Data Analytics for Internet of Things Infrastructure

Today, big data and cloud computing are two mainstream techniques in the field of information technology. Big data and cloud computing are concerned with massive scale of data and infrastructure, respectively. The reason for their adoption as a huge enterprise is the ease of simplification provided by these technologies. Big data indicates a large collection of data which cannot be processed by any source of available processing units. Cloud computing refers to handling and operation of data at the remote place. This chapter enunciates the importance, characteristics, and classification of big data with relevant examples. It also presents the tools and techniques used for the processing of big data. In addition, the concept, working, characteristics, and key features of cloud computing are discussed. Ultimately, this chapter correlates both the technologies – big data and cloud computing in today’s scenario with a case study.

Nowadays, the Internet of Things (IoT) is used everywhere. This ecosystem consists of various smart devices that include sensors, processors, and communication hardware. In the traditional IoT system, the IoT hub serves as an intermediary between tiny underlying sensors and the cloud, assisting applications to collect, send, and analyzed the acquired data in real time. In the traditional system, the IoT hub and sensors are two different isolated layers that make the system complex and increase network dependency. In this respect, the objective of this paper is to analyze and modify the IoT architecture for designing an autonomous and distributed IoT module. The module enabled with Machine to Machine (M2M) communication the above-mentioned problem to be reduced and scalability to be added into IoT devices. The designed module can be built on IoT devices, to directly connect with the network through the Ethernet or Wi-Fi and provide the users with an interface (cloud server, person, device) to communicate with each other. Moreover, the proposed modules are independent and can easily interface using any programming language. In addition, resource management and the security of the IoT systems have been taken into consideration. Hence, the performance of the IoT system could be increased.

Computer-mediated communication (CMC) within the internet of things (IoT) environment has made it easy for people to interact with news in the world. CMC in journalism and development of online newspapers have replaced printed newspapers and have helped people access even the oldest form of news articles on the internet. This chapter includes a social approach to the development of online newspapers within IOT environment and discusses how CMC plays a significant role in online journalism. CMC in journalism enables the readers to comment their thoughts and views under the comments section provided for online newspapers. Communication within IOT environmental, online newspapers have three inherent characteristics. First, comments by online newspaper users in CMC are a prominent discursive genre. Second, CMC illustrates the potential effects of multimedia content and interactivity in online publications. Third, computer mediates the process of publishing, provision of access, and consumption of news in online newspapers. Each of the above factors play a significant role in constructing online newspapers, which impacts the future research on CMC’s potential use in developing online newspapers and mass communication. On the other hand, emerging issues of CMC within IOT environment are also discussed in this chapter, including the repercussions of increased anonymity of interlocutors, concerns regarding the CMC with respect to the territorial point of view, concerns related to media artifacts, and its social outcomes and mutual shaping of users and technologies.

The vehicular ad hoc network (VANET) facilitates communication between automobiles, pedestrians, trucks, motorbikes, and trains. Mobile Ad hoc Networks are the ancestor of VANETs. Due to the fact that broadcast messages are used for vehicle-to-vehicle communication, vehicular ad hoc networking is vulnerable to malicious assault. The packet will be dropped and the message can be altered by malicious users. This is the prominent issue that needs to be concentrated more. Black hole attack is the major attack in VANET, which tries to hack the entire communication network by dropping the transmitted packet or by introducing itself as a node having the shortest path. The whole network and routing path get affected leading to an injection of false messages and trying to divert the node user. To avoid communication with an attacked node, a Trust-Based Authentication Scheme can be used to choose a genuine node for communication or sending packets. Fuzzy logic can be used to evaluate the nodes as an attacked node or genuine node. In this chapter, FATS (Fuzzy Authentication to provide Trust-Based Security) is used to find black hole node and block the node from communication. FATS system uses Trust factor as the major parameter to estimate a genuine node from a malicious node. The simulation is carried out in MATLAB for parameter estimation and implemented using Network Simulator 2.28 software.

The area of e-marketing can benefit from the usage of digital technology like chatbots. This study aimed to determine the impact of chatbots on customers’ purchase intentions. An empirical study was carried out on the impact of chatbot agent’s informational support, Emotional Credibility, and trust on purchasing intentions. The data was collected through an online survey from 223 Delhi-NCR customers who use chatbots while making online purchases. PLS-SEM was used to analyze the data that was collected. The results of structural equation modeling (SEM) showed a significant impact on informational support, emotional credibility, and trust of chatbots on purchase intentions of customers. The results of the study can be used as guidance by marketers to achieve a competitive edge in the changing business environment. The findings of the present study will encourage marketers to use technologies such as chatbots and help customers to get information. The marketers are encouraged to utilize and monitor chatbots efficiently and effectively. This study intends to contribute to the field of e-marketing.

The stock market is a tough forum for investment and requires ample deliberation before investing hard-earned money into buying stocks. The stock market is one of a number of sectors that buyers are committed to. For this reason, the inventory forecast is a hotly debated topic for researchers from each economic and technical domain. In this chapter, the primary goal is to construct a country-of-art-work prediction for pricing that focuses on quick changes in price predictions. The cryptocurrency market is nowhere near as stable as traditional commodity markets. The stock market can be plagued by numerous technical, emotional, and challenging factors, though, making it extremely volatile, risky, uncertain, and unpredictable. This chapter analyses the shortcomings of the current market tendencies and constructs a time-series version for mitigating most of them by using greater-efficient algorithms. An expert machine is proposed to predict the uncertainty of market risk and to predict the guaranteed amount of return. Fuzzy inference is deployed to predict uncertainty. A real-time data set, the Nifty 50 stock list records (2000–2021), from Kaggle, is used as a test bed to validate the proposed version. Finally, fourfold cross validation is carried out to assess the overall outcome or performance of the proposed model.

In this study, low-profile probe-excited crossed antenna with sandwiched metasurface is proposed for very small and ultra-small spacecrafts at X-band. The designed metasurface antenna is lightweight, low cost, and occupies physical size suitable for very small/ultra-small spacecrafts. The main targets of this study are the use of an optimized sandwiched metasurface for increasing return loss and peak gain of proposed antenna at X-band. Moreover, the sandwiched metasurface is used for minimizing levels of generated back lobes and so interferences with electronic components inside the spacecraft box. The constructed sandwiched metasurface antenna, therefore, achieves a bandwidth of about 350 MHz and increases the antenna total gain by about 1.10 dBi at X-band despite its very small size. These results are in general very suitable for very small and ultra-small spacecrafts communications.

Measurement of race car laps is a crucial parameter. Car racing is one of the most popular sport activities all over the world. Lap crossing decides the winners based on time lines. Therefore, calculation of timing plays an important role in racing. As every millisecond or microsecond difference cannot be viewed by the naked eye, accurate time calculation should be computed so that the exact winner can be awarded. The proposed system uses laser beam transmitter and receiver for detecting laps crossing. The ATMEGA 328 controller continuously triggers the laser transceiver. The detection of vehicle is based on beam cutting, and the beam cutting time is compared with the existing fully automatic testing systems. The developed system is dedicated to sense the number of lapses made by the racing vehicle, and lapse details are uploaded to the google cloud. The proposed system supports maximum laps width of 12 m and a computing time delay of 1–10 ns.

In this new age of cutting-edge computerized advancements, a gigantic measure of information is being produced quickly from varying backgrounds. All the industries dealing with large and complex datasets have been facing the need to deal with enormous information being generated/delivered by different sources. This humongous data exists in all forms, for example, structured, unstructured, and semi-structured, which are produced through various sources, such as IOT devices, sensors, social media data, imaging, and geographical data, from day-to-day trials and experiments. This humongous and distinguishable plethora of data accumulated using different data sources needs to be accumulated, studied, and analyzed through statistical tools to perform smarter predictions and analysis. With the introduction of big data technologies, cloud computing, and different types of data analytics technique, it now became easier to combine real-world data and data generated from scientific experiments to extract meaningful insights and use them in real-world scenarios. The cloud platforms such as Amazon Web Services (AWS) and Google Cloud Platform (GCP) can be used for data analytics in different industries. Cloud computing services from AWS and GCP are used for collecting, processing, storing, and mining of various types of data. This chapter studies the provisioning and usage of cloud-based architecture of AWS and GCP for building a data analytics platform.

Recommendation systems for suggesting products are crucial, particularly in streaming services. Recommendation algorithms are crucial for helping viewers find new movies they like on streaming movie platforms like Netflix. In this chapter, we create a smart algorithm that makes an optimistic choice to design a collaborative filtering system that forecasts movie ratings for a user based on a significant database of user ratings. According to the genres that users like to watch, it suggests movies that are the greatest fit for them. The cumulative influence of user ratings and reviews produces the list of suggested films. A statistical analysis is performed to develop a pilot survey model to analyze the real-time dataset. Ant Colony Optimization (ACO) is deployed to determine the rating of the group members’ for future recommendation. In this way, sparsity problems will be optimized in a recommender system. A real-time dataset named as Movielens is used to validate the proposed model. Finally, deploy k-fold cross validation to evaluate the performance metric.

For traffic and security surveillance, moving object detection and segmentation are critical. Detecting moving objects in dynamic environments is more difficult than it is in static environments. In this paper, all the research articles published between 2011 and 2022 in IEEE Xplore, ScienceDirect conferences, and various journals were referenced for a systematic review on identifying different objects from images/videos taken under adverse environmental conditions. We used different tags and keywords to search for papers on the topic under study. All the papers were studied, the proposed techniques were analyzed, and information was gathered. On the basis of this analysis, we present some future prospects for the area under study. We also present a survey of various techniques proposed by various researchers to detect moving objects under various environmental conditions over a period of time.

The Coronavirus outbreak has influenced education around the globe. Schools have made an attempt to fully or partially close to contain the pandemic, and students at all levels are required to learn from home by using technological platforms. This unprecedented abrupt remote teaching and learning mode exposes potential challenges hindering social interaction, an integral part of second and foreign language (L2) acquisition, which is hypothesized to develop learners’ L2 development, particularly communication competence. From a psychological view, remote learning may make learners feel that they do not belong to a learning community. This chapter reviews related studies and perspectives on technology-assisted language teaching and learning to make recommendations for administrators, teachers, and learners. It first reviews current perspectives on technology-assisted teaching and learning. Then, it critically examines second language acquisition theories aligning with computer-mediated communication. Also, it reviews recent research on remote language teaching and learning during lockdowns worldwide. Technology Acceptance Model, a conceptual framework for accepting a technology, and Bloom’s digital taxonomy are also discussed. Finally, the chapter makes recommendations for administrators, teachers, and learners.

Organizations that want to increase their productivity, transparency, and profitability stand to gain the most from the recent rise of the Internet of Things (IoT) and artificial intelligence (AI). Both artificial intelligence and the Internet of Things are game-changing technologies. More companies than ever before are tapping into the IoT’s potential and recognizing its value. Machine learning, artificial intelligence, rapid feedback, and remote monitoring and operations are not far-off pipe dreams. They have already arrived, and they don’t plan on stopping any time soon. Businesses who get in on the IoT revolution early may take advantage of its growing popularity and benefit from its rapid expansion. As we look forward to 2023 and start to appreciate the effect IoT with AI will have on all sectors, the companies that successfully transform and empower themselves via the benefits of IoT may establish insurmountable competitive advantages. IoT technologies are expected to play ever-expanding roles in commercial and social contexts. Current trends in IoT and AI are extremely noticeable in most sectors, but sector-specific developments shouldn’t be overlooked. As both IoT and AI continue to expand, it’s important to keep an eye on emerging trends at the intersection of the two fields. The latest developments in IoT technology that make use of AI methods are the primary focus of this study. All the newest developments and potential future applications from combining IoT and AI are being reviewed. The objective of this study is to showcase the current trend of IoT with AI, along with future directions. Many domains have been analyzed and shown in tabular format, where the methodological advantages and future scope of AI-assisted IoT technologies are identified.

The goal of approaches to automatic text summarization is to construct summaries while extracting the essential information from one or more input texts. Large models could be trained thanks to the ability to examine text non-sequentially, which led to the Transformer becoming the most well-known NLP model. Big data and associated methodologies are frequently used to handle and alter these massive volumes of information. This chapter looks at large data methodologies and method such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer 2 (GPT 2) models for multi-document summarization. The Transformer, BERT and GPT and GPT 2 models in text summarization give very close results in terms of accuracy and they need to be compared to give a model that performs better. In this chapter, the two models have been compared and our results have shown that BERT performs better than GPT 2. This is found based on the results given by ROUGE metrics on a news article dataset containing 100 text files to summarize.

One of the major causes of election violence is the incompetent existing voting methodologies including the paper and ballot as well as e-voting systems. Electronic voting was suggested as an alternative in order to overcome some of the major shortcomings of the paper and ballot system including the huge manpower required for vote counting. But along with solving such challenges, the e-voting systems raise humongous security threats that may lead to vote tampering and other issues like revealing the identity of the voter. This chapter suggests voting systems based on blockchain as a better alternative to the existing e-voting systems as they would solve the major concerns of the traditional e-voting systems. The decentralized and immutable nature of blockchain will prevent any kind of mischievous activity conducted with the objective of vote tampering as well as it would also protect the anonymity of the user. This chapter provides detailed information on the blockchain concept along with its features and types. This chapter also discusses a few of the major consensus algorithms being frequently utilized in different blockchain networks. Further, the major challenges faced by existing voting methodologies have been addressed and then this chapter explains that integrating the e-voting systems with blockchain technology can help overcome most of these challenges. This chapter concludes that the decentralized voting system using blockchain is the future of e-voting.

In recent years, the world has been moving toward connected smart and integrated devices like the Internet of Things (IoT), where devices communicate with each other with sensing and actuating capabilities. The diverse nature and wide range of applications raise various security issues and make these networks vulnerable. Beginning with an overview of the distributed applications of IoT, this chapter then presents various security issues, which are divided into layers. Various types of security attacks and threats are discussed to elaborate on the security problems in IoT networks. This chapter also covers security challenges and their countermeasures. Moreover, the chapter lays out the existing security models and solutions. The last section concludes the chapter by detailing many of the future trends in IoT.

Advances in the Internet-of-Things (IoT) field have allowed a wide variety of devices to be connected and send information continuously to the Internet. Thanks to this increase in data communication, machine learning (ML) and data science have been able to be applied to analyze and extract valuable intelligence from the IoT. In this sense, the IoT has also contributed to improving the design and implementation of soft sensors. Soft sensors are used to predict features that are difficult to measure directly because the sensor to do so does not exist or is very expensive. IoT real-time monitoring can be used in conjunction with ML techniques to infer those parameters that are difficult to achieve with specific sensors. There exist methodologies for the development of soft sensors, but there is a lack of a common tool to support the design and implementation of them, covering the phases from model training to visualization of predictions. In this chapter, we present a methodology to support soft-sensor development based on Kafka-ML, an open-source framework to manage ML pipelines. Kafka-ML will allow researchers to develop, train, and validate ML models and visualize real-time predictions using streaming data. To demonstrate the viability of our proposal, we developed a soft sensor that predicts nitrate levels from river watersheds.