Clustered vehicle routing problem for waste collection with smart operational management approaches

A transformative revolution has been triggered by the inclusion of Internet of Things (IoT) sensors in healthcare’s real-time tracking capabilities. Recent advancements in miniaturization and low-power designs, wireless connectivity, and sensor fusion (integration of information from multiple sensors) continue to have a profound impact on the monitoring of health. Intelligent embeddability and superior sensing capabilities facilitate seamless patient data tracking, and strong security protects data against breaches. The role of IoT sensors in healthcare is to connect devices, such as biometric and temperature sensors or imaging and environmental sensors. IoT sensors are used in multiple applications such as fitness and vital signs tracking information, and patient care in smart healthcare facilities. Such technologies enable IoT-based medication adherence systems so that patients follow prescribed treatments, thus improving outcomes and reducing the possibility of hospital readmissions. Compared to regular monitoring devices, IoT sensors provide continuous data capture, increased accuracy, improved patient mobility and personalized healthcare solutions. Healthcare IoT adoption faces challenges in data privacy, regulation, and the complexity of technical integration of healthcare IoT in existing infrastructures. Ethical considerations must also be addressed to ensure equitable access and unbiased use of IoT technology. However, despite these obstacles, the future of IoT-based healthcare technologies remains an attractive proposition with continuous technology evolution likely to improve the quality of care, optimal resource allocation, and decision-making in real-time. This chapter explores the evolution of healthcare monitoring through IoT sensors, detailing their technological mechanisms, applications, benefits, and the potential for transforming the healthcare landscape.

Nowadays, modern industries generate their energy by using renewable solar. The rapid increase in photovoltaic (PV) module installations provides a better energy conversion, but their life cycle is a major concern. This research paper focuses on the recycling process for solar PV modules using the Internet of Things in industries. The smart bin with the Internet of Things (IoT) utilizes a machine learning approach to collect solar waste. The proposed smart bin uses k-Nearest Neighbor's algorithm (k-NN) and Long Short-Term Memory (LSTM), a network-based learning algorithm. These algorithms are useful in updating the level of the bin via alert messages. It also helps in identifying the type of waste material. The k-NN algorithm provides 83% accuracy in predicting the bin level in a real-time testing environment. The smart dust bin classifies the waste materials, and notifies its level to the collection center through the IoT platform when the level reaches a prescribed threshold, the signal corresponding to the level is passed to the common waste collection unit. IoT is connected to Cloud Server. It helps to predict the level of the smart bin. Delay is introduced in the order of 3-8s while the alert message is sent to the common waste collection unit. The system monitors the smart bin levels and sends the notifications to alert and initiate the collection unit. Real-time mobile app monitors the bin's level and location. The cloud IoT analytics analyze the solar e-waste in a different locations in industries.The proposed system works better and provides accurate results by using machine learning approach.

Enhancing flood verification using Signal Detection Theory (SDT) and IoT Sensors: A spatial scale evaluation

Authentication in cloud-driven IoT-based big data environment: Survey and outlook

Wastewater management is a mechanism that is used to extract and refine pollutants from wastewater or drainage that can be recycled to the water supply with minimal environmental effects. New methods and techniques are required to ensure safe and smart wastewater management systems in smart cities because of the present deteriorating environmental state. Wireless sensor networks and the Internet of Things (IoT) represent promising wastewater treatment technologies. The elaborated literature survey formulates a conceptual framework with an Internet of Things (IoT)-based wastewater management system in smart cities (IoT-WMS) using blockchain technology. Blockchain technology is now being used to store information to develop an incentive model for encouraging the reuse of wastewater. Concerning the quality and quantity of recycled wastewater, tokens are issued to households/industries in smart cities. Nevertheless, this often encourages tampering with the information from which these tokens are awarded to include certain rewards. Anomaly detector algorithms are used to identify the possible IoT sensor data which has been tampered with by intruders. The model employs IoT sensors together with quality metrics to measure the amount of wastewater produced and reused. The simulation analysis shows that the proposed method achieves a high wastewater recycling rate of 96.3%, an efficiency ratio of 88.7%, a low moisture content ratio of 32.4%, an increased wastewater reuse of 90.8%, and a prediction ratio of 92.5%.

Purpose: The purpose of this article is the integration of Internet of Things (IoT) devices and deep learning techniques have been investigated to enhance smart city applications. This investigation addresses a critical challenge: the absence of standardized methods for data collection, processing, and analysis that optimize the interplay between these technologies. Materials and Methods: The research design employed in this study is a qualitative approach. Using extensive data acquisition from IoT sensors, urban infrastructure metrics, and evaluations of deep sustainability outcomes. This data is processed using deep learning algorithms to provide actionable insights. This study underscores the importance of interdisciplinary collaboration in the advancement of smart city solutions, as it facilitates more responsive and adaptive healthcare services in smart cities. Findings: The findings of this research reveal that the synergistic application of IoT and deep learning streamlines data-driven decision-making processes and increases operational efficiencies within urban healthcare systems. The study identified that the deep-learning models utilized large datasets, which holds promise for real-time analytics in urban environments. Implications to Theory, Practice and Policy: Moreover, it contributes to theoretical frameworks that elucidate the integration pathways for IoT and deep learning in smart cities, thus filling key gaps. Key findings revealed that by leveraging IoT for data acquisition and utilizing deep learning for data analysis, cities can improve urban management functions, such as traffic control, public safety, and resource allocation.

The rapid urbanization and technological advancement have catalyzed the emergence of smart cities as a transformative paradigm for sustainable urban development. This paper presents a comprehensive framework for modeling smart cities through the systematic integration of Internet of Things (IoT), big data, and analytics technologies. We propose a multi-layered architectural model that addresses the technical, operational, and governance challenges inherent in smart city implementations. The research examines how IoT sensors and devices generate massive volumes of heterogeneous data, which are subsequently processed through big data platforms to extract actionable insights via advanced analytics techniques. Our framework encompasses data acquisition, storage, processing, and visualization layers, while incorporating machine learning algorithms and real-time analytics for intelligent decision-making. Through case studies of various smart city domains including transportation, energy management, public safety, and healthcare, we demonstrate the practical applicability of our integrated model. The paper also addresses critical challenges such as data privacy, security, interoperability, and scalability that must be overcome for successful smart city deployment. Our findings reveal that effective integration of these three technological pillars enables cities to optimize resource allocation, enhance service delivery, improve quality of life for citizens, and achieve sustainability goals. The proposed model provides urban planners, policymakers, and technology implementers with a structured approach to design and deploy smart city solutions that are both technologically robust and contextually relevant.

This study aims to develop a smart model for the two-dimensional (2D) inundation simulation based on the derived artificial neural network (ANN) model with real-time measurements at the roadside IoT (Internet of Things) sensors; in detail, the flooding zones and associated area can be quantified by combining the inundation-depth estimates at the ungauged locations (defined by the virtual IoT sensor, VIOT) via the corresponding inundation-estimation equations, established using the ANN-derived model with the measurements at the IoT sensors (named SM_EID_VIOT model). Moreover, the resulting inundation-depth estimates at the ungauged locations from the proposed SM_EID_VIOT model can be improved by means of the real-time error-correction approach for the 2D inundation simulation. To demonstrate the reliability of the results from the proposed SM_EID_VIOT model, 1000 simulations of the rainfall-induced flood events within the study area of the Miaoli City of Northern Taiwan are generated as the model-training and validation datasets. Consequently, the proposed SM_EID_VIOT could estimate the inundation depths with an acceptable accuracy at the ungauged locations in time and space based on a low root mean square error (RMSE) of under 0.01 m and a high coefficient of determination (R2) of over 0.8; and it also can delineate the flooding zone to quantify the corresponding area in high reliability in terms of the precision ratio of about 0.7.

The role of IoT sensor in smart building context for indoor fire hazard scenario: A systematic review of interdisciplinary articles

The Internet of things (IoT) plays important roles in the modern digital world. It has several important roles in the modern power systems and power grids. IoT presents a lot of potential in the power systems and power grids. Some of the support functions are direct and several others are found to be indirect. Either way, IoT can play a lot of important roles in the modern power systems. It can help significantly in the measurement, control, and monitoring of the physical parameters in the power grids. It helps to in the reduction of energy consumption in the power electronic components. It has the potential to provide a lot of operational flexibilities in the power electronic components. Implementation of advanced operational algorithms using artificial intelligence and machine learning is facilitated by the IoT based sensors, actuators and other key components. It can provide smart operational assistance to the power electronic systems used in the power grids. Due to their logical flexibilities IoT sensors can be deployed alongside the power electronic components to track their performances. Consequently, using the IoT sensors' information, the actuators are driven to deliver optimal outcome. IoT sensors' information can be sent directly to the central servers in regular intervals to monitor the overall performances of the power electronic components. In addition to the aforesaid applications, several other potential uses of IoT in power electronics include monitoring of critical power grid parameters such as temperature, current, voltage and vibration at different key locations. In this paper, we analyze the use of IoT in power electronic components in the modern power systems.

In the smart cities, objects can smartly communicate with the people through Internet of Things (IoT). It will make smart cities a greener place by detecting pollution through IoT and environmental sensors. In order to maintain the sustainability of green place in smart cities, the emerging technology, i.e. Green IoT automatically and intelligently makes smart cities sustainable in a collaborative manner. Governments and a lot of organizations around the world are doing a lot of efforts to campaign the importance of the reduction of energy consumption and carbon production as well as emphasize on the Green IoT for smart cities. Various IoT related smart cities architectures are already presented in literature. But this work presents the concept of the “Green IoT” to create a green environment which will apprehend the idea of energy saving in smart cities. In this chapter, design of Green IoT architecture is proposed for smart cites with the focus to reduce energy consumption at each stage and ensure realization of IoT toward green. This proposed Green IoT architecture is based on the cloud based system which reduces the hardware consumption.

In Smart City (SC), things can connect with humans intelligently through the Internet of Things (IoT). IoT and ecological sensors will enable the detection of pollutants, contributing to developing environmentally friendly SC. Green Internet Architecture (GIA) in SC ensures the long-term viability of friendly spaces by enabling autonomous and intelligent sustainability practices through collaboration. Governments and organizations worldwide are promoting the significance of reducing energy usage and carbon emissions while highlighting the relevance of using GIA in SC. Several SC layouts connected to the IoT have previously been proposed in the literature. However, this study introduces the "Green Internet" notion of providing a friendly setting that focuses on conserving energy in SC. This research presents a suggested design for a GIA in SC. The main objective is to minimize energy usage at every level and ensure the implementation of IoT in a friendly manner. The suggested GIA is founded on a cloud-based system that diminishes hardware usage with Blockchain technology.

Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions

The Internet of Things (IoT) has emerged as a transformative technology that connects the physical and digital worlds, revolutionizing how devices, objects, and systems interact. As the world becomes increasingly interconnected, IoT paves the way for the development of smart cities, homes, industries, and healthcare systems. This paper presents a comprehensive analysis of the Internet of Things and its role in shaping the smart world.The first section of this paper delves into the foundational concepts of IoT, outlining its history, principles, and key components. The discussion highlights the rapid growth of IoT devices, sensors, and communication protocols, which have enabled seamless data exchange and connectivity on a global scale. The potential benefits and challenges of IoT adoption are addressed, emphasizing the need for robust security and privacy measures to protect against potential threats. The second section explores the pivotal role of IoT in shaping smart cities. With urbanization on the rise, the need for efficient resource management, sustainable infrastructure, and enhanced citizen services becomes more pressing. IoT-driven solutions empower cities to optimize traffic flow, manage energy consumption, improve waste management, and enhance public safety through real-time data analytics and predictive modeling.In the context of smart homes, the third section investigates how IoT applications have revolutionized the way people interact with their living spaces. Smart home devices, such as smart thermostats, lighting systems, and voice-activated assistants, provide users with increased convenience and energy efficiency. However, concerns surrounding data privacy and potential vulnerabilities necessitate a discussion on the best practices for securing smart home ecosystems. The fourth section examines the impact of IoT on industries, from manufacturing to agriculture. IoT-enabled industrial processes streamline production, enhance supply chain management, and facilitate predictive maintenance, leading to increased productivity and reduced operational costs. Additionally, precision agriculture applications of IoT offer farmers real-time data on soil conditions, weather patterns, and crop health, leading to improved crop yield and resource utilization.The fifth section addresses the transformation of healthcare systems through IoT integration. From remote patient monitoring to wearable health devices, IoT has opened new avenues for personalized and proactive healthcare. This section emphasizes the potential of IoT to revolutionize healthcare delivery by facilitating timely interventions, reducing hospital readmissions, and empowering patients to take an active role in their health management.The sixth section discusses the challenges and barriers hindering the widespread adoption of IoT and the smart world. Interoperability issues, standardization, and the complexity of integrating diverse systems pose significant challenges for stakeholders. Moreover, concerns regarding data ownership, privacy, and cyber-security remain key obstacles that need to be addressed to build public trust in IoT technologies. The final section explores future trends and possibilities for IoT and the smart world. With the proliferation of 5G networks, edge computing, and artificial intelligence, the potential for IoT applications is boundless. The paper concludes by emphasizing the importance of collaboration among governments, industries, and researchers to shape a sustainable and inclusive smart world powered by IoT technologies.In summary, this paper provides a comprehensive analysis of the Internet of Things and its role in shaping the smart world. From its foundational principles to its diverse applications across various sectors, IoT has the potential to transform societies and redefine how we interact with technology. However, addressing challenges related to security, privacy, and standardization is crucial to unlock the full potential of IoT and pave the way for a truly interconnected and intelligent future.

The construction industry is the largest emitter of greenhouse gases, accounting for 37% of global emissions, among which embodied carbon of modular buildings cannot be ignored. Although many studies have been conducted on monitoring the embodied carbon of modular buildings, there are still limitations, such as a lack of real-time and transparent data, fragmented and isolated systems, and inconsistent measurement and reporting standards. Therefore, this study aims to develop a real-time embodied carbon monitoring system for modular buildings. The system consists of three essential parts: (1) Firstly, through the Internet of Things (IoT) sensors, the system can automatically monitor and collect data from equipment such as factory processing machines, transport vehicles, and on-site installation equipment, etc. (2) Secondly, the data from the isolated system and the data collected by IoT sensors are stored in blockchain, which can ensure the transparency and credibility of the data. (3) Thirdly, the system has a visual web platform that presents and reports the embodied carbon, which uses a uniform life cycle assessment (LCA) method using the collected data. The study developed a novel multi-technological embodied carbon monitoring system, which will be applied to the Kowloon Tong student hostel being constructed by the Hong Kong Polytechnic University to test its effectiveness. We anticipate that the system can help monitor and manage carbon emissions during construction. This system provides a valuable implication and practical demonstration for the embodied carbon monitoring of modular buildings.