Search Results (14)

The accurate detection of surface defects in bamboo is critical to maintaining product quality. Traditional inspection methods rely heavily on manual labor, making the manufacturing process labor-intensive and error-prone. To overcome these limitations, TEB-YOLO is introduced in this paper, a lightweight and efficient defect detection model based on YOLOv5s. Firstly, EfficientViT replaces the original YOLOv5s backbone, reducing the computational cost while improving feature extraction. Secondly, BiFPN is adopted in place of PANet to enhance multi-scale feature fusion and preserve detailed information. Thirdly, an Efficient Local Attention (ELA) mechanism is embedded in the backbone to strengthen local feature representation. Lastly, the original CIoU loss is replaced with EIoU loss to enhance localization precision. The proposed model achieves a precision of 91.7% with only 10.5 million parameters, marking a 5.4% accuracy improvement and a 22.9% reduction in parameters compared to YOLOv5s. Compared with other mainstream models including YOLOv5n, YOLOv7, YOLOv8n, YOLOv9t, and YOLOv9s, TEB-YOLO achieves precision improvements of 11.8%, 1.66%, 2.0%, 2.8%, and 1.1%, respectively. The experiment results show that TEB-YOLO significantly improves detection precision and model lightweighting, offering a practical and effective solution for real-time bamboo surface defect detection. Full article

Human Activity Recognition (HAR) is a vital technology in domains such as healthcare, fitness, and smart environments. This paper presents an innovative HAR system that leverages machine-learning algorithms deployed on the B-L475E-IOT01A Discovery Kit, a highly efficient microcontroller platform designed for low-power, real-time applications. The system utilizes wearable sensors (accelerometers and gyroscopes) integrated with the kit to enable seamless data acquisition and processing. Our model achieves outstanding performance in classifying dynamic activities, including walking, walking upstairs, and walking downstairs, with high precision and recall, demonstrating its reliability and robustness. However, distinguishing between static activities, such as sitting and standing, remains a challenge, with the model showing a lower recall for sitting due to subtle postural differences. To address these limitations, we implement advanced feature extraction, data augmentation, and sensor fusion techniques, which significantly improve classification accuracy. The ease of use of the B-L475E-IOT01A kit allows for real-time activity classification, validated through the Tera Term interface, making the system ideal for practical applications in wearable devices and embedded systems. The novelty of our approach lies in the seamless integration of real-time processing capabilities with advanced machine-learning techniques, providing immediate, actionable insights. With an overall classification accuracy of 90%, this system demonstrates great potential for deployment in health monitoring, fitness tracking, and eldercare applications. Future work will focus on enhancing the system’s performance in distinguishing static activities and broadening its real-world applicability. Full article

This paper presents a method to enhance extended interaction oscillator (EIO) output power based on a dual-cavity parallel structure (DCPS). This stucture consists of two conventional ladder-line structures in parallel through a connecting structure, which improves the coupling efficiency between the cavities. The dual output power fusion structure employs an H-T type combiner as the output coupler, which can effectively combine the two input waves in phase to further increase the output power. The dispersion characteristics, coupling impedance, and field distribution of the DCPS are investigated through numerical and simulation calculations, and the optimal operating parameters and output structure are obtained by PIC simulation. At an operating voltage of 12.6 kV, current density of 200 A/${\mathrm{cm}}^2$, and longitudinal magnetic field of 0.5 T, the DCPS EIO exhibits an output power exceeding 600 W at a frequency of 140.6 GHz. This represents a nearly three-fold enhancement compared with the 195 W output of the conventional ladder-line EIO structure. These findings demonstrate the significant improvement in output power and interaction efficiency achieved by the DCPS for the EIO device. Full article

Early detection of plant leaf diseases is a major necessity for controlling the spread of infections and enhancing the quality of food crops. Recently, plant disease detection based on deep learning approaches has achieved better performance than current state-of-the-art methods. Hence, this paper utilized a convolutional neural network (CNN) to improve rice leaf disease detection efficiency. We present a modified YOLOv8, which replaces the original Box Loss function by our proposed combination of EIoU loss and $\alpha$-IoU loss in order to improve the performance of the rice leaf disease detection system. A two-stage approach is proposed to achieve a high accuracy of rice leaf disease identification based on AI (artificial intelligence) algorithms. In the first stage, the images of rice leaf diseases in the field are automatically collected. Afterward, these image data are separated into blast leaf, leaf folder, and brown spot sets, respectively. In the second stage, after training the YOLOv8 model on our proposed image dataset, the trained model is deployed on IoT devices to detect and identify rice leaf diseases. In order to assess the performance of the proposed approach, a comparative study between our proposed method and the methods using YOLOv7 and YOLOv5 is conducted. The experimental results demonstrate that the accuracy of our proposed model in this research has reached up to 89.9% on the dataset of 3175 images with 2608 images for training, 326 images for validation, and 241 images for testing. It demonstrates that our proposed approach achieves a higher accuracy rate than existing approaches. Full article

The aging population has posed a challenge to China’s carbon neutrality pledge. To study the household carbon footprint in an aging society, this paper has combined the age-specific consumption pattern and environmental input-output life cycle assessment (EIO-LCA) to calculate the carbon footprint of household consumption across age groups, and then identified the key pathways of carbon emissions via structural path analysis (SPA). Results indicate that the elderly contribute 11.65% to total consumption-based carbon emissions. The working group (ages 15–64) has the highest average carbon footprint (0.85 tCO₂e), while the elderly group (ages 65 and above) has the lowest average carbon footprint (0.82 tCO₂e). Urban households of all ages have a higher carbon footprint than rural households. Housing and food are the dominant sources of the elderly carbon footprint. Notably, the production and distribution of electric power and heat power sector associated with housing energy consumption plays a leading role in the carbon emissions pathways of elderly consumption. Measuring the carbon footprint of older people can support policy designs and decision making in key sectors along the supply chain, and further encourage low-carbon lifestyles among China’s elderly. Additionally, the findings of this study have broad applications, especially for developing countries undergoing demographic transitions. Full article

With the global expansion of urban infrastructure and development of 5G communication technology, advanced information and communications technology has been applied to power systems and the use of smart grids has increased. Smart grid systems collect energy data using Internet-of-Things (IoT) devices, such as data concentrator units (DCUs) and smart meters, to effectively manage energy. Services and functions for energy management are being incorporated into home IoT devices. In this paper, the IoT for energy management in smart cities and smart homes is referred to as the E-IoT. Systems that use the E-IoT can efficiently manage data, but they present many potential security threats, because the E-IoT devices in such homes and enterprises are networked for energy management. Therefore, in this study, to identify vulnerabilities in the E-IoT device systems, digital forensics is applied to the E-IoT device systems. E-IoT devices supplied to Korean power systems were used to build a digital forensic test bed similar to actual E-IoT environments. For digital forensics application, E-IoT data acquisition and analysis methodology was proposed. The proposed methodology consisted of three methods—network packet data analysis, hardware interface analysis, and mobile device paired with E-IoT—which were applied to a DCU, smart meter, smart plug, smart heat controller, smart microwave, and smart monitoring system. On analyzing the user and system data acquired, artifacts such as the device name and energy consumption were derived. User accounts and passwords and energy-usage logs were obtained, indicating the possibility of leakage of personal information and the vulnerabilities of E-IoT devices. Full article

The Internet of Things (IoT) paradigm aims to enhance human society and living standards with the vast deployment of smart and autonomous devices, which requires seamless collaboration. The number of connected devices increases daily, introducing identity management requirements for edge IoT devices. Due to IoT devices’ heterogeneity and resource-constrained configuration, traditional identity management systems are not feasible. As a result, identity management for IoT devices is still an open issue. Distributed Ledger Technology (DLT) and blockchain-based security solutions are becoming popular in different application domains. This paper presents a novel DLT-based distributed identity management architecture for edge IoT devices. The model can be adapted with any IoT solution for secure and trustworthy communication between devices. We have comprehensively reviewed popular consensus mechanisms used in DLT implementations and their connection to IoT research, specifically identity management for Edge IoT devices. Our proposed location-based identity management model is generic, distributed, and decentralized. The proposed model is verified using the Scyther formal verification tool for security performance measurement. SPIN model checker is employed for different state verification of our proposed model. The open-source simulation tool FobSim is used for fog and edge/user layer DTL deployment performance analysis. The results and discussion section represents how our proposed decentralized identity management solution should enhance user data privacy and secure and trustworthy communication in IoT. Full article

Authenticated key agreement is a process in which protocol participants communicate over a public channel to share a secret session key, which is then used to encrypt data transferred in subsequent communications. LLAKEP, an authenticated key agreement protocol for Energy Internet of Things (EIoT) applications, was recently proposed by Zhang et al. While the proposed protocol has some interesting features, such as putting less computation on edge devices versus the server side, its exact security level is unclear. As a result, we shed light on its security in this paper through careful security analysis against various attacks. Despite the designers’ security claims in the random oracle model and its verification using GNY logic, this study demonstrates that this protocol has security weaknesses. We show that LLAKEP is vulnerable to traceability, dictionary, stolen smart glass, known session-specific temporary information, and key compromise impersonation attacks. Furthermore, we demonstrate that it does not provide perfect forward secrecy. To the best of our knowledge, it is the protocol’s first independent security analysis. To overcome the LLAKEP vulnerabilities, we suggested the LLAKEP${}^{+}$ protocol, based on the same set of cryptographic primitives, namely the one-way hash function and ECC point multiplication. Our comprehensive security analysis demonstrates its resistance to different threats, such as impersonation, privileged insider assaults, and stolen smart glass attacks, along with its resistance to sophisticated assaults, such as key compromised impersonation (KCI) and known session-specific temporary information (KSTI). The overhead of the proposed protocol is acceptable compared to the provided security level. Full article

The authenticated key exchange (AKE) protocol can ensure secure communication between a client and a server in the electricity transaction of the Energy Internet of things (EIoT). Park proposed a two-factor authentication protocol 2PAKEP, whose computational burden of authentication is evenly shared by both sides. However, the computing capability of the client device is weaker than that of the server. Therefore, based on 2PAKEP, we propose an authentication protocol that transfers computational tasks from the client to the server. The client has fewer computing tasks in this protocol than the server, and the overall latency will be greatly reduced. Furthermore, the security of the proposed protocol is analyzed by using the ROR model and GNY logic. We verify the low-latency advantage of the proposed protocol through various comparative experiments and use it for EIoT electricity transaction systems in a Metaverse scenario. Full article

With the development of the smart grid, massive electric Internet of Things (EIoT) devices are deployed to collect data and offload them to edge servers for processing. However, the task of offloading optimization still faces several challenges, such as the differentiated quality of service (QoS) requirements, decision coupling among multiple devices, and the impact of electromagnetic interference. In this paper, a low-complexity delay and energy-efficiency-balanced task offloading algorithm based on many-to-one two-sided matching is proposed for an EIoT. The proposed algorithm shows its novelty in the dynamic tradeoff between energy efficiency and delay as well as in low-complexity and stable task offloading. Specifically, we firstly formulate the minimization problem of weighted difference between delay and energy efficiency to realize the joint optimization of differentiated QoS requirements. Then, the joint optimization problem is transformed into a many-to-one two-sided matching problem. Through continuous iteration, a stable matching between devices and servers is established to cope with decision coupling among multiple devices. Finally, the effectiveness of the proposed algorithm is validated through simulations. Compared with two advanced algorithms, the weighted difference between the energy efficiency and delay of the proposed algorithm is increased by 29.01% and 45.65% when the number of devices is 120, and is increased by 11.57% and 22.25% when the number of gateways is 16. Full article

The Internet of things (IoT) will accommodate several billions of devices to the Internet to enhance human society as well as to improve the quality of living. A huge number of sensors, actuators, gateways, servers, and related end-user applications will be connected to the Internet. All these entities require identities to communicate with each other. The communicating devices may have mobility and currently, the only main identity solution is IP based identity management which is not suitable for the authentication and authorization of the heterogeneous IoT devices. Sometimes devices and applications need to communicate in real-time to make decisions within very short times. Most of the recently proposed solutions for identity management are cloud-based. Those cloud-based identity management solutions are not feasible for heterogeneous IoT devices. In this paper, we have proposed an edge-fog based decentralized identity management and authentication solution for IoT devices (IoTD) and edge IoT gateways (EIoTG). We have also presented a secure communication protocol for communication between edge IoT devices and edge IoT gateways. The proposed security protocols are verified using Scyther formal verification tool, which is a popular tool for automated verification of security protocols. The proposed model is specified using the PROMELA language. SPIN model checker is used to confirm the specification of the proposed model. The results show different message flows without any error. Full article

The sinkhole attack in an edge-based Internet of Things (IoT) environment (EIoT) can devastate and ruin the whole functioning of the communication. The sinkhole attacker nodes ( $SHA$ s) have some properties (for example, they first attract the other normal nodes for the shortest path to the destination and when normal nodes initiate the process of sending their packets through that path (i.e., via $SHA$ ), the attacker nodes start disrupting the traffic flow of the network). In the presence of $SHA$ s, the destination (for example, sink node i.e., gateway/base station) does not receive the required information or it may receive partial or modified information. This results in reduction of the network performance and degradation in efficiency and reliability of the communication. In the presence of such an attack, the throughput decreases, end-to-end delay increases and packet delivery ratio decreases. Moreover, it may harm other network performance parameters. Hence, it becomes extremely essential to provide an effective and competent scheme to mitigate this attack in EIoT. In this paper, an intrusion detection scheme to protect EIoT environment against sinkhole attack is proposed, which is named as SAD-EIoT. In SAD-EIoT, the resource rich edge nodes (edge servers) perform the detection of different types of sinkhole attacker nodes with the help of exchanging messages. The practical demonstration of SAD-EIoT is also provided using the well known NS2 simulator to compute the various performance parameters. Additionally, the security analysis of SAD-EIoT is conducted to prove its resiliency against various types of $SHA$ s. SAD-EIoT achieves around $95.83\%$ detection rate and $1.03\%$ false positive rate, which are considerably better than other related existing schemes. Apart from those, SAD-EIoT is proficient with respect to computation and communication costs. Eventually, SAD-EIoT will be a suitable match for those applications which can be used in critical and sensitive operations (for example, surveillance, security and monitoring systems). Full article

In recent years, edge computing has emerged as a new concept in the computing paradigm that empowers several future technologies, such as 5G, vehicle-to-vehicle communications, and the Internet of Things (IoT), by providing cloud computing facilities, as well as services to the end users. However, open communication among the entities in an edge based IoT environment makes it vulnerable to various potential attacks that are executed by an adversary. Device authentication is one of the prominent techniques in security that permits an IoT device to authenticate mutually with a cloud server with the help of an edge node. If authentication is successful, they establish a session key between them for secure communication. To achieve this goal, a novel device authentication and key management mechanism for the edge based IoT environment, called the lightweight authentication and key management scheme for the edge based IoT environment (LDAKM-EIoT), was designed. The detailed security analysis and formal security verification conducted by the widely used “Automated Validation of Internet Security Protocols and Applications (AVISPA)” tool prove that the proposed LDAKM-EIoT is secure against several attack vectors that exist in the infrastructure of the edge based IoT environment. The elaborated comparative analysis of the proposed LDAKM-EIoT and different closely related schemes provides evidence that LDAKM-EIoT is more secure with less communication and computation costs. Finally, the network performance parameters are calculated and analyzed using the NS2 simulation to demonstrate the practical facets of the proposed LDAKM-EIoT. Full article

A research model using the market price for greenhouse gas (GHG) emissions illustrates how the policies, and economic and environment implications of the carbon price can be formulated using a deterministic equilibrium model. However, with increasing carbon costs, the optimal reverse supply chain (RSC) system is being required to adapt and has undergone many distinct shifts in character as it seeks out new configurations through which costs may be effectively managed and minimized. The model was studied comprehensively in terms of quantitative performance using orthogonal arrays. The results were compared to top-down estimates produced through economic input-output life cycle assessment (EIO-LCA) models, providing a basis to contrast remanufacturing GHG emission quantities with those realized through original equipment manufacturing operations. Introducing a carbon cost of $40/t CO2e increased modeled remanufacturing costs by 2.7%, but also increased original equipment costs by 2.3%. The research presented in this study puts forward the theoretical modeling of optimal RSC systems and provides an empirical case study concerning remanufactured appliances, an area of current industrial literature in which there is a dearth of study. Full article