English

The increasing world population and rapid industrial development is driving the need for sustainable management solutions and preservation of the natural resources and the ecosystem. The Internet of Things (IoT) and big data (BD) analytics are expected to play key role toward sustainability in areas such as water, agriculture, energy, transportation and smart city. In this chapter, the focus is sustainability in water. To address the issues of sustainability of clean accessible water for human use and other living things, a water ecosystem is presented. The water ecosystem consists of five major elements which are water source, treatment, reservoir, consumption and wastage. There are several issues faced in sustainable water supply such as decreasing fresh water resources, loss of revenue due to water loss, complexity in managing the increasing demands. Existing water-management models are not efficient in addressing these issues. In addition, due to seasonal variations, changes in environmental laws, varying plant-operating conditions and other factors, there is a need for effective and efficient monitoring. The application of IoT and BD analytics technology is promising and can provide sustainable management solution in water. The practical deployment of IoT and BD analytics consists of four major components which are IoT devices, communication technology, internet and BD. The emerging low power wide area (LPWA) communication technology is expected to enhance massive connectivity required for water monitoring, data acquisition and promote sustainability in water. Hence, in this chapter, the features of the water ecosystem, the system architecture of IoT and BD and the application in water sustainability, challenges such as cyber security, policy and regulations, accuracy of the data and technology interoperability are discussed.

Recent technologies such as Big Data could be used in order to help the improvement of other fields except for the telecommunication field. Due to the Health field, the Big Data technology could contribute with the aim to help for the purpose of analysis and management of the huge amounts of health data. The main objective of this research proposal is an analytical study of the technologies IoT, Cloud Computing (CC) and large-scale data (Big Data) to resolve various issues facing the health sector in relation to these technologies. The purpose of the research proposal is the collection of medical (e-health) big data in real time. The collection will be performed by sensor devices and actuators, which will wear patients who suffer from various ailments. Then is following, the transfer of these data through a network to a cloud server. Additionally, these data will be processed in the cloud which makes its analysis so as to become meaningless. By the analysis of these data is done the data mining. Finally, to address the various problems in the health sector, the transfer of the analyzed health data will be held by the devices of the relevant persons. Also, in our study, we will deal with the security of medical data which constitute personal data and must be protected.

<p>Internet of Things (IoT) requires cloud infrastructures for data analysis (e.g., temperature monitoring, energy consumption measurement, etc.). Traditionally, cloud services have been implemented in large datacenters in the core network. Core cloud offers high-computational capacity with moderate response time, meeting the requirements of centralized services with low-delay demands. However, collecting information and bringing it into one core cloud infrastructure is not a long-term scalable solution, particularly as the volume of IoT devices and data is forecasted to explode. A scalable and efficient solution, both at the network and cloud level, is to distribute the IoT analytics between the core cloud and the edge of the network (e.g., first analytics on the edge cloud and the big data analytics on the core cloud). For an efficient distribution of IoT analytics and use of network resources, it requires to integrate the control of the transport networks (packet and optical) with the distributed edge and cloud resources in order to deploy dynamic and efficient IoT services. This paper presents and experimentally validates the first IoT-aware multilayer (packet/optical) transport software defined networking and edge/cloud orchestration architecture that deploys an IoT-traffic control and congestion avoidance mechanism for dynamic distribution of IoT processing to the edge of the network (i.e., edge computing) based on the actual network resource state.</p>

In recent times, a massive amount of smart devices or objects are connected that enhances the scale of the digital world. These smart objects are referred as “things” or physical devices that have the potential to sense the real-world physical objects, collect the data, and network with others. The objects are connected through the internet, which crafts the terminology of Internet of Things (IoT). IoT has been developed and become the center of consideration due to the novelty of embedded device and a rapid enhancement in its number. This increase is resulting in the creative applications of smart environments. Smart transportation is a central stake for the quality of life of citizens in smart environment. Smart transportation involves the use of devices and sensors in the control system of vehicle; for example navigation system of cars, traffic signal management system, number recognition system and speed monitoring system. In this research article, we propose architecture for smart transportation system using Big Data analytics, in order to achieve real time processing and facilitate a friendly communication in the environment of IoT based smart transportation. The proposed architecture is a 3-phase scheme which is responsible for the organization and management of Big Data, real-time processing of Big Data and service management. The proposed architecture is a generic solution for the smart transportation planning using real time Big Data processing. The proposed scheme is realized using Spark over single node Hadoop setup with various input libraries. A huge amount of data from different authentic and reliable sources is measured to validate the proposed architecture. In addition, the effectiveness of proposed scheme also highlighted with regard to throughput.

This study explores the role of the Internet of Things (IoT) in machine maintenance, focusing on its integration with artificial intelligence (AI) and its impact on operational efficiency across various industries. A bibliometric analysis was conducted using data from Scopus, identifying key research trends, influential publications, and emerging technologies within the field. The findings highlight the increasing adoption of IoT for predictive maintenance in manufacturing, energy, and agriculture sectors. The study also reveals the growing intersection between IoT, machine learning, and data analytics, which enhances predictive capabilities and resource management. The results emphasize the importance of real-time monitoring and decision-making in improving industrial operations. However, challenges such as data security, interoperability, and adoption costs remain barriers to full implementation. This study provides valuable insights into the current state of research on IoT for machine maintenance, offering a foundation for future technological advancements and research directions.

Traffic congestion is still a challenge faced by most countries of the world. However, it can be solved most effectively by integrating modern technologies such as Internet of Things (IoT), fog computing, cloud computing, data analytics, and so on, into a framework that exploits the strengths of these technologies to address specific problems faced in traffic management. Unfortunately, no such framework that addresses the reliability, flexibility, and efficiency issues of smart-traffic management exists. Therefore, this paper proposes a comprehensive framework to achieve a reliable, flexible, and efficient solution for the problem of traffic congestion. The proposed framework has four layers. The first layer, namely, the sensing layer, uses multiple data sources to ensure a reliable and accurate measurement of the traffic status of the streets, and forwards these data to the second layer. The second layer, namely, the fog layer, consumes these data to make efficient decisions and also forwards them to the third layer. The third layer, the cloud layer, permanently stores these data for analytics and knowledge discoveries. Finally, the fourth layer, the services layer, provides assistant services for traffic management. We also discuss the functional model of the framework and the technologies that can be used at each level of the model. We propose a smart-traffic light algorithm at level 1 for the efficient management of congestion at intersections, tweet-classification and image-processing algorithms at level 2 for reliable and accurate decision-making, and support services at level 4 of the functional model. We also evaluated the proposed smart-traffic light algorithm for its efficiency, and the tweet classification and image-processing algorithms for their accuracy.

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%.

Internet of Things (IoT) sensors are ubiquitous technologies deployed across smart cities, industrial sites, and healthcare systems. They continuously generate time series data that enable advanced analytics and automation in industries. However, challenges such as the loss or ambiguity of sensor metadata, heterogeneity in data sources, varying sampling frequencies, inconsistent units of measurement, and irregular timestamps make raw IoT time series data difficult to interpret, undermining the effectiveness of smart systems. To address these challenges, we propose a novel deep learning model, DeepFeatIoT, which integrates learned local and global features with non-learned randomized convolutional kernel-based features and features from large language models (LLMs). This straightforward yet unique fusion of diverse learned and non-learned features significantly enhances IoT time series sensor data classification, even in scenarios with limited labeled data. Our model's effectiveness is demonstrated through its consistent and generalized performance across multiple real-world IoT sensor datasets from diverse critical application domains, outperforming state-of-the-art benchmark models. These results highlight DeepFeatIoT's potential to drive significant advancements in IoT analytics and support the development of next-generation smart systems.

Survival of the Internet of Things (IoT) is due to the spread of smart devices throughout the world. An enormous amount of data from IoT-enabled devices are getting generated in real-time. Real-time data originates from IoT-enabled devices, builds a knowledge database, and magnifies the wisdom in society. E-governance, smart transport, smart waste management, smart water management, smart energy management makes the smart city. IoT technologies like RFID, Sigfox, Zigbee, Lora, RPMA, Ingenu, NB-IoT, GSM, etc., enables city IoT enabled and helped in making a city smart. Data collected through sensors are analyzed using IoT analytics and which contributes to making smart decisions. Smart city implementation helps in enriching the quality of human life.

With the emergence of fog and edge computing, new possibilities arise regarding the data-driven management of citizens’ mobility in smart cities. Internet of Things (IoT) analytics refers to the use of these technologies, data, and analytical models to describe the current status of the city traffic, to predict its evolution over the coming hours, and to make decisions that increase the efficiency of the transportation system. It involves many challenges such as how to deal and manage real and huge amounts of data, and improving security, privacy, scalability, reliability, and quality of services in the cloud and vehicular network. In this paper, we review the state of the art of IoT in intelligent transportation systems (ITS), identify challenges posed by cloud, fog, and edge computing in ITS, and develop a methodology based on agile optimization algorithms for solving a dynamic ride-sharing problem (DRSP) in the context of edge/fog computing. These algorithms allow us to process, in real time, the data gathered from IoT systems in order to optimize automatic decisions in the city transportation system, including: optimizing the vehicle routing, recommending customized transportation modes to the citizens, generating efficient ride-sharing and car-sharing strategies, create optimal charging station for electric vehicles and different services within urban and interurban areas. A numerical example considering a DRSP is provided, in which the potential of employing edge/fog computing, open data, and agile algorithms is illustrated.

Convolutional neural networks (CNNs) are increasingly adopted on resource-constrained sensors for in-situ data analytics in Internet of Things (IoT) applications. This paper presents a model split framework, namely, splitCNN, in order to run a large CNN on a collection of concurrent IoT sensors. Specifically, we adopt CNN filter pruning techniques to split the large CNN into multiple small-size models, each of which is only sensitive to a certain number of data classes. These class-specific models are deployed onto the resource-constrained concurrent sensors which collaboratively perform distributed CNN inference on their same/similar sensing data. The outputs of multiple models are then fused to yield the global inference result. We apply splitCNN to three case studies with different sensing modalities, which include the human voice, industrial vibration signal, and visual sensing data. Extensive evaluation shows the effectiveness of the proposed splitCNN. In particular, the splitCNN achieves significant reduction in the model size and inference time while maintaining similar accuracy, compared with the original CNN model for all three case studies.

In today's digital age, technology plays an essential role in work practices and employee well-being, especially in dynamic and stressful industries such as the apparel sector. This research examines the impact of new technologies—the Internet of Things (IoT), cloud computing, 3D printing, automation facilities, and big data analytics—on work-life balance (WLB) of female team leaders from selected garment factories in Ampara District, Sri Lanka. Sample size 86 was selected from the population through random sampling. The data was collected through structured questionnaires and analyzed using SPSS using reliability tests, descriptive statistics and regression analysis. Results showed that IoT, cloud computing, automation facilities, and big data analytics positively and significantly affected work-life balance, whereas 3D printing was the only technology not positively and significantly related in this case. Regression analysis showed that new technologies explained 61.9% of the variance accounted for WLB with IoT being the strongest predictor. In summary, these results highlight the importance of increased use of advanced technologies in the apparel sector which can enhance flexibility, provide support to stress, and enhance well-being. 3D printing cannot negatively affect WLB, however, as the use of 3D printing is not sufficiently utilized in operations. The study is an important contribution to the knowledge gaps regarding technology's contribution to maintaining a work life balance, specifically female workers in Sri Lanka's apparel industry. The study recommends that organizations focus on IoT, cloud computing, automation, big data analytics, and in the future: 3D printing as means of enhancing retention and satisfaction.

The oil and gas sector is undergoing a significant transformation driven by digital technologies, with the Internet of Things (IoT) and cloud computing playing pivotal roles. This conceptual model explores the integration of IoT and cloud solutions to optimize operations, enhance efficiency, and drive innovation within the oil and gas industry. As the sector faces increasing pressure to improve safety, reduce costs, and minimize environmental impact, digital transformation provides new avenues for achieving these goals. IoT devices enable real-time data collection from equipment, pipelines, and field operations, offering unprecedented visibility into operational performance. Coupled with cloud-based platforms, these technologies facilitate the seamless storage, analysis, and sharing of vast amounts of data. The conceptual model highlights the potential of IoT and cloud solutions to drive predictive maintenance, asset management, and process optimization, thereby reducing downtime, improving reliability, and extending the lifespan of critical infrastructure. Additionally, the model emphasizes the role of IoT and cloud solutions in enabling data-driven decision-making. By leveraging advanced analytics and machine learning algorithms, companies can gain actionable insights from real-time data, improving operational efficiency, enhancing safety protocols, and supporting regulatory compliance. The model also explores the potential of IoT and cloud solutions in optimizing supply chain management and enhancing collaboration across dispersed teams, ensuring greater agility and responsiveness in a highly competitive market. The research underscores the importance of a holistic approach to digital transformation, integrating IoT, cloud computing, and advanced analytics to create a cohesive system that aligns with the strategic goals of oil and gas companies. This conceptual model serves as a roadmap for the sector, providing actionable insights into how IoT and cloud solutions can revolutionize operations, boost profitability, and contribute to sustainability objectives. In conclusion, the adoption of IoT and cloud solutions in the oil and gas industry represents a transformative opportunity to optimize operations and drive innovation. The model provides a framework for successfully navigating digital transformation in this critical sector Keywords: Digital Transformation, IoT, Cloud Computing, Oil and Gas, Predictive Maintenance, Asset Management, Data-Driven Decision-Making, Operational Efficiency, Sustainability.

This case study features Tecnomulipast, an SME from Southern Italy that specializes in machinery production for the food processing industry. The study is in fact centered on the company's digital transformation process, facilitated by investments in advanced production systems and innovation-driven managerial practices both facilitated by regional co-financing initiatives, including from Regione Puglia. At the center of it all is the integration between a new Industry 4.0-compliant laser welding system in the company's ERP system. Through Internet of Things (IoT) technologies, the system is inherently equipped to collect and transmit batch-level as well as real-time data, instantiating a cyber-physical system for advanced manufacturing. Easy to connect by standard interface (i.e. OPC-UA), the system is tied to an analytics data framework capable of working on structured data (e.g., KPIs, sensors' metrics) as well as on unstructured data (e.g., images), allowing for real-time monitoring, early anomaly signaling, and optimization of processes. Designed for scalability, the related technology architecture is future-proof to include artificial intelligence (AI) integration for augmenting decision-making with predictive and prescriptive analytics. Beyond the technological enhancement, however, the transformation was facilitated by an excellence managerial model that focuses on flexibility, data-driven governance, as well as on constant learning. Tecnomulipast's case offers an replicable template for SMEs—especially in low digital maturity areas—showing that targeted investment, innovation-driven management, and system-level integration might finally eliminate the gap between tech potential and operational performance in Industry 4.0 transitions.

The article comprehensively examines the theoretical and methodological foundations of logistics support for the commercial activities of enterprises in the context of the digital transformation of the economy. It reveals the essence, objectives, and functions of logistics within the structure of a trading enterprise and substantiates its key role in shaping competitive advantages. It is determined that an effective logistics system contributes to cost reduction, improved customer service, and the enterprise’s adaptation to dynamic changes in the market environment. Special attention is given to the implementation of digital technologies in logistics processes, including ERP (Enterprise Resource Planning), WMS (Warehouse Management System), TMS (Transportation Management System), the Internet of Things (IoT), cloud services, GPS monitoring, and Big Data analytics. The capabilities of these tools in automating the management of product flows, warehouse and transport logistics, as well as inventory, order, and data management, are analyzed. Examples of successful digital transformation of logistics systems by leading Ukrainian companies, including Rozetka, Epicentr, Nova Poshta, METRO Ukraine, and Foxtrot, are presented. The article identifies key challenges that hinder the widespread adoption of digital solutions in logistics, such as insufficient digital literacy among personnel, limited funding, complexity of IT system integration, and cybersecurity risks. Practical recommendations are proposed to improve logistics support for the commercial activities of domestic enterprises, taking into account current challenges such as military actions, supply chain disruptions, changes in consumer behavior, global instability, and digital inequality. Particular emphasis is placed on the importance of synchronizing digital logistics with the strategic goals of the enterprise and developing partnership relations within supply chains. A systematic approach to logistics–based on the principles of transparency, adaptability, and technological flexibility–is identified as a key success factor in an unstable external environment and a context of digital competition.