Keywords

1 Introduction

Artificial intelligence (AI), the Internet of Things (IoT), and cloud computing are buzzwords in the modern technological era. AI is the technology that aims at making computers or machines equivalent to the human brain and thus capable of learning and problem-solving [27]. AI-based applications can be integrated easily with other emerging technologies like IoT, cloud, Big Data, and Blockchain [12]. IoT states a system of interrelated, connected objects or things that can collect and transfer data via the Internet. A substantial number of physical things are being associated with the Internet at an exceptional rate recognizing the concept of the IoT. Reports and recent trends show that there are more than 30 billion IoT connections, almost four IoT devices per person on average by the year 2025 (https://iot-analytics.com/state-of-the-iot-2020-12-billion-iot-connections-surpassing-non-iot-for-the-first-time/). These IoT applications generate massive data, and cloud computing delivers a way for those generated data to travel to their endpoint [5]. The adoption of cloud computing is recognized as a data-processing and storage facility. All real-time applications connected with IoT need just-in-time processing and quick action over the clouds. AI and IoT-based data have obtained much attention from researchers, academicians, and industrialists in health care, agriculture, telecommunication, e−/m-commerce, and transportations. Nowadays, AI-based approaches amplify the role of IoT in business monitoring, health-care monitoring, disease prediction, bioinformatics, research and development, stock market prediction, social network analysis, weather analysis, agriculture, transportation, and resource optimization. Implementation of these applications requires data storage and computational capacity generally provided by cloud-based services [13]. AI techniques are used to process the stored data in a high-precision and just-in-time manner. The cloud is a powerful tool for transmitting data through the traditional Internet channels as well as via a devoted direct link. IoT becomes the source of generating huge data, and the clouds become crucial for data storage [9]. Hence, the IoT and clouds are closely integrated to offer commercial business services and generally referred to as cloud-based IoT. Businesses like Amazon Web Services (AWS), Google, and Microsoft have become certain cloud-based IoT services leaders, making the challenge even more worthwhile. Further, cloud-based IoT is used to connect a wide range of smart things in various applications.

AI, IoT, and cloud computing play significant roles in various aspects in the present and in the future too. AI methods aim to gather data from various industries to process and collect the data generated from cloud-based IoT. Integration of AI, IoT, and cloud has transformed the overall storage capacity and digital world [24] and hence has become a hot topic for all researchers and academicians. This chapter aims to emphasize on the role of AI in cloud and IoT-based data storage.

The remainder of the chapter is systematized as follows: Section 2 focuses on cloud-based data storage. Section 3 discusses the role of IoT in clouds. Further, the role of AI in IoT and cloud data storage is introduced in Sect. 4. Section 5 explains the applications of AI, IoT, and clouds in various sectors, and Sect. 6 concludes the chapter.

2 Cloud-Based Data Storage

Cloud storage is an Internet-based storage system in which data are transmitted on remote storage systems. The data generated from IoT and other devices are stored, maintained, managed, backed up, and accessible to users via the Internet. Users usually pay according to their consumption of cloud storage on a monthly basis. The cloud-based primary services are database services, computing services, and storage services. There are four basic types of cloud storage: public, private, hybrid, and community cloud data storage, as shown in Fig. 1.

Fig. 1
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Types of cloud storage

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2.1 Public Cloud Storage

Public cloud storage , also called Software as Services (SaaS), is provisioned for open use by the general public. Public cloud storage may be owned, managed, and functioned by a commercial, a government, or an academic. It offers data storage on a pay-per-use basis. In public cloud storage, the communication links can be assumed to be realized over the public Internet, as shown in Fig. 2. Any number of people, clients, organizations, or businesses can access cloud services by the mean of the public Internet. Examples of public cloud storage are Google App Engine, Microsoft Windows Azure, IBM Smart Cloud, Amazon Elastic Compute Cloud (EC2), etc.

Fig. 2
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Public cloud storage

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Advantages

  1. 1.

    The provider is responsible for establishment, maintenance, technical support, and maintenance of the storage infrastructure and its associated costs.

  2. 2.

    Provider holds overall control of cloud environment. The subscriber’s workload or data can be migrated at any time.

  3. 3.

    The workload can be transferred to data centers where the cost is low.

  4. 4.

    Public clouds potentially have a high degree of flexibility.

  5. 5.

    It is suitable for individual users and mid-sized organizations.

Limitation

  • Data are at a high security risk due to the public domain and overall control of providers.

2.2 Private Cloud Storage

Private cloud storage [31] is a scalable and redundant storage solution where data are stored on distant servers devoted to an individual user, as shown in Fig. 3. Private cloud storage is safer than public cloud storage since it can be placed in the office/premises of the company’s data center or in another company’s data center. Private clouds are divided into two parts:

  • On-Site Private Cloud—This type of private cloud is implemented at the user’s locations.

  • Outsourced Private Cloud—This type of private cloud is employed at the server side, which is applied to a hosting business.

Advantages of Private Cloud Storage

  1. 1.

    The main benefit of private cloud storage is that it permits the user to have complete control over their data.

  2. 2.

    It offers high security, scalability, and reliability.

  3. 3.

    It is suitable for large enterprises or organizations.

Limitation

  • It is expensive compared with public cloud storage.

Fig. 3
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Private cloud storage

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2.3 Hybrid Cloud

Hybrid cloud combines private cloud (on-premises or off-premises) and public cloud as represented in Fig. 4. They have substantial deviations in performance, reliability, and security properties depending upon the type of cloud chosen to build a hybrid cloud. Examples of hybrid clouds are Windows Azure, VMware, and vCloud.

Advantages

  1. 1.

    Customization is easy.

  2. 2.

    It is secure and reliable.

  3. 3.

    It is suitable for small and mid-size companies.

Limitation

  • It consists of the limitation of both public and private clouds.

Fig. 4
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Hybrid cloud storage

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2.4 Community Cloud

Community cloud storage is the deviation of private cloud storage and exclusively useful for a particular community of users from organizations that have common concerns. Any data is stored in the community cloud owned as private cloud storage to manage the community’s security. It offers a unique chance for businesses to work on common assignments. Google Apps for Government and Microsoft Government Community Cloud are some well-known community clouds.

Advantages

  1. 1.

    Community clouds are flexible as they permit to modification of the properties according to the user’s needs.

  2. 2.

    Community clouds enable organizations to interact with their remote employees and provision of diverse heterogeneous devices.

  3. 3.

    Community clouds offer the user block facility. The service provider can block specific users from inserting, deleting, modifying, and downloading certain data sets and services.

Limitation

  • It needs specialized security concerns as various organizations access and control the infrastructure.

3 Role of IoT in Cloud Storage

IoT has practically taken many industries such as health care [17], agriculture [18], transportation [6], telecommunication [28], real estate, etc. IoT offers best-connected environment for devices referred as “things” and generates huge amounts of data to be processed, analyzed, and communicated for the cloud. The public cloud storage assists the IoT services by offering third-party access to the organization. The integration of IoT and cloud can support IoT data or computational components operating over IoT devices [1]. Cloud-based IoT framework is represented in Fig. 5.

Fig. 5
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Cloud and IoT-based environments

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In Fig. 5, there are three components of cloud-based IoT storage. The first component provides IoT-based infrastructure where devices of different applications are connected with clouds using the second component, referred as middleware, which consists of communication technology such as 5G, Internet, Satellite Network, Wi-Fi, etc. The third component is the cloud infrastructure.

IoT-based Infrastructure: IoT-based infrastructure is based on a three-layered architecture, that is, perception layer, network layer, and application layer [7, 22].

  • The perception layer is the physical layer or sensor layer of the architecture. The sensors and actuators are collecting data from things and transmit data for further processing [32]. It senses specific physical parameters or identifies other smart objects in the location.

  • The network layer is liable for linking to objects (things), network devices, and servers.

  • The application layer is the uppermost layer where IoT can be deployed. The application layer is accountable for conveying application-specific facilities to the users such as smart homes, smart transportation, smart agriculture, and smart health.

Cloud-based infrastructure offers cloud-based services such as infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS) [14] for extending the application smarter. Users usually pay according to their usage, such as central processing unit (CPU) per hour, gigabyte (GB) storage per hour, IP usage per hour, etc.

  • Infrastructure as a service (IaaS) : IaaS offers virtualized computing infrastructure over the Internet. In IaaS cloud-based services, it can be paid for services like storage, networking, servers, and virtualization. The example of IaaS is AWS EC2, Rackspace, Google Compute Engine (GCE), etc.

  • Platform as a service (PaaS) : PaaS is a model in which cloud vendors offer developers a platform for building apps. PaaS provides a run-time environment to create, test, run, and deploy the application. Examples of PaaS are AWS Elastic Beanstalk, Heroku, and Windows Azure. Windows Azure is the most commonly used PaaS.

  • Software as a service (SaaS) : By SaaS, one can generally access the software from any device, at any time, without installation of any software on your computer via the Internet. Examples of SaaS are BigCommerce, Google Apps, Salesforce, Dropbox, etc.

The foremost benefit of placing the IoT system in a cloud is that it offers more flexibility, scalability, and reliability to connected applications. Sometimes businesses don’t need consistent requirements of data storage; it may be occasional. For example, online sales are increased particularly at the time of Diwali or Christmas; hence, only for 1–2 weeks, organizations require extra storage and computing infrastructure to meet all businesses’ online requirements.

4 Role of AI in Smart Data Storage

Due to digitalization and recent technological advancements, data are generated enormously. About 90% of the world’s data are generated just during the last couple of years [15]. These generated data can produce interesting patterns, meaningful information, and correlation if stored and processed efficiently. Earlier, these data were stored in data centers. In a data center, data are mostly stored on the premises of the business organization. Some data centers may be in a distributed location and not accessible in an efficient manner. Here, the cloud-based solution comes into the picture. The cloud is entirely off-premises, and entire data are accessible from anywhere and at any place via the Internet. IoT and clouds are making data storage as “smart.” The IoT produces vast amounts of data, and cloud computing conveys a pathway for those data to travel to their destination. Data storage requirements of big industries are also high. These industries mostly rely on hybrid clouds due to the potential advantages of hybrid clouds such as elasticity, agility, cost-efficiency, etc. [20].

Before cloud storage, AI task was very expensive due to large data, hardware, and software requirements. The potential of cloud computing makes AI capabilities in a highly accessible manner. AI-based technologies and algorithms focus on the data to discover patterns or models that can explain or inform and predict. AI offers a new business dimension with cloud computing (cloud data storage) and assists corporations/businesses to organize their data, discover interesting patterns and correlation, deliver customer experiences, and improve workflows. Just imagine about a driverless car where a high level of accuracy is driven by AI and required to process data in terabytes just for a single autonomous car, but there is no provision to store our terabyte to exabyte data before processing. Here, cloud comes into the picture. But, if it does not have the ability to calculate accurate precision on time, then it is impossible to get exact route information timely for the driverless autonomous car. Here, AI takes the leading role. Vehicle-to-vehicle information is also required to avoid accidents, and here, it is important to realize the role of IoT. Overall, a driverless car or self-driving car cannot work without coordinating all these three buzzwords, that is, AI, cloud, and IoT.

It is significant to deal and lead with data before applying AI techniques for businesses. The following are the benefits of AI with cloud computing.

  1. 1.

    AI tools are used to enhance data management. Nowadays, businesses generate and collect massive repositories of data, and AI tools can process data management.

  2. 2.

    AI tools can support and streamline the way data are ingested, updated, and managed to provide precise real-time data to users, predict fraudulent, and identify the risk areas.

  3. 3.

    The customer relationship management (CRM) platform Salesforce and its Einstein AI tool are used to manage and enhance the CRM.

  4. 4.

    Within a cloud environment, AI requires historical data to identify patterns and trends and makes better recommendations for the customer.

  5. 5.

    Leveraging AI with clouds is a cost-efficient initiative. The cloud permits organizations to purchase only the storage they actually need and when they need.

AI and cloud-based storage system are transforming the business at every level. AI with cloud services enhance the AI based application and practices into the business so that the service providers can respond quickly, according to the market’s competitive environment in advance. IoT-based cloud assists in renovating business and changing the world with AI.

5 Applications of AI, IoT, and Cloud in Various Sectors

In the recent age, emerging technologies such as AI, IoT, and clouds are at the bleeding edge of cost optimization and improved quality. AI and IoT have already proven themselves in different areas and sectors like health care, agriculture, transportation, e-commerce, and telecommunication [29] as shown in Fig. 6. In order to work with real-time applications, cloud computing is extended as fog computing that can satisfy the need of time-critical applications [30]. The applications of the AI with IoT and clouds in various sectors are accelerated the implementation of Industry 4.0 [34]. These emerging technologies are ready to adapt to the industry changes in real time and maximize the industry turnover.

Fig. 6
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Various AI, IoT, and cloud-based applications

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5.1 Health-Care Sector

The health-care industry is generating enormous data that are stored in the cloud. Remote patients are connected via IoT sensor devices such as body temperature sensor, an electrocardiogram (ECG) sensor, blood pressure sensor, activity tracker, continuous glucose monitor (CGM), etc. [11]. These devices generate huge data from different locations. Hence, these devices need a cloud for continuous monitoring of specific attributes like blood glucose level, heartbeat, etc., for numerous days at a time by taking evaluations at regular intervals. AI and related techniques like machine learning and deep learning techniques are serving in timely analysis and prediction of diagnosis and prognosis of the disease [25]. Cloud assists in securing patient data and permitting health-care providers to continue conveying advanced technological care. A diversity of public, private, and hybrid cloud platforms supports getting better access to the patient’s records and facilitating coordination between the doctor and patient.

5.2 Agriculture Sector

Automation of the agricultural system has enhanced the soil’s gain and strengthened the fertility of the soil [3]. AI and IoT-based agriculture systems have various nodes such as soil, farmer, weather, irrigation, fertilizer, and crop management. These nodes or agents are like to distribute node that is connected through IoT, stored in a cloud environment for AI analytics. The various sensors are used for monitoring agricultural system. For examples, DHT11 is a low-cost digital temperature and humidity sensor, applied in the agricultural systems, and soil moisture sensor is applied for determining the soil moisture level. Cloud-based IoT is very supportive of integrating all agricultural-related data, such as soil-related data, weather data, crop-related data, farmers, supply chain, fertilizers, and retailers in the cloud [8]. The overall objective is to increase the productivity, prediction, and estimation of farming parameters to enhance economic efficiency.

5.3 Transportation Sector

Currently, transportation automation is the foremost area and emergent topic. IoT, AI, and machine learning techniques are profound for smart lighting systems and smart parking applications [35], as shown in Fig. 7. Moreover, route optimization, parking, and accident/detection seem to be the most popular applications. These applications produce and require a huge amount of data to be stored and processed. Cloud storage fulfills all these requirements of the smart transportation sector. Integration of AI, IoT, and clouds in transportation sectors makes this smart and also referred to as intelligent transport system (ITS) [16].

Fig. 7
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Transportation with cloud, IoT, and AI

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5.4 Telecommunication Sector

Telecommunication is itself facing a problem of poor quality of service (QoS), network selection criteria among multiple options and customer churning problems [4]. Telecommunication and mobile users want to always have the best-connected network. In fact, network infrastructure is a primary aspect of all emerging technologies. Telecommunication sector provides fastest network infrastructure to transmit the huge amount of data generated by AI based application. Emerging technologies also assist telecommunication infrastructure in searching best network, intelligent network selection, resource optimization, and user retention mechanism. Telecommunication network also leveraged with AI for intelligent network selection, resource optimization, and on-demand storage facility offered by cloud computing [23].

5.5 Smart City and Home

Smart city and smart home play a significant role in innovation trends [33]. AI and IoT are combinable with clouds and make the city and home smart [10]. Per and Skouby (2016) suggested an IoT architecture that incorporates smart homes and smart cities via the Cloud of Things (CoT) [19]. CoT virtualizes the IoT that offers monitoring and control [2]. AI, IoT, and clouds will contribute to the smart city/home services developments. Concepts of the smart city, smart hospitals, smart education, smart governance, smart banking, and smart business [21] rely on AI, IoT, and cloud-based data storage. The collection vending machine (CVM) for e-waste management is associated with AWS cloud in order to make a city smart [26].

6 Conclusions

This chapter outlined the potential of AI and IoT in cloud-based data storage system. Recently, IoT-based applications are created by billions of connected devices. These devices are generating huge data, demanding huge data, and processing massive data. Various types of clouds such as public, private, hybrid, and community clouds fulfill the requirement of storage, processing, and maintenance of multiple applications. Further, the role of AI is also explained for analyzing and predicting the response to the IoT-based applications. In addition, cloud provides a place where one can implement the AI-based techniques for data analytics to make predictions about real-time behaviors of various applications such as health care, agriculture, transportation, telecommunication, and smart city/home.