A fusion architecture to deliver multipurpose mobile health services

Mobile Health (mHealth) services typically make use of customized software architectures, leading to development-dependent fragmentation. Nevertheless, irrespective of their specific purpose, most mHealth services share common functionalities, where standard pieces could be reused or adapted to expedite service deployment and even extend the follow-up of appearing conditions under the same service. To harness compatibility and reuse, this article presents a data fusion architecture proposing a common design framework for mHealth services. An exhaustive mapping of mHealth functionalities identified in the literature serves as starting point. The architecture is then conceptualized making use of the Joint Directors of Laboratories (JDL) data fusion model. The aim of the architecture is to exploit the multi-source data acquisition capabilities supported by smartphones and Internet of Things devices, and artificial intelligence-enabled feature fusion. A series of interconnected fusion layers ensure streamlined data management; each layer is composed of microservices which may be implemented or omitted depending on the specific goals of the healthcare service. Moreover, the architecture considers essential features related to authentication mechanisms, data sharing protocols, practitioner-patient communication, context-based notifications and tailored visualization interfaces. The effectiveness of the architecture is underscored by its instantiation for four real cases, encompassing risk assessment for youth with mental health issues, remote monitoring for SARS-CoV-2 patients, liquid intake control for kidney disease patients, and peritoneal dialysis treatment support. This breadth of applications exemplifies how the architecture can effectively serve as a guidance framework to accelerate the design of mHealth services.

Virtual hospital and artificial intelligence: a first step towards the application of an innovative health system for the care of rare cerebrovascular diseases

The development of virtual care options, including virtual hospital platforms, is rapidly changing the healthcare, mostly in the pandemic period, due to difficulties in in-person consultations. For this purpose, in 2020, a neurological Virtual Hospital (NOVHO) pilot study has been implemented, in order to experiment a multidisciplinary second opinion evaluation system for neurological diseases. Cerebrovascular diseases represent a preponderant part of neurological disorders. However, more than 30% of strokes remain of undetermined source, and rare CVD (rCVD) are often misdiagnosed. The lack of data on phenotype and clinical course of rCVD patients makes the diagnosis and the development of therapies challenging. Since the diagnosis and care of rCVDs require adequate expertise and instrumental tools, their management is mostly allocated to a few experienced hospitals, making difficult equity in access to care. Therefore, strategies for virtual consultations are increasingly applied with some advantage for patient management also in peripheral areas. Moreover, health data are becoming increasingly complex and require new technologies to be managed. The use of Artificial Intelligence is beginning to be applied to the healthcare system and together with the Internet of Things will enable the creation of virtual models with predictive abilities, bringing healthcare one step closer to personalized medicine. Herein, we will report on the preliminary results of the NOVHO project and present the methodology of a new project aimed at developing an innovative multidisciplinary and multicentre virtual care model, specific for rCVD (NOVHO-rCVD), which combines the virtual hospital approach and the deep-learning machine system.

Smart water management framework for irrigation in agriculture

Global demand and pressure on natural resources is increasing, which is greater on the availability of pure and safe drinking water. The use of new-age technologies including Smart sensors, embedded devices, and Cloud computing can help deliver efficient and safe management for provisioning drinking water for consumers and irrigation for agriculture. The management actions combined with real-time data gathering, monitoring, and alerting with proactive actions, prevent issues from occurring. This research presents a secure and smart research framework to enhance the existing irrigation system. This involves a low-budget irrigation model that can provide automated control and requirements as per the season, climate by using smart device sensors and Cloud communications. The authors presented four unique algorithms and water management processing rules. This also includes alerting scenarios for device and component failures and water leakage by automatically switching to alternative mode and sending alert messages about the faults to resolve the operational failures.The objective of this research is to identify new-age technologies for providing efficient and effective farming methods and investigate Smart IoT-based water management. The highlights of this research are to investigate IoT water management systems using algorithms for irrigation farming, for which this research presents a secure and smart research framework. This involves a low-budget irrigation model that provides automated control and requirements as per the season, climate by using smart device sensors and Cloud communications. Alerts for device and component failures and water leakage are also in-built for switching to alternative mode to resolve the operational failures.

RobEns: Robust Ensemble Adversarial Machine Learning Framework for Securing IoT Traffic

Recently, Machine Learning (ML)-based solutions have been widely adopted to tackle the wide range of security challenges that have affected the progress of the Internet of Things (IoT) in various domains. Despite the reported promising results, the ML-based Intrusion Detection System (IDS) proved to be vulnerable to adversarial examples, which pose an increasing threat. In fact, attackers employ Adversarial Machine Learning (AML) to cause severe performance degradation and thereby evade detection systems. This promoted the need for reliable defense strategies to handle performance and ensure secure networks. This work introduces RobEns, a robust ensemble framework that aims at: (i) exploiting state-of-the-art ML-based models alongside ensemble models for IDSs in the IoT network; (ii) investigating the impact of evasion AML attacks against the provided models within a black-box scenario; and (iii) evaluating the robustness of the considered models after deploying relevant defense methods. In particular, four typical AML attacks are considered to investigate six ML-based IDSs using three benchmarking datasets. Moreover, multi-class classification scenarios are designed to assess the performance of each attack type. The experiments indicated a drastic drop in detection accuracy for some attempts. To harden the IDS even further, two defense mechanisms were derived from both data-based and model-based methods. Specifically, these methods relied on feature squeezing as well as adversarial training defense strategies. They yielded promising results, enhanced robustness, and maintained standard accuracy in the presence or absence of adversaries. The obtained results proved the efficiency of the proposed framework in robustifying IDS performance within the IoT context. In particular, the accuracy reached 100% for black-box attack scenarios while preserving the accuracy in the absence of attacks as well.

Flextory: Flexible Software Factory of IoT Data Consumers

The success of the Internet of Things (IoT) has driven the development, among others, of many different software architectures for producing, processing, and analyzing heterogeneous data. In many cases, IoT applications share common features, such as the use of a platform or middleware, also known as message broker, that collects and manages data traffic between endpoints. However, in general, data processing is very dependent on the case study (sensors that send temperature data, drones that send images, etc.). Thus, the applications responsible for receiving and processing data, which we call consumers, have to be built ad hoc, since some of their elements have to be specially configured to solve specific needs of the case study. This paper presents Flextory, a software factory tool to make it easier for IoT developers to automatically construct configurable consumer applications, which we call FLEX-consumers. Flextory guides developers through the process of generating Java consumers by selecting some desired features such as, for instance, the particular communication protocol to be used. This way, the developer only has to concentrate on designing the algorithm to process the data. In short, the use of Flextory will result in consumer applications with configurable behavior, namely FLEX-consumers, that can connect to a messaging server (for example RabbitMQ) and process the received messages.

A two-phase spatiotemporal chaos-based protocol for data integrity in IoT

One of the biggest problems with Internet of Things (IoT) applications in the real world is ensuring data integrity. This problem becomes increasingly significant as IoT expands quickly across a variety of industries. This study presents a brand-new data integrity methodology for Internet of Things applications. The "sequence sharing" and "data exchange" stages of the suggested protocol are divided into two parts. During the first phase, each pair of nodes uses a new chaotic model for securely exchanging their identity information to generate a common sequence. This phase's objectives include user authentication and timing calculations for the second phase of the recommended method's packet validation phase. The recommended approach was tested in numerous settings, and various analyses were taken into account to guarantee its effectiveness. Also, the results were compared with the conventional data integrity control protocol of IoT. According to the results, the proposed method is an efficient and cost-effective integrity-ensuring mechanism with eliminates the need for third-party auditors and leads to reducing energy consumption and packet overhead. The results also show that the suggested approach is safe against a variety of threats and may be used as a successful integrity control mechanism in practical applications.

Internet of Things (IoT)-enabled framework for a sustainable Vaccine cold chain management system

Vaccines are a unique category of drugs sensitive to temperature and humidity and whose effectiveness directly impacts public health. There has been an increase in vaccine-related adverse events worldwide, particularly in developing countries, attributed to suboptimal temperatures during transport and storage. At the same time, the Internet of Things (IoT) has ushered in a paradigm shift in vaccine information and storage monitoring, enabling continuous 24/7 tracking. This further reduces the dependence on limited human resources and significantly reduces the associated errors and losses. This paper presents an IoT-driven framework that aims to improve the sustainability of medical cold chain management. The framework promotes trust and transparency in vaccine surveillance data by accessing and authenticating IoT devices. The proposed system aims to improve the safety and sustainability of vaccine management. Moreover, we provide detailed insights into the design and hardware components of the proposed framework. In addition, the specific use of the framework in a particular province is highlighted, covering the design of the software platform and the analysis of the hardware equipment.

A Comprehensive Review of Behavior Change Techniques in Wearables and IoT: Implications for Health and Well-Being

This research paper delves into the effectiveness and impact of behavior change techniques fostered by information technologies, particularly wearables and Internet of Things (IoT) devices, within the realms of engineering and computer science. By conducting a comprehensive review of the relevant literature sourced from the Scopus database, this study aims to elucidate the mechanisms and strategies employed by these technologies to facilitate behavior change and their potential benefits to individuals and society. Through statistical measurements and related works, our work explores the trends over a span of two decades, from 2000 to 2023, to understand the evolving landscape of behavior change techniques in wearable and IoT technologies. A specific focus is placed on a case study examining the application of behavior change techniques (BCTs) for monitoring vital signs using wearables, underscoring the relevance and urgency of further investigation in this critical intersection of technology and human behavior. The findings shed light on the promising role of wearables and IoT devices for promoting positive behavior modifications and improving individuals' overall well-being and highlighting the need for continued research and development in this area to harness the full potential of technology for societal benefit.

SDACS: Blockchain-Based Secure and Dynamic Access Control Scheme for Internet of Things

With the rapid growth of the Internet of Things (IoT), massive terminal devices are connected to the network, generating a large amount of IoT data. The reliable sharing of IoT data is crucial for fields such as smart home and healthcare, as it promotes the intelligence of the IoT and provides faster problem solutions. Traditional data sharing schemes usually rely on a trusted centralized server to achieve each attempted access from users to data, which faces serious challenges of a single point of failure, low reliability, and an opaque access process in current IoT environments. To address these disadvantages, we propose a secure and dynamic access control scheme for the IoT, named SDACS, which enables data owners to achieve decentralized and fine-grained access control in an auditable and reliable way. For access control, attribute-based control (ABAC), Hyperledger Fabric, and interplanetary file system (IPFS) were used, with four kinds of access control contracts deployed on blockchain to coordinate and implement access policies. Additionally, a lightweight, certificateless authentication protocol was proposed to minimize the disclosure of identity information and ensure the double-layer protection of data through secure off-chain identity authentication and message transmission. The experimental and theoretical analysis demonstrated that our scheme can maintain high throughput while achieving high security and stability in IoT data security sharing scenarios.

Optimizing Greenhouse Design with Miniature Models and IoT (Internet of Things) Technology-A Real-Time Monitoring Approach

The market for smart greenhouses has been valued at USD 1.77 billion in 2022 and is expected to grow to 3.39 billion by 2030. In order to make this more efficient, with the help of Internet of Things (IoT) technology, it is desired to eliminate the problem of traditional agriculture, which has poor monitoring and accuracy control of the parameters of a culture. Climate control decisions in a greenhouse are made based on parameter monitoring systems, which can be remotely controlled. Instead of this adjustment of the measured parameters, it would be preferable from the point of view of energy consumption that they should be calculated at optimal values from the design phase of the greenhouse. For this reason, it would be better to perform an energy simulation of the greenhouse first. For the study carried out in this work, a small greenhouse (mini-greenhouse) was built. It was equipped with an IoT sensor system, which measured indoor climate parameters and could send data to the cloud for future recording and processing. A simplified mathematical model of the heat balance was established, and the measured internal parameters of the mini-greenhouse were compared with those obtained from the simulation. After validating the mathematical model of the mini-greenhouse, this paper aimed to find the optimal position for placing a normal-sized greenhouse. For this, several possible locations and orientations of the greenhouse were compared by running the mathematical model, with which the most unfavorable positions could be eliminated. Then, some considerably cheaper "mini-greenhouses" were made and placed in the locations with the desired orientations. Using sensor systems and technologies similar to those presented in this work, the parameters from all mini-greenhouses can be monitored in real time. This real-time monitoring allows for the simultaneous analysis of all greenhouses, without the disadvantages of data collection directly in the field, with all data being recorded in the cloud and other IoT-specific advantages being made use of. In the end, we can choose the optimal solution for the location of a real-size greenhouse.

Soil Moisture and Heat Level Prediction for Plant Health Monitoring Using Deep Learning with Gannet Namib Beetle Optimization in IoT

Plant health monitoring is crucial in ensuring a constant food supply to satisfy the growing demand for food. Hence, it is essential to monitor plant health to maximize the yield and minimize the risk of various diseases. Soil moisture and temperature are of critical importance in plant growth, and predicting them enables farmers to take preventive actions, thereby mitigating the issues affecting plant health. This work presents a plant health monitoring approach by forecasting soil moisture and heat levels by collecting data in an Internet of Things (IoT) environment. Here, for transmitting the soil data acquired by the IoT nodes, a cluster head (CH) selection and routing technique using Gannet Namib beetle optimization (GNBO) is used. The data is routed to a prediction module, wherein soil moisture and heat levels are predicted by Convolutional long short term memory (Conv-LSTM). Furthermore, the hyperparameters of the Conv-LSTM are optimized by the GNBO algorithm. The efficiency of the GNBO-Conv-LSTM is examined based on link life time (LLT), energy, delay, distance, negative predictive value (NPV), positive predictive value (PPV), and true negative rate (TNR) and is observed to have achieved values of 0.675, 0.478 J, 0.092 ms, 50.200 m, 0.885, 0.882, and 0.875, correspondingly.

Realization of Humanoid Doctor and Real-Time Diagnostics of Disease Using Internet of Things, Edge Impulse Platform, and ChatGPT

Humanoid doctor is an AI-based robot that featured remote bi-directional communication and is embedded with disruptive technologies. Accurate and real-time responses are the main characteristics of a humanoid doctor which diagnoses disease in a patient. The patient details are obtained by Internet of Things devices, edge devices, and text formats. The inputs from the patient are processed by the humanoid doctor, and it provides its opinion to the patient. The historical patient data are trained using cloud artificial intelligence platform and the model is tested against the patient sample data acquired using medical IoT and edge devices. Disease is identified at three different stages and analyzed. The humanoid doctor is expected to identify the diseases well in comparison with human healthcare professionals. The humanoid doctor is under-trusted because of the lack of a multi-featured accurate model, accessibility, availability, and standardization. In this letter, patient input, artificial intelligence, and response zones are encapsulated and the humanoid doctor is realized.

[Design and Development of ECG Monitoring Cloud Platform Based on the Internet of Things Electrocardiograph]

The design and development of electrocardiogram（ECG） monitoring cloud platform based on the Internet of Things（IoT） electrocardiograph is introduced. The platform is mainly composed of ECG acquisition module, algorithm module, diagnostic model comparison module, warning module, positioning module and medical aid system. The ECG acquisition module collects ECG signals of patients and displays them in real time on the mobile terminals. Then they are uploaded to the ECG monitoring cloud platform through the IoT. The algorithm module and the diagnostic model comparison module mark, process, analyze and diagnose the ECG. Meanwhile, the ECG diagnosis and warning results are pushed to patients and 120 emergency centers through the IoT. Furthermore, the cloud platform will guide patients to self-rescue and the emergency platform will open the green channel to save patients in time.The platform realizes from the onset to diagnosis and treatment in one step, and saves lives against time.

[Design of Remote Slit Lamp Diagnosis Platform Based on IoT Technology]

In order to realize the diagnosis of slit lamp in cross-regional patients and improve the real-time and convenience of diagnosis, a remote slit lamp diagnosis platform based on Internet of Things (IoT) technology is designed. Firstly, the feasibility of remote slit lamp is analyzed. Secondly, the IoT platform architecture of doctor/server/facility (D/S/F) is proposed and a remote slit lamp is designed. Finally, the performance of the remote slit lamp diagnostic platform is tested. The platform solves the communication problem of distributed slit lamps and realizes respectively numerical control of multi-area slit lamp by multi-eye experts. The test results show that the remote control delay of the platform is less than 20 ms, which supports multiple experts to diagnose multiple patients separately.

High-Performance Coaxial Counter-Rotating Triboelectric Nanogenerator with Lift-Drag Hybrid Blades for Wind Energy Harvesting

Wind energy holds potential for in-situ powering large-scale distributed wireless sensor nodes (WSNs) in the Internet of Things (IoT) era. To achieve high performance in wind energy harvesting, a coaxial counter-rotating triboelectric nanogenerator with lift-drag hybrid blades, termed CCR-TENG, has been proposed. The CCR-TENG, which can work in non-contact and soft-contact modes, realizes low-speed wind energy harvesting through a combination of counter-clockwise rotating lift-type blades and clockwise rotating drag-type blades. Non-contact CCR-TENG realizes low-speed wind energy harvesting at wind speeds as low as 1 m/s. The output of a CCR-TENG, working in soft-contact mode, achieves 41% promotion with a maximum short-circuit current of 0.11 mA and a peak surface power density of 6.2 W/m2 with two TENGs connected in parallel. Furthermore, the power density per unit of wind speed achieves 746 mW/m3·s/m. Consequently, two fluorescent lamps were successfully illuminated and six temperature sensors were continuously lit by the CCR-TENG. The reported CCR-TENG significantly improves low-speed environmental wind energy utilization and demonstrates broad application prospects for in-situ power supply of distributed wireless transmission devices and sensors in the era of the IoT.

A machine learning based framework for IoT devices identification using web traffic

Identification of the Internet of Things (IoT) devices has become an essential part of network management to secure the privacy of smart homes and offices. With its wide adoption in the current era, IoT has facilitated the modern age in many ways. However, such proliferation also has associated privacy and data security risks. In the case of smart homes and smart offices, unknown IoT devices increase vulnerabilities and chances of data theft. It is essential to identify the connected devices for secure communication. It is very difficult to maintain the list of rules when the number of connected devices increases and human involvement is necessary to check whether any intruder device has approached the network. Therefore, it is required to automate device identification using machine learning methods. In this article, we propose an accuracy boosting model (ABM) using machine learning models of random forest and extreme gradient boosting. Featuring engineering techniques are employed along with cross-validation to accurately identify IoT devices such as lights, smoke detectors, thermostat, motion sensors, baby monitors, socket, TV, security cameras, and watches. The proposed ensemble model utilizes random forest (RF) and extreme gradient boosting (XGB) as base learners with adaptive boosting. The proposed ensemble model is tested with extensive experiments involving the IoT Device Identification dataset from a public repository. Experimental results indicate a higher accuracy of 91%, precision of 93%, recall of 93%, and F1 score of 93%.

An IoT-based low-cost architecture for smart libraries using SDN

In the evolving landscape of smart libraries, this research pioneers an IoT-based low-cost architecture utilizing Software-Defined Networking (SDN). The increasing demand for more efficient and economical solutions in library management, particularly in the realm of RFID-based processes such as authentication, property circulation, and book loans, underscores the significance of this study. Leveraging the collaborative potential of IoT and SDN technologies, our proposed system introduces a fresh perspective to tackle these challenges and advance intelligent library management. In response to the evolving landscape of smart libraries, our research presents an Internet of Things (IoT)-based low-cost architecture utilizing SDN. The exploration of this architectural paradigm arises from a recognized gap in the existing literature, pointing towards the necessity for more efficient and cost-effective solutions in managing library processes. Our proposed algorithm integrates IoT and SDN technologies to intelligently oversee various library activities, specifically targeting RFID-based processes such as authentication, property circulation management, and book loan management. The system's architecture, encompasses components like the data center, SDN controllers, RFID tags, tag readers, and other network sensors. By leveraging the synergy between RFID and SDN, our innovative approach reduces the need for constant operator supervision in libraries. The scalability and software-oriented nature of the architecture cater to extensive library environments. Our study includes a two-phase investigation, combining practical implementation in a small-scale library with a simulation environment using MATLAB 2021. This research not only fills a crucial gap in current knowledge but also lays the foundation for future advancements in the integration of IoT and SDN technologies for intelligent library management.

A Flexible, Adaptive, and Self-Powered Triboelectric Vibration Sensor with Conductive Sponge-Silicone for Machinery Condition Monitoring

Vibration sensors for continuous and reliable condition monitoring of mechanical equipment, especially detection points of curved surfaces, remain a great challenge and are highly desired. Herein, a highly flexible and adaptive triboelectric vibration sensor for high-fidelity and continuous monitoring of mechanical vibration conditions is proposed. The sensor is entirely composed of flexible materials. It consists of a conductive sponge-silicone layer and a fluorinated ethylene propylene film. It can detect vibration acceleration of 5 to 50 m s-2 and vibration frequency of 10 to 100 Hz. It has strong robustness and stability, and the output performance barely changes after the durability test of 168 000 working cycles. Additionally, the flexible sensor can work even when the detection point of the mechanical equipment is curved, and the linear fit of the output voltage and acceleration is very close to that when the detection point is flat. Finally, it can be applied to monitoring the working condition of blower and vehicle engine, and can transmit vibration signal to mobile phone application through Wi-Fi module for real-time monitoring. The flexible triboelectric vibration sensor is expected to provide a practical paradigm for smart, green, and sustainable wireless sensor system in the era of Internet of Things.

An autonomous wheelchair with health monitoring system based on Internet of Thing

Assistive powered wheelchairs will bring patients and elderly the ability of remain mobile without the direct intervention from caregivers. Vital signs from users can be collected and analyzed remotely to allow better disease prevention and proactive management of health and chronic conditions. This research proposes an autonomous wheelchair prototype system integrated with biophysical sensors based on Internet of Thing (IoT). A powered wheelchair system was developed with three biophysical sensors to collect, transmit and analysis users' four vital signs to provide real-time feedback to users and clinicians. A user interface software embedded with the cloud artificial intelligence (AI) algorithms was developed for the data visualization and analysis. An improved data compression algorithm Minimalist, Adaptive and Streaming R-bit (O-MAS-R) was proposed to achieve a higher compression ratio with minimum 7.1%, maximum 45.25% compared with MAS algorithm during the data transmission. At the same time, the prototype wheelchair, accompanied with a smart-chair app, assimilates data from the onboard sensors and characteristics features within the surroundings in real-time to achieve the functions including obstruct laser scanning, autonomous localization, and point-to-point route planning and moving within a predefined area. In conclusion, the wheelchair prototype uses AI algorithms and navigation technology to help patients and elderly maintain their independent mobility and monitor their healthcare information in real-time.

Multi-scenario PM2.5 distribution and dynamic exposure assessment of university community residents: Development and application of intelligent health risk management system integrated low-cost sensors

Exposure scenario and receptor behavior significantly affect PM2.5 exposure quantity of persons and resident groups, which in turn influenced indoor or outdoor air quality & health management. An Internet of Things (IoT) system, EnvironMax+, was developed to accurately and conveniently assess residential dynamic PM2.5 exposure state. A university community "QC", as the application area, was divided into four exposure scenarios and five groups of residents. Low-cost mobile sensors and indoor/outdoor pollution migration (IOP) models jointly estimated multi-scenario real-time PM2.5 concentrations. Questionnaire was used to investigate residents' indoor activity characteristics. Mobile application (app) "Air health management (AHM)" could automatic collect residents' activity trajectory. At last, multi-scenario daily exposure concentrations of each residents-group were obtained. The results showed that residential exposure scenario was the most important one, where residents spend about 60 % of their daily time. Closing window was the most significant behavior affecting indoor contamination. The annual average PM2.5 concentration in the studied scenarios: residential scenario (RS) < public scenario (PS) < outdoor scenario (OS) < catering scenario (CS). Except for CS, the outdoor PM2.5 in other scenarios was higher than indoor by 5-10 μg/m3. The multi-scenario population weighted annual average exposure concentration was 37.1 μg/m3, which was 78 % of the annual average outdoor concentration. The exposure concentration of 5 groups: cooks > outdoor workers > indoor workers > students > the elderly, related to their daily activity time proportion in different exposure scenario.

Energy-efficient routing protocol for reliable low-latency Internet of Things in oil and gas pipeline monitoring

The oil and gas industries (OGI) are the primary global energy source, with pipelines as vital components for OGI transportation. However, pipeline leaks pose significant risks, including fires, injuries, environmental harm, and property damage. Therefore, maintaining an effective pipeline maintenance system is critical for ensuring a safe and sustainable energy supply. The Internet of Things (IoT) has emerged as a cutting-edge technology for efficient OGI pipeline leak detection. However, deploying IoT in OGI monitoring faces significant challenges due to hazardous environments and limited communication infrastructure. Energy efficiency and fault tolerance, typical IoT concerns, gain heightened importance in the OGI context. In OGI monitoring, IoT devices are linearly deployed with no alternative communication mechanism available along OGI pipelines. Thus, the absence of both communication routes can disrupt crucial data transmission. Therefore, ensuring energy-efficient and fault-tolerant communication for OGI data is paramount. Critical data needs to reach the control center on time for faster actions to avoid loss. Low latency communication for critical data is another challenge of the OGI monitoring environment. Moreover, IoT devices gather a plethora of OGI parameter data including redundant values that hold no relevance for transmission to the control center. Thus, optimizing data transmission is essential to conserve energy in OGI monitoring. This article presents the Priority-Based, Energy-Efficient, and Optimal Data Routing Protocol (PO-IMRP) to tackle these challenges. The energy model and congestion control mechanism optimize data packets for an energy-efficient and congestion-free network. In PO-IMRP, nodes are aware of their energy status and communicate node's depletion status timely for network robustness. Priority-based routing selects low-latency routes for critical data to avoid OGI losses. Comparative analysis against linear LEACH highlights PO-IMRP's superior performance in terms of total packet transmission by completing fewer rounds with more packet's transmissions, attributed to the packet optimization technique implemented at each hop, which helps mitigate network congestion. MATLAB simulations affirm the effectiveness of the protocol in terms of energy efficiency, fault-tolerance, and low latency communication.

Dickson polynomial-based secure group authentication scheme for Internet of Things

Internet of Things (IoT) paves the way for the modern smart industrial applications and cities. Trusted Authority acts as a sole control in monitoring and maintaining the communications between the IoT devices and the infrastructure. The communication between the IoT devices happens from one trusted entity of an area to the other by way of generating security certificates. Establishing trust by way of generating security certificates for the IoT devices in a smart city application can be of high cost and expensive. In order to facilitate this, a secure group authentication scheme that creates trust amongst a group of IoT devices owned by several entities has been proposed. The majority of proposed authentication techniques are made for individual device authentication and are also utilized for group authentication; nevertheless, a unique solution for group authentication is the Dickson polynomial based secure group authentication scheme. The secret keys used in our proposed authentication technique are generated using the Dickson polynomial, which enables the group to authenticate without generating an excessive amount of network traffic overhead. IoT devices' group authentication has made use of the Dickson polynomial. Blockchain technology is employed to enable secure, efficient, and fast data transfer among the unique IoT devices of each group deployed at different places. Also, the proposed secure group authentication scheme developed based on Dickson polynomials is resistant to replay, man-in-the-middle, tampering, side channel and signature forgeries, impersonation, and ephemeral key secret leakage attacks. In order to accomplish this, we have implemented a hardware-based physically unclonable function. Implementation has been carried using python language and deployed and tested on Blockchain using Ethereum Goerli's Testnet framework. Performance analysis has been carried out by choosing various benchmarks and found that the proposed framework outperforms its counterparts through various metrics. Different parameters are also utilized to assess the performance of the proposed blockchain framework and shows that it has better performance in terms of computation, communication, storage and latency.

Smart plant disease net: Adaptive Dense Hybrid Convolution network with attention mechanism for IoT-based plant disease detection by improved optimization approach

Plant diseases are rising nowadays. Plant diseases lead to high economic losses. Internet of Things (IoT) technology has found its application in various sectors. This led to the introduction of smart farming, in which IoT has been utilized to help identify the exact spot of the diseased affected region on the leaf from the vast farmland in a well-organized and automated manner. Thus, the main focus of this task is the introduction of a novel plant disease detection model that relies on IoT technology. The collected images are given to the Image Transmission phase. Here, the encryption task is performed by employing the Advanced Encryption Standard (AES) and also the decrypted plant images are fed to the pre-processing stage. The Mask Regions with Convolutional Neural Networks (R-CNN) are used to segment the pre-processed images. Then, the segmented images are given to the detection phase in which the Adaptive Dense Hybrid Convolution Network with Attention Mechanism (ADHCN-AM) approach is utilized to perform the detection of plant disease. From the ADHCN-AM, the final detected plant disease outcomes are obtained. Throughout the entire validation, the offered model shows 95% enhancement in terms of MCC showcasing its effectiveness over the existing approaches.

Multipurpose Modular Wireless Sensor for Remote Monitoring and IoT Applications

Today, maintaining an Internet connection is indispensable; as an example, we can refer to IoT applications that can be found in fields such as environmental monitoring, smart manufacturing, healthcare, smart buildings, smart homes, transportation, energy, and others. The critical elements in IoT applications are both the Wireless Sensor Nodes (WSn) and the Wireless Sensor Networks. It is essential to state that designing an application demands a particular design of a WSn, which represents an important time consumption during the process. In line with this observation, our work describes the development of a modular WSn (MWSn) built with digital processing, wireless communication, and power supply subsystems. Then, we reduce the WSn-implementing process into the design of its modular sensing subsystem. This would allow the development and launching processes of IoT applications across different fields to become faster and easier. Our proposal presents a versatile communication between the sensing modules and the MWSn using one- or two-wired communication protocols, such as I2C. To validate the efficiency and versatility of our proposal, we present two IoT-based remote monitoring applications.

Security and Reliability Analysis of the Power Splitting-Based Relaying in Wireless Sensors Network

This paper studies the security and reliability of the power splitting (PS)-based relaying in the Internet of Things (IoT) networks with the help of a jammer. Based on the considered system model, we derive outage probability (OP) and intercept probability (IP) under two distinguished schemes, namely, the static PS relaying (SPSR) scheme and the dynamic PS relaying (DPSR) scheme. More precisely, the PS ratio of the former is a constant number, while the latter is optimally adjusted in order to minimize the OP and counts only on the channel gain of the second hop. Numerical results are provided to not only verify the accuracy of the proposed mathematical framework but also identify the trends of both OP and IP with respect to several important parameters. Our findings unveil that the OP and IP have contradictory behavior with respect to the transmit power and number of sources. Moreover, the performance of the DPSR scheme is superior to that of the SPSR scheme.

Automatic Tracking Based on Weighted Fusion Back Propagation in UWB for IoT Devices

The global population is progressively entering an aging phase, with population aging likely to emerge as one of the most-significant social trends of the 21st Century, impacting nearly all societal domains. Addressing the challenge of assisting vulnerable groups such as the elderly and disabled in carrying or transporting objects has become a critical issue in this field. We developed a mobile Internet of Things (IoT) device leveraging Ultra-Wideband (UWB) technology in this context. This research directly benefits vulnerable groups, including the elderly, disabled individuals, pregnant women, and children. Additionally, it provides valuable references for decision-makers, engineers, and researchers to address real-world challenges. The focus of this research is on implementing UWB technology for precise mobile IoT device localization and following, while integrating an autonomous following system, a robotic arm system, an ultrasonic obstacle-avoidance system, and an automatic leveling control system into a comprehensive experimental platform. To counteract the potential UWB signal fluctuations and high noise interference in complex environments, we propose a hybrid filtering-weighted fusion back propagation (HFWF-BP) neural network localization algorithm. This algorithm combines the characteristics of Gaussian, median, and mean filtering, utilizing a weighted fusion back propagation (WF-BP) neural network, and, ultimately, employs the Chan algorithm to achieve optimal estimation values. Through deployment and experimentation on the device, the proposed algorithm's data preprocessing effectively eliminates errors under multi-factor interference, significantly enhancing the precision and anti-interference capabilities of the localization and following processes.

Convolutional Neural Networks in the Inspection of Serrasalmids (Characiformes) Fingerlings

Aquaculture produces more than 122 million tons of fish globally. Among the several economically important species are the Serrasalmidae, which are valued for their nutritional and sensory characteristics. To meet the growing demand, there is a need for automation and accuracy of processes, at a lower cost. Convolutional neural networks (CNNs) are a viable alternative for automation, reducing human intervention, work time, errors, and production costs. Therefore, the objective of this work is to evaluate the efficacy of convolutional neural networks (CNNs) in counting round fish fingerlings (Serrasalmidae) at different densities using 390 color photographs in an illuminated environment. The photographs were submitted to two convolutional neural networks for object detection: one model was adapted from a pre-trained CNN and the other was an online platform based on AutoML. The metrics used for performance evaluation were precision (P), recall (R), accuracy (A), and F1-Score. In conclusion, convolutional neural networks (CNNs) are effective tools for detecting and counting fish. The pre-trained CNN demonstrated outstanding performance in identifying fish fingerlings, achieving accuracy, precision, and recall rates of 99% or higher, regardless of fish density. On the other hand, the AutoML exhibited reduced accuracy and recall rates as the number of fish increased.

Smart Grid Security: An Effective Hybrid CNN-Based Approach for Detecting Energy Theft Using Consumption Patterns

In Internet of Things-based smart grids, smart meters record and report a massive number of power consumption data at certain intervals to the data center of the utility for load monitoring and energy management. Energy theft is a big problem for smart meters and causes non-technical losses. Energy theft attacks can be launched by malicious consumers by compromising the smart meters to report manipulated consumption data for less billing. It is a global issue causing technical and financial damage to governments and operators. Deep learning-based techniques can effectively identify consumers involved in energy theft through power consumption data. In this study, a hybrid convolutional neural network (CNN)-based energy-theft-detection system is proposed to detect data-tampering cyber-attack vectors. CNN is a commonly employed method that automates the extraction of features and the classification process. We employed CNN for feature extraction and traditional machine learning algorithms for classification. In this work, honest data were obtained from a real dataset. Six attack vectors causing data tampering were utilized. Tampered data were synthetically generated through these attack vectors. Six separate datasets were created for each attack vector to design a specialized detector tailored for that specific attack. Additionally, a dataset containing all attack vectors was also generated for the purpose of designing a general detector. Furthermore, the imbalanced dataset problem was addressed through the application of the generative adversarial network (GAN) method. GAN was chosen due to its ability to generate new data closely resembling real data, and its application in this field has not been extensively explored. The data generated with GAN ensured better training for the hybrid CNN-based detector on honest and malicious consumption patterns. Finally, the results indicate that the proposed general detector could classify both honest and malicious users with satisfactory accuracy.

User Perception of Smart Home Surveillance Among Adults Aged 50 Years and Older: Scoping Review

BACKGROUND

Smart home technology (SHT) can be useful for aging in place or health-related purposes. However, surveillance studies have highlighted ethical issues with SHTs, including user privacy, security, and autonomy.

OBJECTIVE

As digital technology is most often designed for younger adults, this review summarizes perceptions of SHTs among users aged 50 years and older to explore their understanding of privacy, the purpose of data collection, risks and benefits, and safety.

METHODS

Through an integrative review, we explored community-dwelling adults' (aged 50 years and older) perceptions of SHTs based on research questions under 4 nonmutually exclusive themes: privacy, the purpose of data collection, risk and benefits, and safety. We searched 1860 titles and abstracts from Ovid MEDLINE, Ovid Embase, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials, Scopus, Web of Science Core Collection, and IEEE Xplore or IET Electronic Library, resulting in 15 included studies.

RESULTS

The 15 studies explored user perception of smart speakers, motion sensors, or home monitoring systems. A total of 13 (87%) studies discussed user privacy concerns regarding data collection and access. A total of 4 (27%) studies explored user knowledge of data collection purposes, 7 (47%) studies featured risk-related concerns such as data breaches and third-party misuse alongside benefits such as convenience, and 9 (60%) studies reported user enthusiasm about the potential for home safety.

CONCLUSIONS

Due to the growing size of aging populations and advances in technological capabilities, regulators and designers should focus on user concerns by supporting higher levels of agency regarding data collection, use, and disclosure and by bolstering organizational accountability. This way, relevant privacy regulation and SHT design can better support user safety while diminishing potential risks to privacy, security, autonomy, or discriminatory outcomes.

Examination of Traditional Botnet Detection on IoT-Based Bots

A botnet is a collection of Internet-connected computers that have been suborned and are controlled externally for malicious purposes. Concomitant with the growth of the Internet of Things (IoT), botnets have been expanding to use IoT devices as their attack vectors. IoT devices utilise specific protocols and network topologies distinct from conventional computers that may render detection techniques ineffective on compromised IoT devices. This paper describes experiments involving the acquisition of several traditional botnet detection techniques, BotMiner, BotProbe, and BotHunter, to evaluate their capabilities when applied to IoT-based botnets. Multiple simulation environments, using internally developed network traffic generation software, were created to test these techniques on traditional and IoT-based networks, with multiple scenarios differentiated by the total number of hosts, the total number of infected hosts, the botnet command and control (CnC) type, and the presence of aberrant activity. Externally acquired datasets were also used to further test and validate the capabilities of each botnet detection technique. The results indicated, contrary to expectations, that BotMiner and BotProbe were able to detect IoT-based botnets-though they exhibited certain limitations specific to their operation. The results show that traditional botnet detection techniques are capable of detecting IoT-based botnets and that the different techniques may offer capabilities that complement one another.

IHIBE: A Hierarchical and Delegated Access Control Mechanism for IoT Environments

Ensuring authorized access control in the IoT is vital for privacy and safety protection. Our study presents the novel IHIBE framework, which combines IOTA (a distributed ledger technology) with hierarchical identity-based encryption (HIBE), thereby enhancing both IoT security and scalability. This approach secures access tokens and policies while reducing the computational demand on data owners. Our empirical findings reveal a significant performance gap, with access rights delegation on the Raspberry Pi 4 exceeding those on AWS by over 250%. Moreover, our analysis uncovers optimal identity policy depths: up to 640 identities on AWS and 640 on the Raspberry Pi 4 for systems with higher tolerable delays, and 320 identities on AWS versus 160 on the Raspberry Pi 4 for systems with lower tolerable delays. The system shows practical viability, exhibiting insignificant operational time differences compared to Zhang et al.'s schemes, particularly in access rights verification processes, with a minimal difference of 33.35%. Our extensive security assessment, encompassing scenarios like encrypted token theft and compromise of authority, affirms the efficacy of our challenge-response and last-word challenge (LWC) mechanisms. This study underscores the importance of platform choice in IoT system architectures and provides insights for deploying efficient, secure, and scalable IoT environments.

A Survey on Heterogeneity Taxonomy, Security and Privacy Preservation in the Integration of IoT, Wireless Sensor Networks and Federated Learning

Federated learning (FL) is a machine learning (ML) technique that enables collaborative model training without sharing raw data, making it ideal for Internet of Things (IoT) applications where data are distributed across devices and privacy is a concern. Wireless Sensor Networks (WSNs) play a crucial role in IoT systems by collecting data from the physical environment. This paper presents a comprehensive survey of the integration of FL, IoT, and WSNs. It covers FL basics, strategies, and types and discusses the integration of FL, IoT, and WSNs in various domains. The paper addresses challenges related to heterogeneity in FL and summarizes state-of-the-art research in this area. It also explores security and privacy considerations and performance evaluation methodologies. The paper outlines the latest achievements and potential research directions in FL, IoT, and WSNs and emphasizes the significance of the surveyed topics within the context of current technological advancements.

IOTA-Based Distributed Ledger in the Mining Industry: Efficiency, Sustainability and Transparency

The paper presents a traceability framework founded upon a methodological approach specifically designed for the integration of the IOTA-based distributed ledger within the mining industry. This framework constitutes an initial stride towards the certification and labelling of sustainable material production. The efficacy of this methodology is subject to real-world evaluation within the framework of the European Commission funded project DIG_IT. Within the architectural framework, the integration of decentralized identifiers (DIDs) and the IOTA network are instrumental in effecting the encryption of data records, with associated hashes securely anchored on the explorer. Recorded environmental parameters, encompassing metrics such as pH level, turbidity, electrical conductivity, and emissions, serve as tangible evidence affirming their adherence to prevailing regulatory standards. The overarching system architecture encompasses a sophisticated Industrial Internet of Things platform (IIoTp), facilitating the seamless connection of data from a diverse array of sensors. End users, including governmental entities, mining managers, and the general public, stand to derive substantial benefits from tailored dashboards designed to facilitate the validation of data for emission compliance.

Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling

This article explores the convergence of artificial intelligence and its challenges for precise planning of LoRa networks. It examines machine learning algorithms in conjunction with empirically collected data to develop an effective propagation model for LoRaWAN. We propose decoupling feature extraction and regression analysis, which facilitates training data requirements. In our comparative analysis, decision-tree-based gradient boosting achieved the lowest root-mean-squared error of 5.53 dBm. Another advantage of this model is its interpretability, which is exploited to qualitatively observe the governing propagation mechanisms. This approach provides a unique opportunity to practically understand the dependence of signal strength on other variables. The analysis revealed a 1.5 dBm sensitivity improvement as the LoR's spreading factor changed from 7 to 12. The impact of clutter was revealed to be highly non-linear, with high attenuations as clutter increased until a certain point, after which it became ineffective. The outcome of this work leads to a more accurate estimation and a better understanding of the LoRa's propagation. Consequently, mitigating the challenges associated with large-scale and dense LoRaWAN deployments, enabling improved link budget analysis, interference management, quality of service, scalability, and energy efficiency of Internet of Things networks.

Field-Programmable Gate Array-Based Implementation of Zero-Trust Stream Data Encryption for Enabling 6G-Narrowband Internet of Things Massive Device Access

With the advent of 6G Narrowband IoT (NB-IoT) technology, IoT security faces inevitable challenges due to the application requirements of Massive Machine-Type Communications (mMTCs). In response, a 6G base station (gNB) and User Equipment (UE) necessitate increased capacities to handle a larger number of connections while maintaining reasonable performance during operations. To address this developmental trend and overcome associated technological hurdles, this paper proposes a hardware-accelerated and software co-designed mechanism to support streaming data transmissions and secure zero-trust inter-endpoint communications. The proposed implementations aim to offload processing efforts from micro-processors and enhance global system operation performance by hardware and software co-design in endpoint communications. Experimental results demonstrate that the proposed secure mechanism based on the use of non-repeating keys and implemented in FPGA, can save 85.61%, 99.71%, and 95.68% of the micro-processor's processing time in key block generations, non-repeating checks, and data block transfers, respectively.

A Review of IoT Firmware Vulnerabilities and Auditing Techniques

In recent years, the Internet of Things (IoT) paradigm has been widely applied across a variety of industrial and consumer areas to facilitate greater automation and increase productivity. Higher dependability on connected devices led to a growing range of cyber security threats targeting IoT-enabled platforms, specifically device firmware vulnerabilities, often overlooked during development and deployment. A comprehensive security strategy aiming to mitigate IoT firmware vulnerabilities would entail auditing the IoT device firmware environment, from software components, storage, and configuration, to delivery, maintenance, and updating, as well as understanding the efficacy of tools and techniques available for this purpose. To this effect, this paper reviews the state-of-the-art technology in IoT firmware vulnerability assessment from a holistic perspective. To help with the process, the IoT ecosystem is divided into eight categories: system properties, access controls, hardware and software re-use, network interfacing, image management, user awareness, regulatory compliance, and adversarial vectors. Following the review of individual areas, the paper further investigates the efficiency and scalability of auditing techniques for detecting firmware vulnerabilities. Beyond the technical aspects, state-of-the-art IoT firmware architectures and respective evaluation platforms are also reviewed according to their technical, regulatory, and standardization challenges. The discussion is accompanied also by a review of the existing auditing tools, the vulnerabilities addressed, the analysis method used, and their abilities to scale and detect unknown attacks. The review also proposes a taxonomy of vulnerabilities and maps them with their exploitation vectors and with the auditing tools that could help in identifying them. Given the current interest in analysis automation, the paper explores the feasibility and impact of evolving machine learning and blockchain applications in securing IoT firmware. The paper concludes with a summary of ongoing and future research challenges in IoT firmware to facilitate and support secure IoT development.

Human Digital Twin in Industry 5.0: A Holistic Approach to Worker Safety and Well-Being through Advanced AI and Emotional Analytics

This research introduces a conceptual framework designed to enhance worker safety and well-being in industrial environments, such as oil and gas construction plants, by leveraging Human Digital Twin (HDT) cutting-edge technologies and advanced artificial intelligence (AI) techniques. At its core, this study is in the developmental phase, aiming to create an integrated system that could enable real-time monitoring and analysis of the physical, mental, and emotional states of workers. It provides valuable insights into the impact of Digital Twins (DT) technology and its role in Industry 5.0. With the development of a chatbot trained as an empathic evaluator that analyses emotions expressed in written conversations using natural language processing (NLP); video logs capable of extracting emotions through facial expressions and speech analysis; and personality tests, this research intends to obtain a deeper understanding of workers' psychological characteristics and stress levels. This innovative approach might enable the identification of stress, anxiety, or other emotional factors that may affect worker safety. Whilst this study does not encompass a case study or an application in a real-world setting, it lays the groundwork for the future implementation of these technologies. The insights derived from this research are intended to inform the development of practical applications aimed at creating safer work environments.

IoT-Based Framework for Digital Twins in the Industry 5.0 Era

Digital twins are considered the next step in IoT-based cyber-physical systems; they allow for the real-time monitoring of assets, and they provide a comprehensive understanding of a system behavior, allowing for data-driven insights and informed choices. However, no comprehensive framework exists for the development of IoT-based digital twins. Moreover, the existing frameworks do not consider the aspects introduced by the Industry 5.0 paradigm, such as sustainability, human-centricity, and resilience. This paper proposes a framework based on the one defined as the outcome of a project funded by the European Union between 2010 and 2013 called the IoT Architectural Reference Model (IoT-A or IoT-ARM), with the aim of the development and implementation of a standard IoT framework that includes digital twins. This framework establishes and implements a standardized collection of architectural instruments for modeling IoT systems in the 5.0 era, serving as a benchmark for the design and implementation of an IoT architecture focused on digital twins and enabling the sustainability, resilience, and human-centricity of the information system. Furthermore, a proof of concept of a monitoring digital twin for a vertical farming system has been developed to test the validity of the framework, and a discussion of applications in the manufacturing and service sectors is presented.

SWARAM: Osprey Optimization Algorithm-Based Energy-Efficient Cluster Head Selection for Wireless Sensor Network-Based Internet of Things

The Internet of Things (IoT) has transformed various aspects of human life nowadays. In the IoT transformative paradigm, sensor nodes are enabled to connect multiple physical devices and systems over the network to collect data from remote places, namely, precision agriculture, wildlife conservation, intelligent forestry, and so on. The battery life of sensor nodes is limited, affecting the network's lifetime, and requires continuous maintenance. Energy conservation has become a severe problem of IoT. Clustering is essential in IoT to optimize energy efficiency and network longevity. In recent years, many clustering protocols have been proposed to improve network lifetime by conserving energy. However, the network experiences an energy-hole issue due to picking an inappropriate Cluster Head (CH). CH node is designated to manage and coordinate communication among nodes in a particular cluster. The redundant data transmission is avoided to conserve energy by collecting and aggregating from other nodes in clusters. CH plays a pivotal role in achieving efficient energy optimization and network performance. To address this problem, we have proposed an osprey optimization algorithm based on energy-efficient cluster head selection (SWARAM) in a wireless sensor network-based Internet of Things to pick the best CH in the cluster. The proposed SWARAM approach consists of two phases, namely, cluster formation and CH selection. The nodes are clustered using Euclidean distance before the CH node is selected using the SWARAM technique. Simulation of the proposed SWARAM algorithm is carried out in the MATLAB2019a tool. The performance of the SWARAM algorithm compared with existing EECHS-ARO, HSWO, and EECHIGWO CH selection algorithms. The suggested SWARAM improves packet delivery ratio and network lifetime by 10% and 10%, respectively. Consequently, the overall performance of the network is improved.

Office Workers' Views About the Uses, Concerns, and Acceptance of Hand Hygiene Data Collected From Smart Sanitizers: Exploratory Qualitative Interview Study

BACKGROUND

COVID-19 and the prospect of future pandemics have emphasized the need to reduce disease transmission in workplaces. Despite the well-established link between good hand hygiene (HH) and employee health, HH in nonclinical workplaces has received little attention. Smart sanitizers have been deployed in clinical settings to motivate and enforce HH. This study is part of a large project that explores the potential of smart sanitizers in office settings.

OBJECTIVE

Our previous study found that for office workers to accept the deployment of smart sanitizers, they would need to find the data generated as useful and actionable. The objectives of this study were to identify (1) the potential uses and actions that could be taken from HH data collected by smart sanitizers (2) the concerns of office workers for the identified uses and actions and (3) the circumstances in which office workers accept HH monitoring.

METHODS

An interview study was conducted with 18 office workers from various professions. Interview questions were developed using a framework from personal informatics. Transcripts were analyzed thematically.

RESULTS

A wide range of uses of smart sanitizer data was identified including managing hygiene resources and workflows, finding operating sanitizers, communicating the (high) standard of organizational hygiene, promoting and enforcing organizational hygiene policies, improving workers' own hygiene practices, executing more effective interventions, and identifying the causes of outbreaks. However, hygiene is mostly considered as a private matter, and it is also possible that no action would be taken. Office workers were also concerned about bullying, coercion, and use of hygiene data for unintended purposes. They were also worried that the data could be inaccurate or incomplete, leading to misrepresentation of hygiene practices. Office workers suggested that they would be more likely to accept monitoring in situations where hygiene is considered important, when there are clear benefits to data collection, if their privacy is respected, if they have some control over how their data are collected, and if the ways in which the data will be used are clearly communicated.

CONCLUSIONS

Smart sanitizers could have a valuable role in improving hygiene practices in offices and reducing disease transmission. Many actionable uses for data collected from smart systems were identified. However, office workers consider HH as a personal matter, and acceptance of smart systems is likely to be dynamic and will depend on the broad situation. Except when there are disease outbreaks, smart systems may need to be restricted to uses that do not require the sharing of personal data. Should organizations wish to implement smart sanitizers in offices, it would be advisable to consult widely with staff and develop systems that are customizable and personalizable.

A Review on the Recent Advancements and Artificial Intelligence in Tablet Technology

BACKGROUND

Tablet formulation could be revolutionized by the integration of modern technology and established pharmaceutical sciences. The pharmaceutical sector can develop tablet formulations that are not only more efficient and stable but also patient-friendly by utilizing artificial intelligence (AI), machine learning (ML), and materials science.

OBJECTIVES

The primary objective of this review is to explore the advancements in tablet technology, focusing on the integration of modern technologies like artificial intelligence (AI), machine learning (ML), and materials science to enhance the efficiency, cost-effectiveness, and quality of tablet formulation processes.

METHODS

This review delves into the utilization of AI and ML techniques within pharmaceutical research and development. The review also discusses various ML methodologies employed, including artificial neural networks, an ensemble of regression trees, support vector machines, and multivariate data analysis techniques.

RESULTS

Recent studies showcased in this review demonstrate the feasibility and effectiveness of ML approaches in pharmaceutical research. The application of AI and ML in pharmaceutical research has shown promising results, offering a potential avenue for significant improvements in the product development process.

CONCLUSION

The integration of nanotechnology, AI, ML, and materials science with traditional pharmaceutical sciences presents a remarkable opportunity for enhancing tablet formulation processes. This review collectively underscores the transformative role that AI and ML can play in advancing pharmaceutical research and development, ultimately leading to more efficient, reliable and patient-centric tablet formulations.

Intelligent Biosensors Promise Smarter Solutions in Food Safety 4.0

Food safety is closely related to human health. However, the regulation and testing processes for food safety are intricate and resource-intensive. Therefore, it is necessary to address food safety risks using a combination of deep learning, the Internet of Things, smartphones, quick response codes, smart packaging, and other smart technologies. Intelligent designs that combine digital systems and advanced functionalities with biosensors hold great promise for revolutionizing current food safety practices. This review introduces the concept of Food Safety 4.0, and discusses the impact of intelligent biosensors, which offer attractive smarter solutions, including real-time monitoring, predictive analytics, enhanced traceability, and consumer empowerment, helping improve risk management and ensure the highest standards of food safety.

Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions

The Internet of Vehicles (IoV) is a technology that is connected to the public internet and is a subnetwork of the Internet of Things (IoT) in which vehicles with sensors are connected to a mobile and wireless network. Numerous vehicles, users, things, and networks allow nodes to communicate information with their surroundings via various communication channels. IoV aims to enhance the comfort of driving, improve energy management, secure data transmission, and prevent road accidents. Despite IoV's advantages, it comes with its own set of challenges, particularly in the highly important aspects of security and trust. Trust management is one of the potential security mechanisms aimed at increasing reliability in IoV environments. Protecting IoV environments from diverse attacks poses significant challenges, prompting researchers to explore various technologies for security solutions and trust evaluation methods. Traditional approaches have been employed, but innovative solutions are imperative. Amid these challenges, machine learning (ML) has emerged as a potent solution, leveraging its remarkable advancements to effectively address IoV's security and trust concerns. ML can potentially be utilized as a powerful technology to address security and trust issues in IoV environments. In this survey, we delve into an overview of IoV and trust management, discussing security requirements, challenges, and attacks. Additionally, we introduce a classification scheme for ML techniques and survey ML-based security and trust management schemes. This research provides an overview for understanding IoV and the potential of ML in improving its security framework. Additionally, it provides insights into the future of trust and security enhancement.

Towards an AI-Driven Data Reduction Framework for Smart City Applications

The accelerated development of technologies within the Internet of Things landscape has led to an exponential boost in the volume of heterogeneous data generated by interconnected sensors, particularly in scenarios with multiple data sources as in smart cities. Transferring, processing, and storing a vast amount of sensed data poses significant challenges for Internet of Things systems. In this sense, data reduction techniques based on artificial intelligence have emerged as promising solutions to address these challenges, alleviating the burden on the required storage, bandwidth, and computational resources. This article proposes a framework that exploits the concept of data reduction to decrease the amount of heterogeneous data in certain applications. A machine learning model that predicts a distortion rate and its corresponding reduction rate of the imputed data is also proposed, which uses the predicted values to select, among many reduction techniques, the most suitable approach. To support such a decision, the model also considers the context of the data producer that dictates the class of reduction algorithm that is allowed to be applied to the input stream. The achieved results indicate that the Huffman algorithm performed better considering the reduction of time-series data, with significant potential applications for smart city scenarios.

Using Large Language Models to Enhance the Reusability of Sensor Data

The Internet of Things generates vast data volumes via diverse sensors, yet its potential remains unexploited for innovative data-driven products and services. Limitations arise from sensor-dependent data handling by manufacturers and user companies, hindering third-party access and comprehension. Initiatives like the European Data Act aim to enable high-quality access to sensor-generated data by regulating accuracy, completeness, and relevance while respecting intellectual property rights. Despite data availability, interoperability challenges impede sensor data reusability. For instance, sensor data shared in HTML formats requires an intricate, time-consuming processing to attain reusable formats like JSON or XML. This study introduces a methodology aimed at converting raw sensor data extracted from web portals into structured formats, thereby enhancing data reusability. The approach utilises large language models to derive structured formats from sensor data initially presented in non-interoperable formats. The effectiveness of these language models was assessed through quantitative and qualitative evaluations in a use case involving meteorological data. In the proposed experiments, GPT-4, the best performing LLM tested, demonstrated the feasibility of this methodology, achieving a precision of 93.51% and a recall of 85.33% in converting HTML to JSON/XML, thus confirming its potential in obtaining reusable sensor data.

Optimal 2D audio features estimation for a lightweight application in mosquitoes species: Ecoacoustics detection and classification purposes

Mosquitoes are the vector of diseases that kill more than one million people per year worldwide. Surveillance systems are essential for understanding their complex ecology and behaviour. This is fundamental for predicting disease risk caused by mosquitoes and formulating effective control strategies against mosquito-borne diseases such as malaria, dengue, and Zika. Mosquito populations vary heterogeneously in urban and rural landscapes, fluctuating with seasonal and climatic trends and human activity. Several approaches provide environmental data for mosquito mapping and risk prediction. However, they rely traditionally upon labour-intensive techniques such as manual traps. This paper presents the optimal audio features for mosquito identification using ecoacoustics signals to automatically identify different mosquito species from their wingbeat sounds based on popular audio features. The audio selection method uses Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Silhouette coefficient to evaluate the clusters in the data through the optimal-combined audio features. To classify the mosquito species and distinguish them from environmental-urban noise, the method comprises the Gaussian Mixture Model (GMM) and Gibbs approach for Aedes aegypti, and Culex quinquefasciatus, using the acoustic recordings of their wingbeat signals. Finally, comparing GMM and Gibbs, the two have very similar accuracy, but the classification time is much faster for Gibbs sampling, making it a good candidate for a lightweight solution. These are essential when deploying the described models to monitor mosquito vectors in the wild with Internet of Things (IoT) technologies.

Study of the Impact of Data Compression on the Energy Consumption Required for Data Transmission in a Microcontroller-Based System

As the number of Internet of Things (IoT) devices continues to rise dramatically each day, the data generated and transmitted by them follow similar trends. Given that a significant portion of these embedded devices operate on battery power, energy conservation becomes a crucial factor in their design. This paper aims to investigate the impact of data compression on the energy consumption required for data transmission. To achieve this goal, we conduct a comprehensive study using various transmission modules in a severely resource-limited microcontroller-based system designed for battery power. Our study evaluates the performance of several compression algorithms, conducting a detailed analysis of computational and memory complexity, along with performance metrics. The primary finding of our study is that by carefully selecting an algorithm for compressing different types of data before transmission, a significant amount of energy can be saved. Moreover, our investigation demonstrates that for a battery-powered embedded device transmitting sensor data based on the STM32F411CE microcontroller, the recommended transmission module is the nRF24L01+ board, as it requires the least amount of energy to transmit one byte of data. This module is most effective when combined with the LZ78 algorithm for optimal energy and time efficiency. In the case of image data, our findings indicate that the use of the JPEG algorithm for compression yields the best results. Overall, our research underscores the importance of selecting appropriate compression algorithms tailored to specific data types, contributing to enhanced energy efficiency in IoT devices.

Low-Power Consumption IGZO Memristor-Based Gas Sensor Embedded in an Internet of Things Monitoring System for Isopropanol Alcohol Gas

Low-power-consumption gas sensors are crucial for diverse applications, including environmental monitoring and portable Internet of Things (IoT) systems. However, the desorption and adsorption characteristics of conventional metal oxide-based gas sensors require supplementary equipment, such as heaters, which is not optimal for low-power IoT monitoring systems. Memristor-based sensors (gasistors) have been investigated as innovative gas sensors owing to their advantages, including high response, low power consumption, and room-temperature (RT) operation. Based on IGZO, the proposed isopropanol alcohol (IPA) gas sensor demonstrates a detection speed of 105 s and a high response of 55.15 for 50 ppm of IPA gas at RT. Moreover, rapid recovery to the initial state was achievable in 50 μs using pulsed voltage and without gas purging. Finally, a low-power circuit module was integrated for wireless signal transmission and processing to ensure IoT compatibility. The stability of sensing results from gasistors based on IGZO has been demonstrated, even when integrated into IoT systems. This enables energy-efficient gas analysis and real-time monitoring at ~0.34 mW, supporting recovery via pulse bias. This research offers practical insights into IoT gas detection, presenting a wireless sensing system for sensitive, low-powered sensors.

Personalized Fair Split Learning for Resource-Constrained Internet of Things

With the flourishing development of the Internet of Things (IoT), federated learning has garnered significant attention as a distributed learning method aimed at preserving the privacy of participant data. However, certain IoT devices, such as sensors, face challenges in effectively employing conventional federated learning approaches due to limited computational and storage resources, which hinder their ability to train complex local models. Additionally, in IoT environments, devices often face problems of data heterogeneity and uneven benefit distribution between them. To address these challenges, a personalized and fair split learning framework is proposed for resource-constrained clients. This framework first adopts a U-shaped structure, dividing the model to enable resource-constrained clients to offload subsets of the foundational model to a central server while retaining personalized model subsets locally to meet the specific personalized requirements of different clients. Furthermore, to ensure fair benefit distribution, a model-aggregation method with optimized aggregation weights is used. This method reasonably allocates model-aggregation weights based on the contributions of clients, thereby achieving collaborative fairness. Experimental results demonstrate that, in three distinct data heterogeneity scenarios, employing personalized training through this framework exhibits higher accuracy compared to existing baseline methods. Simultaneously, the framework ensures collaborative fairness, fostering a more balanced and sustainable cooperation among IoT devices.

Evaluation of 6LoWPAN Generic Header Compression in the Context of a RPL Network

The Internet of Things (IoT) facilitates the integration of diverse devices, leading to the formation of networks such as Low-power Wireless Personal Area Networks (LoWPANs). These networks have inherent constraints that make header and payload compression an attractive solution to optimise communication. In this work, we evaluate the performance of Generic Header Compression (6LoWPAN-GHC), defined in RFC 7400, for IEEE 802.15.4-based networks running the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). Through simulation and real-device experiments, we study the impact of 6LoWPAN-GHC on energy consumption and delays and investigate for which scenarios 6LoWPAN-GHC is beneficial. We show that all RPL control packets are compressible by 6LoWPAN-GHC, which reduces their transmission delay and as such their vulnerability to interference. However, for the devices under study transmitting at 250 kbit/s, the energy gain obtained from sending a compressed packet is outweighed by the energy needed to compress it. The use of 6LoWPAN-GHC causes an energy increase of between 2% and 26%, depending on the RPL packet type. When the range is more important than the bandwidth and a sub-GHz band is used at 10 kbit/s, an energy gain of 11% to 29% can be obtained, depending on the type of RPL control packet.