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With the integration of sustainable energy, the power grid has become increasingly information-intensive and complex. To address the issue of power grid cyber-physical systems being unable to operate securely and stably when systems suffer false data injection attacks, a two-stage detection method based on Stacking and Maximum Information Coefficient and Dual-layer Confidence Extreme Gradient Boosting (MIC-DCXGB) is proposed by the paper. Firstly, a Stacking classification model consisting of multiple heterogeneous learners detects anomalies in real-time measurement data samples to determine if false data are present. Secondly, the method incorporates the Maximum Information Coefficient (MIC) for feature selection, which non-linearly measures the correlation between data features and fairly removes redundant features by evaluating the amount of information one feature variable contains about another. This approach effectively tackles the high-dimensional redundancy problem commonly faced in false data injection attack detection. Then, the paper introduces a dual-layer confidence Extreme Gradient Boosting (XGBoost) tree with positive feedback information transmission to classify node states. By combining grid topology learning with label correlation, it selectively uses preceding label information to reduce errors in the predictions learned by subsequent classifiers, achieving precise localization of the attack positions. Finally, extensive simulations validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In order to improve the capability of cross-domain collaborative task scheduling of avionics systems in heterogeneous networking, a distributed cross-domain task scheduling method is proposed based on capability and task mapping in this paper. The concept of Mosaic warfare is employed to realize dynamic scheduling of resources according to task requirements. This method constructs perception subnets, decision subnets and response subnets for a complete task flow, and quantifies the capabilities of different subnets in a unified manner. The tasks are decomposed into sub-tasks, and sub-tasks are further decomposed into service requirements matching capabilities. In this way, the mapping between capabilities and service requirements is completed, and collaborative task scheduling is carried out in heterogeneous networking under the mapping conditions. In this paper, OMNeT++ simulation platform was used to compare the method with tactical data link and tactical cloud network. The simulation results show that compared with tactical cloud network architecture, the proposed method can improve packet throughput by 29.1%, reduce task group completion time by 36% on average, and reduce message transmission delay. [ABSTRACT FROM AUTHOR]

Using adaptive filtering to estimate the frequency of power systems has become a popular trend. In recent years, however, few studies have been performed on adaptive frequency estimations in non-stationary noise environments. In this paper, we propose the distributed complex inverse square root algorithm and distributed augmented complex inverse square root algorithm for the frequency estimation of power systems based on the widely linear model and the inverse square root cost function, where the function can restrain both positive and negative large errors, based on its symmetry. Moreover, the wireless sensor networks support monitoring and adaptation for the frequency estimation in the distributed networks, and the proposed approach can ensure good robustness of the balanced or unbalanced three-phase power system with the help of a local complex-value voltage signal generated by Clark's transformation. In addition, the bound of step size is driven by the global vectors, and that low computation complexity do not hinder those performances. The results of several experiments demonstrate that our algorithms can effectively estimate the frequency in impulsive noise environments. [ABSTRACT FROM AUTHOR]

The advancement of communication technologies has facilitated the deployment of numerous sensors, terminal human–machine interfaces, and smart devices in various complex environments for data collection and analysis, providing automated and intelligent services. The increasing urgency of monitoring demands in complex environments necessitates low-cost and efficient network deployment solutions to support various monitoring tasks. Distributed networks offer high stability, reliability, and economic feasibility. Among various Low-Power Wide-Area Network (LPWAN) technologies, Long Range (LoRa) has emerged as the preferred choice due to its openness and flexibility. However, traditional LoRa networks face challenges such as limited coverage range and poor scalability, emphasizing the need for research into distributed routing strategies tailored for LoRa networks. This paper proposes the Optimizing Link-State Routing Based on Load Balancing (LB-OLSR) protocol as an ideal approach for constructing LoRa distributed multi-hop networks. The protocol considers the selection of Multipoint Relay (MPR) nodes to reduce unnecessary network overhead. In addition, route planning integrates factors such as business communication latency, link reliability, node occupancy rate, and node load rate to construct an optimization model and optimize the route establishment decision criteria through a load-balancing approach. The simulation results demonstrate that the improved routing protocol exhibits superior performance in node load balancing, average node load duration, and average business latency. [ABSTRACT FROM AUTHOR]

In the case of resistance-inductance lines in PV station area, the problem of voltage overstep is easy to occur. This article proposes a reactive power compensation control method to improve the voltage stability in the photovoltaic power plant area, which addresses the problem of voltage at the point of common coupling (PCC) exceeding the upper limit due to resistance circuits and exceeding the lower limit due to relatively insufficient reactive power output when the output active power is high. The idea is to achieve dynamic adjustment of PCC voltage by paralleling a static reactive power generator (SVG) at the grid connection point and using a variable droop control method. In addition, a reactive power optimization method based on improved particle swarm optimization (IPSO) algorithm is proposed to address the changes in power flow caused by photovoltaic integration in the distribution network system. The proposed improvement method not only effectively reduces network losses but also significantly improves voltage stability. [ABSTRACT FROM AUTHOR]

Distributed network data has the characteristics of distribution and concurrency, which leads to the complexity of data processing and reduces the effectiveness of security risk assessment. Therefore, a security risk assessment method for distributed network data based on the SimHash algorithm is proposed. The actual support of the distributed network data set is reconstructed by probability distortion technology, and the data mining results after probability transformation are obtained by using the data mining method of random disturbance. In order to avoid the existence of duplicate information and redundant data, duplicate distributed network data is removed by calculating text similarity. Finally, the SimHash algorithm is used to calculate the hash value before and after the distributed network data attack, calculate the security risk assessment value of the distributed network data, and complete the security risk assessment. The analysis of the experimental results shows that the proposed method effectively improves the reliability of risk assessment of distributed network data and reduces the communication overhead of the assessment, with the maximum communication overhead not exceeding 10 bits. Therefore, the research method has high effectiveness and practicability. [ABSTRACT FROM AUTHOR]

In a large-scale distributed network, a naming service is used to achieve location transparency and provide effective content discovery. However, fast and accurate name retrieval in the massive name set is laborious. Approximate set membership data structures, such as Bloom filter and Cuckoo filter, are very popular in distributed information systems. They obtain high query performance and reduce memory requirements through the abstract representation of information, but at the cost of introducing query error rates, which will ultimately affect content service quality. In this paper, in order to obtain higher space utilization and a lower query false positive rate, we propose a flexible fingerprint cuckoo filter (FFCF) for information storage and retrieval, which can change the length and type of fingerprints adaptively. In our scheme, FFCF uses longer fingerprints under low occupancy and has the ability to correct errors by changing the type of stored fingerprints. Moreover, we give a theoretical proof and evaluate the performance of FFCF by experimental simulations with synthetic data sets and real network packets. The results demonstrate that FFCF can improve memory utilization, significantly reduce false positive errors by nearly 90 % at 50 % occupancy and outperform Cuckoo filter in the full range of occupancy. [ABSTRACT FROM AUTHOR]

In the case of resistance-inductance lines in PV station area, the problem of voltage overstep is easy to occur. This article proposes a reactive power compensation control method to improve the voltage stability in the photovoltaic power plant area, which addresses the problem of voltage at the point of common coupling (PCC) exceeding the upper limit due to resistance circuits and exceeding the lower limit due to relatively insufficient reactive power output when the output active power is high. The idea is to achieve dynamic adjustment of PCC voltage by paralleling a static reactive power generator (SVG) at the grid connection point and using a variable droop control method. In addition, a reactive power optimization method based on improved particle swarm optimization (IPSO) algorithm is proposed to address the changes in power flow caused by photovoltaic integration in the distribution network system. The proposed improvement method not only effectively reduces network losses but also significantly improves voltage stability.

In order to solve the problem that the data processing frequency of the traditional centralized data processing system is low, which leads to the poor feedback effect of massive data, this paper proposes a computer data information processing system based on neural network algorithm. Based on the original system hardware, the system replaces the data processor and increases the total number of the processor, so as to realize the distributed synchronous processing of massive data. In the aspect of software design, the interaction between system units and modules is formed through the protocol to improve the data communication mode of the system; Calculate the Euclidean distance and set the distributed processing mode of the system; According to the definition of cloud computing, the classification function is used to determine the constraints, establish the processing frequency equation, and realize the rapid processing of massive data. The experimental results show that the processing frequency of the system designed in this paper is more than 50 % higher than that of the traditional system, which solves the problem of low processing frequency caused by weak analysis ability and slow response speed in the traditional system. Conclusion: compared with the traditional system, the system designed in this paper has faster processing frequency for massive data and better feedback effect to users.

Abstract The data are sent by the nodes taking part in frequency hopping communications (FHC) utilising carrier frequencies and time slots that are pseudo-randomly assigned. Because of this, a high degree of protection against eavesdropping and anti-interference capabilities is provided. When using FHC in an environment, sharing time and frequency resources, avoiding collisions, and differentiating services are all made more complex as a result of this. A protocol for FHC that is based on dispersed wireless networks is presented by the authors of this research. It is a mechanism for multiple access control, which is prioritised and distributed. The ratio of empty channels metric can be found in the previous sentence. It is possible to provide priority in channel access by assigning different preset ratios of empty channel thresholds to the various traffic classes. Frames from frequency spread segments that have a partial collision are included as well. An analytical model is simulated for the analysis in terms of collision probability, transmission probability, and frame service time in order to carry out a theoretical examination of the performance of FHC. The objective of this inquiry is to determine how well FHC works. The analytical model has been proven correct by the exhaustive simulations as well as the theoretical findings. Cloud platforms are often used in the instruction of the most cutting-edge machine learning techniques of today, such as deep neural networks. This is done in order to take advantage of the cloud's capacity to scale elastically. In order to satisfy the criteria of these sorts of applications, federated learning, has been proposed as a distributed machine learning solution. This is done in order to fulfil the requirements of these kinds of applications. In federated learning (FL), even though everyone who uses the system works together to train a model, nobody ever shares their data with anybody else. Each user trains a local model with their own data, and then communicates the updated models with a FL server so that the data can be aggregated and a global model can be constructed. This process ensures that each user's model is unique. This process is repeated until a global model has been developed. This kind of training not only reduces the amount of network overhead that is necessary to transfer data to a centralised server, but it also safeguards the personal information of the users. Within the framework of this work, we looked at the feasibility of using the FL technique of learning on the many devices that are part of the dispersed network. On a centralised server, we conduct an analysis of the performance of the FL model by comparing its accuracy and the amount of time it takes to train using a range of various parameter value combinations. Additionally, the accuracy of these federated models may be made to reach a level that is comparable to that of the accuracy of central models.

This paper proposes a sharing algorithm based on blockchain principles to address the issues of data sharing, low efficiency, and performance in traditional systems. The algorithm is integrated with the domain name system to develop a data storage system based on blockchain. The performance of the sharing algorithm is evaluated, and the data storage system is tested. This demonstrates that the sharing algorithm's average latency is 436 ms and average throughput is 5439 tps. Furthermore, it outperforms the other comparison algorithms. Additionally, the study conducts performance experiments to compare the data storage system. The data storage system proposed in this study demonstrates a higher average throughput of 6.42*108 tps and a faster data access time of 0.15 s than the other comparison systems. The comprehensive results show that the proposed sharing algorithm and data storage system outperform the comparison algorithm and system in terms of latency, throughput, and data access performance. The constructed model exhibits good centralized and distributed storage crawling performance, which can achieve more secure, efficient, and trustworthy data sharing in distributed network data storage. [ABSTRACT FROM AUTHOR]

The data are sent by the nodes taking part in frequency hopping communications (FHC) utilising carrier frequencies and time slots that are pseudo-randomly assigned. Because of this, a high degree of protection against eavesdropping and anti-interference capabilities is provided. When using FHC in an environment, sharing time and frequency resources, avoiding collisions, and differentiating services are all made more complex as a result of this. A protocol for FHC that is based on dispersed wireless networks is presented by the authors of this research. It is a mechanism for multiple access control, which is prioritised and distributed. The ratio of empty channels metric can be found in the previous sentence. It is possible to provide priority in channel access by assigning different preset ratios of empty channel thresholds to the various traffic classes. Frames from frequency spread segments that have a partial collision are included as well. An analytical model is simulated for the analysis in terms of collision probability, transmission probability, and frame service time in order to carry out a theoretical examination of the performance of FHC. The objective of this inquiry is to determine how well FHC works. The analytical model has been proven correct by the exhaustive simulations as well as the theoretical findings. Cloud platforms are often used in the instruction of the most cutting-edge machine learning techniques of today, such as deep neural networks. This is done in order to take advantage of the cloud's capacity to scale elastically. In order to satisfy the criteria of these sorts of applications, federated learning, has been proposed as a distributed machine learning solution. This is done in order to fulfil the requirements of these kinds of applications. In federated learning (FL), even though everyone who uses the system works together to train a model, nobody ever shares their data with anybody else. Each user trains a local model with their own data, and then communicates the updated models with a FL server so that the data can be aggregated and a global model can be constructed. This process ensures that each user's model is unique. This process is repeated until a global model has been developed. This kind of training not only reduces the amount of network overhead that is necessary to transfer data to a centralised server, but it also safeguards the personal information of the users. Within the framework of this work, we looked at the feasibility of using the FL technique of learning on the many devices that are part of the dispersed network. On a centralised server, we conduct an analysis of the performance of the FL model by comparing its accuracy and the amount of time it takes to train using a range of various parameter value combinations. Additionally, the accuracy of these federated models may be made to reach a level that is comparable to that of the accuracy of central models. [ABSTRACT FROM AUTHOR]

[Display omitted] To facilitate the transition of triboelectric nanogenerators (TENG) from early stages to large-scale real-world applications, urgent mass production is imperative, necessitating standardized manufacturing processes. In this study, a novel "pause-and-insert" and "print-in-place" 3D printing approach has been devised, employing widely available fused deposition modeling (FDM) printers along with conductive (as electrodes) and nonconductive (for casing and support) filaments to make encapsulate microbeads triboelectric nanogenerator (MB-TENG). This innovation results in a fully enclosed freestanding-mode TENG pack, seamlessly integrated with a lightweight wind-induced oscillator featuring tuning systems, thus enabling efficient, safe, and noiseless omnidirectional wind energy harvesting. Through adept design and manufacturing techniques, systematic inner-structure optimization has been achieved, yielding superior output and industrialization levels compared to similar efforts. MB-TENG's surface charge density is as high as 19.9 µC/m2. The average power density is 13.8 W/m3. Moreover, the elimination of postprocessing significantly streamlines standard manufacturing, enhancing the prospects for device commercialization. Beyond its wind energy application, the versatile energy packs can serve as both ocean wave and human motion harvesters within network configurations, capable of powering a massive real-time monitoring sensor array. [ABSTRACT FROM AUTHOR]

Mitigating heat is a vital ecosystem service of trees, particularly with climate change. Land surface temperature measures captured at a single time of day (in the morning) dominate the urban heat island literature. Less is known about how local tree canopy and impervious surface regulate air temperature throughout the day, and/or across many days with varied weather conditions, including cloud cover. We use bike-mounted air temperature sensors throughout the day in New Haven, Connecticut, USA, from 2019 to 2021 and generalized additive mixed models across 156 rides to estimate the daily variation in cooling benefits associated with tree canopy cover, and warming from impervious surface cover in 90 m buffers surrounding bike observations. Cooling is inferred by subtracting the bicycle-observed temperature from a reference station. The cooling benefits from tree canopy cover were strongest in the midday (11:00–14:00, −1.62 °C), afternoon (14:00–17:00, −1.19 °C), and morning (8:00–11:00, −1.15 °C) on clear days. The cooling effect was comparatively smaller on cloudy mornings −0.92 °C and afternoons −0.51 °C. Warming from impervious surfaces was most pronounced in the evening (17:00–20:00, 1.11 °C) irrespective of clouds, and during cloudy nights (20:00–23:00) and cloudy mornings 1.03 °C 95 % CI [1.03, 1.04]. Among the hottest observed days (top 25th percentile of reference station daily maxima), tree canopy was associated with lower temperatures on clear afternoons −1.78 °C [-1.78, −1.78], cloudy midday −1.17 °C [-1.19, −1.15], clear midday −1.12 °C [-1.12, −1.11]. We add a broader spectrum of weather conditions by explicitly including clouds, and greater temporal resolution by measuring throughout the day to bike-based urban heat research. Future mobile sampling campaigns may broaden the spatial extent with more environmental variation, representing an opportunity for public science and engagement.

The purpose of this thesis is to combine blockchain technology with teacher title evaluation, and design a blockchain-based teacher title evaluation system using the characteristics of blockchain and IPFS, and other technologies. The system adopts a three-layer architecture consisting of a data storage layer, an application business layer, and a user presentation layer. Among them, the data storage layer builds a distributed network through Ethernet blockchain nodes. At the same time, each node is also an IPFS network node, responsible for uploading teacher certification materials to the IPFS network through the file encryption module in the application business layer and generating IPFS abstracts, which can be subsequently uploaded to the chain only, i.e., ensuring the privacy of file materials and reducing the overhead of storing data. To verify the effect of the title review system, the analysis was conducted in terms of role perceptions, covering teachers’ perceptions of the fairness and motivation of the title review system. In terms of fairness of the review criteria, 60.83% of teachers think it is fair, and 25.83% think it is unfair. 65.83% of teachers say that the career review is helpful for career development, and only less than 10% think it is not helpful for career development. It shows that this teacher title evaluation platform is efficient, intelligent, secure, and trustworthy.

In this paper, we explore video blockchain for establishing connectivity among vehicles in a smart city through utilizing blockchain technology. By leveraging intelligent vehicular systems that provide location-based visualization through multiple deployed cameras in vehicles, we expand the scope of collecting video surveillance data for observation, thereby enhancing overall situational awareness. We utilize the decentralized nature of blockchain to implement a vehicle-based surveillance system across a smart city. To ensure reliability, the integration of two cryptographic functions, hashing and signing, with the blockchain is employed. This integration ensures secure and tamper-proof solutions for the existing intelligent surveillance system. In this paper, our primary focus is on combining blockchain technology to achieve sustainable and robust smart solutions for intelligent vehicular distributed video networks while eliminating the need for third-party intermediaries. Through extensive experiments and analysis, we demonstrate the effectiveness and feasibility of our proposed video blockchain approach. The results indicate that this innovative framework provides enhanced security, privacy, and scalability for intelligent vehicular distributed networks in smart cities, paving the way for a connected and efficient urban environment. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a framework which is used in distributed machine learning to obtain an optimal model from clients' local updates. As an efficient design in model convergence and data communication, cloud-edge-client hierarchical federated learning (HFL) attracts more attention than the typical cloud-client architecture. However, the HFL still poses threats to clients' sensitive data by analyzing the upload and download parameters. In this paper, to address information leakage effectively, we propose a novel privacy-preserving scheme based on the concept of differential privacy (DP), adding Gaussian noises to the shared parameters when uploading them to edge and cloud servers and broadcasting them to clients. Our algorithm can obtain global differential privacy with adjustable noises in the architecture. We evaluate the performance on image classification tasks. In our experiment on the Modified National Institute of Standards and Technology (MNIST) dataset, we get 91% model accuracy-layer HFL-DP, our design is more secure while as being accurate.

Electric vehicles (EVs) improve the power grid by increasing intermittent renewable energy consumption and providing financial support to EV users via vehicle-to-grid (V2G) integration. While estimating these advantages, a number of studies have neglected to consider the effect of driving and charging behavior patterns on their results. This article provides a framework that systematically evaluates EV driving and charging behaviors to improve charge management in the light of recent standards and advancements. In addition, the collected data on driving habits are analyzed in order to provide a consistent and usable dataset. By evaluating the individual and simultaneous charging demand characteristics, the V2G potential is further explored. Moreover, managerial recommendations for EV charging management are offered by improving the time step using the Bootstrap approach for more precise results than lower resolution. It is also addressed that the simultaneous use of a limited number of EVs required minimum time. According to the findings of this study, daily travel habits have a crucial influence in defining seasonal and individual charging demands. In order to continue with EV charging-related assessments with a confidence interval of more than 95%, the findings suggest that time steps of lower than ten minutes must be used. In addition, the purpose of this study is to assist researchers from academia and business with further information as they build initiatives linked to EV charging infrastructure and real-time charging management standards that account environmental aspects.

Due to the technical characteristics and application scenarios of distributed networks, their nodes can easily be invaded and compromised. It will result in information being forged or tampered without difficulty. An effective scheme to guarantee the authenticity and integrity of information is judging how trustworthy nodes are in terms of transmission. In D-S evidence theory (DST), the uncertainty can be expressed to solve the trust fusion issue for multiple nodes. In this paper, for reviewing the DST-based trust evaluation and decision and providing their future research directions systematically. Meanwhile, the DST is briefly reviewed, and two improvements in DST are categorically described. The role and mechanism in DST-based trust models are compared and analyzed. The valuable research directions in the near future are represented. Our contributions could solve the trust problem in resource constrained sensor nodes and improve the decision reliability of network.

This paper proposes a new distributed AC state estimation method. Different from the popular distributed state estimation (DSE) methods based on area partitioning method, the proposed method is a truly distributed method in which the power system is not required to be divided into smaller areas and a centralized state estimator in each area is not needed. In order to achieve fully DSE, the information propagation algorithm is introduced in this paper to help the distributed local state estimators share the measurement data. The information propagation algorithm is developed based on consensus protocol. The proof of the convergence of the information propagation algorithm is provided in this paper. Then, the AC state estimation method is integrated with the information propagation algorithm to realize the proposed method. The proposed method is tested in different standard power system models. The results show that the proposed method reaches the similar accuracy as the traditional centralized state estimation methods and performs faster and more accurate than the existing DSE methods.

Automatic modulation classification (AMC) is an important component in non-cooperative wireless communication networks to identify the modulation schemes of the received signals. In this paper, considering the multipath effect in practical propagation environments, a distributed cooperative AMC (Co-AMC) network based on machine learning is proposed to identify the modulation scheme in non-cooperative wireless communication networks. Specifically, feature vectors are first obtained by applying a cyclic spectrum to facilitate the feature extraction of the received signal. Then, a classifier based on the K-nearest neighbor (KNN) method is designed to obtain the local decision for modulation classification at each distributed node. Meanwhile, the reliability of the local decision is estimated by applying two loss functions to assess the quality of the local decision. Finally, the unified classification result is obtained to fuse the local decisions according to their reliabilities by applying a designed decision fusion algorithm based on the distributed weighted average alternating direction method of multipliers (DWA-ADMM). The simulation results demonstrate that the proposed Co-AMC network achieves superior classification accuracy compared to existing AMC methods across a range of modulation schemes and SNRs. More importantly, the proposed Co-AMC exhibits great flexibility and practicability since it is adaptive to wireless networks with various scales and topologies. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a framework which is used in distributed machine learning to obtain an optimal model from clients' local updates. As an efficient design in model convergence and data communication, cloud-edge-client hierarchical federated learning (HFL) attracts more attention than the typical cloud-client architecture. However, the HFL still poses threats to clients' sensitive data by analyzing the upload and download parameters. In this paper, to address information leakage effectively, we propose a novel privacy-preserving scheme based on the concept of differential privacy (DP), adding Gaussian noises to the shared parameters when uploading them to edge and cloud servers and broadcasting them to clients. Our algorithm can obtain global differential privacy with adjustable noises in the architecture. We evaluate the performance on image classification tasks. In our experiment on the Modified National Institute of Standards and Technology (MNIST) dataset, we get 91% model accuracy-layer HFL-DP, our design is more secure while as being accurate. [ABSTRACT FROM AUTHOR]

The Cyber Physical System (CPS) is a prime target for cyber attacks due to its heterogeneity and connectivity with physical equipment. This paper proposes a model based on a Hierarchical Bayesian Network (HBN) to increase the CPS’s attack detection ability. Denial of Service (DoS) attacks pose a significant threat to production line availability, business services, and human lives. Therefore, this paper focuses on detecting DoS attacks using the Bayesian network model, an efficient algorithm to detect faults in the networking system based on incomplete recognizing information. The standard Bayesian network model is hierarchically enhanced and optimized with a Bacterial Foraging Optimization (BFO) approach to improve the detection process. The developed, optimized model satisfies security, Quality of Service (QoS) requirements, and time consumption by reducing the constraints to obtain system reliability. The efficiency of the proposed model is evaluated using the NSL-KDD dataset and compared with existing approaches in terms of accuracy, precision, recall, F1-score, ROC, and RMSE. Compared to other existing systems, the proposed model achieves an accuracy of 98.4% in detecting DoS attacks with a reduced RMSE of 0.0617. [ABSTRACT FROM AUTHOR]

Sustainable Computing has advanced the technological evolution of the Internet and information-based communication technology. It is nowadays emerging in the form of the Cloud of Medical Things (CoMT) to develop smart healthcare systems. The academic community has lately made great strides for the development of security for the CoMT based application systems, such as e-healthcare systems, industrial automation systems, military surveillance systems, and so on. To the architecture of CoMT based Smart Environment, Chebyshev Chaotic-Map based single-user sign-in (S-USI) is found as a significant security-control mechanism. To ensure the fidelity, the S-USI assigns a unary-token to the legal users to access the various services, provided by a service provider over an IP-enabled distributed networks. Numerous authentication mechanisms have been presented for the cloud-based distributed networks. However, most of the schemes are still persuasible to security threats, such as user-anonymity, privileged-insider, mutual authentication, and replay type of attacks. This paper applies a sensor/sensor-tag based smart healthcare environment that uses S-USI to provide security and privacy. To strengthen the authentication process, a robust secure based S-USI mechanism and a well-formed coexistence protocol proof for pervasive services in the cloud are proposed. Using the formal security analysis, the prominence of the proposed strategies is proven to show the security efficiency of proposed S-USI. From the formal verification, the comparison results demonstrate that the proposed S-USI consumes less computation overhead; and thus it can be more suitable for the telecare medical information systems. [ABSTRACT FROM AUTHOR]

The crowd intelligence-based e-commerce transaction network (CIeTN) is a distributed and unstructured network structure. Smart individuals, such as buyers, sellers, and third-party organizations, can store information in local nodes and connect and share information via moments. The purpose of this study is to design a product search algorithm on the basis of ant colony optimization (ACO) to achieve an efficient and accurate search for the product demand of a node in the network. We introduce the improved ideas of maximum and minimum ants to design a set of heuristic search algorithms on the basis of ACO. To reduce search blindness, additional relevant heuristic factors are selected to define the heuristic calculation equation. The pheromone update mechanism integrating into the product matching factor and forwarding probability is used to design the network search rules among nodes in the search algorithm. Finally, the search algorithm is facilitated by Java language programming and PeerSim software. Experimental results show that the algorithm has significant advantages over the flooding method and the random walk method in terms of search success rate, search time, product matching, search network consumption, and scalability. The search algorithm introduces the idea of improving the maximum and minimum ant colony system and proposes new ideas in the design of heuristic factors in the heuristic equation and the pheromone update strategy. The search algorithm can search for product information effectively.

Node churn, or the constant joining and leaving of nodes in a network, can impact the performance of a blockchain network. The difficulties of performing research on the actual blockchain network, particularly on a live decentralized global network like Bitcoin, pose challenges that good simulators can overcome. While various tools, such as NS-3 and OMNet++, are useful for simulating network behavior, SimBlock is specifically designed to simulate the complex Bitcoin blockchain network. However, the current implementation of SimBlock has limitations when replicating actual node churn activity. In this study, the SimBlock simulator was improved to simulate node churn more accurately by removing churning nodes and dropping their connections, and increasing additional instrumentation for validation. The methodology used in the study involved modeling the Bitcoin node churn behavior based on previous studies and using the enhanced SimBlock simulator to simulate node churn. Empirical studies were then conducted to determine the suitability and limitations of the node churn simulation. This study found that the improved SimBlock could produce results similar to observed indicators in a 100-node network. However, it still had limitations in replicating node churn behavior accurately. It was discovered that SimBlock limits all nodes to operate as mining nodes and that mining is simulated in a way that does not depict churn accurately at any time but only at specific intervals or under certain conditions. Despite these limitations, the study's improvements to SimBlock and the identification of its limitations can be useful for future research on node churn in blockchain networks and the development of more effective simulation tools. [ABSTRACT FROM AUTHOR]

With the global influence of the COVID epidemic, network public opinion control is particularly important especially for the purpose of stabilizing the panic at home and abroad. Effective public opinion collection and caching mechanism has a positive significance for the rapid spread of network public opinion. Therefore, by analyzing the accurate and rapid requirements of public opinion communication, this paper introduces the concept of Information‐Centric Networking (ICN) to build a public opinion communication system. At the same time, the corresponding public opinion collection and caching mechanism is designed to optimize the dissemination process of the public opinion. The natural distributed structure of ICN makes the process of public opinion collection and caching distributed. Specifically, a suitable cache server is added between different public opinion collection servers via the distributed search engines. The experimental results show that the proposed distributed public opinion collection and caching mechanism can effectively deal with the spread of public opinion under the environment of the global COVID epidemic, including improving the accuracy of the public opinion transmission in time. [ABSTRACT FROM AUTHOR]

Modern distribution grids are currently being challenged by frequent and sizable voltage fluctuations, due mainly to the increasing deployment of renewable generation. Considering equipment characteristics, traditional devices (e.g., on-load tap changers) to maintain bus voltages cannot provide frequent regulations. To deal with short-term voltage fluctuations, this paper proposes to cooperatively control reactive and active power through PV inverters and district cooling systems. However, traditional voltage control optimization relies heavily on accurate physical models (e.g., network topology), and brings huge computation burdens with enlarging system scale. In this context, this paper adopts model-free reinforcement learning (RL) to solve the controller without prior knowledge of system models. However, because voltage violations occur not frequently in practice, the irregular occurrence brings sparse reward and biased-distribution experience issues in RL training. Hence, on top of the traditional actor–critic structure, we propose two improvements: 1) a compensator module is designed to cope with the sparse reward issue; 2) a scenario-classified experience replay method is proposed for RL training sampling, which can correct the experience distribution to improve training efficiency for a typical scenario with violated voltages. Numerical studies on a 33-bus network show that, the proposed method can smooth voltage fluctuations better, with negligible temperature impacts on demand-side users. • The active power of district cooling systems and the reactive power of PV inverters are cooperatively controlled for voltage regulations by RL method. • A compensator-based RL scheme is developed to address the reward-sparse issue for enhancing the RL training robustness. • A scenario-classified experience replay is introduced to the sampling process to handle the biased distribution issue. [ABSTRACT FROM AUTHOR]

This article presents the architecture of integration of blockchain technology (BCT) and the Internet of Things with the planning of production processes. The authors proposed a shared concept of a distributed machine database based on BCT. As part of the work, a network of connections for the exchange of production resources was created using nodes communicating in a decentralized system, which at the same time serves as an integration of the virtual and real environment. Particular attention was focused on developing an algorithm for the efficient division of production tasks between all interested network users. BCT is used to conclude smart contracts and transactions and ensure the security of exchanged production data within shared ledgers. The proposed concept is a solution enabling a modern approach to the interdisciplinary management of production resources while maintaining the highest cybersecurity standards. [ABSTRACT FROM AUTHOR]

This research study aims to examine the possibilities of Hyperledger Fabric (HLF) in the healthcare sector. The study addresses the gap in the knowledge base through developing customization techniques to enable the simplicity and efficacy of Electronic Medical Records (EMR) adoption for healthcare industry applications. The focus of this research explores methods of using blockchain technology that prioritise users. The investigation of several concepts used in developing web applications has been determined. The study identified that an open-source project, known as Hyperledger Fabric, can be utilised to construct a novel method of storing EMRs. The framework provides a test network that can be customised to satisfy the need of several projects, including storing medical records. This research additionally outlines the difficulties encountered and problems that need to be resolved before Hyperledger Fabric can be successfully implemented in healthcare systems. Considering all types of blockchains available, the needs are met by Hyperledger Fabric, which offers a distributed and secure environment for healthcare systems. Blockchain has the potential to transform healthcare by putting the patient at the centre of the system and enhancing health data protection and interoperability. Also, by using grant and revoke access mechanisms, patients have complete control over their medical information as well as authorized doctors who are allowed to view records. This functionality is made possible by the chaincode defined in the blockchain platform. The research study has both practitioner and research implications for the development of secure blockchain-based EMRs. [ABSTRACT FROM AUTHOR]

As a lightweight method, runtime verification aims to check whether one program execution satisfies a desired property. For online runtime verification, the approach efficiency and property expressiveness are two key points restricting its wide application. In this paper, we propose a distributed network-based parallel runtime verification approach to verifying full regular temporal properties for a suitable subset of C (named by Xd-C) programs in an online manner. With this approach, an Xd-C program is translated into an equivalent Modeling, Simulation and Verification Language (MSVL) program, and a desired property is specified as a Propositional Projection Temporal Logic (PPTL) formula; during the program execution, segments of the generated state sequence are verified in parallel by distributed multi-core machines. Experimental results show that, our approach has a speedup of 2.5X-5.0X over the state-of-art runtime verification approaches and supports full regular temporal properties, meaning that our approach can not only take full advantage of computing and storage resources in a distributed network, but also support more expressive properties. [ABSTRACT FROM AUTHOR]

Consensus protocol is challenging due to the poor node reliability, low efficiency and decentralization. A comprehensive reputation based Practical Byzantine Fault Tolerance consensus method (CRPBFT) has been proposed. Comprehensive reputation model has been developed to evaluate the credibility of each node from service behavior and consensus process at first. The nodes with higher reputation are selected to participate in the consensus process, which helps to reduce the probability of consensus failure caused by the existence of malicious nodes. A consensus communication structure is optimized by replacing the whole network broadcast structure in the commit phase with a star one. It can be applied to degrade the network communication overhead and improve consensus efficiency. A rotation mechanism for replacing the consensus nodes regularly has been proposed to increase the degree of decentralization and enhance the robustness and dynamic of the consensus network. Some experimental results demonstrate that the developed method has excellent performance by comparisons with some state-of-the-arts. [ABSTRACT FROM AUTHOR]

A precise short-term load-forecasting model is vital for energy companies to create accurate supply plans to reduce carbon dioxide production, causing our lives to be more environmentally friendly. A variety of high-voltage-level load-forecasting approaches, such as linear regression (LR), autoregressive integrated moving average (ARIMA), and artificial neural network (ANN) models, have been proposed in recent decades. However, unlike load forecasting in high-voltage transmission systems, load forecasting at the distribution network level is more challenging since distribution networks are more variable and nonstationary. Moreover, existing load-forecasting models only consider the features of the time domain, while the demand load is highly correlated to the frequency-domain information. This paper introduces a robust wavelet transform neural network load-forecasting model. The proposed model utilizes both time- and frequency-domain information to improve the model's prediction accuracy. Firstly, three wavelet transform methods, variational mode decomposition (VMD), empirical mode decomposition (EMD), and empirical wavelet transformation (EWT), were introduced to transform the time-domain demand load data into frequency-domain data. Then, neural network models were trained to predict all components simultaneously. Finally, all the predicted data were aggregated to form the predicted demand load. Three cases were simulated in the case study stage to evaluate the prediction accuracy under different layer numbers, weather information, and neural network types. The simulation results showed that the proposed robust time–frequency load-forecasting model performed better than the traditional time-domain forecasting models based on the comparison of the performance metrics, including the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE). [ABSTRACT FROM AUTHOR]