needing to worry about deploying, managing, and scaling servers. PaaS, serverless computing allows IoT businesses to offload all of a server’s typical operational backend responsibilities, freeing their developers, and engineers to work on creating new products and services. This service’s architecture solves insights from your global device network with an intelligent IoT platform whose scalable, fully managed integration lets you connect, store, and analyze data at the edge and in the cloud. It is container-based environments; empower to run applications in public, private, and hybrid clouds. It supports people, processes, and technologies to build, deploy, and manage apps that are ready for the cloud. Serverless IoT architecture also help to provide IoT businesses with incredible savings, scalability, and performance. It’s working based on decoupled systems that run in response to actions. This event-driven architecture uses events to trigger and communicate between decoupled services. Electronic Design Automation (EDA) has been here for a long time, but it now has more relevance in the cloud. For the serverless model, there is no server management needed. The serverless model is also quickly scalable (so quick updates and deployment are possible) and it is stateless (see Figure 2.6).

These advanced collaborative platforms facilitates new emerging applications and requires following key principles to follow best practices and work well with integrated platforms and application based use cases of IoT.

2.6.1 Interoperability Issues

Interoperability refers to the basic ability of computerized systems to connect and communicate with one another readily, even if they were developed by different manufacturers in different environment. Being able to exchange information between applications, databases, and other computer systems is crucial for the modern world. The interoperable environment of heterogeneous IoT devices and cloud services is an important issue in a cloud-IoT integrated environment. The key concerning issues of interoperability are technical, syntactic, semantic, and organizational interoperability; that requires a closer look at interactions of the key components in IoT ecosystems regarding different aspects [11].

2.6.2 Energy Efficiency

The integration of the cloud and the IoT is attracting attention to the industry. Particularly, the data (alarm, security, climate, and entertainment) gathered by sensors are transmitted first to the gateway, that then transmits the received sensory data to the cloud that quickly consumes the node energy. Eventually, the cloud stores, analyzes, processes, and transmits the sensed data to the users on demand. During the entire data transmission process, if the data transmission from the sensor nodes to the cloud is not succeeded, data are retransmitted until they are successfully delivered, requires efficient energy mechanism for collaborated environment, and remains a significant open issue. The reliable delivery of data in cloud-based IoT is a big concern for a cloud-IoT environment because of limited power constraint of sensor nodes in IoT [12].

2.6.3 Big Data Generation and Processing

With the evolvement and development of smart home, smart cities, and smart industries; the cloud paradigm empower them to collect huge data by heterogeneous connected devices and exchange to fulfill user needs. As this collaboration technique benefitted users in so many ways some concerning issues include big data is never 100 percent accurate, it’s important to be sure before analyzing data that the sensors function accurately and the quality of the data coming for analysis is reliable and not spoiled with factors such as breakdowns in sensors. The physical layer of paradigm containing connected things generate terabytes of data, and it’s a demanding task to choose which data to store and which to drop while on which data needs quick analysis and which requires deeper analysis needing the advanced cloud models such as edge data analytics, cloud gateway and machine learning modules.

2.6.4 Security and Privacy

The cloud and IoT that refers to the integration of the cloud computing and the IoT, has dramatically changed the way treatments are done in the ubiquitous computing world. This evolving integration has become vital because the important amount of data generated by IoT devices needs the cloud model as a storage and processing infrastructure. The security issues in cloud IoT stay more critical since users and IoT devices continue to share computing as well as networking resources remotely. Among the main concerns of cloud IoT, privacy and data integrity have a great place. The one of main constraint of the exchanged data is sharing of the user’s personal information and keep it safe. Several issues such as the protection of user’s privacy and manufacturer’s IP; the detection of malicious activity and how to block them, come under cloud and IoT security threats [13] as it continuous growth. One particularly important issue that has not yet been solved is how to provide appropriate authorization policies and rules while ensuring that only authorized users have access to the sensitive data.

2.6.5 Integration Methodology

Cloud computing has resolved most of IoT issues. Truly, the IoT and cloud are two comparatively challenging technologies, and are being combined in order to change the current and future environment of Internet-working services [14]. This needed a standardization mechanism, standard protocol formats, high bandwidth, compatible architecture, and standard application programming interfaces to allow for interconnection between heterogeneous smart things and the generation of new services, which make up the successful integration of cloud-based IoT paradigm.

2.7.1 Smart Home Applications

Smart homes are probably the most common and have revolved around the Internet for a long time. Smart home devices gather and disseminate information with one another in an integrated platform and automate their response actions based on the owner’s preference. Building smart home automation using IoT helps us manage our lives but as the technologies advances, the main aim of the manufacturers and designers changes their working principles into reducing the controlling and monitoring methods for all functions of the smart applications. The idea cloud IoT comes into our lives as a boon and enables all devices to use Internet connection and cloud storing platforms to work wirelessly and sometimes operation-based. When sensors are attached to them, as part of physical layer the appliances designed by the intent of the smart home concept are able to be used without touching. The systems are controlled with the help of the applications on smartphones or other devices as well as with the voice recognition option.

2.7.2 Smart Health Care

There is a huge applicability of technology, data, and communication methodologies to improve health-care solutions through telehealth and telemedicine. Health-care queries is emerging across the globe, there is a growing number of patients being remotely treated globally. The smart IoT wearables like fitness bands and blood pressure monitors to help patient’s health. Numerous alert mechanisms put in smart devices to notify doctors or family members in the case of emergencies. For the physicians, IoT revolutionized their dealing with patient as it becomes easy to get into the history of a patient and access real-time health data easily. Efficiency of clinical trials increased day-by-day with real-time health data. Different health insurance companies, nowadays, collect data through IoT devices and store them in the cloud, to track the routine activities of a patient, whether they are obeying to their treatment plans or even looking into the operation processes. These smart systems automate the workflow and provide effective health-care services to the patients.

2.7.3 Smart Cities

Smart city is a futuristic powerful application of IoT creating huge curiosity among the world’s population. Smart scrutiny, computerized transportation, smarter energy management systems, smart water distribution, urban security, and environmental monitoring all are examples of IoT applications for smart cities. In these cities, IoT with collaboration of other enabling technologies is used for several reasons like traffic management, public transportation, parking, utility billing, etc. With the integration of sensors, GPS data collection with cloud platforms, it will be stress-free to monitor traffic conditions of a specific area, plan construction program by predicting its influence on traffic, and finding new alternative routes whenever necessary.

2.7.4 Smart Waste Management

One of the important IoT application is selecting the right route for garbage trucks. With powerful smart waste management, IoT applications can notify truck drivers about filled dustbins and set a route for them so that they do not have to waste time by exploring locations with empty dustbins. These devices and integrated platforms also help in developing smart bins, that is, trash bins that can segregate waste into categories like plastic, metal, glass, or paper.

2.7.5 Tackling Industrial Issues

In the manufacturing Industries, IoT can be used in asset management and inventory management. Implanting IoT in the manufacturing sector can help in tracking the efficiency of the systems being used, detect any errors in the machinery, detect causes of lack of efficiency, etc. IoT in the industry can help in tackling unplanned downtime and system failures can result in life-threatening situations too.

2.7.6 Outdoor Surveillance

In smart cities IoT closed-circuit TV cameras combined with AI and machine vision, automate the surveillance of streets and live streaming. These devices also provide the great line of defense mechanism for homes in indoor or outdoor environments.

2.7.7 Smart Workplace

Smart tech solutions for wearable IoT devices take us into a paperless work environment revolutionized by the COVID-19 pandemic. The augmented reality, virtual reality, and extended reality being adopted bynumber of devices to develop smart workplace for the future of the world.

2.7.8 Smart Metering and Smart Grid

Smart metering and smart grid systems change the way of energy distribution within cities. This application of IoT help consumers for smart decisions about energy consumption, transparency of electricity bills, and value added services of utility companies, A smart grid basically promises to extract information on the behaviors of consumers and electricity suppliers in an automated fashion to improve the efficiency, economics, and reliability of electricity distribution. The smart grid also paves new ways to allow real-time data monitoring and electricity demand. These applications involve computer intelligence for the efficient resources management, outage management, load distribution, and fault detection and repairs.

2.7.9 Smart Farming

Smart farming has gain popularity and potential; that farmers can used for applications for optimizing a lot of diverse activities. These applications increase the efficiency of precise farming operations and reduced the labor extent of farming process. Smart farms can revolutionize the agriculture industry and to boost automated tasks and crop quality and quantity.

2.7.10 Smart Tracking and Monitoring System

Asset tracking is an important part of some businesses Global Positioning System (GPS) or radio frequency (RF) are used to track and monitor assets and equipment. The smart devices can be used for long-range identification and verification of assets; which is highly desirable as the online market is growing fast.

The applications of IoT with cloud technologies are numerous; and touched all aspects of life; because it is adjustable to almost any technology that is capable of providing relevant information about its own operation, about the performance of an activity and even about the environmental conditions that we need to monitor and control at a distance. Some of their key enabling services are listed in Table 2.3 .