Application Requirements Research Articles

Design and Improvement of Tandem Twin-Rotor Aerial-Aquatic Vehicle Based on Numerical Analysis

To enhance the air–water adaptability of the aerial-aquatic vehicles (AAVs), an improved tandem twin-rotor AAV design is proposed based on the characteristics and application requirements of air–water cross-domain movement. Computational fluid dynamics (CFD) software was used to simulate the underwater cruising state and dynamic water entry process of the tandem twin-rotor AAV. Results indicate that the underwater cruise resistance of the improved tandem twin-rotor AAV is relatively small. Among them, the two sets of tandem air power systems account for a relatively large proportion of the underwater drag, about 29.8%, while the drag reduction achieved by improving the head shape is around 15.4%. The head shape, water entry angle and speed have a great influence on the water entry trajectory and attitude of the tandem twin-rotor AAV. Following improvement, the tandem twin-rotor AAV demonstrates an enhancement in reducing the deviation of the water entry trajectory, mitigating water surface ricochet and enhancement of water entry depth to a certain extent. The inclination angle should be in the range of 20–30∘ to alleviate unfavorable outcomes, including prolonged water entry time caused by water surface ricochet at lower entry angles, while concurrently mitigating the heightened impact pressure resulting from steeper entry angles. Higher entry velocities lead to greater impact pressures, necessitating careful consideration of water entry depth and impact pressure to prevent adverse effects on structural integrity.

A Method for Detecting Small Target Weld Defects Based on Feature Reorganization Network

Abstract In the process of intelligent weld seam inspection, small weld seams are often prone to false detections or omissions. Existing methods simply concatenate feature segments during the upsampling process without analyzing the importance of each feature segment, leaving room for further improvement in detection accuracy. To address these issues, this study proposes a Feature Reorganization Network (FRNet) for detecting small target weld defects. First, the C2f-Faster-EMA feature extraction module is designed using GSConv convolution, and the LSKNet is introduced to dynamically adjust the receptive field of the backbone in the Backbone section, enhancing the model's ability to extract small target features. Then, a lightweight CARAFE upsampling module is designed in the neck network, which retains more detailed information through feature reorganization and feature expansion, and introduces the parameter-free attention mechanism SimAM to fully capture the contextual information of small targets, thereby enhancing the proposed model's ability to extract small target features. Finally, the GIoU boundary loss function is used to improve the network's bounding box regression performance, achieving intelligent detection of small target weld defects. Experimental results show that the proposed method achieves a mean average precision, parameter count, and computation volume of 85.6%, 2.5M, and 7.0G, respectively, for weld defect detection, outperforming the comparison models and meeting the requirements of practical engineering applications.

Microstructural Analysis, Compressive Strength, and Wear Properties of Spark-Plasma-Sintered Al–Mg–PPA Composites

One technique for sintering green compacts and imparting the required qualities to meet the specific application requirements is spark plasma sintering (SPS). This study examines the effects of SPS parameters (sintering temperature and pressure, holding time, and heating rate) and plantain peel ash (PPA) reinforcement concentrations (0, 5 wt%, 10 wt%, 15 wt%, and 20 wt%) on the microstructure, compressive strength, and wear characteristics of the fabricated Al–Mg–PPA composites. As a result of the ball milling machine’s high efficiency, the PPA reinforcement was evenly dispersed throughout the aluminum matrix after 90 min of milling. At lower sintering temperatures and pressures, microstructural flaws such as weak grain boundaries, micro-pores, and micro-cracks were more noticeable than at higher ones. The PPA reinforcement and magnesium powder (wetting agent) increased the composites’ compressive strength by improving the wettability between the PPA reinforcement and the Al matrix. At a weight fraction of 5 wt% PPA, the maximum compressive strength of 432 MPa was attained for the sintered composites, which is a 222% improvement over the sintered aluminum matrix. Additionally, the PPA reinforcement enhanced the wear properties of the sintered Al–Mg–PPA composites by reducing the wear loss. Increasing the wear load resulted in a higher wear rate. The COF for the sintered composites ranges from 0.049 to 0.727. The most consistent correlation between the wear rate and the COF is that as the wear rate decreases, the COF decreases, and vice versa. Abrasive wear was the dominant wear mechanism observed. Tear ridges, shear steps, micro-voids, and cleavages were seen on the composites’ fracture surfaces, an indication of a ductile-brittle fracture.

Residual based tilt tri‐rotor UAV actuator fault detection using TSK fuzzy model

AbstractUndetected actuator faults on tilt tri‐rotor UAVs can lead to system failures and uncontrolled crashes. Multiple flight modes result in complex models with strong nonlinearity, making fault detection of their actuators a very challenging task. To address this issue, this article proposes a fault detection method based on residual generated by using TSK fuzzy model. Initially, the flight modes of the tilt tri‐rotor UAV are modeled as the TSK fuzzy model. Following this, the residual generator is employed for rapid detection of actuator failures. To enhance detection accuracy, the kernel principal component analysis (KPCA) algorithm is used for a secondary confirmation. The proposed algorithm was validated using both a simulation platform and real flight data. The results demonstrate that the fault detection algorithm achieves high accuracy and real‐time performance, with a computing time of approximately 41 ms in real controller hardware, thus meeting the requirements of practical applications.

Gate-Tunable High-Responsivity Photodiode Based on 2D Ambipolar Semiconductor

Abstract Electrically tunable homojunctions based on ambipolar two-dimensional materials have attracted widespread attention in the field of intelligent vision. These devices exhibit inherent switchable positive and negative photovoltaic properties, that effectively mimic the behavior of human retinal cells. However, the photovoltaic responsivity of most electrically tunable homojunctions remain significantly low due to the weak light absorption, making it challenging to meet the application requirements for high-sensitivity target detection in the field of intelligent vision. Here, we propose a gate-tunable photodiode based on two-dimensional ambipolar WSe2 with an asymmetric gate electrode, achieving high photovoltaic responsivity. By adjusting the gate voltage and keeping bias voltage zero, we can dynamically realize reconfigurable n--p and n--n homojunction states, as well as gate-tunable photovoltaic response characteristics that range from positive to negative. The maximum photovoltaic responsivity of the electrically tunable WSe2 homojunction is approximately 0.4 A/W, which is significantly larger than the previously reported value ~0.1 A/W in homojunction devices. In addition, the responsivity can be further enhanced to approximately 1.0 A/W when the n--p photodiode operates in reverse bias mode, enabling high-sensitivity detection of targets. Our work paves the way for developing gate-tunable photodiodes with high photovoltaic responsivity and advancing high-performance intelligent vision technology.

Delay-sensitive Quality of Service Routing with Integrated Admission Control for Wireless Mesh Network

Wireless mesh networks (WMNs) extend and improve broadband Internet connectivity for the end-users roaming around the edges of the wired network. Amid the explosive escalation of users sharing multimedia content over the Internet, the WMNs need to support the effective implementation of various multimedia applications. The multimedia applications require assured quality of service (QoS) to fulfill the user requirements. The QoS routing in WMNs needs to guarantee the QoS requirements of multimedia applications. Admission control (AC) is the primary traffic control mechanism used to provide QoS provisioning. AC admits a new flow only if the QoS requirements of already admitted flows are not violated, even after the admission of a new flow. We propose a new QoS routing protocol integrated with AC called Delay-Sensitive QoS Routing with integrated Admission Control (DSQRAC) to control the admission of delay-sensitive flows. A delay-aware cross-layer routing metric is used to find the feasible path. DSQRAC is implemented using ad-hoc on-demand distance vector (AODV) routing protocol, where a delay-sensitive controlled flooding mechanism is used to forward the route request packets. In the proposed work, we adjust/reassign the channels to aid the QoS routing to increase the likelihood of accepting a new flow. The simulation results show that the performance of the proposed QoS routing protocol is better than the existing schemes.

Innovative techniques for joining and processing of some aluminum alloys used in the aircraft industry

The continuous development of various cutting-edge fields of science and technology, including the aerospace industry, is advancing at an accelerated pace. To meet current technical, economic, and environmental demands, new ecological and efficient material joining and processing techniques have been developed, aiming to replace some of the commonly used industrial procedures. For aluminum alloys, friction-based joining and processing methods with a rotating tool element are presented in this paper, with potential applications in the aerospace industry. Data is provided on Friction Stir Welding (FSW), known for its ability to produce high-quality joints without compromising the structural integrity of materials, highlighting its advantages over traditional welding methods. The diversity of metallic materials, their characteristics and properties, as well as the technical requirements of different applications, have led to the development of variants of the friction stir welding process, one of which is Submerged (underwater) Friction Stir Welding (SFSW). Data on FSW and SFSW welding as well as several aluminum alloys that can be used in the aerospace industry are included in the paper. The paper also discusses ISIM's approaches in the FSW/SFSW domain for aluminum alloys EN AW 6082 and EN AW 7075 that are applicable to the aerospace industry because of their relevance and interest. The positive outcomes show that FSW and SFSW welding can be developed for a variety of metallic materials. The goal of ISIM’s current and future research is to apply the principles of the FSW/SFSW process to the FSP/SFSP processing of non-ferrous alloys, which can be used in a variety of industries.

An Inversion-Coefficient Based Methodology for Optimizing the Performance of TIAs

ABSTRACT The transimpedance amplifier (TIA) can be found in several applications such as optical transceivers, biomedical circuits, and signal-processing circuits. In this paper, an investigation of two widely used transimpedance-amplifier configurations is presented. The two adopted configurations are the regulated cascode-based TIA (RGC-TIA) and the common-source based TIA (CS-TIA) with resistive feedback. These two configurations are studied in the following three inversion regimes; weak, moderate, and strong. A general model for the drain current of the MOSFET transistor, that is valid for all levels of inversion, is adopted. Through the study, the inversion coefficient (IC) of the amplifying device is systematically varied and its impact is investigated on various performance metrics such as gain, bandwidth, noise, distortion, and power consumption. Studying the inversion coefficient is crucial for precisely adjusting the amplifier biasing, thereby enabling the design of enhanced performance TIAs that effectively address the requirements of diverse applications. Compact-form expressions are derived for the performance metrics and compared with the simulation results. A figure of merit incorporating multiple performance metrics is proposed for performing a fair comparison for a wide range of the inversion coefficient. The simulation employs the 130-nm CMOS Predictive Technology Model (PTM) with a power-supply voltage, VDD , equal to 1.2 V.

The porosity effect on the buckling analysis of functionally graded plates under thermal environment using a Quasi-3D theory

Functionally graded porous structures are at the forefront of material innovation, providing a flexible solution to suit the wide range of requirements in modern engineering applications. The capacity of these materials to integrate customized porosity with variable mechanical properties not only improves performance but also promotes improvements in performance, efficiency, and durability. One of the main contributions of this research its a comprehensive method of including porosity effects in the thermal buckling load analysis of functionally graded plates. Also, this study aims to apply the Quasi-3D theory to investigate the influence of porosity on the thermal critical buckling load of functionally graded plates. The plates demonstrate microscopic heterogeneity, with material characteristics changing continually according to a modified polynomial function. The primary goal is to investigate how different porosity distributions (even, uneven, and logarithmic uneven) influence the thermal critical buckling load under different thermal load circumstances. The governing equations are derived using a Quasi-3D deformation theory that takes into account transverse normal strain. Navier’s method is used to investigate simply supported FG plates, both perfect and imperfect, under uniform, linear, and nonlinear thermal loads. Numerical simulations are used to calculate the thermal critical buckling for various geometries, thermal loading conditions and porosity characteristics. The results show that the porosity distribution has a substantial influence on increasing the thermal critical buckling load of FG porous plates. The model of even porosity distribution has the largest thermal critical buckling load, whereas the model of logarithmic-uneven porosity distribution has the lowest thermal critical buckling load, indicating that such distributions should be carefully considered in FGM design. The type of thermal loading condition applied to the plate has a significant impact on the thermal critical buckling of the plate. Moreover, various geometries, volume fraction index, and inclusion of thickness stretching are considered to examine the effect on the thermal critical buckling load response. Comparisons with literature are added to validate the accuracy of numerical findings. This research has the potential to offer comprehensive reference and useful guidance in the design and application of FG porous plate structures.

Visible light‐responsive azo‐based smart materials: Design, performance, and applications in energy storage

AbstractAzobenzene and its derivatives are the most extensively investigated and applied molecular photoswitches, which can undergo reversible transformation between trans and cis isomers upon irradiation with light at specific wavelengths. Through structural geometry transformation, the property alterations can be integrated into smart materials to meet diverse application requirements. Most azo‐based photoswitches require UV light for activation. However, complete activation within the visible or even near‐infrared light range could offer several benefits for photoswitch applications, including improved biocompatibility, better light penetration, and enhanced solar light utilization efficiency. This review presents an overview of the development of visible‐light responsive azo‐based materials, covering molecular design strategies and their applications in energy storage. Recent efforts aimed at enhancing the performance of azo‐based energy storage materials are highlighted. According to the different strategies for improving energy storage properties, these materials are categorized as those that directly increase isomerization energy and those that introduce phase transition energy. Furthermore, we discuss the challenges and opportunities in this field with a view to inspire further exploration.

Recent advances in machine learning and deep learning-enabled studies on transition metal dichalcogenides

Abstract The machine learning and deep learning (ML/DL) techniques have significantly advanced the understanding and utilization of TMDs by enabling efficient analysis, prediction, and optimization of their properties. ML/DL methods permit rapid screening, optimization and analysis of two-dimensional (2D) material candidates, potentially accelerating the discovery and development of TMDs with desired electronic, optoelectronic, and energy storage properties. This review provides a comprehensive review of machine learning and deep learning (ML/DL) methods to enhance 2D materials research via the optimization of synthesis conditions, interpretation of complex data sets, and the use of generative adversarial networks and variational autoencoders for innovative material design and image processing tasks. Furthermore, it highlights the potential of ML/DL techniques in predicting and tailoring the electronic, optical, and mechanical properties of 2D materials to meet specific application requirements.

Effects of Symmetric and Asymmetric Pre-Compression Combinations on the Mechanical Properties and Fatigue Behavior of ZK60 Magnesium Alloy

Pre-compression modes influence the microstructure of rolled magnesium alloys, thereby enhancing their mechanical and fatigue properties. This study investigates the distribution of dislocations, extension twins, and texture components in rolled ZK60 magnesium alloy sheets subjected to single-pass pre-compression (S-PT3), symmetric pre-compression (S-PT3R3), and asymmetric pre-compression (S-PT3R6). The results indicate that the variation in the orientation of pre-twin textures, leading to differences in the Schmid factor, is the primary cause of the mechanical performance differences observed under different pre-compression modes. In contrast, the hardening effect due to grain refinement is negligible. As the total amount of pre-compression increases, the hardening effect of pre-dislocations becomes more significant. Single-pass pre-compression exhibits a higher fatigue life under large strain amplitudes. Although asymmetric pre-compressed samples show increased overall strength and hardness, their fatigue life and cyclic stability are inferior. During cyclic loading, different pre-compression modes significantly impact the hysteresis curve, deformation mechanisms, stress ratio, and elastic limit amplitude. Symmetric pre-compression samples exhibit superior cyclic stability and fatigue life. Based on different application requirements, suitable pre-compression treatment methods can be selected to optimize the performance of ZK60 magnesium alloys, providing scientific guidance for practical engineering applications.

Characteristics of Academic Pharmacy Fellowship Programs: Results of a Cross-Sectional Online Search.

The pharmacy academy faces an urgent need for diverse, experienced faculty members. However, while many faculty candidates may come from advanced training backgrounds, many do not have all of the skills required to transition seamlessly into an academic role. Academic Pharmacy Fellowships are a promising strategy to address these gaps. The objectiv of this paper is to describe the information available online about Academic Pharmacy Fellowships. In this cross-sectional review, a list of programs was compiled from multiple online sources over one year. For each program identified, available information related to the institution, application requirements, and program details was compiled into a database. The quality of the data in the database was evaluated using an analysis of missing values and data to error ratio. A total of 29 Academic Pharmacy Fellowships were identified for the 2023-2024 application year, including 19 teaching-intensive and 10 research-intensive programs. In general, there is variability in the clarity and amount of information available online for these programs and a lack of standardization. For applicants seeking careers in academic pharmacy, there are diverse options; however, there is an opportunity for Academic Pharmacy Fellowship programs to develop common language, timelines, and online presence. Further collaboration is necessary to ensure pharmacy faculty and mentors are aware of these opportunities and are able to connect potential applicants to these programs to strengthen this endeavor.

DeepNeXt: a lightweight polyp segmentation algorithm based on multi-scale attention.

Fast and accurate automatic segmentation of polyps in colonoscopy plays a crucial role in the early diagnosis and treatment of colon cancer. However, the current polyp segmentation algorithms based on deep neural networks suffer from the problems of larger models and lower segmentation accuracy. Meanwhile, achieving accurate segmentation of polyps is to improve the diagnostic efficiency of doctors, and this need motivates us to develop a set of lightweight models so that it can be easily embedded in clinical devices to meet the requirements of practical applications. This study aims to provide effective technical support for the rapid and precise segmentation of polyps in clinical applications. DeepNeXt, an innovative polyp segmentation model grounded in multi-scale attention mechanisms. DeepNeXt incorporates a multi-stage, lightweight convolutional encoder module, leveraging several lightweight convolutional layers for efficient and accurate feature extraction. Furthermore, it features a novel multi-stage feature fusion structure designed to circumvent the potential loss of feature information during the encoding phase. Additionally, the model employs a multi-scale attentional feature encoding module that harnesses multi-branch deep strip convolution techniques to extract multi-dimensional information from the feature maps post-encoding, thereby enhancing the neural network's capability to extract diverse feature information. Experimental validation on the Kvasir Segmentation Dataset (Kvasir-SEG dataset) and Colorectal Cancer-Clinic Datasetbase (CVC-ClinicDB datasets) demonstrates that DeepNeXt outperforms mainstream networks such as U-net, U-net++, TransUnet, SwinUnet, and TGANet in terms of parameters and floating-point operations (FLOPs). DeepNeXt achieved a FLOPs metric of only 3.04 G and a parameters (Params) metric of just 1.51 M, while delivering exceptional performance in segmentation. On the Kvasir-SEG dataset, it reached a mean intersection over union (mIOU) of 83.91, and on the CVC-ClinicDB dataset, 87.37. Additionally, the Dice and Recall metrics also showed superior results, highlighting that DeepNeXt strikes an optimal balance between computational efficiency, model compactness, and segmentation accuracy. In conclusion, we have proposed the DeepNeXt network, a novel lightweight multi-scale attention segmentation network tailored for computationally limited medical devices, which provides strong support for accurate and efficient polyp segmentation in clinical applications.

A new approach based on genetic algorithm for computation off-loading optimization in multi-access edge computing networks

The proliferation of smart devices and the increasing demand for resource- intensive applications present significant challenges in terms of computational efficiency, leading to surge in data traffic. While cloud computing offers partial solutions, its centralized architecture raises concerns about latency. Multi-access edge computing (MEC) emerges as promising alternative by deploying servers at the network edge to bring computations closer to user devices. However, op- timizing computation offloading in the dynamic MEC environment remains a complex challenge. This paper introduces novel genetic algorithm-based ap- proach for efficient computation offloading in MEC, considering processing and transmission delays, user preferences, and system constraints. The pro- posed approach integrates computation offloading and resource allocation al- gorithm based on evolutionary principles, combined with a greedy strategy to maximize overall system performance. By utilizing genetic algorithms, the pro- posed method enables dynamic adaptation to changing conditions, eliminating the need for intricate mathematical models and providing an appealing solution to the complexities inherent in MEC. The urgency of this research arises from the critical need to enhance mobile application performance. Simulation re- sults demonstrate the robustness and efficacy of our approach in achieving near- optimal solutions while efficiently balancing computation offloading, minimiz- ing latency, and maximizing resource utilization. Our approach offers flexibility and adaptability, contributing to advancement of MEC networks and addressing the requirements of latency-sensitive applications.

Two-dimensional ASEP model to study density profiles in CVD growth

The growth of two-dimensional (2D) transition metal dichalcogenides using chemical vapor deposition has been an area of intense study, primarily due to the scalability requirements for potential device applications. One of the major challenges of such growths is the large-scale thickness variation of the grown film. To investigate the role of different growth parameters computationally, we use a 2D asymmetric simple-exclusion process (ASEP) model with open boundaries as an approximation to the dynamics of deposition on the coarse-grained lattice. The variations in concentration of particles (growth profiles) at the lattice sites in the grown film are studied as functions of parameters like injection and ejection rate of particles from the lattice, time of observation, and the right bias difference between the hopping probabilities towards right and towards left) imposed by the carrier gas. In addition, the deposition rates at a given coarse-grained site is assumed to depend on the occupancy of that site. The effect of the maximum deposition rate, i.e., the deposition rate at a completely unoccupied site on the substrate, has been explored. The growth profiles stretch horizontally when either the evolution time or the right bias is increased. An increased deposition rate leads to a step-like profile, with the higher density region close to the left edge. In 3D, the growth profiles become more uniform with the increase in the height of the precursor with respect to the substrate surface. These results qualitatively agree with the experimental observations.

Salt template-assisted polymer tethering strategy to achieve high dispersed cobalt single atoms/clusters on porous carbon catalysts for effective Fenton-like reactions

The exploration of the synthesis of porous carbon-supported metal nanocatalysts with high metal loading and dispersion for the development of heterogenous catalysts is important. However, this remains a challenging subject. In this paper, a salt template-assisted polymer tethering strategy was proposed to prepare Co single atom and ultrafine cluster anchored on porous carbon (Co-NPC) with a high-loading of 16.9wt%, thereby exposing high density of reaction sites. The obtained Co-NPC catalyst exhibited excellent catalytic performance for almost 100% degradation of bisphenol A (BPA) within 20min, which is much faster than the CoSA-NPC catalyst with only single atom sites and Co-NC without salt template added. Radical quenching experiments and electron paramagnetic resonance tests verified that both the nonradical oxidation pathway by 1O2, the electron transfer and radical oxidation pathway by •OH, SO4•- appeared during the degradation process. After 3 cycles, the removal rate of BPA did not decrease significantly. The fixed bed experiment revealed that Co-NPC could realize the continuous degradation of methylene blue (MB) with almost 100% degradation efficiency. This research indicated that the strategy by pyrolyzing metal coordinated polymer is a potential method for the synthesis of high metal loading catalyst at gram scale, meeting the requirement of practical applications.

Study on the Application of Vibration Energy Harvester Based on 3D Chip-scale Solenoids Coil in Rail Transit

Introduction: A 3D chip-scale solenoid coil was fabricated by micro-electro-mechanical system (MEMS) and wafer-level micro casting technology, and an electromagnetic vibration energy harvester was manufactured with an NdFeB permanent magnet. Methods: Three coils with different turns were designed, namely 45 turns, 90 turns, and 150 turns. The coils had a wire width of 40 microns, a pitch of 25 microns, and a thickness of 150 microns. The permanent magnet was cylindrical with a diameter of 1.8 mm. According to the length of the coil, three specifications of 3/6/10 mm were selected for the permanent magnet. Special PCB circuit testing tooling was processed to test the actual performance of three kinds of permanent magnet energy harvesters with different specifications. Results: The vibration frequency was set to 10 Hz~150 Hz, and the acceleration was designed to be 50 m/s2~300 m/s2. For the energy harvester with 90 turns, a maximum output power of 75 μW was obtained under vibration conditions of 100 m/s2 & 30 Hz. The experimental data showed that vibration frequency, acceleration, and sample size had a certain influence on the energy conversion and output power of vibration. Conclusion: Through the above study, the design and performance of vibration power generation devices can be optimized better to match the actual application requirements of rail transit.

MQTT Broker Optimization

Optimizing MQTT broker performance is crucial for maintaining efficient message routing in IoT systems, especially under varying workloads and QoS levels. This study compares the Round Robin (RR) and Least Response Time (LRT) algorithms to evaluate their performance across QoS levels 0, 1, and 2 and client loads ranging from 500 to 2,500 clients. Using Apache JMeter, key metrics such as CPU usage, throughput, delay, jitter, and response time were assessed. LRT was found to excel in enhancing response time and reducing delay, particularly under high client loads and in applications requiring minimal latency. However, this comes at the cost of higher CPU usage under heavy loads. In contrast, RR demonstrated optimal performance in maintaining balanced CPU utilization and predictable performance, though with slightly higher response times. Both algorithms demonstrated linear scalability in throughput, confirming their ability to handle increasing workloads without bottlenecks. These findings offer practical guidance for IoT developers: in latency-sensitive environments such as industrial automation, LRT is preferable due to its low-latency benefits, while RR is better suited for resource-constrained IoT systems like environmental monitoring, where stability and even load distribution are prioritized. The trade-offs identified provide valuable insights for selecting appropriate algorithms based on specific application requirements.

Comparative Analysis of Elliptic Curve-Based Cryptographic Approaches for Internet of Things Security

The rapid expansion of the Internet of Things (IoT) introduces significant security challenges, given the resource-constrained nature of most IoT devices. To address these challenges, elliptic curve cryptography (ECC) has emerged as a promising solution due to its ability to deliver high levels of security with shorter key lengths, making it highly efficient for devices with limited computational power and memory. This paper focuses on a comparative analysis of three widely used ECC-based cryptographic algorithms: Elliptic Curve Digital Signature Algorithm (ECDSA), Elliptic Curve Integrated Encryption Scheme (ECIES), and Elliptic Curve Diffie-Hellman (ECDH). Through a detailed evaluation of performance metrics such as key generation speed, execution time, and overall efficiency, the study identifies the strengths and limitations of each algorithm in securing IoT environments. The results reveal that ECDH excels in public key generation speed, making it suitable for applications requiring frequent key exchanges. ECDSA demonstrates the fastest overall execution time, providing an efficient option for digital signatures and authentication. Conversely, ECIES, while slower, offers robust encryption capabilities ideal for scenarios demanding enhanced confidentiality. This comparative study highlights the importance of aligning algorithm selection with specific IoT application requirements, balancing factors like security, performance, resource constraints, and operational complexity. The findings underscore the suitability of ECC-based algorithms in addressing the unique challenges of IoT security.