IoT Applications Research Articles

TaPaFuzz: Hardware-accelerated RISC-V bare-metal firmware fuzzing using rapid job launches

Fuzz testing serves as a key technique in software security aimed at identifying unexpected program behaviors by repeatedly executing the target program with auto-generated random inputs. Testing is integral to IoT device security but is hampered by the minimal observability features of typical in-market IoT devices. Moreover, the slow nature of a RISC-V software emulation on x86 host CPUs and the inaccuracies introduced by compiling IoT applications to a different ISA for execution on host systems pose significant challenges. Our software-hardware co-design surmounts these hurdles. Fuzzing jobs are prepared and evaluated on a host computer, while the actual execution with high-throughput tracing is performed on an FPGA. Advances in the host-to-FPGA interface together with an accelerated reset procedure between Fuzzer jobs effectively hide the costly host-FPGA communication, increasing the single-thread fuzzing performance by up to factor 11.7x that of the leading QEMU-based fuzzer AFL++ running on a very fast x86 CPU. We demonstrate practical usability by evaluating our framework on a collection of bare-metal applications.

Edge-Cloud Continuum: Integrating Edge Computing and Cloud Computing for IOT Applications

Abstract: This research will use a mixed-method approach with qualitative understanding and the quantitative analysis of data to examine how edge and cloud computing blend into the frame of an edge-cloud continuum for IoT applications. It adopts an exploratory and descriptive approach to research in carrying out a holistic assessment of the edge-cloud continuum in efficiency, scalability, and performance. The data required for collection was obtained through simulations using the IoTSim-Osmosis framework and also through the use of other case studies and literature published elsewhere before preparing this document. The data is usual to what has been used in several IoT deployments, including smart cities, the health sector, and industrial automation. Based on those KPIs - latency, 70.83%; bandwidth utilization, 40% reduced; energy consumption, 25% reduced; and task completion rate improved by 18.75% - the edge-cloud continuum significantly outperforms traditional cloud-only systems. Especially a very big reduction in latency is significantly important for real-time applications as it would drive the potential ability to improve responsiveness in those applications, like autonomous cars and smart healthcare. The current study demonstrates that the edge-cloud continuum can efficiently enhance resource allocation and enhance the effectiveness of complex IoT systems. This has wide implications for designing and implementing IoT solutions across industry domains

Hybrid Optimization Framework for MIMO Antenna Design in Wearable IoT Applications Using Deep Learning and Bayesian Method

Hybrid Optimization Framework for MIMO Antenna Design in Wearable IoT Applications Using Deep Learning and Bayesian Method

Hybrid moth flame-shark smell optimization-based secure wireless communications for IoT applications

Hybrid moth flame-shark smell optimization-based secure wireless communications for IoT applications

A comprehensive review of different antennas for IoT applications

A comprehensive review of different antennas for IoT applications

Cyber-Physical Systems and IoT: Transforming Smart Cities for Sustainable Development

Smart cities CPS/ IoT are impacting sustainable development in modern cities. This research explore how CPS and IoT integration into infrastructure systems in cities can improve sustainability in aspects such as energy, waste, and environment. Finally, taking quantitative data on energy and pollution saving and resource utilization from the smart cities around the world, the paper quantifies the case analysis. The research method entails retrieving data from existing smart city projects and then using statistical tools to evaluate the differences in sustainability targets with and without CPS and IoT integration. The main discoveries indicate that places implementing CPS and IoT technologies have recorded reduction in energy use by up to 25 % and enhanced waste management by up to 30% thus exhibiting a reduced carbon footprint. The originality of this research is based on the application of CPS- IoT in analysing their practical implications for urban sustainability with relevant recommendations for policymakers, urban planners and technologists. This study not only serves as the research for the academic literature but also advises cities into how they would be able to build upon their sustainable initiatives through the adoption of advanced technology. The outcomes underscore the possibility of the strategic application of CPS and IoT to enhance the creation of smarter urban contexts.

Securing constrained IoT systems: A lightweight machine learning approach for anomaly detection and prevention

With the advent of advanced technological developments such as IoT, edge, and fog computing, cyber attacks have become increasingly sophisticated. IoT networks facilitate collaborative and intelligent tasks across various domains, including Industry 4.0, digital healthcare, and home automation. However, the proliferation of IoT devices has raised concerns about severe attacks, particularly those targeting resource constraints such as energy and memory. In response to these challenges, Tiny Machine Learning (TinyML) has emerged as a new research area, focusing on machine learning techniques tailored for embedded and IoT systems. This study proposes an ML detection mechanism designed to categorize and detect resource-constrained attacks in IoT devices. We consider IoT devices to be integral components within the continuum of edge and cloud computing, leveraging EdgeML and CloudML for detection purposes. Our paper conducts a comparative analysis of ML models, with a specific focus on energy consumption and memory usage in IoT applications. We compare various ML methodologies, including cloud-based, edge-based, and device-based strategies for both training and detection. The evaluation encompasses the application of these ML techniques to petite IoT devices, utilizing TinyML, as well as cloud and edge devices. Our findings reveal that the Decision Tree algorithm deployed on smart devices surpasses other approaches in terms of training efficiency, resource utilization, and the ability to detect resource-constrained attacks on IoT devices. We demonstrate a high level of accuracy, exceeding 96.9%, across all presented ML models in detecting resource constraint attacks within IoT systems. In summary, this research serves as a guide for implementing effective security measures in the dynamic landscape of IoT and associated technologies.

Cognitive Transformation in Personal IoT: Pioneering Intelligent Automation

ABSTRACT In recent years, IoT has transformed personal environments by integrating diverse smart devices. This paper presents an advanced IoT architecture that optimizes network infrastructure, focusing on the adoption of MQTT protocol and introducing Cognitive Smart Objects for managing personal IoT applications. These objects use Neural Networks to predict optimal actions based on user behavior patterns. A Continuous Learning mechanism enables real-time adaptation of the network to evolving user interactions. The study highlights the role of Cognitive Transformation in Personal IoT, driving intelligent automation and enhancing user experience.

VonEdgeSim: A Framework for Simulating IoT Application in Volunteer Edge Computing

Recently, various emerging technologies have been introduced to host IoT applications. Edge computing, utilizing volunteer devices, could be a feasible solution due to the significant and underutilized resources at the edge. However, cloud providers are still reluctant to offer it as an edge infrastructure service because of the unpredictable nature of volunteer resources. Volunteer edge computing introduces challenges such as reliability, trust, and availability. Testing this infrastructure is prohibitively expensive and not feasible in real-world scenarios. This emerging technology will not be fully realized until dedicated research and development efforts have substantiated its potential for running reliable services. Therefore, this paper proposes VonEdgeSim, a simulation of volunteer edge computing. To the best of our knowledge, it is the first and only simulation capable of mimicking volunteer behavior at the edge. Researchers and developers can utilize this simulation to test and develop resource management models. We conduct experiments with various IoT applications, including Augmented Reality, Infotainment, and Health Monitoring. Our results show that incorporating volunteer devices at the edge can significantly enhance system performance by reducing total task delay, and improving task execution time. This emphasizes the potential of volunteers to provide reliable services in an edge computing environment. The simulation code is publicly available for further development and testing.

An Ultra-Low Power, Low Voltage, Low Line Sensitivity, High PSRR, Voltage Reference for IoT Applications

An Ultra-Low Power, Low Voltage, Low Line Sensitivity, High PSRR, Voltage Reference for IoT Applications

Miniaturized tri-band integrated microwave and millimeter-wave MIMO antenna loaded with metamaterial for 5G IoT applications

This study presents a revolutionary tri-band combined microwave (MW) and millimeter wave (MMW) multiple-input-multiple-output (MIMO) antenna. The antenna is built on a Rogers RT-5880 substrate with dimensions of 32 × 32 × 1.6 mm³. This integration increased the isolation from 18 dB to 27 dB in the MW band and 28 dB–40 dB in the MMW band. The effective bandwidths for the MW and MMW bands were initially 3.3–3.7 GHz, 5.45–9.2 GHz, and 24–28 GHz, respectively. The MTM increased these bandwidths to 2.6–4.1 GHz, 5.3–11.3 GHz for the MW band, and 22.5–29.3 GHz for the MMW band. In addition, the realized gain increased significantly, from 1.5 dBi, 3.5 dBi, and 6.5 dBi to 4.5 dBi, 6.1 dBi, and 11 dBi at 3.5 GHz, 6.9 GHz, and 28 GHz. The antenna's overall efficiency increased by 10–12 % across all operational bandwidths. The suggested antenna has excellent diversity performance, with an envelope correlation coefficient of less than 0.0002, a diversity gain greater than 9.998, and a channel capacity loss of less than 0.12 bits/s/Hz. The proposed antenna addresses the increasing demand for IoT devices by efficiently supporting both MW and MMW frequency bands, offering high performance in wireless communication and modern infrastructures. The proposed design not only improves connectivity and efficiency in IoT devices but also supports the Sustainable Development Goals (SDGs) by contributing to the development of smart city infrastructure (SDG 11) and fostering innovation and sustainable industrial practices (SDG 9), thereby promoting sustainable development and industrial innovation.

ACGAN for Addressing the Security Challenges in IoT-Based Healthcare System

The continuous evolution of the IoT paradigm has been extensively applied across various application domains, including air traffic control, education, healthcare, agriculture, transportation, smart home appliances, and others. Our primary focus revolves around exploring the applications of IoT, particularly within healthcare, where it assumes a pivotal role in facilitating secure and real-time remote patient-monitoring systems. This innovation aims to enhance the quality of service and ultimately improve people’s lives. A key component in this ecosystem is the Healthcare Monitoring System (HMS), a technology-based framework designed to continuously monitor and manage patient and healthcare provider data in real time. This system integrates various components, such as software, medical devices, and processes, aimed at improvi1g patient care and supporting healthcare providers in making well-informed decisions. This fosters proactive healthcare management and enables timely interventions when needed. However, data transmission in these systems poses significant security threats during the transfer process, as malicious actors may attempt to breach security protocols.This jeopardizes the integrity of the Internet of Medical Things (IoMT) and ultimately endangers patient safety. Two feature sets—biometric and network flow metric—have been incorporated to enhance detection in healthcare systems. Another major challenge lies in the scarcity of publicly available balanced datasets for analyzing diverse IoMT attack patterns. To address this, the Auxiliary Classifier Generative Adversarial Network (ACGAN) was employed to generate synthetic samples that resemble minority class samples. ACGAN operates with two objectives: the discriminator differentiates between real and synthetic samples while also predicting the correct class labels. This dual functionality ensures that the discriminator learns detailed features for both tasks. Meanwhile, the generator produces high-quality samples that are classified as real by the discriminator and correctly labeled by the auxiliary classifier. The performance of this approach, evaluated using the IoMT dataset, consistently outperforms the existing baseline model across key metrics, including accuracy, precision, recall, F1-score, area under curve (AUC), and confusion matrix results.

A simple picowatt 7.6 ppm/°C, 0.029 %/V line sensitivity fully-CMOS voltage reference with DIBL cancelation

In this paper, a 4-transistor sub-one-volt voltage reference with picowatt power consumption is presented. To achieve ultra-low power consumption and low-voltage operation, all transistors are designed to operate in subthreshold region. By proper design of the circuit, DIBL effect is also compensated to enhance temperature coefficient of the circuit and line sensitivity. The proposed circuit consists of only four different transistors (i.e. thick oxide, native VTH, medium VTH) in a self-biased scheme such that eliminates the need for any start-up circuit. A prototype of the proposed circuit that generates a 341-mV voltage reference is designed and simulated in a standard 0.18-µm CMOS technology. The proposed reference circuit exhibits an average temperature coefficient of 7.6 ppm/°C across all process corners while the total power consumption is as low as 249 picowatt. The average line sensitivity of the circuit is 0.029 %/V within a wide supply range of 0.6 to 3.3 V. Ultra-low power consumption and line sensitivity, and excellent thermal stability render it ideal for integration into RFID and IoT applications.

A Three-Stage Dynamic Comparator for SAR ADC Optimized for Reduced Kickback Noise and Ultra-Low Delay

This paper presents a novel footless single clock-phase three-stage comparator with internally generated regenerative voltage signals for low kickback noise and high speed. The preamplifier and clocked latch topology of dynamic comparators are considered in this brief. The proposed design has been compared by analyzing and optimizing three state-of-the-art comparator designs: Modified StrongARM, Miyahara’s and Three-Stage. These designs are simulated using the 40[Formula: see text]nm CMOS technology process. The area of the proposed comparator is 28.025[Formula: see text][Formula: see text]m2. When simulated in the SPICE-based simulator HSPICE, under typical performance conditions of 0.9[Formula: see text]V supply voltage, 25∘C, 1 GHz operational frequency, typical process corner (tt), and 1[Formula: see text]mV differential voltage, the proposed comparator produces the best post-layout results, with a minimum delay of 59.9[Formula: see text]ps from meta-stability analysis, energy per comparison of 0.175[Formula: see text]pJ/comparison and a kickback noise of 44.55 [Formula: see text]A. The proposed design also shows a significant improvement in the measured performance parameters over the other state-of-the-art dynamic comparators that have been discussed in this brief. The obtained results show that the proposed comparator is appropriate for 12-bit Successive Approximation Resister Analog-to-Digital Converters (SAR-ADCs) in IoT applications.

A deep reinforcement learning approach towards distributed Function as a Service (FaaS) based edge application orchestration in cloud-edge continuum

Serverless computing has emerged as a new cloud computing model which in contrast to IoT offers unlimited and scalable access to resources. This paradigm improves resource utilization, cost, scalability and resource management specifically in terms of irregular incoming traffic. While cloud computing has been known as a reliable computing and storage solution to host IoT applications, it is not suitable for bandwidth limited, real time and secure applications. Therefore, shifting the resources of the cloud-edge continuum towards the edge can mitigate these limitations. In serverless architecture, applications implemented as Function as a Service (FaaS), include a set of chained event-driven microservices which have to be assigned to available instances. IoT microservices orchestration is still a challenging issue in serverless computing architecture due to IoT dynamic, heterogeneous and large-scale environment with limited resources. The integration of FaaS and distributed Deep Reinforcement Learning (DRL) can transform serverless computing by improving microservice execution effectiveness and optimizing real-time application orchestration. This combination improves scalability and adaptability across the edge-cloud continuum. In this paper, we present a novel Deep Reinforcement Learning (DRL) based microservice orchestration approach for the serverless edge-cloud continuum to minimize resource utilization and delay. This approach, unlike existing methods, is distributed and requires a minimum subset of realistic data in each interval to find optimal compositions in the proposed edge serverless architecture and is thus suitable for IoT environment. Experiments conducted using a number of real-world scenarios demonstrate improvement of the number of successfully composed applications by 18%, respectively, compared to state-of-the art methods including Load Balance, Shortest Path algorithms.

Tribo-hygro-electric generator: Harnessing mechanical energy with liquid-infused porous cellulose for multiple sensing and DC power generation

This study introduces the Tribo-Hygro-Electric Generator (THEG), a novel device that harvests ambient mechanical and water-enabled energies for highly sensitive, self-powered multi-sensing. THEG employs a simple design using liquid-infused porous cellulose (LIPC), created by absorbing functional liquids such as water into porous cellulose, and metal contacts with distinct work functions (e.g., Al and Cu) to generate triboelectric charges through friction. It operates via a three-step process: generating triboelectric charges through sliding motion, establishing an internal electric field across Al/LIPC/Cu interfaces for charge separation, and directing charges to create unidirectional output electron transfer, producing direct current (DC) outputs. The principle behind THEG emphasizes the critical role of the single Schottky contact at the Al/LIPC interface in separating and directing charge transfer, as well as the conductive path formed by the hydrogen-bonded network of water molecules and cellulose’s functional groups. THEG can harvest DC power with a current density of up to 3.4 A/m² and 0.5 V, which can be increased to 2 V with four cells in series. THEG demonstrates impressive multiple-sensing capabilities, with robust linear relationships (correlation coefficients > 0.99) between output current density and stimuli such as pressure, velocity, water absorption, and ion concentration. Demonstrating practical utility, the generated energy powers devices like a commercial calculator. This technology advances mechanical energy harvesting and multiple sensing, reducing dependence on external power and enabling transformative IoT applications.

A Review of Agricultural IoT: Challenges, Applications & Future Research Direction

The field of wireless communication has had significant boom in wireless communication, mostly driven by increasing demand for connectivity and technological advancements in low power transceiver development. An Agriculture IoT (AIoT) refers to the technology to enhance agricultural sustainability, productivity, and efficiency. The agricultural IoT uses artificial intelligence to build a network of interconnected systems by fusing digital sensors, IoT devices, advanced analytics, and cloud computing. Agricultural products will be in high demand by 2030 because of the 40% raise in global population. IoT technology is currently utilized in various industries, including agricultural for automation processes. Therefore time is saved, total productivity is increased and decisions are made more effectively and resulting in higher crop yields. AIoT is also being utilized more to keep updated on the sustainability of agricultural supply chains, environment and water usage. Several IoT applications and problems have been covered in this paper. There is also an outline of potential future agricultural research directions.

Resource‐adaptive and OOD‐robust inference of deep neural networks on IoT devices

AbstractEfficiently executing inference tasks of deep neural networks on devices with limited resources poses a significant load in IoT systems. To alleviate the load, one innovative method is branching that adds extra layers with classification exits to a pre‐trained model, enabling inputs with high‐confidence predictions to exit early, thus reducing inference cost. However, branching networks, not originally tailored for IoT environments, are susceptible to noisy and out‐of‐distribution (OOD) data, and they demand additional training for optimal performance. The authors introduce BrevisNet, a novel branching methodology designed for creating on‐device branching models that are both resource‐adaptive and noise‐robust for IoT applications. The method leverages the refined uncertainty estimation capabilities of Dirichlet distributions for classification predictions, combined with the superior OOD detection of energy‐based models. The authors propose a unique training approach and thresholding technique that enhances the precision of branch predictions, offering robustness against noise and OOD inputs. The findings demonstrate that BrevisNet surpasses existing branching techniques in training efficiency, accuracy, overall performance, and robustness.

Introduction to the Special Issue on FPGA-based Embedded Systems for Industrial and IoT Applications

Introduction to the Special Issue on FPGA-based Embedded Systems for Industrial and IoT Applications

A Crucible of IoT Applications and Services and the Gambia Revenue Generation

The study investigated the relationship between Internet of Things (IoT) applications and services and The Gambia revenue generation, tax evasion and tax compliance respectively. It identified the challenges impeding the adoption and implementation of IoT applications and services in the revenue generation processes by the Gambia Revenue Authority (GRA). Both primary and secondary data were used in the study. Questionnaire and interview were used to generate the primary data. The study population (2188) comprised senior and mid-tier management personnel from the Ministry of Finance and Economic Affairs (MoFEA), staff members of The Gambia Revenue Authority, and the corporate taxpayers. The sampling approach used in this study was multistage. Personnel from the MoFEA and GRA were purposively selected, while corporate taxpayers were randomly selected. Their populations were then divided into various strata to ensure fair representation. Respondents were randomly picked from each stratum. To ensure an appropriate sample size, 327 individuals were selected. Data obtained were analysed using multiple regression, correlation coefficient, and simple regression analysis. Qualitative analyses were done on the interview responses. The study revealed that connectivity, networking, and data collection improve revenue generation, while increased IoT adoption and higher data utilisation and infrastructure investment reduce tax evasion and boost tax compliance. However, implementation of the IoT has encountered challenges in the form of technological barriers, financial constraints, and skill gaps. In view of these, investment must be made in enhancing connectivity and networking infrastructure, training staff in data analytics and cyber-security to optimise IoT data usage and boost IoT adoption through inducements and awareness programs. The study concluded that IOT applications and services are in the right direction to revolutionise The Gambia revenue generation.