Communication Networks Research Articles

How Does Vehicle To Grid Integration (V2G) Impact Energy Management And Grid Stability, And What Are The Potential Benefits And Challenges With Its Implementation?

This research paper addresses, How Does Vehicle to Grid Integration (V2G) Impact Energy Management and Grid Stability, and What are the Potential Benefits and Challenges with its Implementation? The need to switch to electric cars (EVs) due to environmental pollution has made it difficult to manage EV energy consumption and include renewable sources. Bidirectional energy flow between EVs and the grid is made possible by Vehicle-toGrid (V2G) technology, which presents a feasible solution. The potential of V2G to optimize energy use, stabilize grids, and facilitate the integration of renewable energy sources is examined in this research. Energy Management Systems (EMS), communication networks, and bidirectional chargers are important parts. In particular, V2G is vital in areas where energy reliability is a problem. It improves grid stability, offers EV owners financial advantages, and encourages the use of renewable energy. V2G is feasible, as demonstrated by the successful deployments by companies like Nissan and Enel, despite obstacles like infrastructure requirements and battery deterioration. To fully utilize V2G,1 future developments should concentrate on standardization, policy support, and technical optimization

Event-Triggered Model-Free Adaptive Control for Nonlinear Multiagent Systems Under Jamming Attacks.

This article addresses the security problem of tracking control for nonlinear multiagent systems against jamming attacks. It is assumed that the communication networks among agents are unreliable due to the existence of jamming attacks, and a Stackelberg game is introduced to depict the interaction process between multiagent systems and malicious jammer. First, the dynamic linearization model of the system is established by applying a pseudo-partial derivative method. Then, a novel model-free security adaptive control strategy is proposed, so that the multiagent systems can achieve bounded tracking control in the mathematical expectation sense in spite of jamming attacks. Furthermore, a fixed threshold event-triggered scheme is utilized to reduce communication cost. It is worth noting that the proposed methods only require the input and output information of the agents. Finally, the validity of the proposed methods is illustrated through two simulation examples.

Analisis Tema Fantasi Komentar Donasi Palestina Pada Akun Youtube Nusantara Palestina Center

This research aims to examine the influence of actors' positions in communication networks and the role of fantasy themes in Palestinian donation comments on the Nusantara Palestine Center YouTube channel. Employing social network analysis through Labspolsys and Gephi 0.10, the study focuses on the video titled "Extraordinary: Indonesian Citizens Finally Provide Clean Water Assistance for Palestine." The principal findings indicate that key actors such as @Rio_naldo, @Arifhidayat26190, @Anonymous5580, and @Vinijulvia exhibit significant capabilities in disseminating information. These actors contribute to the analysis of fantasy themes that reflect aspirations and values, including justice, freedom, solidarity, hope, change, and religiosity. The results suggest that fantasy themes in comments can shape public perceptions of social movements and bolster support. However, this research is limited in its ability to generalize findings to a broader population. In conclusion, this study sheds light on the role of fantasy themes in influencing narratives and perceptions of Palestinian donation social movements on digital platforms such as YouTube

Advancing the deployment and information management of direct air capture: A solution enabled by integrating consortium blockchain system

Direct air capture (DAC) is a critical and emerging Negative Emissions Technology (NET) that directly removes CO2 from the atmosphere, significantly contributing to climate change. However, the deployment and management of large-scale DAC faces challenges such as collections and analysis of energy consumption data, intricate device and system management, emission prediction and operation strategy, precise carbon footprint tracking, etc. This paper proposes the integration of blockchain technology with DAC systems to address these challenges, utilizing blockchain's inherent properties of immutability, security, and transparency. The implementation strategy includes the development of a DAC consortium blockchain system, leveraging a consensus mechanism11Consensus mechanism: The programming and process used in blockchain systems to achieve distributed agreement about the ledger's state or the state of a data set. (Source: Investopedia), ECDSA encryption22Elliptic Curve Digital Signature Algorithm (ECDSA): In cryptography, the Elliptic Curve Digital Signature Algorithm (ECDSA) offers a variant of the Digital Signature Algorithm (DSA) which uses elliptic-curve cryptography. As with elliptic-curve cryptography in general, the bit size of the private key believed to be needed for ECDSA is about twice the size of the security level, in bits. (Source: Wikipedia), IoT33Internet of things (IoT): describes devices with sensors, processing ability, software and other technologies that connect and exchange data with other devices and systems over the Internet or other communication networks. (Source: Wikipedia) integration, and digital signatures. Preliminary modeling of the proposed system suggests potential improvements in operational efficiency and a reduction in data inaccuracies. The proposed system underscores the system's ability to streamline identity verification, improve data collection accuracy, and facilitate secure, confidential information sharing among DAC stakeholders. By enhancing the efficiency and reliability of DAC operations, this approach supports the scalable and effective deployment of NETs in the global effort to combat climate change. Future research will focus on empirical validation through pilot projects and simulations to further substantiate these claims.

Prospectively investigating the impact of AI on shared decision-making in post kidney transplant care (PRIMA-AI): protocol for a longitudinal qualitative study among patients, their support persons and treating physicians at a tertiary care centre

IntroductionAs healthcare is shifting from a paternalistic to a patient-centred approach, medical decision making becomes more collaborative involving patients, their support persons (SPs) and physicians. Implementing shared decision-making (SDM) into...

A detection model for false data injection attacks in smart grids based on graph spatial features using temporal convolutional neural networks

Due to the intelligence and opening of future generation cyber–physical power systems, smart grids are vulnerable to the emergency of cyber-attacks aiming at the cyber–physical systems. Among these attacks, false data injection (FDI) attacks are particularly concerning in smart grids due to their stealthiness. Attackers can tamper with data transmitted through communication networks, influencing the analysis results of the control center, leading it to make wrong decisions and thereby undermining the stability of smart grids. The inability of traditional bad data detection models to detect false data injection attacks poses huge security risks to smart grids. For this reason, a new attack detection model using spatial–temporal features is proposed. The proposed detection model includes the following two parts: spatial features extraction and temporal convolutional network. First, graph convolutional operations are employed to disentangle the interactions among buses and extract spatial features of measurement; Then, the temporal convolutional network model is used to extract the temporal features. Through these processes, the proposed detection model can effectively identify the injected false data injection attacks in smart grids. The detection performance and robustness of the proposed FDI attack detection model are tested through simulations on IEEE 14-bus and 118-bus grid systems.

Machine Learning Approach for Spatiotemporal Multivariate Optimization of Environmental Monitoring Sensor Locations

Abstract Long-term environmental monitoring is critical for managing the soil and groundwater at contaminated sites. Recent improvements in state-of-the-art sensor technology, communication networks, and artificial intelligence have created opportunities to modernize this monitoring activity for automated, fast, robust, and predictive monitoring. In such modernization, it is required that sensor locations be optimized to capture the spatiotemporal dynamics of all monitoring variables as well as to make it cost-effective. The legacy monitoring datasets of the target area are important to perform this optimization. In this study, we have developed a machine-learning approach to optimize sensor locations for soil and groundwater monitoring based on ensemble supervised learning and majority voting. For spatial optimization, Gaussian process regression (GPR) is used for spatial interpolation, while the majority voting is applied to accommodate the multivariate temporal dimension. Results show that the algorithms significantly outperform the random selection of the sensor locations for predictive spatiotemporal interpolation. While the method has been applied to a four-dimensional dataset (with two-dimensional space, time, and multiple contaminants), we anticipate that it can be generalizable to higher-dimensional datasets for environmental monitoring sensor location optimization.

Exploring the YOLO-FT Deep Learning Algorithm for UAV-Based Smart Agriculture Detection in Communication Networks

Exploring the YOLO-FT Deep Learning Algorithm for UAV-Based Smart Agriculture Detection in Communication Networks

Quantum Networking for Secure and Reliable Communication in Space Exploration

Quantum Networking for Secure and Reliable Communication in Space Exploration

Dynamic brain networks in spontaneous gestural communication

Gestures accent and illustrate our communication. Although previous studies have uncovered the positive effects of gestures on communication, little is known about the specific cognitive functions of different types of gestures, or the instantaneous multi-brain dynamics. Here we used the fNIRS-based hyperscanning technique to track the brain activity of two communicators, examining regions such as the PFC and rTPJ, which are part of the mirroring and mentalizing systems. When participants collaboratively solved open-ended realistic problems, we characterised the dynamic multi-brain states linked with specific social behaviours. Results demonstrated that gestures are associated with enhanced team performance, and different gestures serve distinct cognitive functions: interactive gestures are accompanied by better team originality and a more efficient inter-brain network, while fluid gestures correlate with individual cognitive fluency and efficient intra-brain states. These findings reveal a close association between social behaviours and multi-brain networks, providing a new way to explore the brain-behaviour relationship.

Classical and quantum frequency combs for satellite-based clock synchronization

The next generation of space-based networks for communications, sensing, and navigation will contain optical clocks embedded within satellites. To fully realize the capabilities of such clocks, high-precision clock synchronization across the networks will be necessary. Current experiments have shown the potential for classical frequency combs to synchronize remote optical clocks over free space. However, these classical combs are restricted in precision to the standard quantum limit. Quantum frequency combs, however, which exhibit quantum properties such as squeezing and entanglement, provide pathways for going beyond the standard quantum limit. Here, we present our perspective on the prospects for practical clock synchronization in space using both classical and quantum frequency combs. We detail the current outcomes achievable with a classical frequency comb approach to synchronization, before quantifying the potential outcomes offered by quantum frequency combs. Challenges to be overcome in deploying frequency combs in space are presented, and the implications of almost-perfect synchronization for future space-based applications and experiments are discussed.

Congested traffic patterns of mixed lattice hydrodynamic model combining the perceptual range differences with passing effect

With the aid of Vehicle-to-everything (V2X) technologies for network connectivity, connected autonomous vehicles (CAVs) can broaden the drivers' perceptual boundaries and receive a greater quantity of exogenous vehicle information, thereby governing the vehicle's acceleration information of the next moment. Nonetheless, constrained by contemporary communication networks and sophisticated vehicle control technology, the process of promoting CAVs is long-lasting, and throughout this stage of transition, both CAVs and human-driven vehicles (HDVs) will happen on the road. Moreover, passing behaviour is uncommon in the traffic flow model despite being a fundamental microscopic driving behaviour, particularly within mixed-traffic situations. To bridge that hole, we introduce the percentage ratios of CAVs into the lattice hydrodynamic model by integrating the perceptual range differences between two different types of vehicles with passing effects. Subsequently, the stability norm associated with the new model is ascertained by performing the linear stability analysis. When the above-mentioned stability condition is not achieved, we investigate the complex behaviour of the new model, and the associated existing conditions, as well as the modified Korteweg-de Vries (mKdV) equation, are determined simultaneously. When the passing ratio is inadequate, no jam and kink jam make up the whole phase region; while when the passing ratio surpasses the minimum, the initial unstable region can be segregated into two extra segments: the chaotic sub-region and the kink jam sub-region, the density wave progressively transitioned from being a kink-Bando traffic wave to a chaotic phase. Lastly, the findings of the numerical experiment identify with the theoretical derivation made above.

Design and Operation of IoT System and Power Communication with Digital Twin Technology

Abstract This paper explores how to integrate digital twin technology into IoT system design to achieve more efficient monitoring and maintenance of power communication networks. By introducing digital twin technology, the real-time performance and accuracy of embedded systems in power communication are enhanced, providing strong data support and simulation environments for predictive maintenance and fault diagnosis. This paper combines theoretical research with practical applications to propose an embedded IoT system design based on the ARM9 platform and verifies its effectiveness in power communication networks. In the testing phase, the ARM9-based IoT system demonstrated a processing speed 15% faster than previous designs, with a latency reduction of 18% in communication processes. These enhancements contribute to the overall efficiency and reliability of power communication networks.

Event-Based Asynchronous H∞ Control for Nonhomogeneous Markov Jump Systems With Imperfect Transition Probabilities.

The event-based H∞ control problem is investigated for a class of nonhomogeneous Markov jump systems (MJSs) with partially unknown transition probabilities (TPs). The MJS is characterized by a piecewise nonhomogeneous Markovian chain, where the switching of the system TP matrix is governed by a higher-level chain. A hidden Markov model (HMM) is employed to observe the system mode, which cannot always be correctly detected in practice. Under this framework, the partially unknown TPs existing in both higher-level TPs (HTPs) and conditional TPs (CTPs) are taken into account for practical consideration. Additionally, an observed-mode-dependent event-triggered mechanism (ETM) is employed to design an asynchronous controller, which is expected to alleviate the burden of the communication network. Evidently, the considered scenario is fairly general and covers some special cases. With the above consideration, sufficient conditions are established to guarantee stochastic stability of the resulting closed-loop system with a prescribed H∞ performance. Finally, two examples are presented to demonstrate the effectiveness and applicability of the proposed method.

Silicon Germanium Spins Cellular and Space Communication Networks

Silicon Germanium Spins Cellular and Space Communication Networks

Use of the value-focused thinking methodology to understand health care waste management under the perspective of occupational and environmental health

The increasing generation of healthcare waste has greatly accelerated the concern about its potential negative environmental and public health effects. In this sense, the application of decision-making methods and techniques at a strategic level has become increasingly indispensable for the management of health units. This article aimed to understand the challenges in healthcare waste management (HCWM), the main mechanisms of action and values. The Value-Focused Thinking methodology was used to evidence important gaps and propose improvements in the context of decision on occupational safety and environmental protection, leading to the creation of alternatives not seen previously and guaranteeing the good ideas would be converted into actions. The structure was developed by semi-structured questionnaires, observation lists and interviews, conducted to expose the values of the main stakeholders (managers, health professionals, and cleaning professionals) of 10 public health units selected in Caruaru (Brazil). As a result, the investigation evidenced four fundamental goals for the decision context: improvement in work and environmental safety; improvement in HCWM practices; improvement in resource management; strengthening of the stakeholder communication network. From the analysis of the attributes associated with the fundamental goals, it was possible to identify actions that should be implemented with a higher priority and, in the short term, aiming at obtaining positive results, especially in the management practices and the safe management of health waste. The study resulted from an investigation that awakened the participants about aspects little questioned in everyday life. Implementing actions to segregate and recycle materials, with data monitoring and sharing, will increase the population's trust and the environment's and professionals' safety. The alternatives derived from the VFT method consider the perspectives of key stakeholders in HCWM, helping decision-makers and policymakers develop more assertive and grounded management strategies.

The Antecedents, Consequences and Key Determinants of Corporate Accelerators’ Success: A Systematic Literature Review

Over the past decade, accelerators have demonstrated their pivotal contribution to the economic and entrepreneurial ecosystem growth. In recent years, a large number of businesses and organizations use corporate accelerators to internalize opportunities provided by startups and idea generators outside the organization. Although this phenomenon has received much attention, especially in Iran, there are still many unclear points regarding the structure, processes, and acceleration programs. In this article, we did a systematic review of 49 articles out of 486 articles published in different journals in the last 5 years. Then, through the PRISMA method, the reviewed data was converted into a structured format in Excel software. At the end, we examined the findings of this study by presenting a conceptual framework detailing the antecedents and consequences of corporate accelerators. Also, in this research, we found that the five main success factors of corporate accelerators are the acceleration program, the creation of a large and committed external communication network, mentoring, the determination of accurate evaluation criteria and the competitive process in selecting startups.

Integrated Cognitive Symbiotic Computing and Ambient Backscatter Communication Network

Integrated Cognitive Symbiotic Computing and Ambient Backscatter Communication Network

Trajectory and User Assignment Design for UAV Communication Network With No-Fly Zone

Trajectory and User Assignment Design for UAV Communication Network With No-Fly Zone

HF skywave communication for air-to-ground networks: A cooperative transmission framework

High-Frequency (HF) communication is widely used for long-distance transmission in remote and disaster areas. However, the dynamic nature of the ionosphere and multipath propagation in the HF channel pose significant challenges to designing efficient and robust communication systems. In this paper, we propose a Fixed Station (FS) and frequency matching method, as well as a power allocation method, to improve the sum-rate of air-to-ground HF communication networks. We derive optimal power allocation among users that share the same frequency, based on which a modified water-filling algorithm is used to solve the power allocation problem in multi-user scenarios, while a low-complexity algorithm is proposed to solve the integer optimization problem of frequency-FS matching. Simulation results demonstrate that the proposed algorithm outperforms the naive algorithm, indicating its effectiveness.