A 0.52% nonlinearity,$44.9zF/\sqrt {Hz}$input capacitance noise capacitance-to-voltage (C/V) converter for MEMS accelerometer is proposed in this study. The multi-step successive approximation (SA) output mechanism is proposed to replace the conventional single-step conversion. Meanwhile, combining with the modulation-demodulation (MD) technique in the traditional continuous-time (CT) C/V converter, a much lower nonlinearity and noise CT C/V converter without increasing power consumption is achieved in this work. In addition, a short time reset signal between modulation and demodulation is proposed to reset the integration capacitors in each conversion cycle, which lets the C/V converter achieve an approximate twice dynamic range enhancement (DRE). The proposed C/V converter is implemented in a standard 180 nm CMOS process. It incorporates an on-chip capacitor signal generator (CSG), whose capacitance is tuned from −80 fF to 80 fF using clock signals at frequencies of 10Hz, 1 kHz, 2 kHz, 3 kHz, 4 kHz, and 5 kHz respectively, where measurement results demonstrate that the converter has correct output voltage across this frequency range, with a conversion error of less than$350~ppm/\sqrt {Hz}$. The proposed C/V converter is also tested together with an integrated a MEMS accelerometer element. It achieves a maximum nonlinearity as low as 0.52% within ±10 g acceleration and obtains equivalent input noise power spectral density (PSD) as low as$44.9~zF/\sqrt {Hz}$when the frequency is beyond 50 Hz.

Promoting the synergistic optimization of electric carbon emission reduction and the green transformation of the economy and society is an important aspect of implementing sustainable development. This paper used a coupling coordination model to measure the relationship between the carbon emission intensity of electricity consumption (CIEC) and the comprehensive green transformation (CGT) in the three major urban agglomerations of the Yangtze River Economic Belt (YREB) from 2010 to 2021. On this basis, Kernel density estimation and exploratory spatial data analysis were employed to reveal the spatiotemporal evolution patterns of the coupling coordination relationship. Furthermore, the Dagum Gini coefficient and Markov chain were used to analyze the regional differences and hierarchical transition trends. The results indicated that: (1) The CIEC showed a gradual decrease, while the CGT first increased and then declined slowly. (2) The coupling degree exhibited a downward trend, with a spatial pattern of east-to-west decline. (3) The overall imbalance in the coupling coordination of the YREB improved annually, but spatial disparities among urban agglomerations remained significant. (4) The Yangtze River Delta (YRD) demonstrated high coordination stability and potential for upgrading; the middle reaches of the Yangtze River (MRTR) faced challenges of instability in moderate coordination, while the Chengyu (CY) region had a relatively severe risk of degradation. This study provided significant theoretical underpinnings for formulating regionally differentiated carbon emission reduction policies and advancing high-quality development within YREB.

In large-scale edge computing networks, efficient task scheduling is essential for applications with stringent requirements on reliability and low latency, such as autonomous driving and industrial Internet of Things (IoT). However, traditional scheduling approaches often fall short in dynamic environments involving node failures, user mobility, and network congestion. The dynamic adaptive scheduling (DAS) method based on a dynamic framework is proposed, which combines geometric mapping with a dynamic model to simulate edge node load conditions and the task allocation process, enabling efficient and adaptive task distribution. Experimental results indicate that DAS demonstrates superior adaptability and performance under complex and dynamic scenarios, significantly outperforming traditional methods such as random and greedy scheduling. These results validate the effectiveness of DAS in enhancing system reliability and scheduling efficiency in large-scale edge computing environments.

In this paper, a bandwidth from 100Hz to 100kHz, 40dB gain filter is designed for applications where the low-frequency voltage should be reduced; while the signal should be amplified. The band-pass filter consists of a second-order high-pass filter and a capacitively-coupled chopper instrumentation amplifier (CCIA), in which the resistor of the high-pass filter adopts the current-controlled resistor technique, which greatly reduces the area compared with the traditional circuit. Simulation results show that the band pass filter can achieve effective 1/f noise suppression, reaching 74 dB common mode rejection ratio (CMRR), 73 dB power supply rejection ratio (PSRR) and 0.34% total harmonic distortion (THD) at different process angles. This is superior to other amplifiers in this frequency band.

Cloud-Fog Automation (CFA) represents a fundamental paradigm to facilitate flexible deployment modalities of industrial applications. Adopting B5G/6G technologies, software-defined wireless CFA separates wireless networking and computing functions from proprietary hardware, thereby significantly improving the flexibility and scalability of industrial automation. Despite its advantages, optimizing the end-to-end performance in software-defined wireless CFA is technically challenging, due to computing resource fluctuations and the uncertainties imposed on communication. To tackle these challenges, this study presents a Reinforcement Learning (RL)-based latency optimization scheme for software-defined wireless CFA. Jointly considering the wireless channel conditions and computing resource constraints, it adaptively allocates B5G/6G Radio Access Network (RAN) resources for improved performance on end-to-end (E2E) latency. For validation, a full-stack software-defined CFA testbed was implemented utilizing open-source projects, e.g., srsRAN and Open5GS. The numerical data indicate that the proposed scheme surpasses the benchmark, simultaneously achieving lower end-to-end latency and improved performance stability.

Edge computing is vital in developing smart cities by providing on-site computational resources to support the surging Internet of Things demands. However, the distributed nature of edge nodes and large scale of tasks distributed in expansive urban spaces challenge task scheduling and resource allocation. In this paper, a novel framework is developed to achieve efficient task scheduling (assignment and offloading) and resource allocation for large-scale edge computing in both wired and wireless smart-city applications. To overcome overparameterization in existing optimization-based heuristic algorithms, the geometrized task scheduling problem is addressed by transforming the assignment of clustered tasks into a regional partition problem in a two-dimensional graph and applying a Tetris-like task offloading strategy for edge-cloud cooperation. These approaches avoid combinatorial explosion and NP-hardness, and the regional partition problem is solved by multiplicative weighted Voronoi diagrams with polynomial computational complexity. Furthermore, an adaptive resource allocation algorithm is proposed to overcome the dynamic, uncertain, and highly concurrent task requests. An online learning algorithm is adopted to adjust the sliding window length according to the evolving conditions. Comparison results show that the proposed framework significantly reduces the average task deadline violation rate, i.e., up to 4.72% of (more than 20 times better than) those using the other schemes, especially when handling large-scale workloads.

Load forecasting is critical to the task of energy management in power systems, for example, balancing supply and demand and minimizing energy transaction costs. There are many approaches used for load forecasting such as the support vector regression (SVR), the autoregressive integrated moving average (ARIMA), and neural networks, but most of these methods focus on single-step load forecasting, whereas multistep load forecasting can provide better insights for optimizing the energy resource allocation and assisting the decision-making process. In this work, a novel sequence-to-sequence (Seq2Seq)-based deep learning model based on a time series decomposition strategy for multistep load forecasting is proposed. The model consists of a series of basic blocks, each of which includes one encoder and two decoders; and all basic blocks are connected by residuals. In the inner of each basic block, the encoder is realized by temporal convolution network (TCN) for its benefit of parallel computing, and the decoder is implemented by long short-term memory (LSTM) neural network to predict and estimate time series. During the forecasting process, each basic block is forecasted individually. The final forecasted result is the aggregation of the predicted results in all basic blocks. Several cases within multiple real-world datasets are conducted to evaluate the performance of the proposed model. The results demonstrate that the proposed model achieves the best accuracy compared with several benchmark models.

Industrial automation systems are pivotal in enhancing the digitization and intelligence of the industrial sector. In recent years, wireless communication technologies (such as B5G/6G) and Cloud Fog Automation (CFA) have revolutionized the industrial sector, providing unparalleled flexibility and scalability. However, the prevailing CFA architecture predominantly concentrates on network and system levels, often neglecting the optimization of the physical layer communication process. To achieve the optimal system-level performance of wireless CFA, this study introduces CFA-OpenRAN, an integrated architecture encompassing communication, computing, and control. By leveraging the open design, standardized interfaces, and software-defined paradigm of O-RAN, CFA-OpenRAN facilitates cross-domain optimization of communication, computing, and industrial control in wireless CFA. In the end, an application scenario of joint resource allocation and the corresponding experiment setup was developed.

This paper investigates the multi-sensor distributed fusion to estimate the state of target involved in industrial agriculture under heavily-constrained communication. To fully address problems of both random communication delay and limitted transmission rate, we derive a multi-sensor distributed tracklet fusion based on augment state (AS-DTF) method. The derived AS-DTF leverages the augmented equivalent measurement to remove correlations of state estimation errors across different sensor at multiple times and is able to fuse multiple multi-step out-of-sequence tracks (OOSTs) in an optimal manner. The simulation results validate the proposed AS-DTF achieves the same state estimation accuracy as the optimal fusion benchmark.

With the development of the Internet of Things (IoT) and cloud computing, various multimedia data such as audio, video, and images have experienced explosive growth, ushering in the era of big data. Large-scale computing tasks in the Multimedia Internet of Things (M-IoT), such as mathematical optimization problems, have begun to be outsourced from IoT devices with limited computing power to cloud servers for execution. However, outsourcing computation brings security concerns, because the behaviors of clouds are invisible to users. The leakage of privacy data in outsourced optimization problems leads to immeasurable losses. The mutual distrust between clouds and users causes that the correctness of the optimal decisions and the fairness of the payment activities are not guaranteed. Blockchain technology has the characteristic of immutability and has become a new security paradigm for eliminating multi-party trust concerns. In this article, we propose a Bitcoin-based secure outsourcing scheme to address the aforementioned security concerns. To prevent confidential data leakage, the proposed scheme designs a computable privacy-preserving method for the outsourced optimization problems. To judge the correctness of the optimal decision and reduce verification costs, the proposed scheme designs a low-cost two-layer verification mechanism based on dual theory and blockchain technology. Blockchain nodes reach a consensus on the problem solutions and trigger an automatic fair payment protocol-based Bitcoin. Security analysis and experimental results demonstrate that our scheme guarantees privacy, fairness, and computational efficiency.

Residential energy consumption continues to climb steadily, requiring intelligent energy management strategies to reduce power system pressures and residential electricity bills. However, it is challenging to design such strategies due to the random nature of electricity pricing, appliance demand, and user behavior. This article presents a novel reward shaping (RS)-based actor–critic deep reinforcement learning (ACDRL) algorithm to manage the residential energy consumption profile with limited information about the uncertain factors. Specifically, the interaction between the energy management center and various residential loads is modeled as a Markov decision process that provides a fundamental mathematical framework to represent the decision-making in situations where outcomes are partially random and partially influenced by the decision-maker control signals, in which the key elements containing the agent, environment, state, action, and reward are carefully designed, and the electricity price is considered as a stochastic variable. An RS-ACDRL algorithm is then developed, incorporating both the actor and critic network and an RS mechanism, to learn the optimal energy consumption schedules. Several case studies involving real-world data are conducted to evaluate the performance of the proposed algorithm. Numerical results demonstrate that the proposed algorithm outperforms state-of-the-art RL methods in terms of learning speed, solution optimality, and cost reduction.

High-precision clock synchronization is required for many industrial Internet of Things (IIoTs) supporting real-time monitoring and control for applications, such as the smart grid. For high-precision clock synchronization in IIoTs, several approaches can be used, such as the IEEE 1588 protocol, the Network Time Protocol (NTP), and the Global Navigation Satellite System (GNSS) based methods. Among them, the GNSS-based methods are more appropriate for IIoTs distributed in wide areas. It has been reported that nanosecond-level time synchronization can be achieved by GNSS receivers. However, the end-to-end accuracy of the system clocks in the application processor is a different story. In this study, an experimental testbed has been built using the open-source Linux system, the BeiDou receiver, and the open-source Raspberry Pi 4 hardware. The Pulse-Per-Second (PPS) signals from the BeiDou receiver are used as the reference to synchronize the system clocks. With a one-second synchronization interval, experiments indicate that the system clock achieves an accuracy of 1 µs with a success rate of 92.7% and 2 µs with a success rate of 99.7%. While increasing the synchronization interval, the system clock relies more on the local crystal oscillator, and the timing error increases to 100 µs in 500 seconds.

Advances in information and communication technologies have significantly influenced the operation of low-voltage distribution grids. As essential elements of distribution grids, user-side smart meters find many smart grid applications, for example to measure electrical energy use and facilitate communications. However, the service models of distribution grids remain under development in association with upgrading of user-side smart meters. These meters are resource constrained, and challenging to upgrade on a large scale. To address this issue, this article describes a container-driven service architecture, in which containers are used to create a virtual dedicated agent (digital twin) for each user-side smart meter. The agent can be deployed either in the cloud or on an edge system, and can be upgraded to support emerging smart grid applications, thus minimizing the future upgrading requirements of user-side smart meters. We built experimental test beds to verify the proposed architecture and evaluated its performance in real-world experiments.

As a distributed computing paradigm, edge computing has become a key technology for providing timely services to mobile devices by connecting Internet of Things (IoT), cloud centers, and other facilities. By offloading compute-intensive tasks from IoT devices to edge/cloud servers, the communication and computation pressure caused by the massive data in Industrial IoT can be effectively reduced. In the process of computation offloading in edge computing, it is critical to dynamically make optimal offloading decisions to minimize the delay and energy consumption spent on the devices. Although there are a large number of task offloading-decision models, how to measure and evaluate the quality of different models and configurations is crucial. In this article, we propose a novel simulation platform named ChainFL, which can build an edge computing environment among IoT devices while being compatible with federated learning and blockchain technologies to better support the embedding of security-focused offloading algorithms. ChainFL is lightweight and compatible, and it can quickly build complex network environments by connecting devices of different architectures. Moreover, due to its distributed nature, ChainFL can also be deployed as a federated learning platform across multiple devices to enable federated learning with high security due to its embedded blockchain. Finally, we validate the versatility and effectiveness of ChainFL by embedding a complex offloading-decision model in the platform, and deploying it in an Industrial IoT environment with security risks.

With the development of mobile computing, crowd-sourcing has become one of the key technologies supporting collaborative tasks. It has been widely used in areas of real-time localization, such as data collection and fingerprint calibration. Currently, crowdsourcing-based localization are mainly designed for WiFi signals. However, due to the excellent performance on real-time positioning, Ultra-Wideband (UWB) has attracted the attention of major smart device makers. It is highly expected that UWB will be supported by smart devices in the near future. With this consideration, a crowdsourcing localization scheme is introduced based characteristics of UWB signals. Compared to existing positioning technologies, the proposed scheme does not require direct connection between UWB anchors and smart devices. Instead, the smart devices collaborate with peers to complete the positioning process. Finally, a simulation example is provided as a demonstration and to evaluate the performance.

Neighborhood area networks (NANs) are essential communication infrastructure in smart grid. They support communications for various applications including time-critical ones. A typical NAN communication scenario is to send commands from a control center simultaneously to a large number of nodes, demanding low-latency broadcast communications. This is challenging due to the limited bandwidth and large number of nodes in wireless NANs. While some broadcast schemes, e.g., opportunistic flooding, have been developed for general wireless sensor networks, they are not optimized for smart grid NANs with unique characteristics and low-latency requirements for time-critical applications. Therefore, a constrained broadcast scheme with minimized latency (CBS-ML) is presented in this paper for low-latency NAN communications. To avoid traffic congestion, it constrains the broadcast to a small number of core nodes. Theoretical developments are presented to show how to select core nodes based on network topology and link reliability. Simulations are conducted to demonstrate the proposed CBS-ML.

In smart grid, demand-side communications play a significant role in real-time applications, such as demand response and advanced metering. With recent progresses on Internet-of- Things (IoTs), different communication technologies are available for the demand side, such as ZigBee, Bluetooth, Wi-Fi, etc. To improve the inter-operability of various IoTs in demand side of smart grid, ZigBee and Homeplug Alliances have jointly developed the Smart Energy Profile 2.0 (SEP 2.0)as the upper-layer communication protocol, which has been approved as an international standard, namely IEEE 2030.5. Despite of its promising application in demand side, the development on constrained IoT devices is challenging. To address this issue, an upper middleware for SEP 2.0 standard is developed based on the SimpleLink Wi- Fi technology of Texas Instruments (TI). It enables SimpleLink Wi-Fi devices to support SEP 2.0-based communications in the demand side of smart grid. In the end, an experimental system is built to demonstrate the effectiveness of the presented upper middleware.

Demand response (DR) smart grid technology provides an opportunity for electricity consumers to actively participate in the management of power systems. Industry is one of the major consumers of electric power. In this study, we propose a DR energy management scheme for industrial facilities based on the state task network (STN) and mixed integer linear programming (MILP). The scheme divides the processing tasks in industrial facilities into nonschedulable tasks (NSTs) and schedulable tasks (STs), and takes advantage of distributed energy resources (DERs) to implement DR. Based on day-ahead hourly electricity prices, the scheme determines the scheduling of STs and DERs in order to shift the demand from peak periods (with high electricity prices) to off-peak periods (with low electricity prices), which not only improves the reliability of the electric power system, but also reduces energy costs for industrial facilities.

. To achieve high levels of performance in parallel geoprocessing, the underlying spatial structure and relations of spatial models must be accounted for and exploited during decomposition into parallel processes. Spatial models are classified from two perspectives, the domain of modelling and the scope of operations, and a framework of strategies is developed to guide the decomposition of models with different characteristics into parallel processes. Two models are decomposed using these strategies: hill-shading on digital elevation models and the construction of Delaunay Triangulations. Performance statistics are presented for implementations of these algorithms on a MIMD computer.