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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]

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 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]

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]

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]

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]

One of the most common approaches for enhancing network performance is to retrieve data from nearby data holders that have previously obtained the desired data, not only from the original data source itself. In this case, since a data receiver cannot identify a practical data sender, it is necessary to verify both the received data and the data sender. Moreover, a data sender generally fragments the data into several small segments and sends them. Therefore, if these segments are retrieved from multiple unknown senders, the receiver must verify every segment to safely use the data. MHT (Merkle hash tree) is suitable for efficiently verifying the set of segments shared in the network. NDN (named-data networking) and Bitcoin utilize MHT to verify transmitted data. However, a data authentication scheme based on the MHT has an inefficient factor that repeatedly computes the same node values of the MHT and are repeatedly computed. The larger the size of the MHT is, the greater the number of calculation iterations. Therefore, as a result, the authentication scheme's inefficiency is also more severe. When a sender transmits data consisting of many segments through NDN, the data authentication time may take longer than the data transmission time. Hence, in this paper, the degree of the MHT's inefficiency and the pattern of the iterated operation of the MHT are analyzed first. The proposed improvement is to find repeatedly used node values, store them internally, and use the stored node values without recalculation when required to reuse them. For that process, a rule to select such node values is given. Additionally, when verifying the leaf node value of the MHT, the MHT-based authentication scheme asks a verifier to compute all node values on the path from the leaf node to the root node of the MHT. This paper demonstrates the proposed shortest path selection for verifying the leaf node value. The proposed scheme, using saved node values and the shortest path, reduces the computational overhead of the MHT and improves service latency. It has been proven from performance evaluations that the proposed scheme decreases the computational overhead by more than one-third if the number of segments is more than 1024. [ABSTRACT FROM AUTHOR]

The increasing integration of renewable sources into distributed networks results in multiple protection challenges that would be insufficient for conventional protection strategies to tackle because of the characteristics and functionality of distributed generation. These challenges include changes in fault current throughout various operating modes, different distribution network topologies, and high-impedance faults. Therefore, the protection and reliability of a photovoltaic distributed network relies heavily on accurate and adequate fault detection. The proposed strategy utilizes the Variational Mode Decomposition (VMD) and ensemble bagged trees method to tackle these problems in distributed networks. Primarily, VMD is used to extract intrinsic mode functions from zero-, positive-, and negative-sequence components of a three-phase voltage signal. Next, the acquired intrinsic mode functions are supplied into the ensemble bagged trees mechanism for detecting fault events in a distributed network. Under both radial and mesh-soft normally open-point (SNOP) topologies, the outcomes are investigated and compared in the customarily connected and the island modes. Compared to four machine learning mechanisms, including linear discriminant, linear support vector mechanism (SVM), cubic SVM and ensemble boosted tree, the ensemble bagged trees mechanism (EBTM) has superior accuracy. Furthermore, the suggested method relies mainly on local variables and has no communication latency requirements. Therefore, fault detection using the proposed strategy is reasonable. The simulation outcomes show that the proposed strategy provides 100 percent accurate symmetrical and asymmetrical fault diagnosis within 1.25 ms. Moreover, this approach accurately identifies high- and low-impedance faults. [ABSTRACT FROM AUTHOR]

Due to the increased integration of distributed generations in distributed networks, their development and operation are facing protection challenges that traditional protection systems are incapable of addressing. These problems include variations in the fault current during various operation modes, diverse distributed network topology, and high impedance faults. Therefore, appropriate and reasonable fault detection is highly encouraged to improve the protection and dependability of the distributed network. This paper proposes a novel technique that employs an improved Hilbert–Huang Transform (HHT) and ensemble learning techniques to resolve these challenges in a photovoltaic distributed network. First, improved HHT is utilized to extract energy features from the current signal. Second, variational mode decomposition (VMD) is applied to extract the intrinsic mode function from the zero component of the current signal. Then, the acquired energy feature and intrinsic mode function are input to the ensemble learning technique for fault detection and classification. The proposed technique is implemented using MATLAB software environment, including a classification learner app and SIMULINK. An evaluation of the results is conducted under normal connected mode (NCM) and island mode (ISM) for radial and mesh-soft normally open point (SNOP) configurations. The accuracy of the ensemble bagged trees technique is higher when compared to the narrow-neural network, fine tree, quadratic SVM, fine-gaussian SVM, and wide-neural network. The presented technique depends only on local variables and has no requirements for connection latency. Consequently, the detection and classification of faults using the proposed technology are reasonable. The simulation results demonstrate that the proposed technique is superior to the neural network and support vector machine, achieving 100%, 99.2% and 99.7% accurate symmetrical and unsymmetrical fault detection and classification throughout NCM, ISM, and dynamic operation mode, respectively. Moreover, the developed technique protects DN effectively in radial and mesh-SNOP topologies. The suggested strategy can detect and classify faults accurately in DN with/without DGs. Additionally, this technique can precisely detect high and low impedance faults within 4.8 ms. [ABSTRACT FROM AUTHOR]

Hospitals have their own database structures and maintain their data in a closed manner. For this reason, it is difficult for researchers outside of institutions to access multi-center data. Therefore, if the data maintained by all hospitals follow a commonly shared format, researchers can analyze multi-center data using the same method. To safely analyze data using a common data model (CDM) in a distributed multi-center network environment, the objective of this study is to propose and implement the processes for distribution, executing the analysis codes, and returning the results. A secure CDM-based data analysis platform (SCAP) consists of a certificate authority (CA), authentication server (AS), code signer (CS), ticket-granting server (TGS), relaying server (RS), and service server (SS). The AS, CS, TGS, and RS form the central server group of the platform. An SS is stored on a hospital server as an agent for communication with the server group. We designed the functionalities and communication protocols among servers. To safely conduct the intended functions, the proposed protocol was implemented based on a cryptographic algorithm. An SCAP was developed as a web application running on this protocol. Users accessed the platform through a web-based interface. [ABSTRACT FROM AUTHOR]

Considering the increasing scale and severity of damage from recent cybersecurity incidents, the need for fundamental solutions to external security threats has increased. Hence, network separation technology has been designed to stop the leakage of information by separating business computing networks from the Internet. However, security accidents have been continuously occurring, owing to the degradation of data transmission latency performance between the networks, decreasing the convenience and usability of the work environment. In a conventional centralized network connection concept, a problem occurs because if either usability or security is strengthened, the other is weakened. In this study, we proposed a distributed authentication mechanism for secure network connectivity (DAM4SNC) technology in a distributed network environment that requires security and latency performance simultaneously to overcome the trade-off limitations of existing technology. By communicating with separated networks based on the authentication between distributed nodes, the inefficiency of conventional centralized network connection solutions is overcome. Moreover, the security is enhanced through periodic authentication of the distributed nodes and differentiation of the certification levels. As a result of the experiment, the relative efficiency of the proposed scheme (REP) was about 420% or more in all cases. [ABSTRACT FROM AUTHOR]

Blockchain is not widely applied in various fields due to the critical issue of scalability as part of the blockchain trilemma. This issue arises during consensus among the nodes in a public blockchain. To address the issue of low scalability with proof-of-work (PoW) consensus, various methods have been proposed for transaction per second (TPS) improvement. However, no such methods include an improvement in the consensus step. Therefore, to improve PoW public blockchain scalability, it is important to shorten the time required for PoW consensus. This paper proposes a method for minimizing the block intervals that occur during consensus over a PoW blockchain network. A shortened block interval leads to an increase in the probability of three different attacks: selfish mining, double-spending, and eclipse attacks. According to an experiment using Ethereum, with a typical PoW blockchain, it is inevitable to provide rewards for stable block mining in competition between mining pools. To find an optimal block interval in the PoW consensus algorithm, we conducted a four-step experiment. The purpose of this experiment was to verify the difficulty level and issues with Mainnet security. Therefore, considering stale block mining rewards, an optimal block interval is proposed. The Ethereum TPS was improved by at least 200%. Given this finding, it is considered possible to achieve a similar improvement in a different PoW blockchain. On balance, even if the block interval is shorter than that of the PoW Mainnet, network security falls by only 1.21% in Testnet, even with a rise in the stale block rate, while performance is increased at up to 120 TPS, which is three times higher than that in Mainnet. [ABSTRACT FROM AUTHOR]

The effectiveness of collaboration in distributed networks, such as sensor networks and multi-agent systems, relies on nodes' ability to exchange information. The availability of various communication protocols with different technical properties opens the possibility to guarantee connectivity during a system's operation in any condition. A communication network can be represented by a graph on which connectivity can be expressed by a well-known algebraic connectivity value or Fiedler value. It is one of the most important tools used in many applications where connectivity preservation is required. In this paper, a trust-based consensus algorithm for algebraic connectivity estimation has been implemented. To guarantee the accomplishment of the global objective and the system's performance, our contributions include: (i) a novel decentralized framework for combining multiple communication channels in a resulting channel and (ii) a decentralized algebraic connectivity control law that dynamically changes the number of agents in the system during operation. The proposed algebraic connectivity control strategy has been evaluated in simulations and in a real multi-robot system using two channels with different properties and initial topologies. [ABSTRACT FROM AUTHOR]

The top priority of today's healthcare system is delivering medicine directly from the manufacturer to end-user. The pharmaceutical supply chain involves some level of commingling of a collection of stakeholders such as distributors, manufacturers, wholesalers, and customers. The biggest challenge associated with this supply chain is temperature monitoring as well as counterfeit drug prevention. Many drugs and vaccines remain viable within a specific range of temperatures. If exposed beyond this temperature range, the medicine no longer works as intended. In this paper, an Internet of Things (IoT) sensor-based blockchain framework is proposed that tracks and traces drugs as they pass slowly through the entire supply chain. On the one hand, these new technologies of blockchain and IoT sensors play an essential role in supply chain management. On the other hand, they also pose new challenges of security for resource-constrained IoT devices and blockchain scalability issues to handle this IoT sensor-based information. In this paper, our primary focus is on improving classic blockchain systems to make it suitable for IoT based supply chain management, and as a secondary focus, applying these new promising technologies to enable a viable smart healthcare ecosystem through a drug supply chain. [ABSTRACT FROM AUTHOR]

The commercial sector in Nigeria has been greatly hampered due to the poor availability of reliable electricity. In a 2014 World Bank report, nearly half of the firms doing business in Nigeria identified electricity as a major constraint, with over a quarter of them listing electricity as their biggest obstacle. The business losses due to electrical outages have been significant, with losses averaging about 16% of annual sales. The lack of access to reliable electricity is one of the biggest challenges to economic growth in Nigeria. This paper proposes a means of powering the commercial sector in Nigeria using urban swarm electrification. It outlines a conceptual framework for using a distributed network made up of grid-connected home solar PV systems as a viable option for providing the commercial sector with more reliable access to electricity. It further addresses the policy implications for the commercial sector with the enablement of more electrification options, implications that include strong economic impact, as well as the expansion and creation of new industries. [ABSTRACT FROM AUTHOR]

Precise localization is one of the key requirements in the deployment of UAVs (Unmanned Aerial Vehicles) for any application including precision mapping, surveillance, assisted navigation, search and rescue. The need for precise positioning is even more relevant with the increasing automation in UAVs and growing interest in commercial UAV applications such as transport and delivery. In the near future, the airspace is expected to be occupied with a large number of unmanned as well as manned aircraft, a majority of which are expected to be operating autonomously. This paper develops a new cooperative localization prototype that utilizes information sharing among UAVs and static anchor nodes for precise positioning of the UAVs. The UAVs are retrofitted with low-cost sensors including a camera, GPS receiver, UWB (Ultra Wide Band) radio and low-cost inertial sensors. The performance of the low-cost prototype is evaluated in real-world conditions in partially and obscured GNSS (Global Navigation Satellite Systems) environments. The performance is analyzed for both centralized and distributed cooperative network designs. It is demonstrated that the developed system is capable of achieving navigation grade (2–4 m) accuracy in partially GNSS denied environments, provided a consistent communication in the cooperative network is available. Furthermore, this paper provides experimental validation that information sharing is beneficial to improve positioning performance even in ideal GNSS environments. The experiments demonstrate that the major challenges for low-cost cooperative networks are consistent connectivity among UAV platforms and sensor synchronization. [ABSTRACT FROM AUTHOR]