The Business Value of IoT and Big Data Analytics

The Internet of Things (IoT) has become a key component of the ICT infrastructure of smart sustainable cities due to its great potential to advance the different areas of sustainability. The IoT is associated with big data analytics, which is clearly on a penetrative path across urban systems and domains for optimizing and enhancing operations, functions, services, designs, and strategies. As such, the IoT-based big data applications can play a key role in enabling sustainable cities to improve their contribution to sustainability under what has been termed as smart sustainable cities. However, topical studies tend to deal largely with the IoT and big data analytics in the realm of smart cities, leaving important questions involving the role and potential of these advanced technologies in the realm of sustainable cities barely explored to date. Specifically, the integration of the IoT and big data analytics is an unexplored research area as regards the new opportunities it offers in terms of responding to the goals of sustainable development. With that in regard, this chapter provides a state-of-the-art review of the IoT and big data analytics in terms of their core enabling technologies and practical applications within smart cities and smart sustainable cities. Further, it proposes an integrated framework for smart sustainable cities, which is intended to illustrate how the informational landscape of smart cities based on the IoT and big data analytics could enhance the physical landscape of sustainable cities as regards their strategies in ways that can enhance their performance on the basis of the IoT-enabled big data applications. The proposed framework represents a conceptual structure intended to serve as a guide for building a model of smart sustainable cities that can expand the structure into something useful. This should be grounded in further qualitative analyses, empirical investigations, and practical implementations. This work serves to inform various city stakeholders about the benefits that can be realized from developing and implementing smart sustainable cities on the basis of the IoT and big data analytics.

HICSS-48 marks the beginning of a new mini-track on topics at the intersection of the Internet of Things and Big Data Analytics. The mini-track addresses issues organizations face as they seek to make use of data collected from mobile tracking devices such as RFID and other tracking and sensor technologies. Big data analytics is an increasingly important activity that is driven by the pervasive diffusion and adoption of RFID, mobile devices, social media tools, and the Internet of Things (IoT). The IoT allows for the connection and interaction of smart devices as they move and exist within today’s value chain. This allows for unprecedented process visibility that creates tremendous opportunities for operational and strategic benefits. However, the effective management of this visibility for improved decision making requires the combination and analysis of data from item-level identification using RFID, sensors, social media feeds, and cell phone GPS signals; in short, big data analytics. While the IoT and big data analytics have tremendous potential for transforming various industries, many scholars and practitioners are struggling to capture the business value from combining the IoT and big data analytics. In addition, little research has been conducted to assess the potential of the IoT using big data analytics. In this mini-track, we hope to develop a stream of research where researchers will share new and interesting theoretical and methodological perspectives on this topic. We believe the papers represented in this inaugural mini-track are a good kickoff to what we hope will be more exciting and enlightening each year. We open the mini-track with a paper entitled “Research Directions on the Adoption, Usage and Impact of the Internet of Things through the Use of Big Data Analytics” where Fred Riggins and Samuel Fosso Wamba bring into focus several of the important research questions this mini-track will address. The paper begins by defining current perspectives on the IoT and highlights current research in this area. It then proposes a framework for analyzing the adoption, usage and impact of the IoT enabled through big data analytics. The framework is applied to several research questions that need to be examined if researchers are to understand the non-technical issues related to the emergence of the IoT. Specifically, research questions are posed at four levels of analysis: the individual, organizational, industry, and societal levels. The second paper by Robert Minch is entitled “Location Privacy in the Era of the Internet of Things and Big Data Analytics.” As the IoT emerges there is concern that loss of privacy may occur that could impact individuals’ incentives to belong to online networks, interact using online social media, and engage in activities associated with being digital citizens. These privacy issues involve sensing activities, identification and authentication of identities, storage of personal information, processing of this information, incentives to share information, and the range of activities available to use this information. These six phases of information flow all take place within three different contexts: technical, social, and legal contexts. This paper examines these issues across these six phases of information flow and identifies example privacy measures that are being used, and can be used, for each phase. A literature review of existing research on the technical, social, and legal measures is provided. The third paper, “Dynamic Price Prediction for Amazon Spot Instances” by Vivek Kumar Singh and Kaushik Dutta illustrates the importance of being able to dynamically and efficiently price services in contexts such as the IoT. In the case examined in this paper, cloud vendors, such as Amazon Web Services, provide “spot instances” of cloud-based resources that are dynamically priced through an auction mechanism. This paper develops a novel algorithm for spot price prediction that shows high accuracy of 9.4% Mean Absolute Percent Error (MAPE) for short term forecasting (one day ahead) and less than 20% MAPE for long term forecasting (five days ahead). Such novel pricing algorithms will find a place within the context of the IoT as spot services will need to be negotiated, priced, and provided with a short lead time. 2015 48th Hawaii International Conference on System Sciences

Internet of Things (IoT); the new dawn technology that describes how data, people and interconnected physical objects act based on communicated information, and big data analytics have been adopted by diverse domains for varying purposes. Manufacturing, agriculture, banks, oil and gas, healthcare, retail, hospitality, and food services are few of the sectors that have adopted and massively utilized IoT and big data analytics. The transportation industry is also an early adopter, with significant attendant effects on its processes of tracking shipment, freight monitoring, and transparent warehousing. This is recorded in countries like England, Singapore, Portugal, and Germany, while Malaysia is currently assessing the potentials and researching a purpose-driven adoption and implementation. This paper, based on review of related literature, presents a summary of the inherent prospects in adopting IoT and big data analytics in the Malaysia transportation system. Efficient and safe port environment, predictive maintenance and remote management, boundary-less software platform and connected ecosystem, among others, are the inherent benefits in the IoT and big data analytics for the Malaysia transportation system.

Chapter 7 - Semantic interoperability in IoT and big data for health care: a collaborative approach

Internet of Things (IoT) is characterized by a colossal scale of smart objects that collaborate impeccably with each other through a worldwide network. Data obtained from these smart objects can be used to recognize, inspect and manage complicated environments around us, enabling better understanding, intelligent decision-making, and performance optimisation. Data analytics plays an incredible part in creating efficient IoT applications. It is used to dig out consequential insights from IoT data and these insights are typically in the form of smart management decisions, trends, and statistics that assist IoT applications in making potent decisions. Hence, exploitation of data analytics in IoT applications provides tremendous benefits including enhanced quality, increased efficiency, automation and better decision-making. This paper provides a knowhow of data analytics in IoT and presents the benefits of data analytics in IoT applications. Moreover, the data analytical frameworks for delay-tolerant and delay-critical applications are also presented.

Chapter 3 - Internet of things (IoT) and big data analytics (BDA) in healthcare

In the last decade, with the advent of Internet of Things (IoT) and Big Data phenomenons, data security and privacy have become very crucial issues. A significant portion of the problem is due to not utilizing appropriate security and privacy measures in data and computational infrastructures. Secure multiparty computation (secure MPC) is a cryptographic tool that can be used to deal with the mentioned problems. This computational approach has attracted increasing attention, and there has been significant amount of advancement in this domain. In this paper, we review the important theoretical bases and practical advancements of secure multiparty computation. In particular, we briefly review three common cryptographic primitives used in secure MPC and highlight the main arithmetic operations that are performed at the core of secure MPC protocols. We also highlight the strengths and weaknesses of different secure MPC approaches as well as the fundamental challenges in this domain. Moreover, we review and compare the state-of-the-art secure MPC tools that can be used for addressing security and privacy challenges in the IoT and big data analytics. Using secure MPC in the IoT and big data domains is a challenging task and requires significant expert knowledge. This technical review aims at instilling in the reader an enhanced understanding of different approaches in applying secure MPC techniques to the IoT and big data analytics.

In This paper proposes a new model for analytical big data in Internet of Things (IoT) environments, utilizing Digital Twin (DT) techniques to enhance manufacturing monitoring. This suggests a fresh approach for analyzing large amounts of data in IoT settings, by incorporating Digital Twin methods to improve monitoring in manufacturing. The new model was created to tackle issues with data management and analytics in manufacturing settings, where processing real-time data is essential, through the integration of digital twin methodologies. The goal is to enhance operational efficiency, predictive maintenance, and decision-making processes in manufacturing monitoring environments by leveraging big data analytics techniques within IoT settings. This research will analyze the model's efficiency in a manufacturing setting through and Monitoring, seeking to add to the expanding knowledge base in IoT and big data analytics. The model will combine advanced analytics, machine learning, and digital twin technology to allow real-time monitoring, predictive maintenance, and process optimization in manufacturing environments. The suggestion will use prior research and top industry methods to create a plan for putting the model into action, concentrating on improving resource distribution, accuracy, predictability, and innovation. The proposed design for manufacturing and monitoring in IoT environment utilizes a new Analytical Big Data Model with DT techniques to reduce redundancy and specify technology interactions. The proposal will draw on existing research and industry best practices to develop a framework for the implementation of the model, with a focus on optimizing resource allocation, enhancing accuracy and predictability. The new model of new Analytical Big Data Model in IoT environment Using DT Technics: A new Design for Manufacturing and Monitoring. Keywords—Big data in IoT, Big data analysis, Manufacturing Monitoring, DT.

The IoT for smart sustainable cities of the future: An analytical framework for sensor-based big data applications for environmental sustainability

With one-tap check-ins, digital concierge services, voice-activated gadgets, chatbots, smart in-room technology, and advanced analytics, the hotel sector has been quickly embracing new technologies to meet and exceed consumer expectations and digitize the customer experiences. The traditional hotel management with internet-based customer service could not handle dynamic real-time data efficiently due to increased data volume. Thus, this work analyzes hotel management practices with the internet of things (IoT) and big data. The IoT and big data significantly impact the guest experience since businesses can provide consumers with unique services to their needs. Automated check-in and checkout, pre-booking, registration, and user-chosen payment methods are just a few self-services that improve the visitor experience. For enhancing visitor satisfaction and offering tailored services, this paper looks at how IoT and big data analytics can help the hotel sector. It moreover examines how IoT can be used within the business. This extended research finds excellent results in hotel management through IoT and big data. A client occupancy detection model (CODM) simulation scenario finds the best detection accuracy of 97.51%.

The main objective of the report is to show the impact of IoT and Big Data Analytics in the field of virtual education space while learning and applying different e-learning approaches. The main subject of research by the authors is a conceptual model of virtualization of education in terms of the Internet of Things (IoT) paradigm.

Recycling and advanced management of the aircraft at the end of life (EoL) is a novel challenge in the aviation industry. The new paradigm of the internet of things (IoT), Industry 4.0 and big data analytics in the logistics and operation management have a considerable impact on the sustainability of EoL aircraft treatment. This paper aims to highlight the role of IoT and big data analytics in the future of EoL aircraft management based on the waste hierarchy approach. Strategy, architecture, and infrastructure of IoT enabled EoL treatment is proposed, and an empirical study based on secondary data is provided to show the application. This study confirms the merits of using IoT and big data analytics to improve EoL aircraft treatment process in sustainability and performance.

The Internet of Things (IoT) has made it possible for previously unconnected items, such as vehicle engines, to be connected to the network, leading to the emergence of numerous active data streams. The IoT and big data analytics have made considerable strides, opening up intriguing new possibilities for medical and healthcare solutions. Many organisations still struggle with the usage of AI and ML technology when attempting to expand their digital transformation programmes and utilise IoT data. The most current trends involve modifying IoT data for smart applications using artificial intelligence techniques. Numerous apps use data science and analytics to extract conclusions from gigabytes of data. However, these applications do not deal with the issue of constantly identifying patterns in IoT data. The introduction of the IoT and the cloud has further enhanced things by offering smart business recommendations as well as insights into how people operate and how lives are changing. We discuss a variety of AI capabilities and how to apply them to IoT devices in Hands-On AI for IoT. The logic-based substrate provides low energy footprints and higher cognitive accuracy during training and inference, which is a crucial requirement for effective AI with long operating life. The use of AI in the industrial sector has enormous potential. However, it frequently necessitates expensive and resource-intensive machine learning professionals as well as in-depth knowledge of complex statistics and how they are implemented in practical use cases.

Traditional manufacturing systems are currently undergoing digital transformation by integrating Identification, Sensing, and Communication technology. Mass Unstructured data from structured and unstructured data sources are generated by smart manufacturing equipment and applications in Internet of Things (IoT) powered smart factories. The rapidly changing manufacturing environment produces a variety of challenges in ensuring production and operation efficiency and delivering business values. There's an urgent need for businesses to harness, analyze and gain intelligence from these derived data. Big Data Analytics (BDA), a pioneer in the manufacturing field, focuses on dealing with these 4 V's of Big Data (Volume, Variety, Velocity, and Veracity) through advanced data processing, integration, analysis, machine learning, predictive and prescriptive modelling that assist with data-driven decision-making and optimization in the manufacturing process. While several BDA techniques such as preprocess data, build descriptive models, perform huge-scale data mining and run machine learning predictive models are developing in the manufacturing field, nowadays many manufacturers lag behind in adopting BDA into their operations. This paper, through a systematic literature review, aims to analyze the styles of the existing research and see if they can provide panoramic views toward the integration of BDA and smart manufacturing systems. Seven foundational perspectives based on which the reviewed papers are classified include definitions, applications, architecture, models, methods or techniques, implementations, and reviews or surveys.

In this chapter, a discussion on the integration of distributed streaming Big Data Analytics with the Internet of Things is presented. The chapter begins with the introduction of these two technologies by discussing their features and characteristics. Discussion on how the integration of these two technologies benefit in efficient processing of IoT device generated sensor data follows next. Such data centric processing of IoT data powered by cloud, services and other enablers will be the architecture of most of the realtime systems involving sensors and real-time monitoring and actuation. The Volume, Variety and Velocity of sensor generated data make it a Big Data scenario. In addition, the data is real time and requires decisions or actuations immediately. This chapter discusses how IoT data can be processed using distributed, scalable stream processing systems. The chapter is concluded with future directions of such real time Big Data Analytics in IoT.