IoT and Cloud Computing for Societal Good

Cloud computing has revolutionized the IT industry by allowing the supply of elastic computing tools on demand as pay-as-you-go services such as SaaS, PaaS, and IaaS (Beloglazov, Energy-efficient management of virtual machines in data centers for cloud computing. PhD thesis, 2013; Farahnakian et al., Energy and performance: management of virtual machines: provisioning, placement, and consolidation. 2016). Cloud computing has contributed to the reinventing of the way technology resources such as servers, storage, private networks, application platforms, and software services are procured and provisioned. A cloud consumer only requires an Internet connection to connect to a cloud service provider to use these services without placing undue pressure on the resources of their computer or mobile device. A medium-sized enterprise may outsource the demand for computing resources to the Cloud and escape high front-end costs that could be spent on private computational resources and thereby save costs that may arise from hardware and software maintenance and upgrades. A giant enterprise, meanwhile, can also create its own private cloud data center to satisfy the demand for computing services and enhance the process of resource management and provisioning. However, cloud computing infrastructure requires massive energy, sometimes even exceeding the energy requirements of a mid-size town. This chapter discusses the need for energy efficiency and the methodological approach needed to achieve energy efficiency.

The poor quality of air is hazardous to human health as it causes numerous health problems. It results in heart strokes, increased levels of blood sugar causing diabetes and high blood pressure. These pre-existing medical conditions are identified as the root cause of the spread of coronavirus and raise the chances of death from COVID-19. Prominent research has proven that inhaling the polluted air worsens the impact of COVID-19. It suggests that air pollution is increasing COVID-19 mortality rate. It is evident from SARS like outbreaks and dangerous respiratory infections that breathing more polluted air increases the risks of death. This study proposes IoT-based smart air quality control system as a preventive measure to community spread of virus, i.e. COVID-19. It detects the quality of the air using multiple air pollution sensors. The installed sensors are capable to monitor the presence of air pollutants in the ambiance both indoor and outdoor environments. The objective of this study is to bring attention to environmental issues beyond the scope of the human eye and serve to prevent spread of COVID-19 disease. It stores the collected data on a cloud to monitor and control the quality of air. The accuracy of proposed smart unit is verified for the purpose of monitoring the air quality and the storing the data on cloud for applications.

Air pollution causes a number of pulmonary and cardiovascular diseases. Recording of air pollution via real-time low-cost IoT-based monitoring systems and its subsequent forecasting are likely to help timely warn people about prevailing air pollution across a large number of sites. In this paper, we propose and compare a real-time low-cost IoT-based air pollution monitoring system against an existing, accurate, and expensive industry-grade system. Furthermore, we undertake the task of predicting the accurate values of the industry-grade system from values recorded by the low-cost system. For forecasting, a Vector Autoregressive (VAR) model, a Vector Autoregressive Moving Average (VARMA) model, a Seasonal Autoregressive Integrated Moving Average with Exogenous variable (SARIMAX) model, and a weighted ensemble model of VAR, VARMA, and SARIMAX models were trained and tested on particular matter data. Data for forecasting were collected from the low-cost monitoring system and the industry-grade system over a period of time. Results revealed that the low-cost monitoring system predicted the values of the industry-grade system accurately. Furthermore, the ensemble model performed the best among all models in forecasting of accurate particular matter values of the industry-grade system by using the output of the low-cost system. We highlight the implication of using low-cost systems for monitoring of air pollution.

The Internet of Things (IoT) playing an important role in every sector. The use of number of IoT devices becomes double every year. Smart agriculture is evolving as a need. Automation is playing an important role in agriculture sector. The quality, growth, and productivity of yield is increased due to automation. Manual segregation of fruits decreases the quality. A lot of time is wasted in grading of fruits. As sorting being one of the most important industrial challenge, a reliable segregation and taxonomy system is needed. In the proposed system, by using the multiple processed algorithmic photos, the system will recognize suitable things on the basis of its color, look, size and the amount of damage. System will provide good kind of photo that can provide facility for packing to the farmers if they want to. By the assistance of preparing and analyzing pictures, the system will find the items dependent on its shading, surface, size, and deformities. The procedure will provide a higher caliber of the image with a reason to help comparatively for bundling the items in ventures. The utilization of raspberry pi together with the sensor and flapper instrument, the method of programmed bundling will enhance the nature of outcomes in a higher manner. Right from plucking to packing all things are connected with the IoT which helps to perform tasks more smoothly, timely and improves the connectivity between different units of smart agriculture. The proposed system is more reliable, efficient and gives better performance as compared to existing one.

Every day there are more and more new services are being offered, which differ significantly from their predecessors in terms of functionality, speed, user interface, support and upgradation of old services. All this determines the relevance of the implementation of the web-based national platform “bike sharing” and is a reason to pay special attention to the possibility of creating a city cycling system and its mobile support by creating a mobile application. The aim of the study is to form a conceptual approach for creating a web-based platform “bike sharing” to meet the diverse transport needs of cities and their users based on the formalization of the components of business processes. The tasks of the work are analysis of foreign experience of city bicycle rental; identifying the advantages and disadvantages of existing bicycle rental systems; and description and research of business processes based on existing systems. The article presents the concept of renting city bicycles based on foreign experience in the operation of transport infrastructure, the stability of the technology platform, and optimization of bicycle distribution. During the research, IDEF0 notation diagrams show business processes of city bicycle rental systems (activity of bicycle rental service, customer registration, service payment), based on existing services. The effect of such business processes will be characterized by studying an array of security documents of different countries and open sources of scientific and technical information in order to identify among them a description of a technical solution similar to the studied, which will be the basis of promising.

IoT will dominion the world with its deployment in all fields. Device to device conveying is a gigantic procurement of Internet of Things. One and all fields get beneficial out of IoT. It has a role to play in all emerging fields. IoT will become an essential aspect of our routine life. IoT contrives the globe self-propelling and also provides efficient utilization of resources. IoT assists in establishing smart cities, farming, home, sanitation, and a prominent role in safeguarding the globe and its subsistence from multitudinous degradation. IoT has overwhelming applications inclusion surveilling and degrade air pollution. Ascribed to industrialization air gets contaminated to a great extent. IoT assists in degrading air pollution and prevents the living entity from air endured diseases. IoT has crucial deployment in Medical applications. It predicts the diseases at embryonic stages and salvage lives. IoT surveils the health status of patients and conveys it to the medical experts in case of emergency. The significant deployment of IoT in agriculture applications inclusive of smart irrigation, surveil the proliferation of harvest, assist in prevent crops from pests, surveillance using drones conclusively contribute in escalating yields. IoT in industrial automation is a transpire career of investigation. The smart car is an immense deployment of IoT. IoT in inclusion offers Smart sanitation facilities superior sanitation provender are obligatory to maneuver a robust habitation. It automatically collects the wastes if any around the garbage can and intimates the municipal office regarding the wastes enable to keep the city clean and hygienic. Smart homes assure to utilize the resources effectively. It imparts the cloud assistance to superintend home provisions to restore them on a cloud framework and enable the user to have authority upon the yield of smart sensors affiliated with home provisions inclusive of lamps, fans, refrigerator, induction stove, Television.

Artificial Intelligence (AI) and Cloud Computing (CC) applications are venturing into regions that were previously thought to be solely the domain of healthcare specialists. During the past decade, several advancements in AI and CC have been made that may surmount to the alleviation of the tedious diagnostic procedures. In this review paper, the breakthroughs of artificial intelligence and cloud computing in healthcare in recent years have been outlined. The commonly used AI and CC tools have been explained. The applications, limitations, and future prospects of the state of AI and CC applications currently have also been discussed. A comparative analysis of AI and CC tools with the respective specialists is presented to provide context.

The Internet of Things has increased among residential consumers. The main drivers for the growth of smart home market will be video doorbells, voice-assisted technologies, and surveillance systems due to the growing increase in consumer demand.

Meanwhile, the world is aging rapidly. Elderly people want to live healthy and active in their homes; however, this population presents key challenges with smart homes. Most of the smart things targeting elderly population are focussed on improving the Quality of Life rather than providing a seamless and learning ecosystem for breaking their fears with technology.

We described a personal assistant that allows the users to learn how to work with smart sensors, giving them different contexts to test, use, learn and at the same time to keep active and update regarding the next generation of homes. This personal assistant will promote voice interface and learning in the following contexts: to control television and router; to control lights; and to care their plants.

The emergence of digital technology in the twenty-first century has led the evolution of learning practices in the modern workforce. The growing demand for skilled employees, distributed workforce, and opportunities offered by new technologies has triggered the growth of digital learning in the form of massive open online courses (MOOCs), user-generated content, and other online learning experience platforms. Empirical research on measuring the impact of digital learning shows disappointment; however, organizations have launched digital hubs and embraced digital learning techniques to upskill employees (Sheehan Glob Focus EFMD Bus Mag 10(3):10–13, 2016). This chapter primarily focuses on defining a digital learning framework and how organizations are working on implementing digital platforms and tools to harness the impact of digital learning.

This study attempts to examine the factors that are important in shaping the behavioral intention of users to adopt digital learning applications and the Internet of things. The study is carried out with the help of the TAM to find out the acceptance intention of the user. This study provides an upgraded TAM model for digital learning app wherein reflective variables of ease of use and perceived usefulness, that is, utility and learning, student engagement, data security, feedback and rating, accessibility, user interface, accessibility, and entertainment, are used as new constructs of TAM; however, the formative constructs remain the same. Purposive sampling technique is applied to choose 398 participants from the Delhi capital region, and PLS-SEM is used to measure the relationships among constructs. Findings suggest that user-friendly applications are easily adopted by users, whereas data security, utility, and security of data have less importance. The implications of this study are discussed.

Since the Industry 4.0 nature is a world-scale phenomenon, lot of information are available today from several sources, presenting different points of view. From the scientific papers, the research is focused more on the theoretical steps for a future implementation and the benefits achievable, with important information on the development path followed by the industry to reach today’s situation. Other important resources are represented by the companies operating on the consultant and high-tech sectors, because the revolution is completely intertwined with the digital world and their reports can provide interesting information. At the end, the government reports of the most important manufacturing countries have been a good information source for the practical steps and the necessary requirements in order to create a favorable field for the implementation. Therefore, this chapter is intended to contribute over all these mentioned areas of knowledge, providing a brief state of the art regarding the Industry 4.0, the role of IoT and cloud computing in today’s and future factories, as well as a view along all those enabling technologies that complement among each other in the diverse industrial scenarios.

Recognizing human hand gestures through digital inputs is becoming the feed for human-controlled machines and action-based systems. Detecting the patterns of the human hand faces a variety of issues such as classification and correlation due to the time and physical structures. To leverage the success rate of gesture identification, this paper presents multimodal feature analysis through precise feature classification and analysis. Based on the features, the coordinates are segregated to detect the precise correlation with the existing dataset, to improve the rate of success. The proposed method relies on pixel extraction from the classified features in a recurrent manner, to achieve a better success rate over the varying classification instances and inputs. The experimental results prove the consistency of the proposed analysis method by achieving less recognition time.

In this contribution, a mathematical model will be shown in order to determine the optimal frequency of maintenance considering costs, risk, and performance, where the application of tools based on IoT and cloud may provide useful advantages. With this purpose, it starts introducing the topic and indicating the usual decisions related to asset management. Then, the main variables (reliability and risk, operational costs, asset life extension) for the optimization of maintenance frequency are depicted, in order to define after that some mathematical models for the proactive replacement. Finally, an example applied to a steam turbine installed in a petrochemical plant is discussed, together with some conclusions.

This work presents the analysis, design, development, and validation of a middleware based on the interceptor pattern to provide interoperability among application layer protocols in the context of Internet of Things (IoT) protocol stack. The middleware has been designed considering an analysis of the technical characteristics of the most important IoT protocols. From the design point of view, a modular architectural methodology and a technology-independent approach have been adopted to build the middleware, so that it can be easily deployed and managed in IoT environments with a low integration effort. A first proof of concept of the middleware is presented for two IoT protocols, demonstrating interoperability between them. The results show that the existence of the middleware ensures technical interoperability without a significant impact on the degradation of communications under an unstable network environment and with a large number of nodes transmitting at the same time.

The spread of information through the network is called as information diffusion. It is necessary to identify influential nodes to initiate the diffusion process. Most popular models identify influential users but do not consider the activeness of the user; therefore, the results obtained are non-realistic. The proposed model, i.e., Active Influential Node Miner (AINM), can identify active influential nodes considering topological structure and activeness over a period of time in a temporal environment. This is achieved by ant behavior based algorithm incorporated with proposed edge activation probability initializer. The experimental results show AINM that provides a balanced and meaningful spread of information over the network than other considered models.

IoT is the amalgamation of sensor and actuators monitoring to accomplish a task. These instruments with different capabilities communicate over a common platform. IoT with AI aids the machines to learn and incorporate the methodology in many samples. The previous contribution uses artificial intelligence embedded in the instrument to assist and certify the structure. Computer-based intelligence is a geographic area of imitation cognition in which PC procedure is engaged to pick up, in actuality, framework and inference. As learning occurs, the capacities interior of the system become continuously sharp, and the program ends up acceptable for settling on taught decisions. In the framework, two of the most acclaimed strategies are fake system frameworks (ANNs) and inherited computations. ANNs imitate the nerve cell and synapses in the psyche to trade collection for mapping, realizing, besides, essential leadership. They are in use inside IoT arrangement to screen the domain of IoT instruments and to settle on instructed conclusion. The authors have suggested the use of ANN to get acquainted with the sound state of a system and related contraptions. The proposal aims to increase reliability by 4.17% and security by 9.5%.

With the upgrade of ICT infrastructure at a rocketed pace, diversification of applications and involvement of the Internet have increased. This evolution has led to the production, development, and implementation of various smart software/hardware solutions in multiple dimensions to make the process faster, smooth, accessible, and inclusive. The story of IoT (Internet of things) is a game-changer in several fields at different levels, starting from an office to home and industries. The term IoT was coined in 1999 by Kevin Ashton, since then it has seen an exponential growth, and now it has become ubiquitous. IoT may be summarized as the group of interrelated/interconnected devices embedded with sensors, software, actuator, and technology over the network for exchanging the data over the Internet without human involvement. Like any other technology, IoT has its pros and cons. In the last decade, cyber criminals have exploited many attack vectors, several of which can be used to exploit and launch attacks on IoT devices too. IoT attacks have increased substantially over the years, and there has been a jump of 900% in such attacks in 2019. Due to various constraints, IoT solutions don’t possess traditional security solutions or mechanisms to identify anomalies. Multiple security issues in IoT devices are persistent because of the limitation of computational power, hardware, and storage. These limitations make IoT devices more prone to cyber-attacks. IoT cyber-attacks are ranging from DDoS, MITM, brute-forcing, eavesdropping, privilege scaling to more sophisticated ransomware attacks and many more. This chapter will discuss multiple cyber-attacks, their mechanisms and TTPs, along with their impact on IoT infrastructure. This will also include security flaws that were exploited and related challenges in a holistic way at a single place. Statistical data leading to trend analysis along with shares of various attack campaigns shall also be highlighted in this study to give the readers more insight. In order to overcome these issues, remedial measures and mitigation policies will also be discussed.

Internet of things (IoT) is integrating human society and physical systems. It makes a better relationship between humans and nature. IoT is a technology integrator, which improves accessibility and usability. Cloud infrastructure has a lot of operational facilities and gives better collaboration among the team anywhere in the world. Congestion in the network reduces the quality of service (QoS), which increases queuing delay and loss of packets, thereby degrading the performance. The low utilization links and high utilization links in a network are identified for congestion estimation. The low utilization links are combined to increase the speed of the routing process. The proposed privacy-secure link utilization routing algorithm (PSLU) uses the packet encapsulation technique to protect the data while in transmission and aims to reduce congestion to improve QoS in routing for cloud and IoT environment.

Security is a major problem in cloud and in IoT.

The primary purpose of scientific research in the world today is to facilitate life. This paper illustrates a proposed technique to develop such type of microcontroller (ATmega328p)-based secured device control system which will work through an app for Android mobile phones. The name of the intended application is given as “HACA-App” device which stands for “home appliances controlling application” device. At the outset, it would be necessary to pair with connections, i.e., mobile (cell) data/Bluetooth connectivity/Wi-Fi link, to activate this application. In this security system, pattern recognition, fingerprint, and face/image detection and recognition features are added to improve the system reliability. Nevertheless the program as well as the device would have a dual password security system. The application will be prepared for use after entering the password to monitor the ON/OFF load timing of household appliances. Without even using switches, the load could be managed by using this smartphone app. Moreover, this application system will be connected with the smart meter for the calculation of energy consumption through the household. The smart meter will be operated in the app through two different pathways; one for high-voltage appliances and another one for low-voltage appliances. An automatic switch will be introduced with scheduling for the household appliances. For completion of government smart city initiatives, this application can be used in every household of the city, and the total energy consumption can be calculated accurately for the whole city or district. The application intends to standardize human lives to stop the lack of energy use, making regular life more convenient for humans.