IoT for Sustainable Smart Cities and Society

Nowadays, smart city is the latest research domain that is continuously attracting new researchers in its different domains like smart transportations, smart grids, and smart education. It is a very well-known fact that the world’s population is growing at an extraordinary rate today, and not only does half of the population live in urban areas, but it is also estimated to rise by 50% by the year 2050. The amount of population living in these megapolis, thus, puts enormous strain on the environment, which needs to be managed smartly, and thankfully, smart technologies like the Internet of Things (IoT) combined with machine learning (ML) and deep learning (DL) have the potential to tame the pressures of urbanization by creating new and smarter experiences for making day-to-day living more comfortable. The concept of IoT has always been considered the key infrastructure in smart cities since its introduction, and in this chapter, we aim to explain the role that IoT and ML, as well as DL, play in smart cities. There are various approaches in ML and DL that, when it comes to IoT, will result in a powerful solution for the implementation of smart city applications. This chapter provides a rigorous review on IoT-enabled cities using ML and DL.

Surveillance is an imminent part of a smart city model. The persistent possibility of terrorist attacks at public and secured locations raises the need for powerful monitoring systems with subsystems for embedded object tracking. Object tracking is one of machine vision’s basic challenges and has been actively researched for decades. Object tracking is a process to locate a moving object over time across a series of video frames. Object tracking powered with the Internet of Things (IoT) technology provides a broad range of applications such as smart camera surveillance, traffic video surveillance, event prediction and identification, motion detection, human-computer interaction, and perception of human behavior. Real-time visual tracking requires high-response time sensors, tracker speed performance, and large storage requirements. Researchers have ascertained and acknowledged that there is a significant change in the efficacy of drone-based surveillance systems towards object tracking with the inception of the deep learning technologies. Several tracking approaches and models have been proposed by researchers in the area of object tracking and have experienced major improvements with advancement in methods, but object tracking is still considered to be a hard problem to solve. This chapter explains state-of-the-art object tracking algorithms and presents views on current and future trends in object tracking and deep learning surveillance. It also provides an analytical discussion on multi-object tracking experiments based on various datasets available for surveillance and the corresponding results obtained from the research conducted in the near past. FairMOT, GNNMatch, MPNTrack, Lif T, GSDT, and Tracktor++ are among the methods investigated. For the MOT16 and MOT17 datasets, FairMOT generated accuracy of 74.9 and 73.7, respectively, whereas GSDT provided accuracy of 60.7 and 67.1 for the 2DMOT15 and MOT20 datasets. FairMOT is an efficient tracker among the models tested, while MPNTrack is significantly more stable and retains tracklet IDs intact across frames in a series. This concludes FairMOT being an efficient tracker and MPNTrack a stable one. It also discusses a case study on the application of IoT in multi-object tracking and future prospects in surveillance.

In spite of the ubiquitous evolution of the Internet and the continuous improvements in technology, a miniscule bit has been done to simplify the lives of the key caregivers of infants, senior citizens, and the physically disabled. Recovery from an accident or mishap in golden-ager is sluggish. A simple fall can be life threatening. Likewise, a protracted wet bed for infants may lead to ailments. Young mothers and custodians of the elderly and the incapacitated often have to lay out their whole lives for everyday monitoring errands. Through this work, we propose a dependable, real-time smart supervision system for basic supervision tasks of infants, senior citizens, and the physically disabled. With the aid of the Internet of Things and ThingSpeak cloud, we recommend to construct a solution that records and tracks numerous diverse vitals of babies, elderly, and the incapacitated like blood pressure, body temperature, and heart rate. Furthermore, we also proposition to utilize sound and moisture sensors to amass a regular record of the activities of infants, elderly, and the incapacitated, thus tracking their routine and communicating weekly analysis of the same to the custodians, aiding them in the regulation of healthy lifestyle patterns. Moreover, crucial warnings would be issued and communicated to the custodian’s mobile from anywhere in alarming situations. This study is particularly advantageous for the employed class for supervision of their babies and aged while alleviating uncertainties of the mounting competitive employment situation and upholding monetary stability.

The proliferation of the Internet of Things (IoT) has generated immense possibilities in the healthcare domain. IoT has the potential to transform the healthcare sector by changing its current focus from curative approach to ensuring complete wellness of an individual. However, this domain is still in infancy, and a number of aspects must be examined before its full potential can be realized. In order to assist the researchers, this chapter provides a 360-degree view of IoT around the healthcare domain. The chapter also presents a distant view as well as in-depth study of the five-layered IoT architecture with reference to the healthcare domain; compares a number of communication protocols for IoT-based applications on the basis of their data rate, range coverage, energy requirements, cost, and other notable parameters; discusses the role of cloud as well as fog for processing a large amount of data collected through an IoT ecosystem and their associated security aspects; discusses the applicability of IoT in preventive and curative healthcare with respect to various operational areas including disease monitoring, age-based monitoring, physical abnormality monitoring, and profile-based monitoring; and presents open research questions, as well as future research directions in relation to the use of IoT in healthcare.

This chapter focuses on IIoT (Industrial Internet of Things) and its different applications such as the manufacturing industry, healthcare, and food industry. In all of these different application domains, the core technology and the core technological ideas remain the same; the only thing that changes is the type of sensors that would be used. Traditional manufacturing posed different challenges like unavailability of real-time data, unbalanced workload, and longer changeover time. Smart factory tries to overcome these challenges by integrating IoT and operational technology (OT). The chapter also presents how IIoT can help in transforming present-day healthcare and making healthcare much more affordable, much more efficient, and much more autonomous. IIoT solutions can be used to alleviate some of the problems that are encountered by people with respect to health. There are different sensors like ECG sensor, blood pressure sensor, glucose-monitoring sensor, and temperature sensor that can be procured by patients themselves for monitoring their health conditions at their homes. Further, these systems can be internetworked so that if any patient has a critical condition, different levels of alerts would be sent to the hospital to which this patient is registered. The chapter also discusses IIoT implementation in the food industry. The process involves sowing seeds, growing crops, applying fertilizers, applying pesticides, maturity of crops, harvesting crops, food grain processing, packaging of food grains, and transporting to a wholesale market and finally to the retail market. This is called the supply chain from field to plate. IIoT devices can be used in the agricultural field for monitoring the sowing of seeds, for growth of crops, for applying fertilizers, and for irrigation. These devices can also be used at each step. We describe each of these applications in detail in this chapter.

Analysis of traffic conduct, mainly in densely populated urban areas, provides an excellent opportunity to study traffic patterns and extract useful information to help in planning and development. During activities that draw in a massive number of people, such as religious pilgrimages or sporting events, collisions of automotive traffic flows can result in interruptions and unsafe situations for the subjects, often creating chaos and congestion. The scenario becomes more ambitious in Hajj when millions of pilgrims move in a restricted area during a fixed period of time. Hajj is a 5-day Islamic pilgrimage whereby millions of pilgrims from across the globe assemble in Makkah to perform a number of spatiotemporal rituals every year on fixed dates. This chapter presents an interactive platform that utilizes large-scale GPS traces to detect the motion of buses during Hajj. For a period of 2 months, GPS traces are gathered for over 17,000 buses used to carry pilgrims performing Hajj activities. An interactive big data platform was developed to analyze and visualize the massive amount of spatial data. The analysis was done for various stakeholders, including the bus companies. Using our map-based visualization, they were able to visualize the movement of buses; identify drivers’ behavior, speed violations, and location of the violations; and determine the quality of data provided by various AVL providers. The information extracted can be used to generate an intelligent transportation system featuring schedule, evacuation, sustainability, resource optimization, and environmental and economic efficiencies to benefit stakeholders and improve the mobility of pilgrims throughout Hajj.

In the present day, the dashboard of most vehicles consists of a speedometer, a temperature indicator, a pressure indicator, and fuel gauges. No provision has been provided for indicating load on the vehicle. The load of the vehicle is measured in the specific weight range. This chapter presents the hardware implementation of sensing of the load on the vehicle and displaying on the LCD wherein the deflection of leaf spring is used to measure the load. The load will get displayed on the display module which will be integrated in the driver cabinet. Automatically calculating or estimating the total payload delivered to the vehicle by the excavator’s work tool is one way to measure the total weight of the material loaded into a truck. This approach of measuring the load on the vehicle has proved to be effective in solving many issues related to drivers, vehicle owners, and government along with providing various safety measures. The hardware developed in this chapter will help in keeping track of the total weight of each payload.

Internet of Things portrays a general notion for capability of devices to sense and collect data and share it via the Internet, where it can be refined and applied in numerous applications. This chapter gives an overview of IoT architecture and different components of IoT, along with some applications. IoT-based smart room application has been implemented and presented in this work in detail. The hardware that has been used for smart room application is a NodeMCU controller board and some sensors such as proximity sensor, gas sensor, flame sensor, and temperature and humidity sensor. Smart room application has been implemented with the help of IoT protocols like MQTT, HTTP, and WebSocket. All the hardware components and protocols used in the implementation of the application have been discussed in the chapter. Also, the theoretical comparison of three protocols—MQTT, WebSocket, and HTT—has been drawn. For implementation of design using IoT, Node-RED is used. Node-RED is an open-source software used to wire together all the hardware devices and APIs. The response time delays of all these protocols for the smart room test bed have been found and compared. Also, the present work has been compared with some of the related existing work in literature.

Smart cities, with fast increment in urban development, could be a concerning issue, indeed for created nations. It is developing as one of the complex systems around the world with the increment in request and supply based on assets and administrations. In this modern era, brilliant gadgets are very much required within the building of the foundation of a savvy city. The increment in the populace has expanded challenges in the organization and management of keen cities. These sorts of challenges can be restrained by the usage of specialized progressions by the inhabitants. However, the requirements of a keen city and the improvements around it ought to give benefits, not as it were to the living environment but to consider the human-centered administrations. Additionally, keeping up a more advantageous environment needs the improvement of intelligent information frameworks with enabled IoT innovations. In building a keen city, the method ought to be intuitively kind, so an IoT-based stage is necessary. Malware penetration is getting more regrettable day by day and is considered one of the greatest security dangers to the web. Malware is any pernicious program with the expectation to perform noxious exercises on a focused-on framework. In this term paper, we have examined the two wide methods that are utilized in arrange to viably perform Malware examination and location on venture frameworks to diminish the harm of malware assaults.

In everyday life, weather conditions play a major role. Collection, monitoring, and analysis of data about the different parameters of the weather are necessary to plan various activities in day-to-day life. The weather conditions are required to be monitored for numerous reasons, like the dependency of agriculture, aerial, and marine transport services on the weather; detection of air quality and particulate matter for the health of humans and the environment; to ensure a safe working environment in industries; to predict and forecast climatic phenomena, etc. For ages due to the unavailability of accurate forecast data, due to irregular measurement and analysis of weather conditions, many natural calamities and disasters take place resulting in the loss of millions of lives. Data collected from satellites is for a larger geographical area and not for a pinpointed area at the ground level let us say for a city and therefore in some cases some mismatching of data may occur. If data from the ground level are provided as an added aid to the Meteorological department along with the data collected through various other means to perform analysis there are chances of better prediction and forecasting of natural phenomena. The dissertation is a solution to overcome these limitations.

In today’s world, some major areas of application of a smart, real-time, efficient, low-cost, accurate, low-power, portable, high-speed, Internet of Things (IoT)-based Weather Monitoring station are: Airport operations, Coastal area weather detection, Construction of high rising structures, Agricultural greenhouse, and warehouse condition monitoring, Air Pollution, Solar-based technological industries. Therefore, the need of the hour is to design an Automated Weather Monitoring System which will enable enhanced data collection in real-time for different parameters, such as light intensity, humidity, temperature, air quality, and wind speed without the intervention of humans. This Weather Monitoring Station circuit will be designed to provide an automatic monitoring mechanism to authorities and for the people who pass by the location at which the station is installed. For this purpose, competitive strategy tools and equipment are required to design hardware to fetch the required data and provide it for analysis.

This chapter describes the model designed for this automatic monitoring, and the main component used in this model is Raspberry Pi, which will control all the sensors and upload the real-time data collected using the sensors to a cloud and display the same on an LCD screen/panel installed onsite. The Weather Monitoring Station prototype uses a Light intensity sensor (LDR), Anemometer, Temperature sensor, and Air Quality detection sensor, the continuous analog readings of which will be converted to digital using an ADC IC MCP3208.

The future era utilizes IoT in order to provide long-lasting energy and ensure its benefits and optimal usage in smart cities. But IoT device usage may not ensure sustainability and may rely on non-ambient sources of energy, most commonly the batteries, which can be quickly deployed but require periodic battery replacement. Energy harvesting provides solutions by utilizing ambient energy, which is ubiquitous and shall enable green communications. Energy harvesting is the collection of ambient energy in small amounts to power wireless devices. This proves promising where usage of batteries is impractical, say body sensor networks, in particular. It converts available energy to electrical energy which can be used later. It shall provide self-sustaining energy from the environment’s ambient sources. This paper presents a significant focus on energy and energy harvesting methods that rely on solar, thermal, kinetic, and Radio Frequency. Also, Thermal electricity is one of the most crucial aspects in the field of bioelectricity. It has been found through the literature surveys that thermoelectric is found to be the most sustainable form of electricity. The implications and reasons for harvesting energy are discussed. This paper derives the conclusion that energy harvesting is one of the crucial aspects which needs to be widely adopted in order to save the environment and achieve sustainability.

Renewable energy is gradually being used to offset the impact of climate change and global warming. Various forecasting techniques have been introduced to enhance renewable energy prediction ability. The objective of this research is structured as follows: Firstly, this study examines machine learning methods for forecasting renewable energy resources. Secondly, this survey demonstrates the process implemented in the machine learning model for forecasting the performance of the machine learning model. Thirdly, the analyses of renewable energy forecasting models were conducted on the basis of mean absolute percentage error and correlation coefficient. Finally, at the conclusion of this study, several possible future work opportunities were identified.

Presently, the globe is going through development in smart cities as the Internet of Things (IoT) is capable of connecting with almost every object in the environment through sensors, cloud, end user devices, and user interface. This is possible because of the uprising in information technology day by day, thereafter contributing to the social and economic welfare of citizens. The Internet is becoming more integrated in daily life as managed by the Internet of Things. As IoT devices embrace novel endless opportunities to make life easier for people, they also increase the risk of data breaches, unsuspected users, and malicious attacks in the IoT framework. So preserving security and privacy from threats and attacks is an endowing challenge that is faced by IoT devices. It is known to an extent that data are more vulnerable in terms of security and privacy, because of integrated features implemented in the Internet of Things. Consequently, it is more prone to cyber threats and attacks despite the fact that it brings unbounded comfort and social security. Resolving these challenges and promising security and privacy of information against threats and attacks should be the prime priority while designing and implementing the architecture of Internet of things (IoT). However, the end user ought to trust that IoT devices and services provided by them are safe and secure. IoT devices protection needs to be considered while designing the framework, to protect it from any kind of threat and attack while keeping in mind ethics and policies which are utilized by the Internet of Things mechanization. In this chapter, privacy and security issues are discussed along with threats and attacks. Overview of the Interest of Things (IoT) is discussed in the beginning. Research work done in the past is also reviewed and examined with state of the art whereupon highlighting techniques utilized including the objectives and limitations. Additionally, this chapter covers layers of the Internet of Things (IoT) with the network of IoT and security and challenges in each layer of IoT architecture. Major security issues are also considered, followed by a discussion about Smart City Applications with their threats and solutions precisely to preserve concealment of Internet of Things (IoT) devices.

Textile Effluents include dyes that have color variation, though this is considered as a physical parameter of water. Due to their hazardous nature, dyes are considered to have devastating chemical effects on the bio-ecological system. The elimination of dye-color from the industrial wastewater assimilation process gives a classification in treatment technology, mainly if the adsorbent is cost-effective and naturally available. Banana bark and orange peel were used as adsorbent materials in this pilot research. The outcomes of the research shows a variation in potential of Hydrogen, contact of materials in time and specific concentration, assimilation of the adsorbent materials indicated the removal of pollutants present in the textile effluent. This investigation revealed that there is a significant difference between the use of orange peels which were more essential than when compared to the banana bark for the assimilation of pollutants from the textile wastewater. In this paper, attempts have been made to remove color, pH, TS, TDS, TSS, COD, and BOD by using two adsorbents. The highest color removal competence has been noticed as 65% and 75% for banana bark and orange peel, respectively. It result concludes a variation in the parameter like pH has decreased from 8.02 to 8.4. The TDS has observed the maximum percent removal of 50 and 85%. The investigational outcome shows the efficacy of the materials that have superior capability to eliminate color from wastewater and act as cost-effective adsorbent materials.