Search Results (7,212)

Proportion Integration Differentiation (PID) is a common clock disciplining algorithm. In satellite clock source equipment and in Internet of Things (IoT) sensor nodes it is usually required that both time and frequency signals have high accuracy. Because the traditional PID clock disciplining method used in the equipment only performs PID calculation and feedback control on single variable, such as frequency, the time accuracy error of the clock source is large and even has inherent deviation. By using the integral relationship between frequency and time, a new two variables PID control algorithm for high-precision clock disciplining is proposed in this paper. Time is taken as the constraint variable to make the time deviation converge. It can guarantee a high accuracy of time and high long-term stability of frequency. At the same time, frequency is taken as the feedback variable to make frequency obtain fast convergence. It can ensure high short-term stability of the frequency and the continuity of time. So, it can make the time and frequency of the disciplined clock have high accuracy and stability at the same time. In order to verify the effectiveness of the proposed algorithm, it is simulated based on the GNSS disciplined clock model. The GNSS time after Kalman filtering is used as the time reference to discipline the local clock. The simulation results show that the time deviation range of a local clock after convergence is −0.38 ns∼0.31 ns, the frequency accuracy is better than $1\times {10}^{-15}$ averaging over one day, and the long-term time stability (TDEV) for a day is about 7 ps when using the two variables PID algorithm. Compared with the single variable PID algorithm, the time accuracy of the two variables PID algorithm is improved by about one order of magnitude and the long-term time stability (TDEV) is improved by about two orders of magnitude. The research results indicate that the two variables PID control algorithm has great application potential for the development of clock source equipment and other bivariate disciplining scenarios. Full article

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. Full article

Future 6G networks will inherit and develop Network Function Virtualization (NFV) architecture. With the NFV-enabled network architecture, it becomes possible to establish different virtual networks within the same infrastructure, create different Virtual Network Functions (VNFs) in different virtual networks, and form Service Function Chains (SFCs) that meet different service requirements through the orderly combination of VNFs. These SFCs can be deployed to physical entities as needed to provide network functions that support different services. To meet the highly dynamic service requirements in the future 6G Internet of Things (IoT) scenario, the highly flexible and efficient SFC reconfiguration algorithm is the key research direction. Deep-learning-based algorithms have shown their advantages in solving this type of dynamic optimization problem. Considering that the efficiency of the traditional Actor Critic (AC) algorithm is limited, the policy does not directly participate in the value function update. In this paper, we use the Proximal Policy Optimization (PPO) clip function to restrict the difference between the new policy and the old policy, to ensure the stability of the updating process. We combine PPO with AC, and further bring the historical decision information as the network knowledge to offer better initial policies, to accelerate the training speed. We also propose the Knowledge = Assisted Actor Critic Proximal Policy Optimization (KA-ACPPO)-based SFC reconfiguration algorithm to ensure the Quality of Service (QoS) of end-to-end services. Simulation results show that the proposed KA-ACPPO algorithm can effectively reduce computing cost and power consumption. Full article

With the development of deep learning foundation model technology, the types of computing tasks have become more complex, and the computing resources and memory required for these tasks have also become more substantial. Since it has long been revealed that task offloading in cloud servers has many drawbacks, such as high communication delay and low security, task offloading is mostly carried out in the edge servers of the Internet of Things (IoT) network. However, edge servers in IoT networks are characterized by tight resource constraints and often the dynamic nature of data sources. Therefore, the question of how to perform task offloading of deep learning foundation model services on edge servers has become a new research topic. However, the existing task offloading methods either can not meet the requirements of massive CNN architecture or require a lot of communication overhead, leading to significant delays and energy consumption. In this paper, we propose a parallel partitioning method based on matrix convolution to partition foundation model inference tasks, which partitions large CNN inference tasks into subtasks that can be executed in parallel to meet the constraints of edge devices with limited hardware resources. Then, we model and mathematically express the problem of task offloading. In a multi-edge-server, multi-user, and multi-task edge-end system, we propose a task-offloading method that balances the tradeoff between delay and energy consumption. It adopts a greedy algorithm to optimize task-offloading decisions and terminal device transmission power to maximize the benefits of task offloading. Finally, extensive experiments verify the significant and extensive effectiveness of our algorithm. Full article

Triboelectric nanogenerators (TENGs), as novel electronic devices for converting mechanical energy into electrical energy, are better suited as signal-testing sensors or as components within larger wearable Internet of Things (IoT) or Artificial Intelligence (AI) systems, where they handle small-device power supply and signal acquisition. Consequently, TENGs hold promising applications in self-powered sensor technology. As global energy supplies become increasingly tight, research into self-powered sensors has become critical. This study presents a self-powered sport sensor system utilizing a triboelectric nanogenerator (TENG), which incorporates a thermoplastic polyurethane (TPU) film doped with graphene and polytetrafluoroethylene (PTFE) as friction materials. The graphene-doped TPU nanocomposite film-based TENG (GT-TENG) demonstrates excellent working durability. Furthermore, the GT-TENG not only consistently powers an LED but also supplies energy to a sports timer and an electronic watch. It serves additionally as a self-powered sensor for monitoring human movement. The design of this self-powered motion sensor system effectively harnesses human kinetic energy, integrating it seamlessly with sport sensing capabilities. Full article

This article describes the development and implementation of a water resource management system utilizing open technologies such as the ESP32 microcontroller and MicroPython. This system stands out for its low cost, high efficiency and adaptability to various environments, thanks to the integration of free or low-cost communications such as LoRaWAN and NB-IoT, as well as the use of open-source programming, which offers flexibility. The article details the use of JSN-SR04T ultrasonic sensors, manufactured by JINZHAN, a company based in China, for water-level measurement and the use of 3D printing to manufacture customized components, demonstrating a scalable and replicable solution for efficient water management. Full article

In a context-aware Internet of Things (IoT) environment, the functional contexts of devices and users will change over time depending on their service consumption. Each iteration of an IoT middleware algorithm will also encounter changes occurring in the contexts due to the joining/leaving of new/old members; this is the inherent nature of ad hoc IoT scenarios. Individual users will have notable preferences in their service consumption patterns; by leveraging these patterns, the approach presented in this article focuses on how these changes impact performance due to functional-context switching over time. This is based on the idea that consumption patterns will exhibit certain time-variant correlations. The maximum likelihood estimation (MLE) is used in the proposed approach to capture the impact of these correlations and study them in depth. The results of this study reveal how the correlation probabilities and the system performance change over time; this also aids with the construction of the boundaries of certain time-variant correlations in users’ consumption patterns. In the proposed approach, the information gleaned from the MLE is used in arranging the service information within a distributed service registry based on users’ service usage preferences. Practical simulations were conducted over small (100 nodes), medium (1000 nodes), and relatively larger (10,000 nodes) networks. It was found that the approach described helps to reduce service discovery time and can improve the performance in service-oriented IoT scenarios. Full article

The field of wireless communication has undergone revolutionary changes driven by technological advancements in recent years. Central to this evolution is wireless ad hoc networks, which are characterized by their decentralized nature and have introduced numerous possibilities and challenges for researchers. Moreover, most of the existing Internet of Things (IoT) networks are based on ad hoc networks. This study focuses on the exploration of interference management and Medium Access Control (MAC) schemes. Through statistical derivations and systematic simulations, we evaluate the efficacy of guard zone-based MAC protocols under Rayleigh fading channel conditions. By establishing a link between network parameters, interference patterns, and MAC effectiveness, this work contributes to optimizing network performance. A key aspect of this study is the investigation of optimal guard zone parameters, which are crucial for interference mitigation. The adaptive guard zone scheme demonstrates superior performance compared to the widely recognized Carrier Sense Multiple Access (CSMA) and the system-wide fixed guard zone protocol under fading channel conditions that mimic real-world scenarios. Additionally, simulations reveal the interactions between network variables such as node density, path loss exponent, outage probability, and spreading gain, providing insights into their impact on aggregated interference and guard zone effectiveness. Full article

In the present day, the health of the populace is significantly jeopardized by the presence of contaminated water, and the majority of the population is unaware of the distinction between safe and unsafe water consumption. Agricultural, industrial, and other human-induced activities are causing a significant decline in the availability of drinking water. Consequently, the issue of ensuring the safety of ingesting water is becoming increasingly prevalent. People should be aware of the purity of the water and the locations where it can be used in order to resolve this situation. There are numerous IoT-based system architectures that are capable of monitoring water parameters; however, the majority of these architectures do not allow for real-time water quality prediction or visualization. In order to achieve this, we suggest a wireless framework that is based on the Internet of Things (IoT). The sensors are able to capture water parameters and transmit the data to the cloud, where a machine learning (ML) model operates to classify the water quality. After that, Grafana enables us to effortlessly visualize the real-time data and predictions from any location. We employed a multi-class dataset from China for the model’s construction. GridSearchCV was implemented to identify the optimal parameters for model optimization. The proposed model is a combination of the Random Forest (RF), Extreme Gradient Boosting (XGB), and Histogram Gradient Boosting (HGB) models. The accuracy of the model for the China dataset was 99.80%. To assess the robustness of the proposed model, we acquired a new dataset from the Bangladesh Water Development Board (BWDB) and used it to test the proposed model. The model’s accuracy for this dataset was 99.72%. In summary, the proposed wireless IoT framework enables individuals to effortlessly monitor the purity of water and view its parameters from any location. Full article

In the Internet of Things (IoT), the selection of mobile users with IoT-enabled devices plays a crucial role in ensuring the efficiency and accuracy of data collection. The reputation of these mobile users is a key indicator in selecting high-quality participants, as it directly reflects the reliability of the data they submit and their past performance. However, existing approaches often rely on a trusted centralized server, which can lead to single points of failure and increased vulnerability to attacks. Additionally, they may not adequately address the potential manipulation of reputation scores by malicious entities, leading to unreliable and potentially compromised user selection. To address these challenges, we propose PRUS, a privacy-preserving and quality-aware user selection scheme for IoT. By leveraging the decentralized and immutable nature of the blockchain, PRUS enhances the reliability of the user selection process. The scheme utilizes a public-key cryptosystem with distributed decryption to protect the privacy of users’ data and reputation, while truth discovery techniques are employed to ensure the accuracy of the collected data. Furthermore, a privacy-preserving verification algorithm using reputation commitment is developed to safeguard against the malicious tampering of reputation scores. Finally, the Dirichlet distribution is used to predict future reputation values, further improving the robustness of the selection process. Security analysis demonstrates that PRUS effectively protects user privacy, and experimental results indicate that the scheme offers significant advantages in terms of communication and computational efficiency. Full article

The advent of the Fourth Industrial Revolution has positioned the Internet of Things as a pivotal force in intelligent vehicles. With the source of vehicle-to-everything (V2X), Internet of Things (IoT) networks, and inter-vehicle communication, intelligent connected vehicles are at the forefront of this transformation, leading to complex vehicular networks that are crucial yet susceptible to cyber threats. The complexity and openness of these networks expose them to a plethora of cyber-attacks, from passive eavesdropping to active disruptions like Denial of Service and Sybil attacks. These not only compromise the safety and efficiency of vehicular networks but also pose a significant risk to the stability and resilience of the Internet of Vehicles. Addressing these vulnerabilities, this paper proposes a Dynamic Forest-Structured Ensemble Network (DFSENet) specifically tailored for the Internet of Vehicles (IoV). By leveraging data-balancing techniques and dimensionality reduction, the DFSENet model is designed to detect a wide range of cyber threats effectively. The proposed model demonstrates high efficacy, with an accuracy of 99.2% on the CICIDS dataset and 98% on the car-hacking dataset. The precision, recall, and f-measure metrics stand at 95.6%, 98.8%, and 96.9%, respectively, establishing the DFSENet model as a robust solution for securing the IoV against cyber-attacks. Full article

With the development of the Internet of Things (IoT) and edge computing, more and more devices, such as sensor nodes and intelligent automated guided vehicles (AGVs), can serve as edge devices to provide Location-Based Services (LBS) through the IoT. As the number of applications increases, there is an abundance of sensitive information in the communication process, pushing the focus of privacy protection towards the communication process and edge devices. The challenge lies in the fact that most traditional location privacy protection algorithms are not suited for the IoT with edge computing, as they primarily focus on the security of remote servers. To enhance the capability of location privacy protection, this paper proposes a novel K-anonymity algorithm based on clustering. This novel algorithm incorporates a scheme that flexibly combines real and virtual locations based on the requirements of applications. Simulation results demonstrate that the proposed algorithm significantly improves location privacy protection for the IoT with edge computing. When compared to traditional K-anonymity algorithms, the proposed algorithm further enhances the security of location privacy by expanding the potential region in which the real node may be located, thereby limiting the effectiveness of “narrow-region” attacks. Full article

Trigger–action programming (TAP) enables users to automate Internet of Things (IoT) devices by creating rules such as “IF Device1.TriggerState is triggered, THEN Device2.ActionState is executed”. As the number of IoT devices grows, the combination space between the functions provided by devices expands, making manual rule creation time-consuming for end-users. Existing TAP recommendation systems enhance the efficiency of rule discovery but face two primary issues: they ignore the association of rules between users and fail to model collaborative information among users. To address these issues, this article proposes a graph contrastive learning-based recommendation system for TAP rules, named GCL4TAP. In GCL4TAP, we first devise a data partitioning method called DATA2DIV, which establishes cross-user rule relationships and is represented by a user–rule bipartite graph. Then, we design a user–user graph to model the similarities among users based on the categories and quantities of devices that they own. Finally, these graphs are converted into low-dimensional vector representations of users and rules using graph contrastive learning techniques. Extensive experiments conducted on a real-world smart home dataset demonstrate the superior performance of GCL4TAP compared to other state-of-the-art methods. Full article

This research aims to optimize the solar–hydrogen energy system at Kangwon National University’s Samcheok campus by leveraging the integration of artificial intelligence (AI), the Internet of Things (IoT), and machine learning. The primary objective is to enhance the efficiency and reliability of the renewable energy system through predictive modeling and advanced fault detection techniques. Key elements of the methodology include data collection from solar energy production and fault detection systems, energy potential analysis using Transformer models, and fault identification in solar panels using CNN and ResNet-50 architectures. The Transformer model was evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and an additional variation of MAE (MAE2). Known for its ability to detect intricate time series patterns, the Transformer model exhibited solid predictive performance, with the MAE and MAE2 results reflecting consistent average errors, while the MSE pointed to areas with larger deviations requiring improvement. In fault detection, the ResNet-50 model outperformed VGG-16, achieving 85% accuracy and a 42% loss, as opposed to VGG-16’s 80% accuracy and 78% loss. This indicates that ResNet-50 is more adept at detecting and classifying complex faults in solar panels, although further refinement is needed to reduce error rates. This study demonstrates the potential for AI and IoT integration in renewable energy systems, particularly within academic institutions, to improve energy management and system reliability. Results suggest that the ResNet-50 model enhances fault detection accuracy, while the Transformer model provides valuable insights for strategic energy output forecasting. Future research could focus on incorporating real-time environmental data to improve prediction accuracy and developing automated AIoT-based monitoring systems to reduce the need for human intervention. This study provides critical insights into advancing the efficiency and sustainability of solar–hydrogen systems, supporting the growth of AI-driven renewable energy solutions in university settings. Full article

Theoretical mathematics is a key evolution factor of artificial intelligence (AI). Nowadays, representing a smart system as a mathematical model helps to analyze any system under development and supports different case studies found in real life. Additionally, the Markov chain has shown itself to be an invaluable tool for decision-making systems, natural language processing, and predictive modeling. In an Internet of Things (IoT), Machine-to-Machine (M2M) traffic necessitates new traffic models due to its unique pattern and different goals. In this context, we have two types of modeling: (1) source traffic modeling, used to design stochastic processes so that they match the behavior of physical quantities of measured data traffic (e.g., video, data, voice), and (2) aggregated traffic modeling, which refers to the process of combining multiple small packets into a single packet in order to reduce the header overhead in the network. In IoT studies, balancing the accuracy of the model while managing a large number of M2M devices is a heavy challenge for academia. One the one hand, source traffic models are more competitive than aggregated traffic models because of their dependability. However, their complexity is expected to make managing the exponential growth of M2M devices difficult. In this paper, we propose to use a Markov-Modulated Poisson Process (MMPP) framework to explore Human-to-Human (H2H) traffic and M2M heterogeneous traffic effects. As a tool for stochastic processes, we employ Markov chains to characterize the coexistence of H2H and M2M traffic. Using the traditional evolved Node B (eNodeB), our simulation results show that the network’s service completion rate will suffer significantly. In the worst-case scenario, when an accumulative storm of M2M requests attempts to access the network simultaneously, the degradation reaches 8% as a completion task rate. However, using our “Coexistence of Heterogeneous traffic Analyzer and Network Architecture for Long term evolution” (CHANAL) solution, we can achieve a service completion rate of 96%. Full article