Search

Internet of Things (IoT) devices use advanced sensors to support many applications related to monitoring of health metrics, transportation systems, and industrial parameters. A major challenge in such IoT networks is that the local task processing in IoT devices can lead to increased latency, energy consumption, network congestion, and resource competition. This challenge can be addressed by fog computing as it presents a promising solution by extending cloud capabilities closer to the edge of the network, and therefore, facilitating localized data processing. Addressing the challenge of task allocation and offloading for minimal energy consumption, we propose an effective algorithm for task offloading in D2D-assisted IoT networks that use the stable matching technique, i.e., Gale–Shapley. The D2D communication between the IoT devices and fog nodes (FNs) (in the surrounding) can be established to support IoT devices for task offloading. In particular, we develop novel preference profiles for IoT devices and FNs based on the energy consumption and utilize Gale–Shapley to match IoT devices with FNs. The main idea is to optimize offloading policies to reduce the energy consumption. Through simulations, it is demonstrated that the Gale–Shapley matching reduces the overall energy consumption by about 40–300% compared to the other compared schemes at transmission power of 0 dB. [ABSTRACT FROM AUTHOR]

Maintenance and service are important tasks for any industrial enterprise. This article presents a methodology for technical maintenance that employs a smart glove equipped with tactile sensors, an electronic unit responsible for processing and transmitting information, and a unit designed to interpret the results. Tactile sensors are graphene-based. The main idea of the method is to use sensors to record the strength of contact between the operator's fingertips and the equipment. Afterwards, these values are recorded, transferred to processing, and the output signal from the sensors is compared with the steps of various repair works. The work contains methods for creating each component of the glove, their effectiveness is evaluated, and experiments are described to assess the feasibility of using the developed device for the maintenance and repair of equipment. The device discussed in this work is a wearable device. The obtained results demonstrate the applicability of the smart glove for equipment maintenance and repair. [ABSTRACT FROM AUTHOR]

The widespread adoption of Internet of Things (IoT) devices has increased exponentially in recent years. Consequently, the security risks and vulnerabilities related to these unsecured IoT devices are also continuously increasing. Among the significant challenges facing the IoT environment is the threat of Distributed Denial of Service (DDoS) attacks. Several solutions are available in the literature to detect DDoS attacks. However, these detection mechanisms can easily be evaded by attackers using advanced tools and techniques, posing difficulty in detecting such lethal attacks in real time. Therefore, this paper proposes a novel distributed ensemble method for detecting lethal IoT traffic-based DDoS attacks. This method comprises two key stages: first, developing a distributed ensemble method using the breathtaking capabilities of the H2O.ai distributed machine learning platform and the ensemble learning technique. Secondly, this method was deployed on the Apache Storm stream processing framework, to swiftly analyze incoming network streams and categorize them into eleven distinct classes, including benign traffic and ten types of attacks, in near real time. The proposed method accurately identifies specific target categories within a multi-attack classification scenario by utilizing the expertise of various models. Ultimately, the prediction for a target class is determined based on the model with the highest detection rate. The effectiveness of this method has been examined using different configured scenarios. The experimental results show that our method can identify various attack categories more accurately with 99%+ accuracy and 8.45 s quicker than non-ensemble methods. [ABSTRACT FROM AUTHOR]

Air pollution remains a critical global health concern, with 91% of the world's population exposed to air quality exceeding World Health Organization (WHO) standards and indoor pollution causing approximately 3.8 million deaths annually due to incomplete fuel combustion. Subways, as major public transportation modes in densely populated cities, can exhibit fine particulate matter (PM) levels that surpass safety limits, even in developed countries. Contributing factors include station location, ambient air quality, train frequency, ventilation efficiency, braking systems, tunnel structure, and electrical components. While elevated PM levels in underground platforms are recognized, the vertical and horizontal variations within stations are not well understood. This study examines the vertical and horizontal distribution of PM2.5 and PM10 levels at Shibuya Station, a structurally complex hub in the Tokyo Subway System. Portable sensors were employed to measure PM concentrations across different platform levels—both above and underground—and at various locations along the platforms. The results indicate that above-ground platforms have significantly lower PM2.5 and PM10 levels compared to underground platforms (17.09 μg/m3 vs. 22.73 μg/m3 for PM2.5; 39.54 μg/m3 vs. 56.98 μg/m3 for PM10). Notably, the highest pollution levels were found not at the deepest platform but at the one with the least effective ventilation. On the same platform, PM levels varied by up to 63.72% for PM2.5 and 120.23% for PM10, with elevated concentrations near the platform extremities compared to central areas. These findings suggest that ventilation efficiency plays a more significant role than elevation in vertical PM variation, while horizontal differences are likely influenced by piston effects from moving trains. This study underscores the risk of exposure to unsafe PM2.5 levels in underground platforms, particularly at platform extremities, highlighting the need for improved ventilation strategies to enhance air quality in subway environments. [ABSTRACT FROM AUTHOR]

This study focused on ensuring the security of Internet of Things (IoT) devices in homes, given the growing adoption of automation technologies. Evaluating their reliability was crucial to protecting privacy and data security. The main objective was to conduct a systematic review of the current state of domestic IoT devices. An exploratory, cross-sectional, and qualitative methodology was employed, following the PRISMA method. Articles were searched in academic databases such as IEEE Xplore, Springer Link, ScienceDirect, SciELO, and Google Scholar. Inclusion criteria focused on studies from the last five years on trends, challenges, and vulnerabilities in the security of IoT devices in homes.The review identified common vulnerabilities such as lack of encryption and weak passwords, recommending the implementation of encryption, password management, and regular updates. The benefits of IoT in homes include convenience and energy efficiency, while limitations encompass privacy concerns and compatibility issues. Anonymization and pseudonymization techniques are crucial for protecting personal data. Additionally, the importance of adopting common standards to improve interoperability and security in the domestic IoT ecosystem was highlighted. [ABSTRACT FROM AUTHOR]

A common research focus in deep learning is human activity recognition (HAR), which involves detecting human activities using sensor data from magnetometers, accelerometers, and gyroscopes. For real-time HAR applications, it’s crucial to develop a model that is both cost-effective and efficient in terms of resource and processing power usage. To achieve this, our approach trains the deep learning model on channels of variable width instead of adjusting the number of neurons or layers. To reduce computational overhead, random sampling is applied to the lower triangular convolution layer. The model detects human activity from streaming sensor data using adaptive window sizes, which are designed to address sudden changes in activity, known as drifts, such as falls or collapses. The adaptive window strategy is a key to manage dynamic window sizes and handle drifts effectively. The model’s usability and practicality are evaluated on a range of IoT devices and tested on five real-world datasets, as well as one synthetic dataset generated in real-time using a Raspberry Pi 3B and a NodeMCU. Experimental results show that our model achieves a higher accuracy of 97.84% on the WISDM dataset with a width of 0.85, outperforming other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

As the prevalence of Internet of Things (IoT) devices increases, Cyber incidents are also increasing significantly. These Cyber incidents are mainly caused by various attacks, such as Distributed Denial of Service (DDoS), Denial of Service (DoS), intrusions, and web-based attacks. This type of attacks can severely impact valuable IoT system resources, compromise stored data, and lead to substantial financial losses if not adequately mitigated. Detecting these attacks within network traffic is complex and requires intelligent Intrusion Detection Systems (IDS). This paper proposes a Machine Learning (ML) based hybrid IDS model for edge computing networks. The feature selection process employs the 'Recursive Feature Elimination technique' (RFE) combined with 'Random Forest' (RF) to identify optimal features for attack detection. The Hybrid IDS model integrates 'Random Forest' (RF), 'Decision Tree' (DT), 'Extra Tree' (ET), and 'K-Nearest Neighbor' (KNN) algorithms. The Hybrid IDS model is evaluated on four datasets: 'CIC-IDS-2017', 'NSL-KDD', 'UNSW-NB15', and 'CSE-CIC-IDS-2018'. The results of the proposed model show maximum prediction accuracy of 99.92%, 99.89%, 99.50%, and 99.13%, and F1-score values obtained are 99.95%, 99.90%, 99.23%, and 99.13% on 'CIC-IDS-2017', 'NSL-KDD', 'UNSW-NB15', and 'CSE-CIC-IDS2018' datasets, respectively. The experimental results clearly demonstrate that the proposed model performs better than the models reported in the existing studies. [ABSTRACT FROM AUTHOR]

As IoT devices with microcontroller (MCU)-based firmware become more common in our lives, memory corruption vulnerabilities in their firmware are increasingly targeted by adversaries. Fuzzing is a powerful method for detecting these vulnerabilities, but it poses unique challenges when applied to IoT devices. Direct fuzzing on these devices is inefficient, and recent efforts have shifted towards creating emulation environments for dynamic firmware testing. However, unlike traditional software, firmware interactions with peripherals that are significantly more diverse presents new challenges for achieving scalable full-system emulation and effective fuzzing. This paper reviews 27 state-of-the-art works in MCU-based firmware emulation and its applications in fuzzing. Instead of classifying existing techniques based on their capabilities and features, we first identify the fundamental challenges faced by firmware emulation and fuzzing. We then revisit recent studies, organizing them according to the specific challenges they address, and discussing how each specific challenge is addressed. We compare the emulation fidelity and bug detection capabilities of various techniques to clearly demonstrate their strengths and weaknesses, aiding users in selecting or combining tools to meet their needs. Finally, we highlight the remaining technical gaps and point out important future research directions in firmware emulation and fuzzing. [ABSTRACT FROM AUTHOR]

By applying the building theory from case study research by Kathleen M. Eisenhardt, this article describes a parsimonious and coherent process that puts into light as Self Sovereign Identity technology integrated into smart grids allows to give individuals back control of their energy data. First three research questions were defined. Next, significant cases studies were selected. Then the process moves forward by iteratively cycling through the case studies, developing theory, and cycling through white papers and existing literature articles until reaching the closure hence theoretical saturation. Through this process, focus of the article is delineating the potentialities and the challenges to face to integrate this cutting-edge technology in smart grid, highlighting as this technology allows to create a more reliable and transparent energy system. Results highlight for example as SSI paradigm ensures a high level of identity assurance for IoT devices within a grid, and allows users to maintain ultimate control over devices, hence over their energy data.

The rapid deployment of Internet-of-Things (IoT) devices for a few years has been impressive, and the progressive deployment of 5G will accelerate things even further. Indeed, this standard opens the door to a new generation of standards aimed at a convergence of networks and communication protocols (WiFi, LTE, 4G etc.). This results in the need for flexible implementations of different families of codes, such as, LPDC, NB-LDPC, turbo codes and polar codes. In this context, the work presented in this article proposes the design of a flexible instruction set processor for an IoT context. The objective is to improve the performance level of low-complexity processor cores through instruction set extensions for Error Correction Code (ECC) decoding. The approach discussed is supported by experimental results obtained based on a RISC-V architecture to which specific instruction sets have been added. The results demonstrate a reduction in the required processing clock cycles up to 44.1% for polar codes, 39.2% for LDPC codes, 21.8% for NB-LDPC codes, and 24.3% for turbo codes (4G LTE) codes with a classical Single Instruction Single Data (SISD) approach. Moreover, Single Instruction Multiple Data (SIMD) parallelization strategy enables execution time savings that are far more impressive. The number of clock cycles required to decode a data bit is reduced by 65.6% to 76.9%, with a limited hardware over-cost from 0.6% to 34% (depending on the error correction code family and the targeted RISC-V core).

The Internet of Things encapsulates a vision of a world in which billions of objects, called connected devices, are endowed with intelligence, communication capabilities, and integrated sensing and actuation capabilities. They often outsource their storage and computing needs to more powerful resources in the cloud. In Edge computing, it is the device itself that collects and processes data and must make real-time decisions. A model that combines the edge and the cloud allows for instant processing of large amounts of data, making devices increasingly intelligent. One fundamental aspect of Artificial Intelligence, particularly Machine Learning (ML), is that it requires a substantial amount of data to learn. The GPT model can be adapted to operate within a cloud environment, which enables instant processing of large quantities of data. In this article, our approach relies on a hybrid of Cloud computing and Edge computing, merging the best features of both approaches by using Edge for real-time processing and the cloud for storage and large-scale data analysis. We will explore the possibilities of integrating the GPT model into IoT devices. This extension allows devices to offer processing capabilities at the local level, which can be extremely beneficial for many applications. We propose a platform for simulating connected objects augmented by the LSTM neural network model to predict the energy consumption of IoT devices in a smart city scenario. [ABSTRACT FROM AUTHOR]

This paper presents the design, implementation, and characterization of a digital IoT platform capable of generating brain rhythm frequencies using synchronous digital logic. Designed with the Google SkyWater 130 nm open-source process design kit (PDK), this platform emulates Alpha, Beta, and Gamma rhythms. As a proof of concept and the first of its kind, this device showcases its potential applications in both industrial and academic settings. The platform was integrated with an IoT device to optimize and accelerate research and development efforts in embedded systems. Its cost-effective and efficient performance opens opportunities for real-time neural signal processing and integrated healthcare. The presented digital platform serves as a valuable educational tool, enabling researchers to engage in hands-on learning and experimentation with IoT technologies and system-level hardware–software integration at the device level. By utilizing open-source tools, this research demonstrates a cost-effective approach, fostering innovation and bridging the gap between theoretical knowledge and practical application. Furthermore, the proposed system-level design can be interfaced with various serial devices, Wi-Fi modules, ARM processors, and mobile applications, illustrating its versatility and potential for future integration into broader IoT ecosystems. This approach underscores the value of open-source solutions in driving technological advancements and addressing skills shortages.

The increasing number of domestic Internet of Things (IoT) devices in our lives leads to numerous benefits, but also comes with an increased risk of cybersecurity breaches. These breaches have psychological consequences for the users. We examined the nature of the psychological impact of cybersecurity breaches on domestic IoT by investigating emotional experiences in a scenario study (Study 1) and a field experiment (Study 2) using the five emotion components of the Component Process Model (CPM) and emotion regulation as a framework. We replicated a three-dimensional structure for emotional experiences found in a previous study, with an addition of an ancillary fourth dimension in the second study. The first dimension represents emotional intensity. The second bipolar dimension describes constructive vs. unconstructive action tendencies. On the third dimension, also bipolar, cognitive and motivational emotion features are opposed to affective emotion features. The fourth dimension, labeled distress symptoms, mainly reflects negative emotions. In Study 2, most of the introduced frequent irregularities on IoT devices were not noticed, and the intensity of emotional reactions and tendencies to react in a constructive way decreased throughout the phases of the experiment. These findings reveal that we are not emotionally equipped to identify potential threats in the cyber world. [ABSTRACT FROM AUTHOR]

This paper presents the design, implementation, and characterization of a digital IoT platform capable of generating brain rhythm frequencies using synchronous digital logic. Designed with the Google SkyWater 130 nm open-source process design kit (PDK), this platform emulates Alpha, Beta, and Gamma rhythms. As a proof of concept and the first of its kind, this device showcases its potential applications in both industrial and academic settings. The platform was integrated with an IoT device to optimize and accelerate research and development efforts in embedded systems. Its cost-effective and efficient performance opens opportunities for real-time neural signal processing and integrated healthcare. The presented digital platform serves as a valuable educational tool, enabling researchers to engage in hands-on learning and experimentation with IoT technologies and system-level hardware–software integration at the device level. By utilizing open-source tools, this research demonstrates a cost-effective approach, fostering innovation and bridging the gap between theoretical knowledge and practical application. Furthermore, the proposed system-level design can be interfaced with various serial devices, Wi-Fi modules, ARM processors, and mobile applications, illustrating its versatility and potential for future integration into broader IoT ecosystems. This approach underscores the value of open-source solutions in driving technological advancements and addressing skills shortages. [ABSTRACT FROM AUTHOR]

Summary: This work presents a compact two‐element multi‐input‐multi‐output (MIMO) antenna for 5G‐enabled IoT devices. The antenna operates over a wide frequency range of 24.6 to 31.4 GHz (28‐GHz band) and 57.6 to 60.2 GHz (60‐GHz band). Each MIMO element consists of an inverted L‐shaped slotted radiator with a partial ground plane. The antenna offers a peak gain of 5.45 and 5.56 dBi across two operating bands. The minimum isolation between the two ports is −26.5 dB, reaching a maximum value of over −45 dB. The investigation of MIMO metrics like "envelope correlation coefficient (ECC)," "diversity gain (DG)," "mean effective gain (MEG)," "channel capacity loss (CCL)," and "total active reflection coefficient (TARC)" also show favorable characteristics. The antenna is fabricated on a 10 × 22 × 0.503 mm3 Rogers 5880 substrate. The experimental results are in close agreement with that of the simulation results. The distinguishing features of the proposed antenna such as its compact design, simple geometrical configuration, wide operating bandwidth, low ECC, and high isolation make it a strong candidate for 5G‐enabled IoT devices. [ABSTRACT FROM AUTHOR]

Tomato (biological name: Solanum lycopersicum) is an important food crop worldwide. However, due to climatic changes and various diseases, the yield of tomatoes decreased significantly, being detrimental from an economic point of view. Various diseases infect the tomato leaves, such as bacterial and septorial leaf spots, early blight and mosaic virus, to name a few. If uncared, these tomato leaf diseases (TLDs) can spread to other leaves and the fruit. Hence it is vital to detect these diseases as early as possible. Leaf examination is one of the standard techniques to identify and control the spread of diseases. Big Data has made substantial progress, and with the help of computer vision and deep learning techniques to analyse data, we can identify the diseased leaves and help control the disease's spread further. This study used three lightweight midgeneration convolutional neural networks (CNNs) classification network architectures which has the scope to be deployed in IoT devices to help the agricultural community tackle TLDs. It also shows the efficacy of the models with and without geometric data augmentation. The model was trained on a Kaggle data set containing a more significant number of samples to make a robust model aware of broader data distribution and validated on the Plant Village dataset to test its efficacy. The results show that applying transfer learning using ImageNet weights to the MobileNet Architecture using geometrically augmented sample images yields a train and test accuracy of 99.71% and 99.49%, respectively. [ABSTRACT FROM AUTHOR]

The Internet of Things refers to billions of devices around us connected to the wireless internet. These IoT devices are memory-constrained devices that can collect and transfer data over the network without human assistance. Recently, IoT is materialized in retail commerce, transforming from recognition service to post-purchase engagement service. IoT examples in retail commerce are smart refrigerators, smart speakers, smart washing machines, smart automobiles, and automatic re-purchase of groceries using RFID tags. Despite the rise, one of the significant inconveniences slowing rapid adaption is the "security" of these devices, which are vulnerable to various attacks. One such attack is Distributed Denial-of-Service (DDoS) attacks targeting offline or online sensitive data. Hence, a lightweight cryptographic mechanism needs to establish secure communication among IoT devices. This paper presents the solution to secure communication among IoT devices using a permissioned blockchain network. Specifically, in this work, we proposed a mechanism for identifying and authenticating the smart devices using the Elliptic-curve cryptography (ECC) protocol. This proposed work uses permissioned blockchain infrastructure, which acts as a source of trust that aids the authentication process using ECC cryptosystem. In addition, lightweight Physical Unclonable Function (PUF) technology is also used to securely store the device's keys. Using this technology, the private keys need not be stored anywhere, but it is generated on the fly from the trusted zone whenever the private key is required. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is reshaping the world with its potential to support new and evolving applications in areas, such as healthcare, automation, remote monitoring, and so on. This rapid popularity and growth of IoT‐based applications coincides with a significant surge in threats and malware attacks on IoT devices. Furthermore, the widespread usage of Linux‐based systems in IoT devices makes malware detection a challenging task. Researchers and practitioners have proposed a variety of techniques to address these threats in the IoT ecosystem. Both researchers and practitioners have proposed a range of techniques to counter these threats within the IoT ecosystem. However, despite the multitude of proposed techniques, there remains a notable absence of a comprehensive and systematic review assessing the efficacy of static and dynamic analysis methods in detecting IoT malware. This research work is a systematic literature review (SLR) that aims to offer a concise summary of the latest advancements in the field of IoT malware detection, specifically focusing on the utilization of static and dynamic analytic techniques. The SLR focuses on examining the present status of research, methodology, and trends in the area of IoT malware detection. It accomplishes this by synthesizing the findings from a wide range of scholarly works that have been published in well‐regarded academic journals and conferences. Additionally, the SLR highlights the significance of the empirical process that includes the role of selecting datasets, accurate feature selection and the utilization of machine learning algorithms in enhancing the detection accuracy. The study also evaluates the capability of different analysis techniques to detect malware and compares the performance of various models for IoT malware detection. Furthermore, the review concluded by addressing several open issues and challenges that the research community as a whole must address. [ABSTRACT FROM AUTHOR]

With the rapid proliferation of insecure Internet of Things (IoT) devices, the security of Internet‐based applications and networks has become a prominent concern. One of the most significant security threats encountered in IoT environments is a Distributed Denial of Service (DDoS) attack. This attack can severely disrupt critical services and prevent smart devices from functioning normally, leading to severe consequences for businesses and individuals. It aims to overwhelm victims' resources, websites, and other services by flooding them with massive attack packets, making them inaccessible to legitimate users. Researchers have developed multiple detection schemes to detect DDoS attacks. As technology advances and other facilitating factors have increased, it is a challenge to identify such powerful attacks in real‐time. In this paper, we propose a novel distributed detection scheme for IoT network traffic‐based DDoS attacks by deploying it in a Kafka Streams processing framework named Kafka‐Shield. The Kafka‐Shield comprises two stages: design and deployment. Firstly, the detection scheme is designed on the Hadoop cluster employing a highly scalable H2O.ai machine learning platform. Secondly, a portable, scalable, and distributed detection scheme is deployed on the Kafka Streams processing framework. To analyze the incoming traffic data and categorize it into nine target classes in real time. Additionally, Kafka‐Shield stores each network flow with significant input features and the predicted outcome in the Hadoop Distributed File System (HDFS). It enables the development of new models or updating current ones. To validate the effectiveness of the Kafka‐Shield, we performed critical analysis using various configured attack scenarios. The experimental results affirm Kafka‐Shield's remarkable efficiency in detecting DDoS attacks. It has a detection rate of over 99% and can process 0.928 million traces in nearly 3.027 s. [ABSTRACT FROM AUTHOR]

The widespread adoption of Internet of Things (IoT) devices has resulted in notable progress in different fields, improving operational effectiveness while also raising concerns about privacy due to their vulnerability to virus attacks. Further, the study suggests using an advanced approach that utilizes machine learning, specifically the Wide Residual Network (WRN), to identify hidden malware in IoT systems. The research intends to improve privacy protection by accurately identifying malicious software that undermines the security of IoT devices, using the MalMemAnalysis dataset. Moreover, thorough experimentation provides evidence for the effectiveness of the WRN-based strategy, resulting in exceptional performance measures such as accuracy, precision, F1-score, and recall. The study of the test data demonstrates highly impressive results, with a multiclass accuracy surpassing 99.97% and a binary class accuracy beyond 99.98%. The results emphasize the strength and dependability of using advanced deep learning methods such as WRN for identifying hidden malware risks in IoT environments. Furthermore, a comparison examination with the current body of literature emphasizes the originality and efficacy of the suggested methodology. This research builds upon previous studies that have investigated several machine learning methods for detecting malware on IoT devices. However, it distinguishes itself by showcasing exceptional performance metrics and validating its findings through thorough experimentation with real-world datasets. Utilizing WRN offers benefits in managing the intricacies of malware detection, emphasizing its capacity to enhance the security of IoT ecosystems. To summarize, this work proposes an effective way to address privacy concerns on IoT devices by utilizing advanced machine learning methods. The research provides useful insights into the changing landscape of IoT cybersecurity by emphasizing methodological rigor and conducting comparative performance analysis. Future research could focus on enhancing the recommended approach by adding more datasets and leveraging real-time monitoring capabilities to strengthen IoT devices' defenses against new cybersecurity threats. [ABSTRACT FROM AUTHOR]

This research investigated the real-world operational performance of five purposely designed and built net-zero-energy houses in Melbourne, Australia. The embodied energy and carbon emissions of these houses were calculated based on their architectural and engineering drawings, as well as the relevant databases of embodied energy and emission factors. Operational data, including solar production, consumption, end uses, battery usage, grid import, and grid export, were measured using the appropriate IoT devices from May 2023 to April 2024. The results showed that all the studied houses achieved net-zero energy and net-zero carbon status for operation, exporting between 3 to 37 times more energy than they consumed to the grid (except for house 2, where the consumption from the grid was zero). The embodied carbon of each case study house was calculated as 13.1 tons of CO2-e, which could be paid back within 4 to 9 years depending on the operational carbon. Achieving net-zero cost status, however, was found to be difficult due to the higher electricity purchase price, daily connection charge, and lower feed-in tariff. Only house 2 was close to achieving net zero cost with only AUD 37 out-of-pocket cost. Increasing the energy exported to the grid and storing the generated solar energy may help achieve net-zero cost. The installation of batteries did not affect the net-zero energy or emission status but had a significant impact on net-zero operational costs. However, the calculated payback period for the batteries installed in these five houses ranged from 43 to 112 years, making them impractical at this stage compared to the typical 10-year warranty period of the batteries. With rising electricity purchase prices, decreasing feed-in tariffs (potentially to zero in the future/already the case in some areas), and government incentives for battery installation, the payback period could be reduced, justifying their adoption. Moreover, the installed 13.5 kWh Tesla battery was too big for households with lower energy consumption like houses 2 and 5, which used only 25% of their total battery capacity most of the year. Therefore, selecting an appropriately sized battery based on household consumption could further help reduce the payback period. [ABSTRACT FROM AUTHOR]

The recent breakthroughs in machine learning (ML) and deep learning (DL) have catalyzed the design and development of various intelligent systems over wide application domains. While most existing machine learning models require large memory and computing power, efforts have been made to deploy some models on resource-constrained devices as well. A majority of the early application systems focused on exploiting the inference capabilities of ML and DL models, where data captured from different mobile and embedded sensing components are processed through these models for application goals such as classification and segmentation. More recently, the concept of exploiting the mobile and embedded computing resources for ML/DL model training has gained attention, as such capabilities allow (i) the training of models via local data without the need to share data over wireless links, thus enabling privacy-preserving computation by design, (ii) model personalization and environment adaptation, and (iii) deployment of accurate models in remote and hardly accessible locations without stable internet connectivity. This work summarizes and analyzes state-of-the-art systems research that allows such on-device model training capabilities and provides a survey of on-device training from a systems perspective. [ABSTRACT FROM AUTHOR]

Due to resource shortages and device diversity, energy efficiency and scalability issues are critical in the Internet of Things (IoT) space. Managing edge resources consistently to encourage resource sharing among devices is complex, given IoT's device heterogeneity and dynamic environmental conditions. In response to these challenges, our research presents a suite of intelligent techniques tailored for optimizing resources in IoT devices. Our solution's core component is a thorough full-stack system architecture made to flexibly handle a diverse range of IoT devices, each of which operates under resource limitations. This paradigm centers on the deployment of multiple edge servers, strategically positioned to cater to the unique requirements of IoT devices, which exhibit compatibility with heterogeneity, high performance, and adaptive intelligence. To realize this vision, we create a clustered environment within the realm of heterogeneous IoT devices. We employ an African vulture's optimization algorithm (AVOA), approach to establish connections between Cluster Head (CH) nodes. Following this crucial step, we meticulously select edge nodes situated in close proximity to the data source for transmission, reducing energy consumption and latency. Our proposed Multi-Edge-IoT system sets a new standard for efficiency within the IoT ecosystem, outperforming existing approaches in key metrics such as energy consumption, latency, communication overhead, and packet loss rate. It represents a significant stride towards the harmonious and resource-efficient operation of IoT devices in an increasingly interconnected world. [ABSTRACT FROM AUTHOR]

The trinity of global warming, climate change, and air pollution casts an ominous shadow over society and the environment. At the heart of these threats lie carbon emissions, whose reduction has become paramount. Blockchain technology and the internet of things (IoT) emerge as innovative tools for establishing an efficient carbon credit exchange. This paper presents a blockchain and IoT-centric platform for carbon credit exchange, paving the way for transparent, secure, and effective trading. IoT devices play a pivotal role in monitoring and verifying carbon emissions, safeguarding the integrity and accountability of the trading process. Blockchain technology, with its decentralized and immutable nature, empowers the platform with transparency, reduced fraud, and enhanced accountability. This platform aims to arm organizations and individuals with the ability to actively curb carbon emissions, fostering collective efforts towards global pollution reduction goals. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) devices play a crucial role in the real-time acquisition of photoplethysmography (PPG) signals, facilitating seamless data transmission to cloud-based platforms for analysis. Atrial fibrillation (AF), affecting approximately 1–2% of the global population, requires accurate detection methods due to its prevalence and health impact. This study employs IoT devices to capture PPG signals and implements comprehensive preprocessing steps, including windowing, filtering, and artifact removal, to extract relevant features for classification. We explored a broad range of machine learning (ML) and deep learning (DL) approaches. Our results demonstrate superior performance, achieving an accuracy of 97.7%, surpassing state-of-the-art methods, including those with FDA clearance. Key strengths of our proposal include the use of shortened 15-second traces and validation using publicly available datasets. This research advances the design of cost-effective IoT devices for AF detection by leveraging diverse ML and DL techniques to enhance classification accuracy and robustness. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Chestnut (Castanea sativa Mill.) forests in the Mediterranean region are facing increasing abandonment due to a combination of factors, ranging from climate change to socioeconomic issues. The recovery of chestnut ecosystems and their preservation and valorization are key to ensuring the supply of the wide spectrum of ecosystem services they provide and to preventing detrimental environmental shifts. The study's objective was to provide evidence on the effects of different management options on the ecophysiology of chestnut forests, with diverse pruning intensities (low, medium, and high intensity versus no pruning) tested in an abandoned chestnut stand in central Italy with the aim of recovering and rehabilitating it for fruit production. Innovative Internet of Things (IoT) 'Tree Talker' devices were installed on single trees to continuously monitor and measure ecophysiological (i.e., water transport, net primary productivity, foliage development) and microclimatic parameters. Results show a reduction in water use in trees subjected to medium- and high-intensity pruning treatments, along with a decrease in the carbon sequestration function. However, interestingly, the results highlight that trees regain their usual sap flow and carbon sink activity at the end of the first post-pruning growing season and fully realign during the following year, as also confirmed by the NDVI values. As such, this paper demonstrates the efficacy of recovering and managing abandoned chestnut forests, and the initial setback in carbon sequestration resulting from pruning is rapidly remedied with the advantage of reviving trees for fruit production. Additionally, the reduced water demand induced by pruning could represent a promising adaptation strategy to climate change, bolstering the resilience of chestnut trees to prolonged and intensified drought periods, which are projected to increase under future climate scenarios, particularly in the Mediterranean region. [ABSTRACT FROM AUTHOR]

With the rapid proliferation of the Internet of Things (IoT) in recent years, the number of IoT devices has surged exponentially. These devices collect and transmit vast amounts of data, including sensitive information. Encrypting data is a crucial means to prevent unauthorized access and potential misuse. However, the traditional cryptographic schemes offering robust security demand substantial device resources and are unsuitable for lightweight deployments, particularly in resource-constrained IoT devices. On the other hand, with the automotive industry making strides in autonomous driving, self-driving vehicles are beginning to integrate into people's daily lives. Ensuring the security of autonomous driving systems, particularly in preventing hacker infiltrations, is a paramount challenge currently facing the industry. An emerging lightweight sequence cipher—aiming to strike a balance between security and resource efficiency—has been proposed in this paper based on S-box substitution and arithmetic addition. The designed security threshold is 280. It has been verified that with a slight performance disadvantage, it can reduce memory usage while ensuring the security threshold. The key stream generated by this structure exhibits excellent pseudo-randomness. [ABSTRACT FROM AUTHOR]

In the world of connected devices, there is huge growth of less secure Internet of Things (IoT) devices, and the ease of performing sophisticated cyberattacks using these devices has posed a serious threat to the security of Internet-based services or networks. Distributed Denial of Service (DDoS) attack is one of the most significant cyberattacks. It aims to damage or exhaust victims' resources, services, or networks and make them unavailable to legitimate users. Several solutions are available in the literature to detect DDoS attacks. However, it is difficult to detect them in real-time due to today's high speed or high volume of attack traffic. Therefore, this paper proposes an Apache Storm-based distributed detection approach for IoT network traffic-based DDoS attacks, namely SDDA-IoT. SDDA-IoT is composed of two primary modules: model development and model deployment. In the case of model development, we created five distributed detection models by utilizing a Hadoop cluster and the extremely scalable H2O.ai machine learning platform. In the case of model deployment, we deploy an efficient distributed detection model on the Apache Storm stream processing framework for analyzing ingress streaming data and classifying it into seven classes in near-real-time. To create new models or update existing ones, this module also saves the highly discriminating input features of each network flow along with the predicted outcome in the Hadoop Distributed File System (HDFS). The effectiveness of the SDDA-IoT approach has been examined using a variety of configured scenarios. The experimental results show that the SDDA-IoT approach detects DDoS attacks faster than recent state-of-the-art methods and more accurately with 99%+ accuracy. [ABSTRACT FROM AUTHOR]

This study focuses on trust within water-treatment IoT plants, examining the collaboration between IoT devices, control systems, and skilled personnel. The main aim is to assess the levels of trust between these different critical elements based on specific criteria and to emphasize that trust is neither bidirectional nor transitive. To this end, we have developed a synthetic database representing the critical elements in the system, taking into account characteristics such as accuracy, reliability, and experience. Using a mathematical model based on the (AHP), we calculated levels of trust between these critical elements, taking into account temporal dynamics and the non-bidirectional nature of trust. Our experiments included anomalous scenarios, such as sudden fluctuations in IoT device reliability and significant variations in staff experience. These variations were incorporated to assess the robustness of our approach. The trust levels obtained provide a detailed insight into the relationships between critical elements, enhancing our understanding of trust in the context of water-treatment plants. [ABSTRACT FROM AUTHOR]

The Smart Autonomous Vehicular System (AVS) is designed to combine technologies such as sensors, cameras, radars, and machine learning algorithms in cars. The implementation of Smart AVS in smart cars has the potential to revolutionize the automotive industry and transform the way we think about transportation. In this paper, the implementation of Smart AVS in smart cars includes two steps. Firstly, the architecture is designed using Microsoft Threat Modelling tool. Secondly, with the use of Engineering Software, smart cars are constructed and simulated to verify and validate algorithms related to autonomous driving, path planning, and other intelligent functionalities. Simulating these algorithms in a controlled virtual environment helps to identify and address issues before implementation on physical vehicles. The main advantages of using the proposed model are early detection of vulnerabilities, realistic simulation of sensor inputs, communication protocol testing, cloud integration validation, user interface, and consumer experience, and validation of compliance with security standards. [ABSTRACT FROM AUTHOR]

Efficient energy management is a significant task in Internet-of-Things (IoT) devices because typical IoT devices have the constraint of a limited power supply. In particular, energy harvesting IoT devices must be tolerant of complex and varying temporal/spatial environments for energy availability. Several schemes have been proposed to manage energy usage in IoT devices, such as duty-cycle control, transmission power control, and task scheduling. However, these approaches need to deal with the operating conditions particular to energy harvesting devices, e.g., power depletion according to energy harvesting conditions. In this paper, regarding a wireless sensor network (WSN) as a representative IoT device, we propose an Energy Intelligence Platform Module (EIPM) for energy harvesting WSNs. The EIPM provides harvested energy status prediction, checkpointing, and task execution control to ensure continuous operation according to energy harvesting conditions while minimizing required hardware/software overheads such as additional measurement components and computations. Our experiment results demonstrate that the EIPM successfully enables a device to cope with energy insufficiency under various harvesting conditions. [ABSTRACT FROM AUTHOR]

Internet-of-Things (IoT) services become closely coupled with machine learning and cloud computing, where the 5G network provides the connectivity for the IoT devices. The 5G network can be used not only for connecting the IoT devices to the cloud servers, but also for providing computing resources for 'edge computing'. In this paper, we propose to use the edge node resources of the 5G network for 'inferencing' and 'training' the deep neural network (DNN) models for massive IoT services. More specifically, two types of 5G edge nodes are utilized to this end: (i) the 'IoT controller', which functions as a 5G-UE (user equipment), (ii) the 'edge controller', which is collocated with 5G-UPF (user plane function) in the 5G core network. In the proposed scheme, the downsized DNN models are executed and trained at the IoT controllers. At the edge controller, a deep reinforcement learning (DRL) algorithm is executed to determine the downsizing configuration and the training configuration of the DNN models. The resource constraints of the IoT controllers are considered in these decisions. Extensive evaluations with various DNN models show the effectiveness of the proposed scheme. We show that the proposed scheme achieves proper load balancing even when the resource capacity of individual IoT controllers is very low. For example, fairly complex DNN models for computer vision can be effectively supported by using IoT controllers equipped with the resource capacity of NVIDIA Jetson Nano. [ABSTRACT FROM AUTHOR]

Medicine has advanced significantly, but healthcare challenges remain, especially in rural areas. Telemedicine, supported by the Internet of Things (IoT), offers a solution through remote monitoring of cardiovascular diseases. However, its implementation faces obstacles such as the lack of healthcare professionals and facilities, and insufficient coverage and technological infrastructure for efficient and secure transmission of medical data. It is crucial to review the use of IoT devices in telemedicine for heart disease, highlighting trends and challenges in an updated state-of-the-art analysis for future proposals. Using the PRISMA methodological framework, 22 studies from Scopus, IEEE Xplore and ACM databases were included. The key factors for implementing these models were found to be economic and social. The most common devices are body temperature, electrocardiogram and heart rate sensors, which, together with technologies such as Wifi and Bluetooth, play important roles in hospital systems, such as real-time monitoring and decision making. This represents a move towards a more seamless and effective integration of emerging technologies in medicine, promoting more accurate and accessible medical care. [ABSTRACT FROM AUTHOR]

In farming, clashes between humans and animals create significant challenges, risking crop yields, human well-being, and resource depletion. Farmers use traditional methods like electric fences to protect their fields but these can harm essential animals that maintain a balanced ecosystem. To address these fundamental challenges, our research presents a fresh solution harnessing the power of the Internet of Things (IoT) and deep learning. In this paper, we developed a monitoring system that takes advantage of ESP32-CAM and Raspberry Pi in collaboration with optimised YOLOv8 model. Our objective is to detect and classify objects such as animals or humans that roam around the field, providing real-time notification to the farmers by incorporating firebase cloud messaging (FCM). Initially, we have employed ultrasonic sensors that will detect any intruder movement, triggering the camera to capture an image. Further, the captured image is transmitted to a server equipped with an object detection model. Afterwards, the processed image is forwarded to FCM, responsible for managing the image and sending notifications to the farmer through an Android application. Our optimised YOLOv8 model attains an exceptional precision of 97%, recall of 96%, and accuracy of 96%. Once we achieved this optimal outcome, we integrated the model with our IoT infrastructure. This study emphasizes the effectiveness of low-power IoT devices, LoRa devices, and object detection techniques in delivering strong security solutions to the agriculture industry. These technologies hold the potential to significantly decrease crop damage while enhancing safety within the agricultural field and contribute towards wildlife conservation. [ABSTRACT FROM AUTHOR]