Wired Networks Research Articles

An efficient federated learning solution for the artificial intelligence of things

Federated Learning (FL) has gained popularity due to its advantages over centralized learning. However, existing FL research has primarily focused on unconstrained wired networks, neglecting the challenges posed by wireless Internet of Things (IoT) environments. The successful integration of FL into IoT networks requires tailored adaptations to address unique constraints, especially in computation and communication. This paper introduces Communication-Aware Federated Averaging (CAFA), a novel algorithm designed to enhance FL operations in wireless IoT networks with shared communication channels. CAFA primarily leverages the latent computational capacities during the communication phase for local training and aggregation. Through extensive and realistic evaluations in dedicated FL-IoT framework, our method demonstrates significant advantages over state-of-the-art approaches. Indeed, CAFA achieves up to a 4x reduction in communication costs and accelerates FL training by as much as 70%, while preserving model accuracy. These achievements position CAFA as a promising solution for the efficient implementation of FL in constrained wireless networks.

Considering traffic characteristics: Roadside unit deployment optimization algorithm based on dynamic division of road network subareas

AbstractGiven that the overall coverage deployment method fails to meet information needs in important areas, there are redundancies and deficiencies in the information provided. To enhance communication stability for roadside units (RSUs), improve information coverage at critical intersections and optimize algorithm efficiency. Here, a method for deploying RSUs is proposed that aims to optimize revenue in road network subareas. The road network is divided into several subareas based on critical intersections, node similarity, road segment correlations, and characteristics of RSU information transmission. Then, a roadway accessibility algorithm is developed that accounts for channel fading. Considering the robustness of wire network deployment, an improved traveling salesman problem (TSP) problem is proposed that includes candidate locations and constructs a model for optimal RSU deployment that maximizes consolidated revenue. Finally, using the Sioux Falls network as an example, the RSU deployment strategy is evaluated for the overall network and the road network after being subdivided. The results indicate that subdividing the road network improves the efficiency of the optimization solution, the information coverage of critical intersections increases by 1.8 times. The deployment optimization scheme of RSUs is directly influenced by various parameters such as bandwidth capacity and cost coefficient. When deploying RSUs in road network subareas, variations in total demand have minimal impact on RSU deployment, ensuring a stable deployment scheme.

Analysis of Wireless Security and Networks using COPRAS Method

The wireless networks have become integral to our daily lives, powering everything from smart phones to smart home devices. However, alongside their benefits, wireless networks also bring forth significant security challenges. Wireless networks, unlike traditional wired networks, transmit data over the airwaves, making them susceptible to various forms of unauthorized access and data breaches. Ensuring the security of these networks is crucial to safeguard sensitive information, protect user privacy, and prevent cyber-attacks. The significance of research in wireless security and networks lies in the critical role that wireless technologies play in our interconnected world. As wireless networks become increasingly prevalent in various sectors, including communication, commerce, healthcare, transportation, and more, ensuring their security becomes paramount. Research in wireless security and networks contributes to the advancement of secure and reliable wireless technologies, protecting users' privacy, data, and overall well-being in an increasingly connected world. The COPRAS-G method requires identifying selection criteria, evaluating information related to these criteria, and developing methods to evaluate Meeting the participant's needs Criteria for doing in order to assess the overall performance of the surrogate. Decision analysis involves a Decision Maker (DM) Situation to do consider a particular set of alternatives and select one among several alternatives, usually with conflicting criteria. For this reason, the developed complexity proportionality assessment (COPRAS) method can be used. A genetic algorithm; Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Differential Evolution (DE). Detection Accuracy, Resource Efficiency; Implementation Complexity; Time-to-Deployment. From the result Differential Evolution (DE) is got the first rank whereas is Ant Colony Optimization (ACO) is having the lowest rank

Network intrusion detection based on machine learning strategies: performance comparisons on imbalanced wired, wireless, and software-defined networking (SDN) network traffics

Network intrusion detection based on machine learning strategies: performance comparisons on imbalanced wired, wireless, and software-defined networking (SDN) network traffics

Wire Databases Generation Using Deep Learning Methods for Rotorcraft Wire Strike Prevention

Helicopter wire strikes have been one of the most common sources of accidents in the past seven years. Poles and wires can be difficult to see for rotorcraft pilots because of their size, their location, and their blending into the background. This motivates the need to provide pilots with more tools and information about wire and pole locations. However, there is currently no comprehensive dataset summarizing the locations of poles and wires. As such, this work aims to leverage various types of data sources to generate a database of wires of interest. The proposed approach consists in first detecting pole and wire locations and then predicting a wire network. The first step combines a semantic segmentation approach applied to satellite imagery and a convolutional neural network classifier applied to street-view imagery to predict wire and pole locations. The second step applies the many-to-many Dijkstra algorithm to connect the predicted locations and generate a predicted wire map. This two-step approach is applied to a portion of Westchester County in New York, where publicly available imagery exists. Results indicate that this approach can successfully predict a wire map with high accuracy for wires alongside roads.

A novel conductive microtubule hydrogel for electrical stimulation of chronic wounds based on biological electrical wires

Electrical stimulation (ES) is considered a promising therapy for chronic wounds via conductive dressing. However, the lack of a clinically suitable conductive dressing is a serious challenge. In this study, a suitable conductive biomaterial with favorable biocompatibility and conductivity was screened by means of an inherent structure derived from the body based on electrical conduction in vivo. Ions condensed around the surface of the microtubules (MTs) derived from the cell’s cytoskeleton are allowed to flow in the presence of potential differences, effectively forming a network of biological electrical wires, which is essential to the bioelectrical communication of cells. We hypothesized that MT dressing could improve chronic wound healing via the conductivity of MTs applied by ES. We first developed an MT-MAA hydrogel by a double cross-linking method using UV and calcium chloride to improve chronic wound healing by ES. In vitro studies showed good conductivity, mechanical properties, biocompatibility, and biodegradability of the MT-MAA hydrogel, as well as an elevated secretion of growth factors with enhanced cell proliferation and migration ability in response to ES. The in vivo experimental results from a full-thickness diabetic wound model revealed rapid wound closure within 7d in C57BL/6J mice, and the wound bed dressed by the MT-MAA hydrogel was shown to have promoted re-epithelization, enhanced angiogenesis, accelerated nerve growth, limited inflammation phases, and improved antibacterial effect under the ES treatment. These preclinical findings suggest that the MT-MAA hydrogel may be an ideal conductive dressing for chronic wound healing. Furthermore, biomaterials based on MTs may be also promising for treating other diseases.

A security-enhanced authentication scheme for quantum-key-distribution (QKD) enabled Internet of vehicles in multi-cloud environment

The Internet of vehicles (IoV) is an essential part of modern intelligent transportation systems (ITS). In the ITS, intelligent connected vehicle can access a variety of latency-sensitive cloud services through the vulnerable wireless communication channel, which could lead to security and privacy issues. To prevent access by malicious nodes, a large number of authentication schemes have been proposed. However, with the diversification of cloud services and the rapid development of quantum computing, there are many drawbacks remain, including timeliness of authentication and resisting quantum computing. In light of this, we propose a lattice-based secure and efficient multi-cloud authentication and key agreement scheme for quantum key distribution (QKD) enabled IoV. Its features are as follows: i) Security-enhanced and Efficient Authentication: We combine the lattice-based lightweight signatures and quantum authentication keys to guarantee security-enhanced authentication. Meanwhile, we propose the quantum security service cloud (QSC) to manage the authentication of all vehicles and cloud server providers (CSPs) to reduce the authentication rounds and improve efficiency. ii) Extended Quantum Key Distribution (eQKD): In wireless networks, quantum key agreement is achieved through the pre-filled quantum keys. In wired networks, quantum key is accomplished by QKD with Bennett-Brassard 1984 (BB84) protocol. Furthermore, formal and informal security demonstrates that the scheme could resist potential security attacks. The performance comparison illustrates that our scheme could decrease the computational overhead by 27.23%-81.78% and authentication rounds by 81.34%-93.10%.

Anisotropy Reversal of Thermal Conductivity in Silicon Nanowire Networks Driven by Quasi-Ballistic Phonon Transport.

Nanostructured semiconductors promise functional thermal management for microelectronics and thermoelectrics through a rich design capability. However, experimental studies on anisotropic in-plane thermal conduction remain limited, despite the demand for directional heat dissipation. Here, inspired by an oriental wave pattern, a periodic network of bent wires, we investigate anisotropic in-plane thermal conduction in nanoscale silicon phononic crystals with the thermally dead volume. We observed the anisotropy reversal of the material thermal conductivity from 1.2 at 300 K to 0.8 at 4 K, with the reversal temperature of 80 K mediated by the transition from a diffusive to a quasi-ballistic regime. Our Monte Carlo simulations revealed that the backflow of the directional phonons induces the anisotropy reversal, showing that the quasi-ballistic phonon transport introduces preferential thermal conduction channels with anomalous temperature dependence. Accordingly, the anisotropy of the effective thermal conductivity varied from 2.7 to 5.0 in the range of 4-300 K, indicating an anisotropic heat manipulation capability. Our findings demonstrate that the design of nanowire networks enables the directional thermal management of electronic devices.

AUTOMATED SYSTEM FOR DETERMINING OF ELECTRICITY CONSUMPTION WITH ELEMENTS OF ARTIFICIAL INTELLIGENCE

Currently, Russia is moving towards automating the collection of information from metering devices for heat, gas, water, electricity, etc. Technologies are being developed that allow the transmission of resource consumption data via various communication channels, both wired and over-the-air. There are already devices and devices that allow transmitting readings of electricity metering devices over existing wired communication networks and this issue is being resolved at the legislative level, Federal Law No. 522-FZ of 07/01/2020 has been adopted. The basis for the research is to save time and effort in obtaining the necessary information to account for electricity readings from substations, increase the speed of decision-making, and be able to predict the situation with big data to identify abnormal values and eliminate them (for example, identified data from electricity meters will reduce the time labor of metering operators and economic losses of an energy supply company). The article proposes hardware and software methods for detecting data on electricity losses based on the use of artificial neural network algorithms and allowing to detect inconsistencies in commercial data readings of electric meters, which will reduce the commercial component of electricity losses. A verification calculation of an artificial neural network was performed for inconsistencies in the data on the transmitted electricity. The design of a receiving and transmitting digital module for remote data recording has been carried out, as well as the development of an automated system designed for remote collection, processing, storage and transmission of information on energy consumption and balance. The developed automated information and measurement system for remote data collection and transmission was tested at the manufacturer of smart electric energy meters with a radio channel.

Creation of a Software Tool with Elements of an Artificial Neural Network for Data Forecasting

Currently, Russia is moving towards automating the collection of information from metering devices for heat, gas, water, electricity, etc. Technologies are being developed that allow transmitting data on resource consumption through various communication channels, both wired and on-air. There are already devices and devices that allow transmitting readings of electricity metering devices over existing wired communication networks, and this issue is being resolved at the legislative level, federal Law No. 522-FZ of 01.07.2020 has been adopted. The basis for the research is to save time and effort to obtain the necessary information to take into account the readings of electricity from substations, increase the speed of decision-making, to be able to predict the situation with big data to identify abnormal values and eliminate them (for example, identified data from electricity meters will reduce the time labor costs of accounting operators and economic losses of the energy supply company). The article proposes software methods for detecting data on electricity losses based on the use of artificial neural network algorithms and allowing to detect inconsistencies of commercial data readings of electric meters, which will reduce the commercial component of electricity losses. A verification calculation of an artificial neural network was performed for inconsistencies in the data on the transmitted electricity.

Enhanced complex wire fault diagnosis via integration of time domain reflectometry and particle swarm optimization with least square support vector machine

AbstractUrban power systems rely on intricate wire networks, known as the power grid, which form the essential electric infrastructure in cities. While these networks transmit electricity from power plants to consumers, they are vulnerable to faults caused by manufacturing errors and improper installation, posing risks to system integrity. Thus, accurate identification and assessment of these faults are crucial to prevent damage and maintain system reliability. The objective of this research is to present an innovative and efficient methodology for diagnosing complex wire networks through the application of time domain reflectometry (TDR) combined with the particle swarm optimization (PSO) and least squares support vector machine (LSSVM) algorithm. This research addresses the imperative need to accurately locate and assess breakage faults within wire networks, emphasizing their role in both power transmission and communication infrastructure. To model the TDR answer of a specific complex wire network, a forward model is established utilizing resistance, inductance, capacitance and conductance (RLCG) parameters and the finite difference time domain (FDTD) method. Subsequently, the PSO‐LSSVM approach is used to solve the inverse problem of localizing faults in complex wire networks. The experimental results validate the practicality of this approach in real‐world systems.

Multifunction Robot for Reducing Sensors Count in Greenhouse Monitoring

Abstract: Over the years, greenhouse systems have been utilized to regulate the ambient conditions of precious crops in an effort to increase agricultural productivity. The crucial elements of a typical greenhouse are the construction of a polyhouse, the placement of artificial lighting, the careful use of vents, watering methods, and the calculated application of insecticides and pesticides, among other things. Numerous factors are crucial for optimum plant development, including soil moisture, temperature, humidity, light intensity, greenhouse layout, and plant spacing. Various monitoring and control methods have been suggested and put into practice for automated greenhouses throughout the years. For parameter monitoring in different plots of a green house, a number of sensors are often needed in green house automation. The interface between these sensors and the monitoring device is crucial. In exchange for a fee, several wired and wireless sensor networking solutions are being tested. This paper focuses on adopting a robotic approach to reduce the sensor count in the automated greenhouse system. The need of multiple sensors is eliminated as the robot can move across the greenhouse to collect data at required locations.

Improving network data security interaction methods under wireless communication

AbstractWith the development of information technology, network data security issues have received widespread attention. Under traditional wired communication networks, it not only has high installation and maintenance costs, requires a lot of manpower and resources, but also has low security performance, which is not significant in improving network data security. How to improve network data security, prevent data from being maliciously leaked, stolen, and effectively transmitted in the network, is a major challenge facing today's society. Wireless communication technology has brought new ideas to improve network data security. This article analyzed the role of wireless communication technology in improving network data security and selected 30 enterprises as the research objects. This article studied the role of traditional wired communication technology and wireless communication technology in network data security protection and compared several indicators based on virus detection rate, defense rate, data transmission speed, and customer satisfaction. The experimental results showed that the average detection rate of Trojan viruses based on wireless communication technology was 80.8%, and the average defense rate against viruses using antivirus software was 58%. In the case of virus attacks, the average transmission speed was 2.4 s, and the average satisfaction of users with wireless communication technology in virus defense was 6.9. It indicates that wireless communication technology has significantly improved virus detection rate, defense rate, data transmission speed, and customer satisfaction in the mode of improving network data security. This technology has significant significance and value for users.

ДОСЛІДЖЕННЯ ЗАЛЕЖНОСТІ ШВИДКОСТІ ПЕРЕДАВАННЯ ДАНИХ В СИСТЕМІ BPL ВІД КІЛЬКОСТІ НАВАНТАЖЕНИХ ВІДГАЛУЖЕНЬ МЕРЕЖІ ВНУТРІШНЬОБУДИНКО-ВОЇ ЕЛЕКТРОПРОВОДКИ

The work is devoted to research of the characteristics of Broadband over Power Lines (BPL) transmission systems (TS) when working on domestic house electrical wiring networks (HEWN). The object of research is the component of domestic electrical wiring network (HEWN) – branch. The influence of the branch load resistance on attenuation and data transmission rate acros HEWN fragment with one branch in the cases of the idle and short type of load, the load with active and reactive resistance type in the case of traditional orthogonal harmonic signals and generalized class orthogonal harmonic signals used in BPL TS was reviewed previously. The influence of number and position of active and reactive character load of branch on attenuation, phase and data transmission rate acros HEWN fragment in the case of traditional orthogonal harmonic signals on BPL TS uses is researched in the paper. Calculations are carried out and the results of research of frequency characteristics of attenuation, phase and data transmission rate acros HEWN fragment, formed with widely used in Ukraine PPV type wire (the cross-sectional area of the conductor is 2,5 mm2), in the range of 2 MHz to 30 MHz are given. The research of dependence between BPL TS data transmission rate acros HEWN fragment (in the case of 25ºMHz-PB frequency plan uses) and number and position of the load branches in the case of the idle type of load, the load with active and reactive resistance type (R, L, C) that connected to the poles of the load on the branch is researched in the paper. A comparison of the data transmission rate acros HEWN fragment for different numbers and variants of the position of the loaded branches that poles of which connected to the loads of different types (idle, R, L, C) was made.

Detection of attacks in Internet of Things networks using machine learning methods

The increase in the volume of digital data generated, in particular, by smart devices of the Internet of Things, has made research related to the application of machine learning methods to detect anomalies in network traffic - the presence of network attacks - relevant. For this purpose, the article proposes a unified approach to detecting attacks at various levels of the Internet of Things network architecture, based on machine learning methods. The article investigates that at the level of a wireless sensor network, the detection of an attack is related to the detection of anomalous behavior of an Internet of Things device, in which the deviation of the behavior of an Internet of Things device from its profile can be considered as a compromise of the device. Building profiles of smart IoT devices is based on statistical characteristics, such as the intensity and duration of packet transmission, the proportion of relayed packets, etc. It has been studied that at the level of a local or global wired network of the Internet of Things, data aggregation takes place, the analysis of which is also performed by machine learning methods. Trained classifiers can become part of a network attack detection system that makes decisions about node compromise "on the fly". Models of network attack classifiers at the level of a wireless sensor network, and at the level of a local or global wired network are experimentally selected. The best results in terms of completeness and accuracy estimates are demonstrated by the random forest method for a wired local and (or) global network and by all considered methods for a wireless sensor network. Practical significance: the proposed classifier models can be used in the design of attack detection systems in Internet of Things networks.

Applying Proportional–Integral–Derivative Controllers on Wired Network TCP’s Queue to Solve Its Incompatibility with the Wireless Ad-Hoc Network

Applying Proportional–Integral–Derivative Controllers on Wired Network TCP’s Queue to Solve Its Incompatibility with the Wireless Ad-Hoc Network

Security Issues on Internet of Things (IoT): A Recent Challenges and Countermeasures

Abstract: This paper contains a surveyas well as an investigation and examination of the present situation statesecurity for the Internet of Things (IoT). The Internet of Things aims to connect anybody with anything, anyplace. In contrast to the fixed Internet, IoT uses multiple wired and wireless networks to connect a large number of machines, limited-resource devices, and sensors. The realization, network, and application layers are the three hypothetical layers that make up an IoT.The security challenges that exist within and across these layers are described in this study. There are also several security ideas that need be implemented at each tier. Previous work on ensuring security for each IoT layer, as well as related countermeasures, is also examined. Finally, the report discusses potential acquisition strategies.

Case Study on Techno Economic Analysis of Solar Roof Top Power Plant in India

The viability of installing a rooftop solar power plant is investigated in this research. Roof top renewable energy has recently become popular for residential buildings. In general, the electricity produced from roof-top solar panels is converted to alternating current (AC) via a power control unit and directed to the existing wire network of the houses intended primarily to serve the Electric Utilities that supply alternating current (AC). The current 1 kilowatt power plant is utilized to assess performance throughout the year. The overall cost of the solar power plant is INR 50,000 with subsidies and it generates 1403.44 kWh of electricity each year. According to government policy, Government purchase the generated solar power in residential areas costs an average of 2.5 INR/kWh, so a yearly INR 3508.60 is generated from the solar power plant if all of the electricity is directly fed into the grid, but if it is used, it will save the consumer money because 1 kWh costs an average of 8.00 rupees in India in 2023 as buyers prospective. Therefore, as the point of view of the purchaser, the buyer or owner of a solar roof top receives 11,227.50 INR every year. The solar roof top power plant has a lifespan of 25 years, hence the annual cost of the solar power plant is 2000 INR. Thus, the benefit cost ratio to the owner is 5.61 if he/she uses energy for their dwelling, and the payback period is only 4.45 years. The proposed work must be considered in terms of an overall environmentally friendly approach that will end up resulting in a new era for energy efficiency and a lower carbon footprint. It will also contribute to the expansion of renewable energy in India.

Adaptive protocol of raft in wireless network

Original distributed consensus algorithms are typically engineered with wired communication networks in mind. However, when these algorithms are applied in wireless networks, the unique characteristics of wireless communication among nodes, including high error rates, variable latency, and dynamic network topology, present novel challenges that necessitate the development of adaptive protocols. This article aims to address these challenges by designing an adaptive protocol specifically for the implementation of the Raft consensus algorithm within wireless environments. We first outline the key stages of this adaptive protocol, focusing on enhancing robustness and efficiency in the state synchronization and routing processes, which are demonstrated through extensive simulations. The proposed adaptive protocol intends to ensure consistent states across nodes in wireless networks, which promote reliable and effective distributed systems for a variety of applications in autonomous wireless networks, including Vehicle-to-everything (V2X), Internet of Things (IoT), and edge computing.

Privacy-Preserving Push-Sum Average Consensus via State Decomposition

Average consensus is extensively used in distributed networks for computation and control, where all the agents constantly communicate with each other and update their states in order to reach an agreement. Under a general average consensus algorithm, information exchanged through wireless or wired communication networks could lead to the disclosure of sensitive and private information. In this paper, we propose a privacy-preserving push-sum approach for directed networks that can protect the privacy of all agents while achieving average consensus simultaneously. Each node decomposes its initial state arbitrarily into two substates, and their average equals to the initial state, guaranteeing that the agent's state will converge to the accurate average consensus. Only one substate is exchanged by the node with its neighbours over time, and the other one is reserved. That is to say, only the exchanged substate would be visible to an adversary, preventing the initial state information from leakage. Different from the existing state-decomposition approach which only applies to undirected graphs, our proposed approach is applicable to strongly connected digraphs. In addition, in direct contrast to offset-adding based privacy-preserving push-sum algorithm, which is vulnerable to an external eavesdropper, our proposed approach can ensure privacy against both an honest-but-curious node and an external eavesdropper. A numerical simulation is provided to illustrate the effectiveness of the proposed approach.