Journal of Wireless Networks and Communication Systems
Journal of Wireless Networks and Communication Systems (E-ISSN: 3107-6432) is a peer-reviewed quarterly online journal aims to service the education professionals, particularly researchers, scientists, academicians all over the world to develop, share and discuss various new issues and developments. This journal publishes original research, reviews, and technical notes and it generally encompasses a broad range of topics related to the theory, design, analysis, implementation, and application of wireless communication technologies.
Structural Health Monitoring of RCC Buildings Using IoT-Enabled Wireless Sensor Networks
Since urban areas are growing at a fast pace, it is crucial to have advanced and immediate structural monitoring systems to ensure that reinforced concrete structures are safe and stable. Usually, conducting inspections of RCC buildings happens only occasionally, is laborious and is done after detection of damages, increasing chances of sudden breakdowns. Since RCC buildings are difficult to monitor, this research offers a new IoT-based method for checking their health using a WSN created for them. Nodes with embedded accelerometers, strain gauges and temperature sensors are linked by the ZigBee and LoRaWAN protocols to a cloud in the center. Through this innovation, a smart algorithm detects abnormalities by examining signals in real-time and utilizing AI built into the infrastructure itself to check for cracks and unusual vibrations. Data received from the sensors is first filtered with an FFT filter, then is analyzed with a CNN trained on data about structural faults. A test version of a reinforced concrete building was built and tested with dynamically applied loads and changes in environment. The system was able to detect cracks with almost complete accuracy, had few false alarms and sent real-time alerts quickly. The proposed structure showed greater energy saving, more effective scaling and greater strength compared to usual wired long-term systems. All in all, this research points to the great benefits of combining IoT, WSN and edge AI for modern civil infrastructure monitoring systems which allows for constant, unsupervised monitoring, early spotting of flaws and predictive upkeep, thus helping create safer and smarter cities.
Copyright (c) 2026 Journal of Wireless Networks and Communication Systems
Design and Analysis of Reconfigurable MIMO Antennas for 5G Wireless Networks
The introduction of 5G systems means a sharp increase in the need for antennas supporting ultra-high rates, quick responses and flexible spectrum use, mainly in the millimeter-wave (mmWave) area. This paper details the creation, modeling and evaluation of a Multiple-Input Multiple-Output (MIMO) antenna system for use in 5G mmWave bands. The system uses varactor diodes, RF Micro-Electro-Mechanical Systems (MEMS) switches and Defected Ground Structures (DGS) to make it reconfigurable in both radiation direction and operating frequency. Because of these elements, the system can turn beams regularly and quickly switch frequencies over the 24–30 GHz band for 5G NR bands n257 and n258. The compact Rogers RT/Duroid 5880 was chosen for the design of the antenna array and CST Microwave Studio was used to optimize it for high isolation (>25 dB), low ECC (<0.02) and high radiation efficiency (>85%). Thorough parametric analysis was used to check how performance metrics such as gain, beam overview and coupling decrease due to different reconfiguration mechanisms. Simulation results are confirmed by tests of the prototype which demonstrate that the system can re-tune frequencies, beam in a specific direction (+/- 30°) and always works well in MIMO mode. Because Channel Capacity Loss (CCL) is below 0.3 bps/Hz and Diversity Gain (DG) is above 9.9 dB, the diversity performance is ideal for situations with many users and lots of pathways. The antenna, as described, gives more choices along with a compact design, together with meeting all the requirements expected by next-generation wireless, vehicular and adaptive 5G base station systems. The scalable and low-power benefits of its hybrid approach mean it is a big step forward in the design of 5G and future wireless network antennas.
Real-Time IoT-Based Smart Traffic Management System Using Edge AI and Sensor Networks
The fast increase in vehicles in cities has brought about serious problems with crowded roads, extended trips and more pollution, prompting the invention of intelligent traffic control systems. The research proposes a smart traffic management system built using IoT infrastructure, edge AI and a spread of sensors that aim to optimize traffic flow, decrease delay at traffic signals and help the environment. Split computing framework makes it possible for decisions related to traffic lights to be made locally, not through the cloud. Data from a mixture of inductive loop detectors, RFID vehicle identifiers, sensors for air quality and IP cameras is sent in real time. For edge deployment, a light CNN is trained to spot vehicles and estimate how many cars are on each lane, while LSTMs predict minor changes in traffic flow to allow managers to plan ahead. Otherwise, agents with reinforcement learning respond using Deep Q-Learning which enables the system to alter traffic signal phases automatically when traffic or conditions change. The system was tested by running simulations and deploying hardware with Raspberry Pi which led to clear increases in how well the system runs. Average vehicle idle time was reduced by 27.2%, intersection throughput increased by 21.1% and CO₂ emissions decreased by 18.1% when these adaptive signals were used in place of conventional ones. The results show that the proposed method can handle large volumes, respond in real time and be maintained which makes it a good fit for future traffic management systems in smart cities.
Optimization of Power Flow in Hybrid Microgrids Using AI-Based Algorithms
Increased renewable energy applications in microgrids imply that there is currently less predictability in power generation, implying that there is a necessity of sophisticated methods to streamline the operations of the microgrid so that it operates effectively and steadily. In this work, a hybrid Artificial Intelligence (AI) framework is proposed to enhance the way power is transferred in AC and DC microgrids with energy storage systems (ESS), distributed energy resources (DERs) and integrated with the normal utility grid. The proposed solution comprised of DDPG Deep Reinforcement Learning and Genetic Algorithms address both real-time and global optimization issues. The DRL agent determines the optimal strategies to exploit RES, ESS and the grid and the GA enhances the initial parameters and critical elements of the model of the agent to achieve optimal performance. The framework is studied under different conditions and load and generation patterns in two simulation environments, one in MATLAB/Simulink and the other in Python during 24 hours. Compared to the outcomes, this DRL-GA approach is a far better solution than the Mixed Integer Linear Programming (MILP) and Particle Swarm Optimization (PSO), leading to an operation cost reduction, enhancing voltage stability as well as increasing renewables utilization. The findings provide a flexible and smart control strategy that will be efficient in microgrid energy management.
Real-Time Harmonic Compensation in Nonlinear Power Systems Using Hybrid Active-Passive Filters
Due to increased use of nonlinear loads such as variable frequency drives, uninterruptible power supplies and rectifier based devices, there is an increase in harmonic pollution which is detrimental to power quality as well as the reliability of the system. In this paper a real time method based on an HAPF is presented that reduces both small and large harmonic distortions in nonlinear distribution systems. A hybrid topology combines a tuned passive filter to aim at selective harmonic reduction and a shunt-connected active filter with a VSC to achieve wide and fast harmonic cancellation. The active filter extracts harmonics using Synchronous Reference Frame (SRF) theory and provides an accurate and fast outcome using a Proportional-Integral (PI) hysteresis current controller during the injection of current. Following the simulations in MATLAB/Simulink, it is depicted that the proposed system significantly enhances the power quality of the system by decreasing the Total Harmonic Distortion (THD) of 23.6 % to 3.1%, which is in compliance with the requirements of IEEE-519. Besides that, simulation in a prototype implemented on a TMS320F28379D DSP demonstrates that the control strategy is functional under dynamic variations of the load. The developed HAPF discovered in the work provides an outstanding harmonic mitigation, improved stability and flexibility, which determine its applicability in the industry and utilities.
Synchronous Reference Frame (SRF) Theory
Optimization of Load Forecasting in Power Systems Using Hybrid AI Models
Load forecasting is useful in efficient running and planning of power systems, predominantly in situations involving more and more renewable energy sources. The previous forecasting techniques tends to overlook the complex non-linear trends in load data. This paper proposed a hybrid AI solution, which combines Long Short-Term Memory networks, Random Forest and ARIMA to improve the precision of load forecasting. The two models together enhance the ability to foretell results and be insusceptible to changes. Various experiments are carried out by loading data of the past and the suggested model is tested against other common methods of forecasting. It can be seen that there are significantly improved forecasts because of lower MAPE and RMSE in the results. What is more, the hybrid model is more efficient and able to support real-time requirements. The work establishes a decent avenue of future developments in load forecasting that helps in the reliability of operating power systems.
Green and Intelligent Signal Processing: Deep Learning Approaches for Energy-Efficient MIMO Systems
Due to the massive expansion of wireless applications, the ever-increasing demands for mobile technology, and the development of Internet of Things technology, wireless networks are confronting an increase in data traffic and resource management issues. Due to their capacity for data storage and spectrum efficiency, fifth-generation cellular networks have attracted a lot of interest. In order to meet a variety of user needs, multiple-input multiple-output (MIMO) networks are dependable solutions for data storage and capacity problems. MIMO systems are crucial for achieving revolutionary improvements in energy efficiency (EE) and area throughput. EE is most economical and one of the simplest strategies to fight global warming, energy reduction bills, and improvize competitive performance. EE and area throughput can be greatly enhanced by Deep Learning (DL). In 5G wireless communication systems, it is essential. The suggested model considered the total energy utilization of the circuit elements and the power amplifier of the BS, as well as the user equipment (UE) with a single antenna. In this paper, Green and Intelligent Signal Processing: Deep Learning Approaches for Energy-Efficient MIMO Systems (GISPDL-EEMIMO). The proposed GISPDL-EEMIMO technique undergoes data collection, data preprocessing, feature extraction, and prediction. A set of experiments has been performed to demonstrate the promising performance of the GISPDL-EEMIMO technique. The comparative findings showed that, in terms of distinct measures, the GISPDL-EEMIMO technique outperforms other existing models.
Copyright (c) 2025 Journal of Wireless Networks and Communication Systems
Anomaly Detection in Wireless Sensor Networks Using Hybrid Machine Learning Models
Nowadays, WSNs, or Wireless sensor networks, have become one of the most widely used wireless technologies for the purpose of sensor communication. WSNs are often designed for specialized applications, involving tracking or monitoring, in indoor or outdoor settings where battery capacity is a major concern. Over the past few years, numerous routing schemes have been developed to resolve these challenges. Currently, researchers use a variety of machine learning (ML) approaches to identify anomalies in WSN. The study presents an Anomaly Detection in Wireless Sensor Networks Using Hybrid Machine Learning Model (ADWSN-HMLM). The ADWSN-HMLM approach undergoes data preprocessing, feature extraction, detection, and classification. The benchmark IDS dataset is used to test the experimental results of the ADWSN-HMLM approach. The simulation outcomes showed that the ADWSN-HMLM approach outperformed other approaches. The infrastructure of WSN and the security issues they encounter are conveniently referenced in this study. Along with discussing the difficulties and suggested solutions for enhancing sensors' capacity to recognize threats, attacks, risks, and malicious nodes through their capacity to learn and self-develop using ML techniques, this study also explores the potential benefits of ML approaches in lowering the security costs of WSN across a number of domains. In addition to 98% for regular traffic, the detection accuracy for scheduling, grayhole, flooding, and blackhole assaults is 97.59%, 96.95%, 96.03%, and 97.05%, respectively. These findings demonstrate that the ADWSN-HMLM methodology can offer the WSN effective anomaly recognition.
RAA-DRL: Renewable-Aware AI-Driven Resource Allocation for Green Communications and Energy-Efficient Wireless Networks
The continual rise in the demand for greater capacity from such systems has evoked concern regarding energy consumption and ultimately, an environmental impact. Traditional resource allocation methods generally prioritize performance and overlook the unpredictability of renewable energy; therefore, such methods could lead to waste and the use of non-renewable resources. To help tackle these aspects, this paper offers RAA-DRL (Renewable-Aware AI-Driven Resource Allocation), an innovative framework for optimizing network performance while incorporating green energy. RAA-DRL utilizes deep reinforcement learning (DRL) to allocate resources in real-time, based on the current traffic and available level of renewable energy. Unlike static methods, the DRL-based system will learn dynamically from historical traffic and renewable generation data regarding long-term energy efficiency, throughput and spectral efficiency. The simulation results show that RAA-DRL can deliver significant improvements in energy efficiency and QoS compared to conventional schemes. It can reduce the reliance on the grid and continue to support high network performance, serving as an example of what AI technology can offer for sustainable communications. This research contributes to the establishment of green communication technologies through a scalable, adaptive, and renewable-aware approach to future wireless networks. Ultimately, RAA-DRL enables viable, sustainable, performance oriented networks for the future 6G and IoT systems.
QHCP: Quantum-Resilient Hybrid Cryptographic Protocol for Secure Data Exchange in Next-Generation Networks
As the next-generation networks are developed, the demands for secure, reliable, and future-proof protocols for data exchange are greater than ever. Traditional cryptographic protocols remain susceptible to seemingly futuristic low-risk, high-reward advancements of commercial quantum computing. We exhibit a novel protocol named QHCP: Quantum-Resilient Hybrid Cryptographic Protocol, which combines the post-quantum resiliency from several post-quantum cryptographic algorithms with efficiency gained from maintaining the use of classical protocols to ensure data remains confidential and/or the integrity of data remains intact over time. The architectural style that QHCP uses is a Layered approach: Performance (ap­plication layer) to Quantum Resistance (crytographical superfluous services). In addition to it, QHCP solves practical issues such as hygienic key handling for minimizing potential risks introduced by cyber breaks and low-latency cycles that are perfect for working in fast, resource-limited environments. On a first glance, we find reasonableness of the security soundness/efficiency trade-offs and range for how (on next-to-future-internet-of-things (of) things systems to) on the future bigger computing infrastructures under 6th generation (6g)) farities of use can possibly be supported by QHCP! All-in-all, QHCP offers a strong path for making the sensitive data minimum quantum-protected through hybrid quantum-classical model resistant to diverse evolving advanced cyber threats.
Adaptive Trust-Aware Energy-Efficient Routing Architecture for Resilient Mobile Ad Hoc Networks in Next-Generation IoT Systems
As per next-generation IoT systems, Mobile Ad Hoc Networks (MANETs) are becoming main technology because they work without fixed setup. Regarding changing environments, these networks can adapt quickly and operate easily. These networks actually face serious problems like changing structures, limited battery life, and security attacks. They definitely need solutions for these major challenges. To solve these problems, we are introducing a new routing framework called TERRA (Trust-Enabled Resilient Routing Architecture). We are seeing that only this approach can handle the trust issues properly. Further, tERRA uses three main ideas: we are seeing trust-based checking where each device only rates its nearby devices based on how well they send data packets, their power use, and past work; energy-smart path choosing that picks routes to save battery and make the network last longer; and flexible multiple-path routing that keeps backup routes for better reliability and safety. We are seeing that TERRA performs better than other well-known protocols like AODV, DSR, and AOMDV when tested using NS-3 and MATLAB simulations only. Also, the proposed method surely achieved 15-22% better packet delivery ratio and reduced delay by 18%. Moreover, it extended the network lifetime by 20% compared to existing approaches. We are seeing that the trust mechanism is only effective against black hole and wormhole attacks, reaching 92% accuracy in finding harmful nodes. TERRA provides a secure and scalable routing solution that uses less energy, making it suitable for IoT applications. Further, the system itself works well for smart healthcare, disaster response, and intelligent transportation systems.
WiSIDS: A Lightweight Machine Learning-Based Intrusion Detection System for Securing Wireless IoT Networks
The rapid proliferation of Internet of Things (IoT) devices in wireless environments has introduced significant security challenges due to their limited computational resources and susceptibility to cyberattacks. Traditional Intrusion Detection Systems (IDS) are often unsuitable for IoT because of their high processing overhead, memory consumption, and latency. This paper proposes WiSIDS (Wireless IoT Secure Intrusion Detection System), lightweight IDS that leverages machine learning to provide accurate and efficient attack detection in wireless IoT networks. WiSIDS employs feature selection and model optimization to minimize computational complexity while maintaining high detection performance. Experimental evaluation is conducted on benchmark IoT security datasets, including NSL-KDD and Bot-IoT, to assess accuracy, precision, recall, F1-score, processing delay, and energy consumption. Results demonstrate that WiSIDS achieves a detection accuracy of over 95%, reduces latency by up to 60%, and lowers resource utilization compared to conventional IDS solutions. These findings highlight the feasibility of deploying WiSIDS in resource-constrained IoT devices to enhance wireless network security without compromising efficiency.
Developing an Intelligent Human-Computer Interaction System Using Deep Learning and Machine Learning Algorithms
As crucial input techniques in Human-Computer Interaction (HCI), speech and gesture recognition have become increasingly popular in virtual reality in recent years. Specifically, the swift advancement of deep learning, artificial intelligence, and other computer technologies has led to revolutionary advancements in voice and gesture detection. Significant technical advancements in the field of Human-Computer Interaction (HCI) have made it possible for educators to deliver high-quality educational services by utilizing intelligent input and output channels. This work proposes a straightforward and efficient way for extracting salient characteristics based on contextual information by conducting in-depth research and analysis on the design of human-computer interaction systems using machine learning algorithms. The findings demonstrate how deep learning and intelligent HCI are extensively used in voice, gesture, emotion, and intelligent robot direction. In related study disciplines, a wide range of recognition techniques were put forth and experimentally validated. When it comes to voice-activated Human–human-machine interfaces (HMIs), context is crucial to enhancing user interfaces. Action recognition accuracy and precision can be significantly increased by combining long short-term memory networks with convolutional neural networks. As a result, more industries will be involved in the use of HCI in the future, and more opportunities are anticipated.
The Role of Wireless and Information Technologies in Healthcare: IoT-based Customized Applications
Conventional healthcare systems have faced historic challenges trying to meet the varying needs of large groups of patients that have been far too inefficient and suboptimum as an outcome. With advances in machine learning (ML) and deep learning (DL) models, we are entering an exciting time in healthcare where hospitals and healthcare systems can begin to deliver value-based care by providing highly personalized and effective treatment for patients. The healthcare systems of the future are expected to provide patients with affordable, high-quality treatments using a range of applications made possible by information and communication technologies. This is because these wirelessly enabled medical systems are able to remotely and continually check on the health of patients in both indoor and outdoor environments, which makes patients feel more at ease and allows them to accomplish more things. The healthcare industry is increasingly being shaped by advancements in information and communication technologies (ICTs). The purpose of the research is to clarify the relationship between ICTs and healthcare by defining the most widely used ICT-based healthcare paradigms and the primary ICTs that support them. The main ICT-based healthcare models that have surfaced recently as a result of ICT breakthroughs are described below. We can also determine the technological foundations of the innovative uses of these developments by examining the scientific literature. The study ends by outlining significant unresolved research questions and concerns, many of which are particularly connected to future IoT-based healthcare systems.
Indoor Air Quality Monitoring Systems: The Impact of using Intelligent Communication System in Living Environments
With the advancement of communication system over the last 10 years, indoor air quality (IAQ) monitoring technology has advanced significantly, improving the monitoring system's real-time performance and lowering the possible effects of pollutants linked to health. "Incorporating the Internet of Things into these cutting-edge developing technologies therefore offers a great opportunity for the development of air quality systems, which require new networking models, strategies, and processes for multiple constraints with sophisticated sensing and communication abilities. A thorough analysis of the hardware requirements, communication methods, and architectures is necessary to handle reasonable data related to building inhabitants' daily activities. Research and understanding of the existing air quality systems serve in making choices by assisting in the matching of appropriate intelligent, adaptive, and reasoning technologies with data flow management. This article will include further details and a detailed examination of new communications protocols, networking systems, sensors, topologies, and technologies related to indoor and outdoor air quality systems. This article uses an intelligent communication system in the living environments to examine and evaluate the advancements in IAQ monitoring technology and the usage of communication technology in this area. The issues and difficulties with the IAQ monitoring system are also covered in this paper, which serves as a resource for researchers and encourages the quick and comprehensive advancement of IAQ monitoring.
Developing an Information and Communication Technology Supply Chain Security Management Model in Cloud Computing
In today's business environment, the impact of information technology (IT) on the agility of supply chain management (SCM) systems is highly apparent. The business environment is impacted by competition pressure as well as ongoing disruptions such as new product and technology developments, declining products, and product proliferation. To gain a competitive advantage and succeed as a business, companies adjust to market shifts. This article combines effective cloud computing techniques with Internet of Things data networking technologies to develop supply chain management models. The development of supply chain security management models is also examined in this study from the viewpoints of participants, activity modes, and module operations. The data has been analyzed using these designs. The development of supply chain security management models from the viewpoints of participants, activity modes, and modular operations is investigated in this study in order to compare data. The model achieves a forecast accuracy of 98.48%, and its average test-running accuracy is approximately 89.60%, according to the testing findings. The total stability of the design across several builds is also around 96.02%, according to stability testing. According to research, the Internet of Things and cloud computing holds great promise for supply chain security management, potentially providing companies with better options in this field.
Enhancing Cloud Security: The Role of Artificial Intelligence in Real-time and Proactive Cyber Threat Detection
Cloud scalability needs to adapt to the constantly shifting needs of the marketplace. But as computing has become a crucial part of modern IT systems, giving businesses flexibility and technology advances, a new era of cybersecurity threats has emerged, with hackers using ever-more-advanced tactics to compromise cloud networks. Wide-ranging effects of such breaches may include lost data, monetary losses, harm to one's reputation, and legal responsibilities. Creating a strong security framework is essential to successfully protecting cloud infrastructure in light of these issues. Advanced capabilities for analyzing vast volumes of data produced in real-time within cloud infrastructures are offered by artificial intelligence (AI) technology. This makes identifying unusual behavior and possible security breaches possible early on. AI systems can more accurately and effectively detect patterns, spot malicious activities, and foresee emerging dangers by utilizing machine learning (ML) algorithms, deep learning models, and natural language processing techniques. By continuously learning from historical data and adjusting to changing attack vectors, AI-driven threat detection systems assist cloud security teams in staying ahead of adversaries and enhancing the resilience and integrity of cloud-based services. In conclusion, the suggested framework is a thorough approach to cloud security, combining cutting-edge technology with ongoing improvement to safeguard cloud infrastructure, reduce risks, and successfully negotiate the always-changing cybersecurity threat landscape.
AI-Driven Cyber Risk Assessment: Protecting Against Cyberthreats Determined with Machine Learning
Digital defense systems are improved by the quick integration of artificial intelligence (AI) into cyber security, which makes it possible for predictive analysis, real-time anomaly detection, and automated threat detection. In order to compromise, avoid, or trick AI-based security models, cybercriminals resorted to hostile AI techniques for creating AI weapons. The present research investigates how machine learning methods might be included into cyber risk assessment to anticipate and stop data theft. These include, among other things, deceptively created inputs or manipulation strategies that take advantage of flaws in machine learning algorithms, enabling an attacker to get around security measures, carry out cyberattacks covertly, and even tamper with AI-driven decision-making systems. The results show that by anticipating threats and improving security protocols, AI-driven models greatly improve cyber resilience. The paper also covers the difficulties and ethical issues surrounding the application of AI in cybersecurity. For companies looking to improve their cybersecurity frameworks using clever risk assessment tools, the findings offer insightful information. Finally, this work suggests that adversarial robustness, model interpretability, and the emerging discipline of explainable AI are all important directions for guaranteeing the safety and reliability of operation in high-risk operational scenarios. The integration of trusted AI models will be key to protecting critical infrastructure, enterprise data systems and national-level digital ecosystems against new threat vectors.
Enhanced Multi-Label Classification Using Fuzzy Deep Neural Networks for Imbalanced Data
In classification issues where each data instance is simultaneously assigned many labels, a classification with multiple labels is a useful method for controlling uncertainty. These kinds of circumstances are common in real-world settings where judgments are based on ambiguous or crowded data and flexible categorization techniques are favored. Nonetheless, the issue of class imbalance is a feature shared by several multi-label datasets, where samples and the labels that belong to them are not distributed uniformly throughout the data space. To address the issue of class imbalance, we present a fuzzy logic-based multi-label classification method in this study. One or more labels could be used to convey this categorization challenge. Deep neural networks, which have shown themselves to be incredibly effective in such situations, are used to do away with the necessity for an expert to ascertain the logical principles of inference. The benefits and drawbacks of each strategy can be balanced by integrating deep neural networks with fuzzy inference systems. Accuracy and model application flexibility will be improved by the suggested system, especially when it comes to time limits brought on by causality of reported series of times. The suggested model performs better for continuous data classification than four baseline models, according to tests conducted on a classification with multiple labels dataset about present-day and voltage profiles of various home appliances.
Improving Stability and Efficiency in MANETs: A Multi-Criteria Approach for Optimal Multipoint Relay Selection
Mobile ad hoc networks (MANETs) were self-sufficient networks made up of several nodes on the move that could connect electronically without the aid of any kind of pre-existing equipment. Because such networks function in extremely unpredictable situations, their capacity to ensure. Mobile area networks, or MANETs, constitute distributed networks with nodes that roam. Energy is a crucial resource because MANET nodes are battery-operated. In OLSR, MPRs are separate nodes to which various nodes decide to send information and manage traffic. As a result, MPR nodes may consume a significant amount of power. Providing Quality of Service (QoS) is a significant research challenge since those networks are susceptible to volatility brought on by wireless communication nor movement. Therefore, when creating routing techniques for MANETs, it is essential to use circuits and mediation facilities that provide assurance, safety, and longer network lives. In order to address the issues of node mobility, energy limitations, and security flaws, this study suggests an advanced algorithm for choosing complicated messengers (MPRs) in MANETs. The suggested method improves networking strength, availability, and endurance by using a multicriteria-weighted methodology that evaluates the dependability, energy consumption, and range of portable nodes. The enhanced technique is tested in NS5 simulations using its Random Waypoint as well as ManhattanGrid motion models. According to the results, the improved algorithm performs better than the conventional OLSR, especially when it comes to throughput, messages distribution, and latency decreases for changeable network situations.
Artificial Intelligence with Machine Learning Model for Resource Management in Wireless Networks
Artificial Intelligence (AI) improves resource management and energy conservation through machine learning, improved forecasting, and quicker data processing. Artificial intelligence (AI) and machine learning (ML) have become essential enablers of this integration in this context. Machine learning and artificial intelligence are necessary for an organization’s survival and expansion, not just a choice. This research explores the growing significance of machine learning and artificial intelligence in resource management. The proposed method, deep reinforcement learning (DRL) based on machine learning is an essential tool for addressing the issues of resource management. Resource management can ensure that certain QoS criteria are met for various applications, such as information or real-time video transmission. Reducing total transmit power allows optimal resource management, while following Quality of Service (QoS) criteria permits a better relay choice. In this paper, the numerical results show that it performs competitively in terms of bandwidth of 97.5%, power consumption of 96%, spectrum efficiency of 91%, and network lifespan of 93 percent. Costs are decreased and environmental responsibility is enhanced through resource management. This research examines how AI might increase bandwidth, power consumption, spectrum efficiency, and resource management, with a particular emphasis on automation, efficiency, and prediction via quality of service.
1D-CNN-based Real-Time Network Intrusion Detection with Privacy-Preserving for IoT
The Internet of Things, or IoT, is a concept that links every device to the Internet and enables them to collaborate to achieve shared goals, including smart automation in the home. The amount of data produced in the rapidly growing Internet of Things (IoT) space has never been higher. Numerous decision-making processes are accelerated by processing this enormous data to get priceless insights. Intrusion detection systems (IDS) are crucial for safeguarding data and computer resources against external threats in computer networks. Modern IDSs struggle to become more effective and flexible in the face of unexpected and unexpected threats. The suggested approach presents a real-time, privacy-preserving; 1D-CNN-based network intrusion detection system for the Internet of Things. This paper introduces an Intrusion Detection System (IDS) that utilizes a dataset to enhance the Internet of Things (IoT). First, the input data is preprocessed using data processing. Due to its ability to decrease convergence, the choice of features is included in the suggested model used to improve linearity-based principal components analysis. Intrusion detection is recognized using algorithms with extreme gradient boosting (XGBoost) hyperparameters. 1D CNN is the best and least costly model for real-time IoT security monitoring, and it excels in the recommended approach of feature extraction. With a binary intrusion detection accuracy of 99.77%, 1D CNN again outperformed LSTM, RNN, and MLP, which obtained 98.25%, 94.52%, and 92.2%, respectively. The results, based on feature extraction, demonstrate that 1D CNN is a highly efficient real-time IoT security monitoring technique. Among all models that are used for analysis and development, it offers the best efficiency and dependability. Lastly, the performance of the proposed model was superior to that of the existing models.
FFDNet: Image Denoising Based on Convolutional Neural Network (CNN)
Image denoising is a tecahnique that aims to reduce or eliminate unwanted sound from images, improving the worth and clearness of the image. During capture and distribution, images frequently have several types of noise, including Gaussian, speckle, and salt-and-pepper noise. Generative adversarial networks (GAN) are identified by high processing costs, improper fitting, and the capacity to introduce mistakes in the training data. These algorithms lose flexibility since their parameters are set and can't be changed while filtering. Based on this, we introduce FFDNet, a quick and adaptable denoising convolutional neural network that uses a stimulus with a programmable degree of sound conjunction. Convolutional neural networks (CNNs), a kind of deep neural network with remarkable item classification accuracy, may be constructed using large databases. The more recent image-denoising method, FFDNet, is based on convolutional neural network architecture. CNN is utilized for spatially varied distortion because it is quick, adaptable, and can efficiently manage various sound stages (i.e., [0, 75]) with just one system. According to the results of experiments, the FFDNet depends on the peak signal-to-noise ratio (PSNR), which is 38.08 dB, 37.63 dB, 37.42 dB, and 36.49 dB, respectively. Based on the result, it is effective and efficient.