Optimal Transmission Research Articles

Particle swarm optimization for beamforming design in a cognitive radio

Beamforming is essential for improving transmission in wireless sensor networks (WSNs), particularly in cognitive radio networks (CRNs) with several secondary users (SU) equipped with transmitting antennas. Optimizing beamforming while minimizing interference with primary users (PU) is of great interest. This study proposes an improved particle swarm optimization (PSO) algorithm to enhance beamforming performance. This approach aims to maximize the power of the beam directed to the SU receiver while controlling interference in the PU protection region. The results show that this algorithm constantly improves beam focus and signal-to-noise ratio to effectively optimize beamforming. Firstly, beam focusing becomes narrower as the number of antenna elements increases, generating optimal transmission conditions. Secondly, the algorithm achieves a considerable improvement in signal-to-noise ratio as the number of antenna elements increases. Furthermore, optimization performance improves as the number of antenna elements increases, as shown by the best fitness values. The simulations also illustrate the performance of the proposed method.

Optimal Transmission Schedule With Privacy Preservation for Cyber-Physical System Against Eavesdropping Attack

Optimal Transmission Schedule With Privacy Preservation for Cyber-Physical System Against Eavesdropping Attack

Enhancement in Reliability of IEEE 802.15.4 WBAN Using Greedy Spider Monkey Algorithm

Wireless Body Area Networks (WBANs) hold immense potential in healthcare monitoring, but ensuring reliable data transmission is crucial. While the IEEE 802.15.4 standard offers low-power operation and basic security, its Contention-Based Access (CBA) mechanism can lead to packet collisions and reduced reliability, especially in congested scenarios. This paper proposes a novel approach to enhance reliability in 802.15.4 WBANs by incorporating a Greedy Spider Monkey Optimization Algorithm (GSMA). The GSMA mimics the intelligent foraging behaviour of spider monkeys, enabling dynamic channel selection and optimal transmission scheduling. Our approach aims to minimize packet collisions by selecting channels with lower traffic based on historical data and real-time network conditions; the GSMA reduces the likelihood of collisions and data loss. Secondly, the algorithm prioritizes critical data packets based on their urgency and channel availability, ensuring the timely delivery of essential medical information. Finally, by reducing collisions and optimizing scheduling, the GSMA helps maintain efficient data flow within the WBAN. This paper presents the design and implementation of the GSMA-based approach within the 802.15.4 framework. Simulation results are presented to evaluate the effectiveness of the proposed method in improving reliability, reducing packet loss, and enhancing overall network performance in WBANs. The findings demonstrate the potential of the GSMA to address the limitations of 802.15.4 and contribute to developing more reliable and efficient WBAN solutions for healthcare applications.

An Optimized Transmission Mechanism for Mitigating Jamming Attacks in Multi-Hop Wireless Networks

Aim: To address the vulnerability of Multi-Hop Wireless Network Systems (MHWNs) to jamming attacks and propose an effective solution to maintain communication integrity and Quality of Service (QoS). Background: In MHWNs, the open-access nature makes them susceptible to jamming attacks, which disrupt communication by interfering with authenticated nodes in the wireless medium. Existing methods primarily focus on tracking and countering jammers but lack effectiveness in preventing communication disruptions. Objective: The objective of this study is to introduce a novel algorithm, Optimized Transmission Mechanism (OTM), to mitigate the impact of jamming attacks on MHWNs. OTM aims to optimize node handover and packet routing to bypass jammed areas, ensuring uninterrupted packet transmission and preserving QoS. Methods: The proposed OTM algorithm determines the optimal transmission route based on radio transmitter location and connection quality. It prioritizes routes with the highest connection quality to maintain QoS even in jammed conditions. Additionally, it incorporates mechanisms for packet redirection away from jammed areas to ensure successful transmission. Results: Evaluation of the Extended Optimized Transmission Mechanism (EOTM) demonstrates significant improvements in packet delivery performance compared to existing algorithms. The enhanced algorithm effectively mitigates the impact of jamming attacks, ensuring reliable communication and preserving QoS in MHWNs. Conclusion: The proposed OTM algorithm presents a promising approach to counter-jamming attacks in MHWNs by dynamically routing packets to avoid jammed areas and maintain communication integrity. The results highlight the effectiveness of EOTM in improving packet delivery performance and ensuring uninterrupted communication in the face of jamming threats.

An Effective Congestion and Interference Secure Routing Protocol for Internet of Things Applications in Wireless Sensor Network

AbstractThis paper provides an effective Wireless Sensor Network (WSN) routing solution for Internet of Things (IoT) applications cognizant of congestion, security, and interference. Because several sources try to deliver their packets to a destination simultaneously, which is a common case in IoT applications. The proposed congestion and interference aware safe routing protocol is claimed to work in networks with high traffic. The signal to interference ratio (SINR), congestion level, and survival factor is used in our suggested procedure to estimate the cluster head selection factor first. The adaptive fuzzy c-means clustering method clusters the network nodes based on the cluster head selection factor. After that, data packets are encrypted using Adaptive Quantum Logic-based packet coding. Finally, the Adaptive Krill Herd (AKH) optimization method identifies the least congested corridor, resulting in optimal data transmission routing. The exploratory findings show that the provided strategy outperforms previous methodologies in network performance, end-to-end delay, packet delivery ratio, and node remaining energy level.

Levy Flight-Based Bee Swarm Optimized Optimal Transmission Sequence for PAPR Reduction in 5G NOMA Systems

Levy Flight-Based Bee Swarm Optimized Optimal Transmission Sequence for PAPR Reduction in 5G NOMA Systems

Multimedia Transmission Technique for Smart Ambulance with multi-carrier OFDM in a V2V and V2I Channel model using Software Defined Radio Technology

This research explores the implementation of a cutting-edge Software Defined Radio (SDR) framework to transmit multimedia files that can be assumed to be medical data in smart ambulances. The system utilizes multi-carrier Orthogonal Frequency-Division Multiplexing (OFDM) across V2V and V2I channels. The research is based on the notion that adaptive real-time communication is essential for the uninterrupted supply of key patient data to medical facilities and vehicles in transit, in order to address the problems posed by high mobility and dynamic environmental conditions. A comprehensive SDR system has been constructed and assessed in comparison to conventional communication mechanisms, demonstrating notable advancements in data accuracy and uninterrupted transmission. Our system successfully established stable connections in V2I channels, even in the presence of environmental obstacles. It maintained average power levels of approximately 32.074 dBm and a Peak-to-Average Power Ratio (PAPR) of 1.037 dB. These results indicate a constant signal envelope that promotes optimal signal transmission with excellent fidelity. In V2V scenarios, we successfully maintained data integrity with a low Peak-to-Average Power Ratio (PAPR) of 3.316 dB, even while vehicles were moving at a speed of 20 km/h. Additionally, we secured a high likelihood (94.5%) that the signal power remained close to the average, showing the robustness of our system against Doppler effects and signal dispersion. Text transmissions experienced errors when subjected to a Doppler shift of 20 km/h, which impacted the decoding of the received text. Similarly, image transmissions revealed limitations in bandwidth, as a transmitted image of 3640 KB was received with a degraded 4 KB. This emphasizes the importance of implementing effective error handling and recovery mechanisms. The results illustrate the efficacy of the suggested system in maintaining a high Quality of Service (QoS), offering proof of the effectiveness of contemporary wireless communication technologies in improving emergency medical services and setting new standards in smart ambulance capabilities.

Interline Power Flow Controller Allocation for Active Power Losses Enhancement Using Whale Optimization Algorithm

Transmission networks face continuous electrical and mechanical stresses due to increasing system challenges and power losses. Transmission networks require special focus and detailed studies each time a load or a generator emerges to the grid. The interline power flow controller (IPFC) is a relatively new scheme that is implemented in the transmission network to improve transmission efficiency, decrease transmission losses, and enhance voltage profile. In this paper, the interline power flow controller’s impact on transmission network performance is investigated as it is implemented within the IEEE 5-bus, 14-bus, and IEEE 57-bus systems. In addition, the whale optimization algorithm (WOA) is used to optimize the interline power flow controller locations within the system to achieve optimal transmission system performance. WOA performance is also compared to genetic algorithm (GA) and particle swarm optimization (PSO) algorithms, and the superiority of the proposed WOA-based control is proved. The robustness of the optimized system against load variations is investigated and the results introduced affirm the capability of the interline power flow controller to enhance transmission network efficiency.

Sustainability using Artificial Intelligence: Transformative Solutions in the IT Sector

Sustainability has become a critical priority for businesses and governments as we address environmental changes such as climate change, resource depletion, waste management, and water level increases. This paper investigates the transformative role of artificial intelligence (AI) in sustainable product development, highlighting how advancements in AI technologies are reshaping traditional practices. Traditional product development processes often relied on resource-intensive methods that generated significant waste and offered limited adaptability to sustainability goals. As a cornerstone of the digital economy, the IT sector faces increasing pressure to address its environmental impact, particularly in areas like server management, network optimization, and data transmission. This paper explores the transformative role of Artificial Intelligence (AI) in driving sustainable practices within IT operations. AI-powered solutions, such as dynamic workload management, predictive maintenance, and traffic optimization, enable energy-efficient server and network operations, significantly reducing power consumption. Advanced AI-driven algorithms facilitate data compression, prioritize transmission, and optimize content delivery, minimizing carbon emissions from data-intensive activities. Additionally, AI supports renewable energy integration, tracks carbon footprints, and provides actionable insights for developing sustainable software and IT infrastructure. These innovative applications of AI enhance operational efficiency and contribute to global sustainability goals by reducing the environmental footprint of IT systems. This research highlights the potential of AI to create a greener IT landscape while maintaining the sector’s growth and performance imperatives.

Energy-Efficient Cooperative Transmission in Ultra-Dense Millimeter-Wave Network: Multi-Agent Q-Learning Approach.

In beyond fifth-generation networks, millimeter wave (mmWave) is considered a promising technology that can offer high data rates. However, due to inter-cell interference at cell boundaries, it is difficult to achieve a high signal-to-interference-plus-noise ratio (SINR) among users in an ultra-dense mmWave network environment (UDmN). In this paper, we solve this problem with the cooperative transmission technique to provide high SINR to users. Using coordinated multi-point transmission (CoMP) with the joint transmission (JT) strategy as a cooperation diversity technique can provide users with higher data rates through multiple desired signals. Nonetheless, cooperative transmissions between multiple base stations (BSs) lead to increased energy consumption. Therefore, we propose a multi-agent Q-learning-based power control scheme in UDmN. To satisfy the quality of service (QoS) requirements of users and decrease the energy consumption of networks, we define a reward function while considering the outage and energy efficiency of each BS. The results show that our scheme can achieve optimal transmission power and significantly improved network energy efficiency compared with conventional algorithms such as no transmit power control and random control. Additionally, we validate that leveraging channel state information to determine the participation of each BS in power control contributes to enhanced overall performance.

Effect of Lu content on microstructure and spectral properties of Yb:(LuxSc1-x)2O3 solid-solution sesquioxide transparent ceramics

Yb³⁺-doped (Lux,Sc1-x)2O3 materials demonstrate superior performance in generating short pulse lasers because of the broader emission spectra than single-component sesquioxide, making them a promising laser gain material for high-power ultrafast lasers. By adjusting the concentration of Lu/Sc, laser gain materials with different widths of emission spectra can be obtained. In this work, 5 at.% Yb:(LuxSc1-x)2O3 nano-powders with varying Lu concentrations (x = 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9)were successfully synthesized by the co-precipitation method. Subsequently, Yb:(LuxSc1-x)2O3 transparent ceramics were produced through a combination of vacuum pre-sintering and hot isostatic pressing (HIP) post-treatment at 1700 °C. Influence of Lu content on densification process, microstructure changes and optical transmittance of Yb:(LuxSc1-x)2O3 ceramics was investigated in detail. The average grain sizes of ceramics pre-sintered at 1700 °C decrease from 3.42 μm to 1.43 μm, and all ceramics have a relatively uniform microstructure. After HIP post-treatment, the optimum in-line transmittance of Yb:(LuxSc1-x)2O3 ceramics reaches 77.6 % at 1100 nm when x value = 0.1. With the increase of Lu content, the emission wavelength experiences a gradual blueshift from 1041 nm to 1034 nm. Notably, the Yb:(Lu0.6Sc0.4)2O3 ceramics exhibited the broadest emission bandwidth of 18 nm, which is 1.5 times of the Yb:Sc2O3, suggesting their immense potential for applications in ultrashort pulse lasers.

Corrigendum to “Determination of optimal transmission network constraints for novel close-to-real-time flexible energy market” [Energ. Rep., vol. 12, December 2024, Pages 1014–1024, doi.org/10.1016/j.egyr.2024.07.003

Corrigendum to “Determination of optimal transmission network constraints for novel close-to-real-time flexible energy market” [Energ. Rep., vol. 12, December 2024, Pages 1014–1024, doi.org/10.1016/j.egyr.2024.07.003

Research on High-Efficiency Routing Protocols for HWSNs Based on Deep Reinforcement Learning

In heterogeneous wireless sensor networks (HWSNs), optimizing energy efficiency presents significant challenges due to variations in node energy levels and the complexity of the network environment. This paper introduces an energy efficiency optimization algorithm for HWSNs based on the Deep Q-Network (HDQN). The algorithm aims to address these challenges by selecting the optimal information transmission path. The HDQN leverages energy differences between nodes and real-time environmental data to enhance network efficiency. Its reward function takes into account node distance, remaining energy, and relay node count to balance node participation and minimize overall energy consumption. The Deep Q-Network (DQN) uses the mean squared error for precise reward estimation, and an improved packet header structure supports effective routing decisions. Simulation results show that the HDQN significantly outperforms existing algorithms—EEHCHR, 2L-HMGEAR, NCOGA, DEEC, and SEP—in terms of energy efficiency, network lifetime, and robustness, demonstrating its potential to advance the performance of HWSNs. The research results of the paper provide a theoretical basis for future energy efficiency research in wireless communication and contribute to the study of the new generation of wireless networks.

Slotted ALOHA Based Practical Byzantine Fault Tolerance (PBFT) Blockchain Networks: Performance Analysis and Optimization.

Practical Byzantine Fault Tolerance (PBFT) is one of the most popular consensus mechanisms for the consortium and private blockchain technology. It has been recognized as a candidate consensus mechanism for the Internet of Things networks as it offers lower resource requirements and high performance when compared with other consensus mechanisms such as proof of work. In this paper, by considering the blockchain nodes are wirelessly connected, we model the network nodes distribution and transaction arrival rate as Poisson point process and we develop a framework for evaluating the performance of the wireless PBFT network. The framework utilizes slotted ALOHA as its multiple access technique. We derive the end-to-end success probability of the wireless PBFT network which serves as the basis for obtaining other key performance indicators namely, the optimal transmission interval, the transaction throughput and delay, and the viable area. The viable area represents the minimum PBFT coverage area that guarantees the liveness, safety, and resilience of the PBFT protocol while satisfying a predefined end-to-end success probability. Results show that the transmission interval required to make the wireless PBFT network viable can be reduced if either the end-to-end success probability requirement or the number of faulty nodes is lowered.

Enhancing Greenhouse Efficiency: A Quantum Genetic Algorithm Approach to Optimal Glass Thickness

Greenhouses are pivotal in extending the growing season for various crops by creating conducive environments that enhance plant growth. A critical factor in maximizing the efficiency of greenhouses is the optimization of light transmittance through multi-layer glass structures. This not only supports plant health but also conserves energy by maintaining a warmer internal climate. The application of quantum genetic algorithms (QGAs) to this context represents a significant advancement, as these algorithms excel in handling multi-objective optimization challenges. By targeting the precise calibration of glass thickness, QGAs can substantially influence the amount of solar energy harnessed within the greenhouse, especially when the sun is directly overhead at noon. This paper presents an in-depth study on the use of quantum genetic algorithms to optimize the thickness of three distinct layers of glass in greenhouse settings. Our findings reveal that QGAs are capable of generating various combinations of glass thickness that not only maximize the total energy transmitted but also ensure stability in the energy output across different scenarios. The robustness of QGAs in consistently deriving optimal solutions underscores their potential as a superior technique in architectural design for agricultural applications. Comparative analysis with classical genetic algorithms and random number optimization techniques further demonstrates the superiority of quantum genetic algorithms.

Distribution network reconfiguration optimization based on genetic algorithm and its influence on operation and maintenance management

As one of the advanced application functions of distribution network automation, distribution network reconfiguration is an important optimization means to ensure the normal operation of the power grid. In order to further improve the power supply quality and operation and maintenance efficiency of the distribution network, this paper proposes a reconfiguration method based on improved genetic algorithm, establishes a network topology reconfiguration computational model, and validates the proposed method for the reconfiguration of the distribution network, and the results show that compared with the reconfiguration model of the distribution network constructed by basic genetic algorithm, the algorithm of this paper shows excellent performance in terms of both the comparison of the node voltages and the evolution of the population. The results show that compared with the basic genetic algorithm constructed distribution network reconfiguration model, this paper's algorithm exhibits excellent performance in both node voltage comparison and population evolution, and is capable of realizing optimal power transmission. Finally, the impact of distribution network reconfiguration on operation and maintenance management is analyzed.

A transparent multifunctional integrated meta-window with excellent sound insulation and vibration reduction performance

As the speed of transportation vehicles such as high-speed train continues to increase, there has been a significant rise in both noise and vibration levels, substantially compromising passenger comfort and overall travel experience. In transportation vehicles, the necessity for transparency in windows poses a challenge in incorporating high sound-insulating or vibration-damping materials, rendering windows vulnerable in sound isolation and vibration attenuation. This study employs an integrated material-structural design concept to develop a multifunctional meta-window, which ensures optimal lighting transmission while achieving outstanding sound-insulating and vibration-damping capabilities. Two distinct structures, named ultra-lightweight thin plate-type metamaterial and high-stability thick plate-type metamaterial, are precisely designed to adapt to varied application scenarios. Utilizing a gradient parameter multi-cell parallel synergetic coupling design method broadens the working bandwidth for sound insulation. The meta-window incorporates localized resonance units, enabling acoustic and vibrational energy dissipation through low-frequency resonance, effectively enhancing the window’s sound-insulating and vibration-damping capabilities. Comprising a composite of various transparent materials, the design amalgamates sound insulation, vibration reduction, and light transmission, eliminating the need for opaque sound-insulating or damping materials. Consequently, it holds substantial potential for applications across sectors, including train, aircraft, and architectural domains.

Thermo-hydrodynamic performance and geyser boiling flows of a novel co-axial condensing heat pipe (CCHP) for low-grade green energy exploitations

Due to their excellent heat transfer efficiency, two-phase closed thermosiphons (TPCTs) are essential for solar applications and the cooling of electronics. In this research, a novel coaxial condensing heat pipe (CCHP) was proposed, which has a more compact structure compared to traditional heat pipe, given the same volume and heat transfer area. Numerical modeling and experimental investigation were fully utilized to examine the influences of heat inputs, inclination angles, liquid filling ratios, and working mediums on the heat transmission properties of CCHP. The vapor-liquid flow phenomenon along with the thermal and mass transmission mechanism of CCHP under various operating conditions were explored. Our findings demonstrated that CCHP exhibited optimal thermal transmission performance at the tilt angle of 60° when the filling rate is 50 % and the input power is 100 W, the minimum thermal resistance of CCHP is 0.284K/W. At the tilt angle of 60° and input power of 100 W, CCHP has the lowest thermal resistance of 0.267K/W. Besides, the utilization of ethanol as a working medium could simultaneously reduce the evaporator temperature and enhance the thermal properties of CCHP. Inside CCHP, "geyser boiling" phenomena was further observed. Our experimental findings and numerical ones agreed well for each other. This innovative heat pipe could be confident and robust applied in the solar energy collection and relevant heat transfer enhancement fields.

Dose-efficient phase-contrast imaging of thick weak phase objects via OBF STEM using a pixelated detector.

Optimum bright-field scanning transmission electron microscopy (OBF STEM) is a recently developed low-dose imaging technique that uses a segmented or pixelated detector. While we previously reported that OBF STEM with a segmented detector has a higher efficiency than conventional STEM techniques such as annular bright field (ABF), the imaging efficiency is expected to be further improved by using a pixelated detector. In this study, we adopted a pixelated detector for the OBF technique and investigated the imaging characteristics. Because OBF imaging is based on the thick weak phase object approximation (tWPOA), a non-zero crystalline sample thickness is considered in addition to the conventional WPOA, where the pixelated OBF method can be regarded as the theoretical extension of single side band (SSB) ptychography. Thus, we compared these two techniques via signal-to-noise ratio transfer functions (SNRTFs), multi-slice image simulations, and experiments, showing how the OBF technique can improve dose efficiency from the conventional WPOA-based ptychographic imaging.

Joint jammer selection and power optimization in covert communications against a warden with uncertain locations

In covert communications, joint jammer selection and power optimization are important to improve performance. However, existing schemes usually assume a warden with a known location and perfect channel state information (CSI), which is difficult to achieve in practice. To be more practical, it is important to investigate covert communications against a warden with uncertain locations and imperfect CSI, which makes it difficult for legitimate transceivers to estimate the detection probability of the warden. First, the uncertainty caused by the unknown warden location must be removed, and the optimal detection position (OPTDP) of the warden is derived which can provide the best detection performance (i.e., the worst case for a covert communication). Then, to further avoid the impractical assumption of perfect CSI, the covert throughput is maximized using only the channel distribution information. Given this OPTDP based worst case for covert communications, the jammer selection, the jamming power, the transmission power, and the transmission rate are jointly optimized to maximize the covert throughput (OPTDP-JP). To solve this coupling problem, a heuristic algorithm based on maximum distance ratio (H-MAXDR) is proposed to provide a sub-optimal solution. First, according to the analysis of the covert throughput, the node with the maximum distance ratio (i.e., the ratio of the distances from the jammer to the receiver and that to the warden) is selected as the friendly jammer (MAXDR). Then, the optimal transmission and jamming power can be derived, followed by the optimal transmission rate obtained via the bisection method. In numerical and simulation results, it is shown that although the location of the warden is unknown, by assuming the OPTDP of the warden, the proposed OPTDP-JP can always satisfy the covertness constraint. In addition, with an uncertain warden and imperfect CSI, the covert throughput provided by OPTDP-JP is 80% higher than the existing schemes when the covertness constraint is 0.9, showing the effectiveness of OPTDP-JP.