Securing the future of IoT-healthcare systems: A meta-synthesis of mandatory security requirements

INTRODUCTION

Healthcare-based Internet of Things (Healthcare-IoT) is a turning point in the development of health information systems. This emerging trend significantly contributes to enhancing users' awareness of their health, ultimately leading to an extension in life expectancy. Security and privacy are among the greatest challenges for H-IoT systems. To establish complete safety and security in these systems, the implementation of mandatory security requirements is imperative. For this reason, this study identifies the necessary security requirements for H-IoT systems using a Meta-Synthesis approach.

METHODS

Initially, following the Seven-Stage Sandelowski & Barroso approach, the existing literature was searched in the Scopus and Web of Science databases. Among the 844 extracted articles from the period of 2010 to 2020, 78 final articles were reviewed and analyzed, leading to the identification of 51 security requirements. Subsequently, to assess the quality of the identified requirements and their overlap, interviews were conducted with two experts.

RESULTS

Finally, 14 security requirements, predominantly with technical and quantitative aspects, were identified for designing a Healthcare-IoT system and implementing security mechanisms.

CONCLUSION

The findings of this study emphasize that addressing the identified 14 security requirements is crucial for safeguarding Healthcare-IoT systems and ensuring their robustness in the evolving health information landscape.

Application of artificial intelligence in active assisted living for aging population in real-world setting with commercial devices - A scoping review

BACKGROUND

The aging population is steadily increasing, posing new challenges and opportunities for healthcare systems worldwide. Technological advancements, particularly in commercially available Active Assisted Living devices, offer a promising alternative. These readily accessible products, ranging from smartwatches to home automation systems, are often equipped with Artificial Intelligence capabilities that can monitor health metrics, predict adverse events, and facilitate a safer living environment. However, there is no review exploring how Artificial Intelligence has been integrated into commercially available Active Assisted Living technologies, and how these devices monitor health metrics and provide healthcare solutions in a real-world environment for healthy aging. This review is essential because it fills a knowledge gap in understanding AI's integration in Active Assisted Living technologies in promoting healthy aging in real-world settings, identifying key issues that require to be addressed in future studies.

OBJECTIVE

The aim of this overview is to outline current understanding, identify potential research opportunities, and highlight research gaps from published studies regarding the use of Artificial Intelligence in commercially available Active Assisted Living technologies that assists older individuals aging at home.

METHODS

A comprehensive search was conducted in six databases-PubMed, CINAHL, IEEE Xplore, Scopus, ACM Digital Library, and Web of Science-to identify relevant studies published over the past decade from 2013 to 2024. Our methodology adhered to the PRISMA extension for scoping reviews to ensure rigor and transparency throughout the review process. After applying predefined inclusion and exclusion criteria on 825 retrieved articles, a total of 64 papers were included for analysis and synthesis.

RESULTS

Several trends emerged from our analysis of the 64 selected papers. A majority of the work (39/64, 61%) was published after the year 2020. Geographically, most of the studies originated from East Asia and North America (36/64, 56%). The primary application goal of Artificial Intelligence in the reviewed literature was focused on activity recognition (34/64, 53%), followed by daily monitoring (10/64, 16%). Methodologically, tree-based and neural network-based approaches were the most prevalent Artificial Intelligence algorithms used in studies (32/64, 50% and 31/64, 48% respectively). A notable proportion of the studies (32/64, 50%) carried out their research using specially designed smart home testbeds that simulate the conditions in real-world. Moreover, ambient technology was a common thread (49/64, 77%), with occupancy-related data (such as motion and electrical appliance usage logs) and environmental sensors (indicators like temperature and humidity) being the most frequently used.

CONCLUSION

Our results suggest that Artificial Intelligence has been increasingly deployed in the real-world Active Assisted Living context over the past decade, offering a variety of applications aimed at healthy aging and facilitating independent living for the older adults. A wide range of smart home indicators were leveraged for comprehensive data analysis, exploring and enhancing the potentials and effectiveness of solutions. However, our review has identified multiple research gaps that need further investigation. First, most research has been conducted in controlled testbed environments, leaving a lack of real-world applications that could validate the technologies' efficacy and scalability. Second, there is a noticeable absence of research leveraging cloud technology, an essential tool for large-scale deployment and standardized data collection and management. Future work should prioritize these areas to maximize the potential benefits of Artificial Intelligence in Active Assisted Living settings.

OOA-modified Bi-LSTM network: An effective intrusion detection framework for IoT systems

Currently, the Internet of Things (IoT) generates a huge amount of traffic data in communication and information technology. The diversification and integration of IoT applications and terminals make IoT vulnerable to intrusion attacks. Therefore, it is necessary to develop an efficient Intrusion Detection System (IDS) that guarantees the reliability, integrity, and security of IoT systems. The detection of intrusion is considered a challenging task because of inappropriate features existing in the input data and the slow training process. In order to address these issues, an effective meta heuristic based feature selection and deep learning techniques are developed for enhancing the IDS. The Osprey Optimization Algorithm (OOA) based feature selection is proposed for selecting the highly informative features from the input which leads to an effective differentiation among the normal and attack traffic of network. Moreover, the traditional sigmoid and tangent activation functions are replaced with the Exponential Linear Unit (ELU) activation function to propose the modified Bi-directional Long Short Term Memory (Bi-LSTM). The modified Bi-LSTM is used for classifying the types of intrusion attacks. The ELU activation function makes gradients extremely large during back-propagation and leads to faster learning. This research is analysed in three different datasets such as N-BaIoT, Canadian Institute for Cybersecurity Intrusion Detection Dataset 2017 (CICIDS-2017), and ToN-IoT datasets. The empirical investigation states that the proposed framework obtains impressive detection accuracy of 99.98 %, 99.97 % and 99.88 % on the N-BaIoT, CICIDS-2017, and ToN-IoT datasets, respectively. Compared to peer frameworks, this framework obtains high detection accuracy with better interpretability and reduced processing time.

Development of artificial intelligence edge computing based wearable device for fall detection and prevention of elderly people

Elderly falls are a major concerning threat resulting in over 1.5-2 million elderly people experiencing severe injuries and 1 million deaths yearly. Falls experienced by Elderly people may lead to a long-term negative impact on their physical and psychological health conditions. Major healthcare research had focused on this lately to detect and prevent the fall. In this work, an Artificial Intelligence (AI) edge computing based wearable device is designed and developed for detection and prevention of fall of elderly people. Further, the various deep learning algorithms such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) are utilized for activity recognition of elderly. Also, the CNN-LSTM, RNN-LSTM and GRU-LSTM with and without attention layer respectively are utilized and the performance metrics are analyzed to find the best deep learning model. Furthermore, the three different hardware boards such as Jetson Nano developer board, Raspberry PI 3 and 4 are utilized as an AI edge computing device and the best deep learning model is implemented and the computation time is evaluated. Results demonstrate that the CNN-LSTM with attention layer exhibits the accuracy, recall, precision and F1_Score of 97%, 98%, 98% and 0.98 respectively which is better when compared to other deep learning models. Also, the computation time of NVIDIA Jetson Nano is less when compared to other edge computing devices. This work appears to be of high societal relevance since the proposed wearable device can be used to monitor the activity of elderly and prevents the elderly falls which improve the quality of life of elderly people.

A review of the recent achievements and future trends on 3D printed microfluidic devices for bioanalytical applications

3D printing has revolutionized the manufacturing process of microanalytical devices by enabling the automated production of customized objects. This technology promises to become a fundamental tool, accelerating investigations in critical areas of health, food, and environmental sciences. This microfabrication technology can be easily disseminated among users to produce further and provide analytical data to an interconnected network towards the Internet of Things, as 3D printers enable automated, reproducible, low-cost, and easy fabrication of microanalytical devices in a single step. New functional materials are being investigated for one-step fabrication of highly complex 3D printed parts using photocurable resins. However, they are not yet widely used to fabricate microfluidic devices. This is likely the critical step towards easy and automated fabrication of sophisticated, complex, and functional 3D-printed microchips. Accordingly, this review covers recent advances in the development of 3D-printed microfluidic devices for point-of-care (POC) or bioanalytical applications such as nucleic acid amplification assays, immunoassays, cell and biomarker analysis and organs-on-a-chip. Finally, we discuss the future implications of this technology and highlight the challenges in researching and developing appropriate materials and manufacturing techniques to enable the production of 3D-printed microfluidic analytical devices in a single step.

Plant disease recognition using residual convolutional enlightened Swin transformer networks

Agriculture plays a pivotal role in the economic development of a nation, but, growth of agriculture is affected badly by the many factors one such is plant diseases. Early stage prediction of these disease is crucial role for global health and even for game changers the farmer's life. Recently, adoption of modern technologies, such as the Internet of Things (IoT) and deep learning concepts has given the brighter light of inventing the intelligent machines to predict the plant diseases before it is deep-rooted in the farmlands. But, precise prediction of plant diseases is a complex job due to the presence of noise, changes in the intensities, similar resemblance between healthy and diseased plants and finally dimension of plant leaves. To tackle this problem, high-accurate and intelligently tuned deep learning algorithms are mandatorily needed. In this research article, novel ensemble of Swin transformers and residual convolutional networks are proposed. Swin transformers (ST) are hierarchical structures with linearly scalable computing complexity that offer performance and flexibility at various scales. In order to extract the best deep key-point features, the Swin transformers and residual networks has been combined, followed by Feed forward networks for better prediction. Extended experimentation is conducted using Plant Village Kaggle datasets, and performance metrics, including accuracy, precision, recall, specificity, and F1-rating, are evaluated and analysed. Existing structure along with FCN-8s, CED-Net, SegNet, DeepLabv3, Dense nets, and Central nets are used to demonstrate the superiority of the suggested version. The experimental results show that in terms of accuracy, precision, recall, and F1-rating, the introduced version shown better performances than the other state-of-art hybrid learning models.

Advances in real time smart monitoring of environmental parameters using IoT and sensors

People who work in dangerous environments include farmers, sailors, travelers, and mining workers. Due to the fact that they must evaluate the changes taking place in their immediate surroundings, they must gather information and data from the real world. It becomes crucial to regularly monitor meteorological parameters such air quality, rainfall, water level, pH value, wind direction and speed, temperature, atmospheric pressure, humidity, soil moisture, light intensity, and turbidity in order to avoid risks or calamities. Enhancing environmental standards is largely influenced by IoT. It greatly advances sustainable living with its innovative and cutting-edge techniques for monitoring air quality and treating water. With the aid of various sensors, microcontroller (Arduino Uno), GSM, Wi-Fi, and HTTP protocols, the suggested system is a real-time smart monitoring system based on the Internet of Things. Also, the proposed system has HTTP-based webpage enabled by Wi-Fi to transfer the data to remote locations. This technology makes it feasible to track changes in the weather from any location at any distance. The proposed system is a sophisticated, efficient, accurate, cost-effective, and dependable weather station that will be valuable to anyone who wants to monitor environmental changes on a regular basis.

A scalable blockchain based framework for efficient IoT data management using lightweight consensus

Recent research has focused on applying blockchain technology to solve security-related problems in Internet of Things (IoT) networks. However, the inherent scalability issues of blockchain technology become apparent in the presence of a vast number of IoT devices and the substantial data generated by these networks. Therefore, in this paper, we use a lightweight consensus algorithm to cater to these problems. We propose a scalable blockchain-based framework for managing IoT data, catering to a large number of devices. This framework utilizes the Delegated Proof of Stake (DPoS) consensus algorithm to ensure enhanced performance and efficiency in resource-constrained IoT networks. DPoS being a lightweight consensus algorithm leverages a selected number of elected delegates to validate and confirm transactions, thus mitigating the performance and efficiency degradation in the blockchain-based IoT networks. In this paper, we implemented an Interplanetary File System (IPFS) for distributed storage, and Docker to evaluate the network performance in terms of throughput, latency, and resource utilization. We divided our analysis into four parts: Latency, throughput, resource utilization, and file upload time and speed in distributed storage evaluation. Our empirical findings demonstrate that our framework exhibits low latency, measuring less than 0.976 ms. The proposed technique outperforms Proof of Stake (PoS), representing a state-of-the-art consensus technique. We also demonstrate that the proposed approach is useful in IoT applications where low latency or resource efficiency is required.

Low-cost MEMS accelerometers for earthquake early warning systems: A dataset collected during seismic events in central Italy

This article describes a dataset of acceleration signals acquired from a low-cost Wireless Sensor Network (WSN) during seismic events that occurred in Central Italy. The WSN consists of 5 low-cost sensor nodes, each embedding an ADXL355 tri-axial MEMS accelerometer with a fixed sampling frequency of 250 Hz. The data was acquired from February 2023 to the end of June 2023. During this period, several earthquake sequences affected the area where the sensor network was installed. Continuous data was acquired from the WSN and then trimmed around the origin time of seismic events that occurred near the installation site, close to the city of Pollenza (MC), Italy. A total of 67 events were selected, whose data is available at the Istituto Nazionale di Geofisica e Vulcanologia (INGV) Seismology data center. The traces acquired from the WSN were then manually annotated by analysts from INGV. Annotations include picking time for P and S phases, when distinguishable from the background noise, alongside an associated uncertainty level for the manual annotations. The resulting dataset consists of 328 3 × 25,001 arrays, each associated with its metadata. The metadata includes event data (hypocenter position, origin time, magnitude, magnitude type, etc.), trace-related data (mean, median, maximum, and minimum amplitudes, manual picks, and picks uncertainty), and sensor-specific data (sensor name, sensitivity, and orientation). Furthermore, a small dataset consisting of non-seismic traces is included, with the goal of providing records of noise-only traces, relative to both electronic and environmental/anthropic noise sources. The dataset holds potential for training and developing Machine Learning or signal processing algorithms for seismic data with low signal-to-noise ratios. Additionally, it is valuable for research about earthquakes, structural health monitoring, and MEMS accelerometer performance in civil and seismic engineering applications.

Internet of things-based management versus standard management of home noninvasive ventilation in COPD patients with hypercapnic chronic respiratory failure: a multicentre randomized controlled non-inferiority trial

Background

Effective monitoring and management are crucial during long-term home noninvasive positive pressure ventilation (NPPV) in patients with hypercapnic chronic obstructive pulmonary disease (COPD). This study investigated the benefit of Internet of Things (IOT)-based management of home NPPV.

Methods

This multicenter, prospective, parallel-group, randomized controlled non-inferiority trial enrolled patients requiring long-term home NPPV for hypercapnic COPD. Patients were randomly assigned (1:1), via a computer-generated randomization sequence, to standard home management or IOT management based on telemonitoring of clinical and ventilator parameters over 12 months. The intervention was unblinded, but outcome assessment was blinded to management assignment. The primary outcome was the between-group comparison of the change in health-related quality of life, based on severe respiratory insufficiency questionnaire scores with a non-inferiority margin of -5. This study is registered with Chinese Clinical Trials Registry (No. ChiCTR1800019536).

Findings

Overall, 148 patients (age: 72.7 ± 6.8 years; male: 85.8%; forced expiratory volume in 1 s: 0.7 ± 0.3 L; PaCO2: 66.4 ± 12.0 mmHg), recruited from 11 Chinese hospitals between January 24, 2019, and June 28, 2021, were randomly allocated to the intervention group (n = 73) or the control group (n = 75). At 12 months, the mean severe respiratory insufficiency questionnaire score was 56.5 in the intervention group and 50.0 in the control group (adjusted between-group difference: 6.26 [95% CI, 3.71-8.80]; P < 0.001), satisfying the hypothesis of non-inferiority. The 12-month risk of readmission was 34.3% in intervention group compared with 56.0% in the control group, adjusted hazard ratio of 0.56 (95% CI, 0.34-0.92; P = 0.023). No severe adverse events were reported.

Interpretation

Among stable patients with hypercapnic COPD, using IOT-based management for home NPPV improved health-related quality of life and prolonged the time to readmission.

Funding

Air Liquide Healthcare (Beijing) Co., Ltd.

Cyberattack patterns in blockchain-based communication networks for distributed renewable energy systems: A study on big datasets

Blockchain-based reliable, resilient, and secure communication for Distributed Energy Resources (DERs) is essential in Smart Grid (SG). The Solana blockchain, due to its high stability, scalability, and throughput, along with low latency, is envisioned to enhance the reliability, resilience, and security of DERs in SGs. This paper presents big datasets focusing on SQL Injection, Spoofing, and Man-in-the-Middle (MitM) cyberattacks, which have been collected from Solana blockchain-based Industrial Wireless Sensor Networks (IWSNs) for events monitoring and control in DERs. The datasets provided include both raw (unprocessed) and refined (processed) data, which highlight distinct trends in cyberattacks in DERs. These distinctive patterns demonstrate problems like superfluous mass data generation, transmitting invalid packets, sending deceptive data packets, heavily using network bandwidth, rerouting, causing memory overflow, overheads, and creating high latency. These issues result in ineffective real-time events monitoring and control of DERs in SGs. The thorough nature of these datasets is expected to play a crucial role in identifying and mitigating a wide range of cyberattacks across different smart grid applications.

Knowledge Development Trajectories of Intelligent Video Surveillance Domain: An Academic Study Based on Citation and Main Path Analysis

Smart city is an area where the Internet of things is used effectively with sensors. The data used by smart city can be collected through the cameras, sensors etc. Intelligent video surveillance (IVS) systems integrate multiple networked cameras for automatic surveillance purposes. Such systems can analyze and monitor video data and perform automatic functions required by users. This study performed main path analysis (MPA) to explore the development trends of IVS research. First, relevant articles were retrieved from the Web of Science database. Next, MPA was performed to analyze development trends in relevant research, and g-index and h-index values were analyzed to identify influential journals. Cluster analysis was then performed to group similar articles, and Wordle was used to display the key words of each group in word clouds. These key words served as the basis for naming their corresponding groups. Data mining and statistical analysis yielded six major IVS research topics, namely video cameras, background modeling, closed-circuit television, multiple cameras, person reidentification, and privacy, security, and protection. These topics can boost the future innovation and development of IVS technology and contribute to smart transportation, smart city, and other applications. According to the study results, predictions were made regarding developments in IVS research to provide recommendations for future research.

Smart cities and innovative urban management: Perspectives of integrated technological solutions in urban environments

The increasing urbanization in a poorly planned way accentuates the imbalance between the population's needs and the organized development in urban spaces. The present study is based on the development of a situational diagnosis in the scope of a smart city, for the contextualization of potential opportunities for actions and innovation strategies in urban spaces. This article presents a literature overview covering the innovative actions developed in the scope of smart cities in scientific publications. Furthermore, the scope of the study is identifying innovation initiatives in the performance of actions and solutions for urban spaces. A literature review was developed supported by mappings, couplings, and diagrams, through the use of VOSViewer and SciMat software, and 115 articles were selected and analyzed, considering the articles based on the criterion of the coefficient of the number of citations concerning the year of publication. In the literature overview developed, it was found that the research within the scope of smart cities has been deepened over the years, with the evolution of the number of words related to the theme in the period from 2014 to 2021, as the advance in the number of publications from 2018 is noticeable, which highlights the increase in popularity regarding the topic, as well as its current relevance. The study identified thematic axes with an emphasis on technology and innovation, environment, urbanism, energy, governance, mobility, and accessibility. The results contributed by assembling innovative smart city actions and practices in an interrelated way with technology, innovation, and market-oriented constructs aimed to reach urban demands, as well as the development of innovative solutions between public institutions and business organizations to integrate urban spaces.

Micro energy harvesting for IoT platform: Review analysis toward future research opportunities

Micro-energy harvesting (MEH) is a technology of renewable power generation which is a key technology for hosting the future low-powered electronic devices for wireless sensor networks (WSNs) and, the Internet of Things (IoT). Recent technological advancements have given rise to several resources and technologies that are boosting particular facets of society. Many researchers are now interested in studying MEH systems for ultra-low power IoT sensors and WSNs. A comprehensive study of IoT will help to manage a single MEH as a power source for multiple WSNs. The popular database from Scopus was used in this study to perform a review analysis of the MEH system for ultra-low power IoT sensors. All relevant and important literature studies published in this field were statistically analysed using a review analysis method by VOSviewer software, and research gaps, challenges and recommendations of this field were investigated. The findings of the study indicate that there has been an increasing number of literature studies published on the subject of MEH systems for IoT platforms throughout time, particularly from 2013 to 2023. The results demonstrate that 67% of manuscripts highlight problem-solving, modelling and technical overview, simulation, experimental setup and prototype. In observation, 27% of papers are based on bibliometric analysis, systematic review, survey, review and based on case study, and 2% of conference manuscripts are based on modelling, simulation, and review analysis. The top-cited articles are published in 5 different countries and 9 publishers including IEEE 51%, Elsevier 16%, MDPI 10% and others. In addition, several MEH system-related problems and challenges are noted to identify current limitations and research gaps, including technical, modelling, economic, power quality, and environmental concerns. Also, the study offers guidelines and recommendations for the improvement of future MEH technology to increase its energy efficiency, topologies, design, operational performance, and capabilities. This study's detailed information, perceptive analysis, and critical argument are expected to improve MEH research's viable future.

Advancing sanitary surveillance: Innovating a live-feed sewer monitoring framework for effective water level and chamber cover detections

Efficient sanitation system management relies on vigilant sewage surveillance to uphold environmental hygiene. The absence of robust monitoring infrastructure jeopardizes unimpeded conduit flow, leading to floods and contamination. The accumulation of harmful gases in sewer chambers, coupled with tampered lids, compounds sewer network challenges, resulting in structural damage, disruptions, and safety risks from accidents and gas inhalation. Notably, even vehicular transit is vulnerable, facing collisions due to inadequately secured manholes. The core objective of this research was to deconstruct and synthesize a prototype blueprint for a live-feed sewer monitoring framework (LSMF). This involves creating a data gathering nexus (DGN) and empirically assessing diverse wireless sensing implements (WSI) for precision. Simultaneously, a geographic information matrix (GIM) was developed with algorithms to detect sewer surges, blockages, and missing manhole covers. Three scrutinized sensors-the LiDar TF-Luna, laser TOF400 VL53L1X, and ultrasonic JSN-SR04T-were evaluated for their ability to measure water levels in sewer vaults. The results showed that the TF-Luna LiDar sensor performed favorably within the 1.0-5.0 m range, with a standard deviation of 0.44-1.15. The TOF400 laser sensor ranked second, with a more variable standard deviation of up to 104 as obstacle distance increased. In contrast, the JSN-SR04T ultrasonic sensor exhibited lower standard deviation but lacked consistency, maintaining readings of 0.22-0.23 m within the 2.0-5.0 m span. The insights from this study provide valuable guidance for sustainable solutions to sewer surveillance challenges. Moreover, employing a logarithmic function, TF-Luna Benewake exhibited reliability at approximately 84.5%, while TOF400 VL53L1X adopted an exponential equation, boasting reliability approaching approximately 89.6%. With this navigational tool, TF-Luna Benewake maintained accuracy within ±10 cm for distances ranging from 8 to 10 m, showcasing its exceptional performance.

Intelligent decision making for energy efficient fog nodes selection and smart switching in the IOT: a machine learning approach

With the emergence of Internet of Things (IoT) technology, a huge amount of data is generated, which is costly to transfer to the cloud data centers in terms of security, bandwidth, and latency. Fog computing is an efficient paradigm for locally processing and manipulating IoT-generated data. It is difficult to configure the fog nodes to provide all of the services required by the end devices because of the static configuration, poor processing, and storage capacities. To enhance fog nodes' capabilities, it is essential to reconfigure them to accommodate a broader range and variety of hosted services. In this study, we focus on the placement of fog services and their dynamic reconfiguration in response to the end-device requests. Due to its growing successes and popularity in the IoT era, the Decision Tree (DT) machine learning model is implemented to predict the occurrence of requests and events in advance. The DT model enables the fog nodes to predict requests for a specific service in advance and reconfigure the fog node accordingly. The performance of the proposed model is evaluated in terms of high throughput, minimized energy consumption, and dynamic fog node smart switching. The simulation results demonstrate a notable increase in the fog node hit ratios, scaling up to 99% for the majority of services concurrently with a substantial reduction in miss ratios. Furthermore, the energy consumption is greatly reduced by over 50% as compared to a static node.

Multi-factor optimized mobile sink data collection framework for hybrid WSN-LTE assisted IoT network

The convergence of wireless sensor network-assisted Internet of Things has diverse applications. In most applications, the sensors are battery-powered, and it is necessary to use the energy judiciously to extend their functional duration effectively. Mobile sinks-based data collection is used to extend the lifespan of these networks. But providing a scalable and effective solution with consideration for multi-criteria factors of quality of service and lifetime maximization is still a challenge. This work addresses this problem with a hybrid wireless sensor network-Long term evolution assisted architecture. The problem of maximizing lifetime and providing multi-factor quality of service is solved as a two-stage optimization problem involving clustering and data collection path scheduling. Hybrid meta-heuristics is used to solve the clustering optimization problem. Minimal Steiner tree-based graph theory is applied to schedule the data collection path for sinks. Unlike existing works, the lifetime maximization without QoS degradation is addressed by hybridizing multiple approaches of multi-criteria optimal clustering, optimal path scheduling, and network adaptive traffic class-based data scheduling. This hybridization helps to extend the lifetime and enhance the QoS regarding packet delivery within the proposed solution. Through simulation analysis, the introduced approach yields a noteworthy increase of at least 6% and reduces packet delivery delay by 26% compared to existing methodologies.

Internet of medical things and blockchain-enabled patient-centric agent through SDN for remote patient monitoring in 5G network

During the COVID-19 pandemic, there has been a significant increase in the use of internet resources for accessing medical care, resulting in the development and advancement of the Internet of Medical Things (IoMT). This technology utilizes a range of medical equipment and testing software to broadcast patient results over the internet, hence enabling the provision of remote healthcare services. Nevertheless, the preservation of privacy and security in the realm of online communication continues to provide a significant and pressing obstacle. Blockchain technology has shown the potential to mitigate security apprehensions across several sectors, such as the healthcare industry. Recent advancements in research have included intelligent agents in patient monitoring systems by integrating blockchain technology. However, the conventional network configuration of the agent and blockchain introduces a level of complexity. In order to address this disparity, we present a proposed architectural framework that combines software defined networking (SDN) with Blockchain technology. This framework is specially tailored for the purpose of facilitating remote patient monitoring systems within the context of a 5G environment. The architectural design contains a patient-centric agent (PCA) inside the SDN control plane for the purpose of managing user data on behalf of the patients. The appropriate handling of patient data is ensured by the PCA via the provision of essential instructions to the forwarding devices. The suggested model is assessed using hyperledger fabric on docker-engine, and its performance is compared to that of current models in fifth generation (5G) networks. The performance of our suggested model surpasses current methodologies, as shown by our extensive study including factors such as throughput, dependability, communication overhead, and packet error rate.

Green internet of things and solar energy

The Internet of Things (IoT) stands out as one of the most captivating technologies of the current decade. Its ability to connect people and things anytime and anywhere has led to its rapid expansion and numerous impactful applications that enhance human life. With billions of connected devices and substantial power and infrastructure requirements, the IoT system can pose a threat to the environment. However, the IoT's vast range of resources and capabilities can also be leveraged to assist in environmental conservation in the evolution of technologies due to massive CO2 emissions, climate change, and environmental and health issues. In this study, with the two-way integration of IoT and green practices, two distinct concepts for green IoT are presented. Among green practices, energy solutions play a vital role in greening the IoT. In this study, the energy solutions for the IoT system are divided as reducing energy consumption and using green energy sources. Solutions for reducing IoT energy consumption are studied systematically through a five-layer framework to simplify its modular design and implementation. Then, the use of green energy resources is discussed for all components of the IoT ecosystem. Leveraging IoT to make the environment and other technologies green is the other concept of green IoT. IoT technology plays a crucial role in enhancing both energy management systems and the efficient harvesting of renewable energy sources. Switching to solar energy from fossil fuel energy is one of the most fundamental green practices today. In this study, the mutual relationship between solar energy harvesting and the IoT is addressed specifically. Several promising research directions in the realm of green IoT are also highlighted.

Disruptive supply chain technology assessment for sustainability journey: A framework of probabilistic group decision making

The fourth industrial revolution, commonly recognized as Industry 4.0, has been ushered by modern and innovative intelligence and communication technologies. Concerns about disruptive technologies (DTs) are beginning to grow in developing countries, despite the fact that the trade-offs between implementation difficulties and realistic effects are still unknown. Hence, prioritization and promotion of such technologies should be considered when investing in them to ensure sustainability. The study aims to provide new critical insights into what DTs are and how to identify the significant DTs for sustainable supply chain (SSC). Understanding the DTs' potential for achieving holistic sustainability through effective technology adoption and diffusion is critical. To achieve the goal, an integrated approach combining the Bayesian method and the Best Worst Method (BWM) is utilized in this study to evaluate DTs in emerging economies' supply chain (SC). The systematic literature review yielded a total of 10 DTs for SSC, which were then evaluated using the Bayesian-BWM to explore the most critical DTs for a well-known example of the readymade garment (RMG) industry of Bangladesh. The results show that the three most essential DTs for SSC are "Internet of things (IoT)", "Cloud manufacturing", and "Artificial intelligence (AI)". The research insights will facilitate policymakers and practitioners in determining where to concentrate efforts during the technology adoption and diffusion stage in order to improve sustainable production through managing SC operations in an uncertain business environment.

A low-cost wireless extension for object detection and data logging for educational robotics using the ESP-NOW protocol

In recent years, inexpensive and easy to use robotics platforms have been incorporated into middle school, high school, and college educational curricula and competitions all over the world. Students have access to advanced microprocessors and sensor systems that engage, educate, and encourage their creativity. In this study, the capabilities of the widely available VEX Robotics System are extended using the wireless ESP-NOW protocol to allow for real-time data logging and to extend the computational capabilities of the system. Specifically, this study presents an open source system that interfaces a VEX V5 microprocessor, an OpenMV camera, and a computer. Images from OpenMV are sent to a computer where object detection algorithms can be run and instructions sent to the VEX V5 microprocessor while system data and sensor readings are sent from the VEX V5 microprocessor to the computer. System performance was evaluated as a function of distance between transmitter and receiver, data packet round trip timing, and object detection using YoloV8. Three sample applications are detailed including the evaluation of a vision-based object sorting machine, a drivetrain trajectory analysis, and a proportional-integral-derivative (PID) control algorithm tuning experiment. It was concluded that the system is well suited for real time object detection tasks and could play an important role in improving robotics education.

Ensemble averaging deep neural network for botnet detection in heterogeneous Internet of Things devices

The botnet attack is one of the coordinated attack types that can infect Internet of Things (IoT) devices and cause them to malfunction. Botnets can steal sensitive information from IoT devices and control them to launch another attack, such as a Distributed Denial-of-Service (DDoS) attack or email spam. This attack is commonly detected using a network-based Intrusion Detection System (NIDS) that monitors the network device's activity. However, IoT network is dynamic and IoT devices have many types with different configurations and vendors in IoT environments. Therefore, this research proposes an Intrusion Detection System (IDS) by ensemble-ing traffic from heterogeneous IoT devices. This research proposes Deep Neural Network (DNN) to create a training model from each heterogeneous IoT device. After that, each training model from each heterogeneous IoT device is used to predict the traffic. The prediction results from each training model are averaged using the ensemble averaging method to determine the final result. This research used the N-BaIoT dataset to validate the proposed IDS model. Based on experimental results, ensemble averaging DNN can detect botnet attacks in heterogeneous IoT devices with an average accuracy of 97.21, precision of 91.41, recall of 87.31, and F1-score 88.48.

Design of urban innovation space system using artificial intelligence technology and internet of things

The goal of this paper is to integrate artificial intelligence (AI) and Internet of things (IoT) technology into urban innovation space systems while expediting the construction of urban informatization. The core of the paper is to build an innovation space system, which is developed around three key components: innovation elements, innovation networks and innovation bases. First, the definition of innovation space is investigated in detail, and the essence of innovation space is understood to ensure that the key elements in the innovation process can be accurately captured and analyzed in the follow-up research. Second, it is clear that Chengdu is a representative city in Sichuan Province. Through the research in this area, people can deeply understand the specific background and characteristics of urban innovation space system. Then, the innovation space system is constructed, which is supported by innovation elements, innovation networks and innovation bases. These three components are intertwined, which together constitute the key elements of urban innovation space. Furthermore, the Internet worm technology is integrated with the IoT technology, and the system is visually inspected with the help of AI. The application of IoT technology helps to realize the automation and information sharing of the system, while the use of AI provides a deep insight into the system structure and operation. Through this research process, people can fully understand the construction process of Chengdu innovation space system, and provide deeper insight and support for urban innovation through the application of IoT and AI technology. The results show that while Chengdu's entrepreneurship and innovation enterprises are dispersed throughout all of the city's districts and counties, the city's academic talent and the bulk of its higher education institutions are concentrated in the city's core. There are 275 entrepreneurship and innovation enterprises in the High-tech District of Chengdu, which is the most densely distributed area. An urban innovation space network is being built by eight distinct research and higher education establishments. As urban innovation spaces are being built, emphasis should be given to the regional aggregation features of talents, higher education and research institutions, as well as entrepreneurship and innovation business enterprises. The innovation space system based on Internet worm technology of the IoT shows excellent performance in real-time identification of innovation elements, network connection quality, sensor monitoring, AI visual monitoring and so on. The system performs well in real-time monitoring of new enterprises and projects, and the real-time recognition rate reaches 98 %. The communication quality of the innovation network is relatively stable, and the connection quality reaches 92 %. The accuracy of sensor status monitoring in the IoT is high, reaching 99 %. The coverage of AI vision monitoring system reaches 96 %, effectively monitoring the areas involved in innovative space systems. Generally speaking, through the combination of theory and practice, this paaper provides comprehensive and specific guidance for the construction of urban innovation space system, promotes the research progress in this field, and makes beneficial contributions to the sustainable development of urban innovation and informatization.

The applications of internet of things in smart healthcare sectors: a bibliometric and deep study

The recent attention garnered by Internet of Things (IoT) technology for its potential to alleviate challenges faced by healthcare systems, such as those resulting from an aging population and the rise in chronic illnesses, has underscored the significance of smart healthcare. Surprisingly, no bibliometric study has been conducted on this subject to date. Consequently, this investigation aims to provide a comprehensive overview of the longitudinal state and knowledge structure of IoT in smart healthcare. To achieve this, a content analysis tool is employed for academic research, facilitating the identification of key study themes, the growth trajectory of the research topic, the top journal sources, and the distribution of nations based on subject areas. The bibliometric evaluation encompasses 614 publications published in 14 journals spanning the period from 2016 to 2022. Employing bibliographic coupling analysis, the latest developments in IoT have been uncovered within the domain of smart healthcare. The findings reveal 11 primary research topic areas that have been the focus of scholarly discourse during this period. This study highlights that the computing paradigm and network connectivity emerge as the most prominent topics within this research domain. Blockchain-based security in healthcare closely follows as the second-largest topic discussed by scholars. Additionally, the analysis indicates a significant increase in total publications for the most popular topic, peaking around 2018.

Identifying topology of distribution substation in power Internet of Things using dynamic voltage load fluctuation flow analysis

At present, the reconfiguration, maintenance, and review of power lines play a pivotal role in maintaining the stability of electrical grid operations and ensuring the accuracy of electrical energy measurements. These essential tasks not only guarantee the uninterrupted functioning of the power system, thereby improving the reliability of the electricity supply but also facilitate precise electricity consumption measurement. In view of these considerations, this article endeavors to address the challenges posed by power line restructuring, maintenance, and inspections on the stability of power grid operations and the accuracy of energy metering. To accomplish this goal, this article introduces an enhanced methodology based on the hidden Markov model (HMM) for identifying the topology of distribution substations. This approach involves a thorough analysis of the characteristic topology structures found in low-voltage distribution network (LVDN) substations. A topology identification model is also developed for LVDN substations by leveraging time series data of electricity consumption measurements and adhering to the principles of energy conservation. The HMM is employed to streamline the dimensionality of the electricity consumption data matrix, thereby transforming the topology identification challenge of LVDN substations into a solvable convex optimization problem. Experimental results substantiate the effectiveness of the proposed model, with convergence to minimal error achieved after a mere 50 iterations for long time series data. Notably, the method attains an impressive discriminative accuracy of 0.9 while incurring only a modest increase in computational time, requiring a mere 35.1 milliseconds. By comparison, the full-day data analysis method exhibits the shortest computational time at 16.1 milliseconds but falls short of achieving the desired accuracy level of 0.9. Meanwhile, the sliding time window analysis method achieves the highest accuracy of 0.95 but at the cost of a 50-fold increase in computational time compared to the proposed method. Furthermore, the algorithm reported here excels in terms of energy efficiency (0.89) and load balancing (0.85). In summary, the proposed methodology outperforms alternative approaches across a spectrum of performance metrics. This article delivers valuable insights to the industry by fortifying the stability of power grid operations and elevating the precision of energy metering. The proposed approach serves as an effective solution to the challenges entailed by power line restructuring, maintenance, and inspections.

Bolstering IoT security with IoT device type Identification using optimized Variational Autoencoder Wasserstein Generative Adversarial Network

Due to the massive growth in Internet of Things (IoT) devices, it is necessary to properly identify, authorize, and protect against attacks the devices connected to the particular network. In this manuscript, IoT Device Type Identification based on Variational Auto Encoder Wasserstein Generative Adversarial Network optimized with Pelican Optimization Algorithm (IoT-DTI-VAWGAN-POA) is proposed for Prolonging IoT Security. The proposed technique comprises three phases, such as data collection, feature extraction, and IoT device type detection. Initially, real network traffic dataset is gathered by distinct IoT device types, like baby monitor, security camera, etc. For feature extraction phase, the network traffic feature vector comprises packet sizes, Mean, Variance, Kurtosis derived by Adaptive and concise empirical wavelet transforms. Then, the extracting features are supplied to VAWGAN is used to identify the IoT devices as known or unknown. Then Pelican Optimization Algorithm (POA) is considered to optimize the weight factors of VAWGAN for better IoT device type identification. The proposed IoT-DTI-VAWGAN-POA method is implemented in Python and proficiency is examined under the performance metrics, like accuracy, precision, f-measure, sensitivity, Error rate, computational complexity, and RoC. It provides 33.41%, 32.01%, and 31.65% higher accuracy, and 44.78%, 43.24%, and 48.98% lower error rate compared to the existing methods.

Improving digital twin experience through big data, IoT and social analysis: An architecture and a case study

Industries such as construction and business companies are becoming increasingly digitized. The amount of data to be monitored and processed has increased significantly since the advent of the Internet of Things and the massive use of sensors. In addition to the data from these sensors, large amounts of data that require specific handling and processing are received. Much of this data is eventually represented in digital twins as a monitoring or decision-support tool. In this paper, we present an architecture to improve digital twin-based experiences that need to represent information from multiple sources. This architecture is demonstrated using the specific use case of a digital twin for an office of an Italian company. The implementation leverages the Matterport 3D media platform and integrates different technologies and sensors. An evaluation of the solution has also been carried out. The results show high user acceptance and the opening of multiple possibilities to enrich the virtual model with further data from different sources.

An IoT-fuzzy intelligent approach for holistic management of COVID-19 patients

In this study, an internet of things (IoT)-enabled fuzzy intelligent system is introduced for the remote monitoring, diagnosis, and prescription of treatment for patients with COVID-19. The main objective of the present study is to develop an integrated tool that combines IoT and fuzzy logic to provide timely healthcare and diagnosis within a smart framework. This system tracks patients' health by utilizing an Arduino microcontroller, a small and affordable computer that reads data from various sensors, to gather data. Once collected, the data are processed, analyzed, and transmitted to a web page for remote access via an IoT-compatible Wi-Fi module. In cases of emergencies, such as abnormal blood pressure, cardiac issues, glucose levels, or temperature, immediate action can be taken to monitor the health of critical COVID-19 patients in isolation. The system employs fuzzy logic to recommend medical treatments for patients. Sudden changes in these medical conditions are remotely reported through a web page to healthcare providers, relatives, or friends. This intelligent system assists healthcare professionals in making informed decisions based on the patient's condition.

Low-cost monitoring systems for urban water management: Lessons from the field

Sound urban water management relies on extensive and reliable monitoring of water infrastructure. As low-cost sensors and networks have become increasingly available for environmental monitoring, urban water researchers and practitioners must consider the benefits and disadvantages of such technologies. In this perspective paper, we highlight six technical and socio-technological considerations for low-cost monitoring technology to reach its full potential in the field of urban water management, including: technical barriers to implementation, complementarity with traditional sensing technologies, low-cost sensor reliability, added value of produced information, opportunities to democratize data collection, and economic and environmental costs of the technology. For each consideration, we present recent experiences from our own work and broader literature and identify future research needs to address current challenges. Our experience supports the strong potential of low-cost monitoring technology, in particular that it promotes extensive and innovative monitoring of urban water infrastructure. Future efforts should focus on more systematic documenting of experiences to lower barriers to designing, implementing, and testing of low-cost sensor networks, and on assessing the economic, social, and environmental costs and benefits of low-cost sensor deployments.

Activated carbon derived from wood biochar for Amperometric sensing of Ammonia for early screening of chronic kidney disease

Personalized medicine has emerged as an increasingly efficient and effective approach to addressing disease diagnosis and intervention. Ammonia is a waste product produced by the body during the digestion of protein. The requirement to develop an electrochemical sensing platform for monitoring skin ammonia levels holds great potential as an essential solution to pre-screen chronic kidney disease (CKD). In this research, we have manufactured an innovative electrochemical sensor by employing activated carbon derived from wood biochar as the signal transducer. We conducted a comprehensive analysis of the structural and morphological characteristics of the synthesized materials using various techniques. The hypothesized interaction was investigated using chronoamperometry as a transduction technique. To assess cross-reactivity, we conducted a study using common interferants or chemicals present in the environment. The data presented in this paper represents three replicates and is plotted with a 5 % error bar, demonstrating a 95 % confidence interval in the sensor response. In this study, we have elucidated the functionality and usefulness of a wearable microelectronic research prototype integrated with an HTC-activated carbon @RTIL-based electrochemical sensing platform for detecting ammonia levels released from the skin as a marker for chronic kidney disease screening. By enabling early detection and monitoring, these platforms can facilitate timely interventions, such as lifestyle modifications, medication adjustments, or referral to nephrology specialists. This proactive approach can potentially slow down disease progression, minimize the need for dialysis or transplantation, and ultimately improve the quality of life for CKD patients.

Integration of federated learning with IoT for smart cities applications, challenges, and solutions

In the past few years, privacy concerns have grown, making the financial models of businesses more vulnerable to attack. In many cases, it is hard to emphasize the importance of monitoring things in real-time with data from Internet of Things (IoT) devices. The people who make the IoT devices and those who use them face big problems when they try to use Artificial Intelligence (AI) techniques in real-world applications, where data must be collected and processed at a central location. Federated learning (FL) has made a decentralized, cooperative AI system that can be used by many IoT apps that use AI. It is possible because it can train AI on IoT devices that are spread out and do not need to share data. FL allows local models to be trained on local data and share their knowledge to improve a global model. Also, shared learning allows models from all over the world to be trained using data from all over the world. This article looks at the IoT in all of its forms, including "smart" businesses, "smart" cities, "smart" transportation, and "smart" healthcare. This study looks at the safety problems that the federated learning with IoT (FL-IoT) area has brought to market. This research is needed to explore because federated learning is a new technique, and a small amount of work is done on challenges faced during integration with IoT. This research also helps in the real world in such applications where encrypted data must be sent from one place to another. Researchers and graduate students are the audience of our article.

Towards online surface water quality monitoring technology: A review

The exponential growth of human population and anthropogenic activities have led to the increase of global surface water contamination especially in river, lakes and ocean. Safe and clean surface water sources are crucial to human health and well-being, aquatic ecosystem, environment and economy. Thus, water monitoring is vital to ensure minimal and controllable contamination in the water sources. The conventional surface water monitoring method involves collecting samples on site and then testing them in the laboratory, which is time-consuming and not able to provide real-time water quality data. In addition, it involves many manpower and resources, costly and lack of integration. These make surface water quality monitoring more challenging. The incorporation of Internet of Things (IoT) and smart technology has contributed to the improvement of monitoring system. There are different approaches in the development and implementation of online surface water quality monitoring system to provide real-time data collection with lower operating cost. This paper reviews the sensors and system developed for the online surface water quality monitoring system in the previous studies. The calibration and validation of the sensors, and challenges in the design and development of online surface water quality monitoring system are also discussed.

Machine intelligence and medical cyber-physical system architectures for smart healthcare: Taxonomy, challenges, opportunities, and possible solutions

Hospitals use medical cyber-physical systems (MCPS) more often to give patients quality continuous care. MCPS isa life-critical, context-aware, networked system of medical equipment. It has been challenging to achieve high assurance in system software, interoperability, context-aware intelligence, autonomy, security and privacy, and device certifiability due to the necessity to create complicated MCPS that are safe and efficient. The MCPS system is shown in the paper as a newly developed application case study of artificial intelligence in healthcare. Applications for various CPS-based healthcare systems are discussed, such as telehealthcare systems for managing chronic diseases (cardiovascular diseases, epilepsy, hearing loss, and respiratory diseases), supporting medication intake management, and tele-homecare systems. The goal of this study is to provide a thorough overview of the essential components of the MCPS from several angles, including design, methodology, and important enabling technologies, including sensor networks, the Internet of Things (IoT), cloud computing, and multi-agent systems. Additionally, some significant applications are investigated, such as smart cities, which are regarded as one of the key applications that will offer new services for industrial systems, transportation networks, energy distribution, monitoring of environmental changes, business and commerce applications, emergency response, and other social and recreational activities.The four levels of an MCPS's general architecture-data collecting, data aggregation, cloud processing, and action-are shown in this study. Different encryption techniques must be employed to ensure data privacy inside each layer due to the variations in hardware and communication capabilities of each layer. We compare established and new encryption techniques based on how well they support safe data exchange, secure computing, and secure storage. Our thorough experimental study of each method reveals that, although enabling innovative new features like secure sharing and safe computing, developing encryption approaches significantly increases computational and storage overhead. To increase the usability of newly developed encryption schemes in an MCPS and to provide a comprehensive list of tools and databases to assist other researchers, we provide a list of opportunities and challenges for incorporating machine intelligence-based MCPS in healthcare applications in our paper's conclusion.

AgDataBox-IoT - application development for agrometeorological stations in smart

Currently, Brazil is one of the world's largest grain producers and exporters. Agriculture has already entered its 4.0 version (2017), also known as digital agriculture, when the industry has entered the 4.0 era (2011). This new paradigm uses Internet of Things (IoT) techniques, sensors installed in the field, network of interconnected sensors in the plot, drones for crop monitoring, multispectral cameras, storage and processing of data in Cloud Computing, and Big Data techniques to process the large volumes of generated data. One of the practical options for implementing precision agriculture is the segmentation of the plot into management zones, aiming at maximizing profits according to the productive potential of each zone, being economically viable even for small producers. Considering that climate factors directly influence yield, this study describes the development of a sensor network for climate monitoring of management zones (microclimates), allowing the identification of climate factors that influence yield at each of its stages.•Application of the internet of things to assist in decision making in the agricultural production system.•AgDataBox (ADB-IoT) web platform has an Application Programming Interface (API).•An agrometeorological station capable of monitoring all meteorological parameters was developed (Kate 3.0).

Photovoltaic energy sharing: Implementation and tests on a real collective self-consumption system

This research study analyses different types of photovoltaic (PV) energy sharing in a collective self-consumption (CSC) real-case in the Izarbel technological park in France. The analysis is carried out above all from the point of view of the self-consumption rate (SCR) and the savings. After explaining the emergence of the self-consumption concept for the integration of renewable energies, the study case is described. The PV energy is produced in ESTIA1 building and consumed in ESTIA1, 2 and 4 buildings. The main IoT components used to implement the CSC are smart meters and the Tecsol TICs; devices based on the LoRa protocol to retrieve production and consumption data. Then, the characteristics of PV energy sharing in France are explained, in particular the three possible types of energy sharing/allocation (static, dynamic by default and customised dynamic) and the structure of the electricity bill. Finally, the three types of sharing are compared in four scenarios (without and with a data centre, for low and high solar radiation). The results show that the dynamic allocations lead to increases of the SCR and that the customised dynamic sharing increases savings.

Visual image design of the internet of things based on AI intelligence

Visual object detection has emerged as a critical technology for Unmanned Arial Vehicle (UAV) use due to advances in computer vision. New developments in fields like communication technology and the UAV needs to be able to act autonomously by gathering data and then making choices. These tendencies have brought us to cutting-edge levels of health care, transportation, energy, monitoring, and security for visual image detection and manufacturing endeavors. These include coordination in communication via IoT, sustainability of IoT network, and optimization challenges in path planning. Because of their limited battery life, these gadgets are limited in their range of communication. UAVs can be seen as terminal devices connected to a large network where a swarm of other UAVs is coordinating their motions, directing one another, and maintaining watch over locations outside its visual range. One of the essential components of UAV-based applications is the ability to recognize objects of interest in aerial photographs taken by UAVs. While aerial photos might be useful, object detection is challenging. As a result, capturing aerial photographs with UAVs is a unique challenge since the size of things in these images might vary greatly. The study proposal included specific information regarding the Detection of Visual Images by UAVs (DVI-UAV) using the IoT and Artificial Intelligence (AI). Included in the study of AI is the concept of DSYolov3. The DSYolov3 model was presented to deal with these problems in the UAV industry. By fusing the channel-wise feature across multiple scales using a spatial pyramid pooling approach, the proposed study creates a novel module, Multi-scale Fusion of Channel Attention (MFCAM), for scale-variant object identification tasks. The method's effectiveness and efficiency have been thoroughly tested and evaluated experimentally. The suggested method would allow us to outperform most current detectors and guarantee that the models will be useable on UAVs. There will be a 95 % success rate in terms of visual image detection, a 94 % success rate in terms of computation cost, a 97 % success rate in terms of accuracy, and a 95 % success rate in terms of effectiveness.

Application of artificial intelligence algorithms and low-cost sensors to estimate respirable dust in the workplace

The Internet of Things (IoT) and low-cost sensor technology have become common tools for environmental exposure monitoring; however, their application in measuring respirable dust (RD) in the workplace remains limited. This study aimed to develop a predictive model for RD using artificial intelligence (AI) algorithms and low-cost sensors and subsequently assess its validity using a standard sampling approach. Various low-cost sensors were combined into an RD sensor module and mounted on a portable aerosol monitor (GRIMM 11-D) for two weeks. AI algorithms were used to capture data per minute over 14 days to establish predictive RD models. The best-fitting model was validated using an aluminum cyclone equipped with an air pump and polytetrafluoroethylene filters to sample the 8-hour RD for 5 days at an aircraft manufacturing company. This module was continuously monitored for two weeks to evaluate its stability. The RD concentration measured by GRIMM 11-D in a general outdoor environment over two weeks was 28.1 ± 16.1 μg/m3 (range: 2.4-85.3 μg/m3). Among the various established models, random forest regression was observed to have the best prediction capacity (R2 = 0.97 and root mean square error = 2.82 μg/m3) in comparison to the other 19 methods. Field-based validation revealed that the predicted RD concentration (35.9 ± 4.1 μg/m3, range: 32.7-42.9 μg/m3) closely approximated the results obtained by the traditional method (38.1 ± 8.9 μg/m3, range: 28.1-52.5 μg/m3), and a strong positive Spearman correlation was observed between the two (rs = 0.70). The average bias was -2.2 μg/m3 and the precision was 5.8 μg/m3, resulting in an accuracy of 6.2 μg/m3 (94.2 %). Data completeness was 99.7 % during the continuous two-week monitoring period. The developed sensor module of RD exhibited excellent predictive performance and good data stability that can be applied to exposure assessments in occupational epidemiological studies.

Application of digital technologies for ensuring agricultural productivity

Over the decades, agri-food security has become one of the most critical concerns in the world. Sustainable agri-food production technologies have been reliable in mitigating poverty caused by high demands for food. Recently, the applications of agri-food system technologies have been meaningfully changing the worldwide scene due to both external strengths and internal forces. Digital agriculture (DA) is a pioneering technology helping to meet the growing global demand for sustainable food production. Integrating different sub-branches of DA technologies such as artificial intelligence, automation and robotics, sensors, Internet of Things (IoT) and data analytics into agriculture practices to reduce waste, optimize farming inputs and enhance crop production. This can help shift from tedious operations to continuously automated processes, resulting in increasing agricultural production by enabling the traceability of products and processes. The application of DA provides agri-food producers with accurate and real-time observations regarding different features influencing their productivity, such as plant health, soil quality, weather conditions, and pest and disease pressure. Analyzing the results achieved by DA can help agricultural producers and scholars make better decisions to increase yields, improve efficiency, reduce costs, and manage resources. The core focus of the current work is to clarify the benefits of some sub-branches of DA in increasing agricultural production efficiency, discuss the challenges of practical DA in the field, and highlight the future perspectives of DA. This review paper can open new directions to speed up the DA application on the farm and link traditional agriculture with modern farming technologies.

Dataset of temperature, humidity, and actuator states of an east-facing South African Greenhouse Tunnel

A greenhouse tunnel in Stellenbosch, South Africa was used for testing a generic sensing system for monitoring and control of climatic conditions in the tunnel. Three temperature and humidity sensors were used to record data throughout the day in 5 min intervals. Bambara Nuts, a climate change-resilient and nutritious crop, were grown in a separate study in the tunnel using an aeroponics system. These were chosen as it is regarded as the norm in autonomous greenhouse temperature control in the region. During data collection, the sensors were placed at the front, middle, and back of the tunnel. At the front, there was an industrial extraction fan, and at the back, there was an evaporative cooling wet wall. The fan and wet wall were controlled using the middle sensor data that was averaged every minute to determine if the fan and wet wall should be on or off. The hysteresis band used as a threshold was to turn the fan on when the middle temperature reached 30 °C and to turn it off it was 22 °C. This data collection method extended from 31 December 2022 to 13 June 2023, collecting 162 days of temperature and humidity data for that period.

Towards an improved internet of things sensors data quality for a smart aquaponics system yield prediction

The mobile aquaponics system is a sustainable integrated aquaculture-crop production system in which wastewater from fish ponds are utilized in crop production, filtered, and returned for aquaculture uses. This process ensures the optimization of water and nutrients as well as the simultaneous production of fish and crops in portable homestead models. The Lack of datasets and documentations on monitoring growth parameters in Sub-Saharan Africa hamper the effective management and prediction of yields. Water quality impacts the fish growth rate, feed consumption, and general well-being irrespective of the system. This research presents an improvement on the IoT water quality sensor system earlier developed in a previous study in carried out in conjunction with two local catfish farmers. The improved system produced datasets that when trained using several machine learning algorithms achieved a test RMSE score of 0.6140 against 1.0128 from the old system for fish length prediction using Decision Tree Regressor. Further testing with the XGBoost Regressor achieved a test RMSE score of 7.0192 for fish weight prediction from the initial IoT dataset and 0.7793 from the improved IoT dataset. Both systems achieved a prediction accuracy of 99%. These evaluations clearly show that the improved system outperformed the initial one.•The discovery and use of improved IoT pond water quality sensors.•Development of machine learning models to evaluate the methods.•Testing of the datasets from the two methods using the machine learning models.

Utilizing the intelligence edge framework for robotic upper limb rehabilitation in home

Robotic devices are gaining popularity for the physical rehabilitation of stroke survivors. Transition of these robotic systems from research labs to the clinical setting has been successful, however, providing robot-assisted rehabilitation in home settings remains to be achieved. In addition to ensure safety to the users, other important issues that need to be addressed are the real time monitoring of the installed instruments, remote supervision by a therapist, optimal data transmission and processing. The goal of this paper is to advance the current state of robot-assisted in-home rehabilitation. A state-of-the-art approach to implement a novel paradigm for home-based training of stroke survivors in the context of an upper limb rehabilitation robot system is presented in this paper. First, a cost effective and easy-to-wear upper limb robotic orthosis for home settings is introduced. Then, a framework of the internet of robotics things (IoRT) is discussed together with its implementation. Experimental results are included from a proof-of-concept study demonstrating that the means of absolute errors in predicting wrist, elbow and shoulder angles are 0.8918 0 , 2.6753 0 and 8.0258 0 , respectively. These experimental results demonstrate the feasibility of a safe home-based training paradigm for stroke survivors. The proposed framework will help overcome the technological barriers, being relevant for IT experts in health-related domains and pave the way to setting up a telerehabilitation system increasing implementation of home-based robotic rehabilitation. The proposed novel framework includes:•A low-cost and easy to wear upper limb robotic orthosis which is suitable for use at home.•A paradigm of IoRT which is used in conjunction with the robotic orthosis for home-based rehabilitation.•A machine learning-based protocol which combines and analyse the data from robot sensors for efficient and quick decision making.

Industry 4.0: Machinery integration with supply chain and logistics in compliance with Italian regulations

This paper shows a real overview of the interconnection and automated integration of 4.0 machinery within the supply chain or logistics of two companies in the southern Italian territory. The authors provide an exhaustive analysis of the Italian legislation and the strict requirements in order to assess which investments are part of Industry 4.0 with a focus on business risk. The work also shows the potential of a new framework developed that allows using OPC-UA and Modbus protocols to access the functional variables of the 4.0 machinery in a bidirectional way, directly from cloud applications. The proposed solutions help companies to develop more efficient production processes and to fulfil the requirements imposed by Italian regulations in order to benefit from Industry 4.0 financial aid.

Achieving better indoor air quality with IoT systems for future buildings: Opportunities and challenges

With the development of IoT technology and low-cost indoor air quality (IAQ) sensors, the IoT-based IAQ monitoring platform has garnered significant research interest and demonstrated its potential in enhancing IAQ management. This study presents a comprehensive review of previous research on the development and application of IoT-based IAQ platforms in different built environments. It offers detailed insights into the design and implementation of recent IoT-based IAQ platforms. The findings indicate that the IoT-based IAQ platforms are able to provide reliable information for IAQ monitoring. To ensure quality control of the IoT-based IAQ platform, it is suggested to replace the sensors every 4-6 months for reliable monitoring. In another aspect, integrating data-driven technology into the platform is crucial for IAQ prediction and efficient control of ventilation systems, leveraging the wealth of data available from the IoT platform. According to recent studies that applied data-driven algorithms for IAQ management, it can be confirmed that the data-driven algorithms are able to prompt IAQ by providing either more information or a control strategy. However, it should be noted that only 9.1 % of the developed platforms integrated data-driven models for IAQ management. Based on our findings, current challenges and further opportunities are discussed. Future studies should focus on integrating data-driven algorithms into IoT-based IAQ platforms and developing digital twins that can be used for real building IAQ management. However, there is obvious tension between controlling ventilation for energy efficiency versus better air quality. It is important to make a balance between energy efficiency and better air quality according to the current situations of specific built environments. Also, the next generation of IoT-based IAQ platforms should include occupants in the loop to create a more occupant-centric IAQ management approach.

The elephant in the room: cybersecurity in healthcare

Cybersecurity has seen an increasing frequency and impact of cyberattacks and exposure of Protected Health Information (PHI). The uptake of an Electronic Medical Record (EMR), the exponential adoption of Internet of Things (IoT) devices, and the impact of the COVID-19 pandemic has increased the threat surface presented for cyberattack by the healthcare sector. Within healthcare generally and, more specifically, within anaesthesia and Intensive Care, there has been an explosion in wired and wireless devices used daily in the care of almost every patient-the Internet of Medical Things (IoMT); ventilators, anaesthetic machines, infusion pumps, pacing devices, organ support and a plethora of monitoring modalities. All of these devices, once connected to a hospital network, present another opportunity for a malevolent party to access the hospital systems, either to gain PHI for financial, political or other gain or to attack the systems directly to cause erroneous monitoring, altered settings of any device and even to access the EMR via this IoMT window. This exponential increase in the IoMT and the increasing wireless connectivity of anaesthesia and ICU devices as well as implantable devices presents a real and present danger to patient safety. There has, at the same time, been a chronic underfunding of cybersecurity in healthcare. This lack of cybersecurity investment has left the sector exposed, and with the monetisation of PHI, the introduction of technically unsecure IoT devices for monitoring and direct patient care, the healthcare sector is presenting itself for further devastating cyberattacks or breaches of PHI. Coupled with the immense strain that the COVID-19 pandemic has placed on healthcare and the changes in working patterns of many caregivers, this has further amplified the exposure of the sector to cyberattacks.

Detecting contradictions from IoT protocol specification documents based on neural generated knowledge graph

Due to the boom of Internet of Things (IoT) in recent years, various IoT devices are connected to the Internet and communicate with each other through network protocols such as the Constrained Application Protocol (CoAP). These protocols are typically defined and described in specification documents, such as Request for Comments (RFC), which are written in natural or semi-formal languages. Since developers largely follow the specification documents when implementing web protocols, they have become the de facto protocol specifications. Therefore, it must be ensured that the descriptions in them are consistent to avoid technological issues, incompatibility, security risks, or even legal concerns. In this work, we propose Neural RFC Knowledge Graph (NRFCKG), a neural network-generated knowledge graph based contradictions detection tool for IoT protocol specification documents. Our approach can automatically parse the specification documents and construct knowledge graphs from them through entity extraction, relation extraction, and rule extraction with large language models. It then conducts an intra-entity and inter-entity contradiction detection over the generated knowledge graph. We implement NRFCKG and apply it to the most extensively used messaging protocols in IoT, including the main RFC (RFC7252) of CoAP, the specification document of MQTT, and the specification document of AMQP. Our evaluation shows that NRFCKG generalizes well to other specification documents and it manages to detect contradictions from these IoT protocol specification documents.

Urology: a trip into metaverse

PURPOSE

Metaverse is becoming an alternative world in which technology and virtual experiences are mixed with real life, and it holds the promise of changing our way of living. Healthcare is already changing thanks to Metaverse and its numerous applications. In particular, Urology and urologic patients can benefit in many ways from Metaverse.

METHODS

A non-systematic literature review identified recently published studies dealing with Metaverse. The database used for this review was PubMed, and the identified studies served as the base for a narrative analysis of the literature that explored the use of Metaverse in Urology.

RESULTS

Virtual consultations can enhance access to care and reduce distance and costs, and pain management and rehabilitation can find an incredible support in virtual reality, reducing anxiety and stress and improving adherence to therapy. Metaverse has the biggest potential in urologic surgery, where it can revolutionize both surgery planning, with 3D modeling and virtual surgeries, and intraoperatively, with augmented reality and artificial intelligence. Med Schools can implement Metaverse in anatomy and surgery lectures, providing an immersive environment for learning, and residents can use this platform for learning in a safe space at their own pace. However, there are also potential challenges and ethical concerns associated with the use of the metaverse in healthcare.

CONCLUSIONS

This paper provides an overview of the concept of the metaverse, its potential applications, challenges, and opportunities, and discusses the implications of its development in Urology.

The use of smart environments and robots for infection prevention control: A systematic literature review

BACKGROUND

Infection prevention and control (IPC) is essential to prevent nosocomial infections. This manuscript aims at investigating the current use and role of robots and smart environments on IPC systems in nosocomial settings METHODS: The systematic literature review was performed following the PRISMA statement. Literature was searched for articles published in the period January 2016 to October 2022. Two authors determined the eligibility of the papers, with conflicting decisions being mitigated by a third. Relevant data was then extracted using an ad-hoc extraction table to facilitate the analysis and narrative synthesis.

RESULTS

The search strategy returned 1520 citations and 17 papers were included. This review identified 3 main areas of interest: hand hygiene and personal protective equipment compliance, automatic infection cluster detection and environments cleaning (ie, air quality control, sterilization). This review demonstrates that IPC practices within hospitals mostly do not rely on automation and robotic technology, and few advancements have been made in this field.

CONCLUSIONS

Increasing the awareness of healthcare workers on these technologies, through training and involving them in the design process, is essential to accomplish the Health 4.0 transformation. Research priorities should also be considering how to implement similar or more contextualized alternatives for low-income countries.

Predictive worker safety assessment through on-site correspondence using multi-layer fuzzy logic in outdoor construction environments

Construction sites remain highly perilous work environments globally, exposing employees to numerous hazards that can result in severe injuries or fatalities. To resolve this several solutions based on quantitative approaches have been developed. However the wide adoption of preexisting solutions is hindered by lack of accuracy. To this aim the development of an efficient fuzzy inference system has become a de-facto necessity. In this paper, we propose an edge inference framework based on multi-layered fuzzy logic for safety of construction workers. The proposed system employs an edge computing-based framework where IoT devices collect, store, and manage data to offer safety services. Multi-layer fuzzy logic is applied to infer the worker safety index based on rules that consist of construction environment factors. The multi-layer fuzzy logic is fed with weather, building and worker data collected from IoT nodes as inputs. The safety risk assessment process involves analyzing various factors. Weather information, such as temperature, humidity, and rainfall data, is considered to assess the risk to safety. The condition of the building is evaluated by analyzing load, strain, and inclination data. Additionally, the safety risk to workers is analyzed by taking into account their heart rate and location information. The initial layer's outputs are utilized as inputs for the subsequent layer, where an integrated safety index is inferred. Ultimately, the safety index is generated as the final outcome. The system's results are conveyed through warnings and an error measurement on a safety scale ranging from 1 to 10. Furthermore, web service is developed to allow the construction management to check the worker safety condition of the construction site in real-time, while also monitoring the operational status of the IoT devices, allowing for the early detection of sensor malfunction and the subsequent guarantee of worker safety. Extensive evaluations conducted to test the performance of the developed framework verify its efficiency to provide improved risk assessment, real-time monitoring, and proactive safety actions, encouraging a safer and more productive work environment.

New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson's disease

Parkinson's disease (PD) is characterized by the loss of neuronal cells, which leads to synaptic dysfunction and cognitive defects. Despite the advancements in treatment strategies, the management of PD is still a challenging event. Early prediction and diagnosis of PD are of utmost importance for effective management of PD. In addition, the classification of patients with PD as compared to normal healthy individuals also imposes drawbacks in the early diagnosis of PD. To address these challenges, artificial intelligence (AI) and machine learning (ML) models have been implicated in the diagnosis, prediction, and treatment of PD. Recent times have also demonstrated the implication of AI and ML models in the classification of PD based on neuroimaging methods, speech recording, gait abnormalities, and others. Herein, we have briefly discussed the role of AI and ML in the diagnosis, treatment, and identification of novel biomarkers in the progression of PD. We have also highlighted the role of AI and ML in PD management through altered lipidomics and gut-brain axis. We briefly explain the role of early PD detection through AI and ML algorithms based on speech recordings, handwriting patterns, gait abnormalities, and neuroimaging techniques. Further, the review discuss the potential role of the metaverse, the Internet of Things, and electronic health records in the effective management of PD to improve the quality of life. Lastly, we also focused on the implementation of AI and ML-algorithms in neurosurgical process and drug discovery.

A non-pharmacological multi-modal therapy to improve sleep and cognition and reduce mild cognitive impairment risk: Design and methodology of a randomized clinical trial

Aging populations are at increased risk of sleep deficiencies (e.g., insomnia) that are associated with a variety of chronic health risks, including Alzheimer's disease and related dementias (ADRD). Insomnia medications carry additional risk, including increased drowsiness and falls, as well as polypharmacy risks. The recommended first-line treatment for insomnia is cognitive behavioral therapy for insomnia (CBTi), but access is limited. Telehealth is one way to increase access, particularly for older adults, but to date telehealth has been typically limited to simple videoconferencing portals. While these portals have been shown to be non-inferior to in-person treatment, it is plausible that telehealth could be significantly improved. This work describes a protocol designed to evaluate whether a clinician-patient dashboard inclusive of several user-friendly features (e.g., patterns of sleep data from ambulatory devices, guided relaxation resources, and reminders to complete in-home CBTi practice) could improve CBTi outcomes for middle- to older-aged adults (N = 100). Participants were randomly assigned to one of three telehealth interventions delivered through 6-weekly sessions: (1) CBTi augmented with a clinician-patient dashboard, smartphone application, and integrated smart devices; (2) standard CBTi (i.e., active comparator); or (3) sleep hygiene education (i.e., active control). All participants were assessed at screening, pre-study evaluation, baseline, throughout treatment, and at 1-week post-treatment. The primary outcome is the Insomnia Severity Index. Secondary and exploratory outcomes span sleep diary, actiwatch and Apple watch assessed sleep parameters (e.g., efficiency, duration, timing, variability), psychosocial correlates (e.g., fatigue, depression, stress), cognitive performance, treatment adherence, and neurodegenerative and systemic inflammatory biomarkers.