Wireless Networks Research Articles

IoT Wearable Machine Devices Based on Optical Sensors and Wireless Networks Application in Community Fitness Data Analysis

IoT Wearable Machine Devices Based on Optical Sensors and Wireless Networks Application in Community Fitness Data Analysis

Optimalisasi Penentuan Posisi Access Point Pada Ruang Tertutup Menggunakan Algoritma Genetika

WiFi is a term used for wireless network devices (WLAN) based on IEEE 802.11 standard. Access Point is a WLAN device that is used as a transmitter or receiver. One of the parameters that can determine the performance of a WiFi is the coverage area of an access point. Placement it is not appropriate with environmental conditions will cause some of an area that is not covered or a blankspot area. In this research, the position of the access point was optimized using a genetic algorithm based on position and coverage area. Total coverage area is obtained by determining the union coverage area of all access points which appropriate with the range that is determined using indoor propagation model. From the research results, the most optimal number of access points is 5 access points. The existing condition with 5 access points has a coverage area of 75,27%, while optimization using 5 access points has a coverage area of 96,64%. The optimization can increase the coverage area by 21,38% and reduce the blank spot area to 3,36%.

Artificial Intelligence Workload Allocation Method for Vehicular Edge Computing

Real-time applications such as smart transportation systems require minimum response time to increase performance. Incorporating edge computing, processing units near end devices, achieving fast response time. The collaboration between edge servers and cloud servers is beneficial in achieving the lowest response time by using edge servers and high computational resources by using cloud servers. The workload allocation between edge–cloud servers is challenging, especially in a highly dynamic system with multiple factors varying over time. In this paper, the workload allocation decisions among the edge servers and cloud are considered for autonomous vehicle systems. The autonomous vehicle system generates multiple tasks belonging to different AI applications running on the vehicles. The proposed method considers allocating the tasks to edge or cloud servers. The cloud servers can be reached through a cellular network or a wireless network. The proposed method is based on designing a neural network model and using a high number of features that contribute to the decision-making process. A huge dataset has also been generated for the implementation. The EdgeCloudSim is used as a simulator for implementation. The competitor's methods considered for the comparison are random, simple moving average (SMA) based, multi-armed bandit (MAB) theory-based, game theory-based, and machine learning-based workload allocation methods. The result shows an improvement in the average Quality of Experience (QoE), ranging from 8.33% to 28.57%, while the average failure rate achieved enhancement up to 50%.

Hardware-software implementation of a local Wi-Fi network for the transmission of biomedical signals

The object of this study is a wireless local Wi-Fi network for broadcasting biomedical signals, its structure, and principles of construction. The task of minimizing the power consumption of a Wi-Fi transmitter has been addressed, which provides the possibility of building a wireless system for long-term monitoring of biomedical signals. As a result, a functional diagram of a wireless Holter monitoring system based on an ESP32 microcontroller was constructed, which includes a subsystem for setting up and diagnosing system units using MATLAB software packages, an ECG signal generator, and a multifunctional PCIe board from National Instruments. Evaluation criteria and methods for minimizing power consumption by an autonomous Wi-Fi transmitter have been proposed. Methods for synchronizing the working cycles of the transmitter and receiver of the Holter monitoring system were determined. A procedure for determining the optimal biosignal measurement frequency is presented, at which the distortion of ECG signals would be minimal, which means that the signal could be transmitted without losses. The concept of constructing an algorithm for implementing a program for a Wi-Fi transmitter has been developed, ensuring parallel execution of ECG signal measurement operations and their transmission over a local network. The data from semi-naturalistic tests with an experimental Holter monitoring system with a pre-setup subsystem and using external measuring devices, a computer, and the MATLAB software environment are presented. A comparative analysis of the experimental data with primary ECG signals and ECG signals at the receiver output showed a fairly stable correspondence between the input and output ECG signals. The proposed algorithms make it possible to reduce the average current consumption of the ESP32 microcontroller to 50.5 mA. The results of the study demonstrate the possibility of constructing an energy-efficient wireless system for long-term monitoring of biomedical signals based on the Wi-Fi interface

GSAAN with War Strategy Optimization Algorithm for Cyber Security in a 5G Wireless Communication Network

In this paper, the graph sample and aggregate-attention network with war strategy optimization algorithm for cyber security in the 5G wireless communication network (CS-5GWCN-GSAAN-WSOA) is proposed in 5G mobile networks to identify cyber threats. Initially, the input data are amassed from the 5G-NIDD dataset. Then the input data are fed to preprocessing, here redundancy elimination and missing value replacement are performed utilizing the contrast limited adaptive histogram equalization filtering (CLAHEF) method. After preprocessing, optimal features are selected by the stochastic gradient boosting based recursive feature elimination process. The selected features are fed to the graph sample and aggregate-attention network (GSAAN) to detect cyber-attacks in 5G wireless communication. Generally, the GSAAN approach does not adopt any optimization techniques for determining optimal parameters and guaranteeing accurate attack detection. Therefore, the war strategy optimization algorithm (WSOA) contemplates to optimize the GSAAN weight parameters. The CS-5GWCN-GSAAN-WSOA method is activated on Python. The CS-5GWCN-GSAAN-WSOA method attains 22.51%, 20.35%, and 35.54% higher accuracy, 19.45%, 27.80%, and 18.93% higher sensitivity analysed with existing models, like cyber security attack identification in 5G wireless systems utilizing machine learning (CS-5GWCN-SVM), enhanced dropping attacks detection in 5G networks depending on deep learning models (CS-5GWCN-KNN), and cyber security issues in 5G enabled attack detection through deep learning (CS-5GWCN-RNN), respectively.

Security, privacy and performance concerns in ultra dense networks

Ultra Dense Networks (UDNs) have emerged as a pivotal technology in meeting the exponential growth in data demand and providing seamless connectivity in 5G and beyond networks. However, the high density of small cells in UDNs introduces significant challenges related to security, privacy, and performance. This survey paper presents a comprehensive review of the current state-of-the-art in addressing these concerns. It begins by exploring the unique security vulnerabilities inherent to UDNs, including the increased risk of eavesdropping, denial of service attacks, and unauthorized access due to the close proximity of small cells. The paper then discusses privacy issues, particularly the risks of location tracking and user data exposure, exacerbated by the dense deployment of base stations. In terms of performance, the paper evaluates the impact of interference, handover management, and resource allocation on network efficiency. Various proposed solutions, such as advanced encryption techniques, privacy-preserving algorithms, and interference mitigation strategies, are analyzed and compared. The survey concludes by identifying open research challenges and future directions, emphasizing the need for integrated approaches that simultaneously address security, privacy, and performance to ensure the robust operation of UDNs in next-generation wireless networks.

Analysis and Classification Of Wireless LAN Network Service Quality Using The Naive Bayes Algorithm In The Department Of Informatics and Computer Engineering, Faculty Of Engineering, State University Of Makassar

This study aims to determine the Quality of Service outcomes and the classification results of the Naive Bayes Algorithm on the Wireless LAN network quality in the Department of Informatics Engineering and Computer Faculty of Engineering at Makassar State University. This study uses the Action Research method with a quantitative approach. It conducts 54 tests using Wireshark in two different sessions to be compared for each time conducted during the research. The results of the research indicate that the status of the Wireless LAN network in the Department of Informatics Engineering and Computer Faculty of Engineering at Makassar State University falls into the "Good" category with the highest value of 59.26% , with an algorithm accuracy of 65.43%. Both testing sessions, morning session from 08:30 to 09:00 and afternoon session from 13:30 to 14:00, are categorized as "Good" according to the TIPHON standardization category as well as based on the Naive Bayes Algorithm classification results. Based on the research findings, it is recommended that the measurement of network service quality can utilize software other than Wireshark and increase the amount of test data to improve the accuracy of the Naive Bayes algorithm.

A hound-inspired pre-hybridized genetic approach for router placement in wireless mesh networks

In the last years, wireless mesh networks (WMNs) have gained more and more popularity in many research and industrial applications thanks to their easy implementation, maintenance, and great reliability at a low cost. Nevertheless, for a large number of nodes, the performance of such networks is heavily influenced by the positioning of routers and gateways over the area to be covered. In this paper, we tackle the router placement problem, which is known to be NP-hard, and its approximate solution through a meta-heuristic approach. The proposed solution empowers the benefits offered by a genetic algorithm pre-hybridized with a local search approach inspired by the behavior of hound dogs. The basic idea is to exploit the dogs’ capabilities in moving throughout the solution space to effectively explore it by placing themselves in areas that are more favorable for achieving a high-quality approximate solution in a reasonable time. Experimental results on several benchmarking instances and comparisons with the most effective state-of-the-art algorithms have demonstrated the potential of the proposed approach. This is evidenced by very high connectivity and coverage, a low number of generations, and a small GA population required for convergence. This results in low computational effort and significant time savings, which are of paramount importance in IoT and edge scenarios. We remark that, although offering potential, at the current state, our proposal is not able to adapt to areas with obstacles or irregular shapes.

ABBR: An augmented BBR for collaborative intelligent transmission over heterogeneous networks in IIoT

In the era of Industry 5.0, with the deep convergence of Industrial Internet of Things (IIoT) and 5G technology, stable transmission of massive data in heterogeneous networks becomes crucial. This is not only the key to improving the efficiency of human–machine collaboration, but also the basis for ensuring system continuity and reliability. The arrival of 5G has brought new challenges to the communication of IIoT in heterogeneous environments. Due to the inherent characteristics of wireless networks, such as random packet loss and network jitter, traditional transmission control schemes often fail to achieve optimal performance. In this paper we propose a novel transmission control algorithm, aBBR. It is an augmented algorithm based on BBRv3. aBBR dynamically adjusts the sending window size through real-time analysis to enhance the transmission performance in heterogeneous networks. Simulation results show that, compared to traditional algorithms, aBBR demonstrates the best comprehensive performance in terms of throughput, latency, and retransmission. When random packet loss exists in the link, aBBR improves the throughput by an average of 29.3% and decreases the retransmission rate by 18.5% while keeping the transmission delay at the same level as BBRv3.

A dynamic symmetric key generation at wireless link layer: information-theoretic perspectives

The expansion of wireless communication introduces security vulnerabilities, emphasizing the essential need for secure systems that prioritize confidentiality, integrity, and other key aspects of data protection. Since computational security acknowledges the possibility of breaches when adequate computational resources are available, that is why information-theoretic security is being explored, which suggests the existence of unbreakable cryptographic systems even in the presence of limitless processing power. Secret key exchange has traditionally relied on RSA or DH protocols, but researchers are now exploring innovative approaches for sharing secret keys among wireless network devices, leveraging physical or link layer characteristics. This research seeks to revolutionize secure multi-party key acquisition in wireless networks, capitalizing on information-theoretic security and collaborative data extraction. The proposed secret key generation framework comprehensively organizes and explains the information-theoretic aspects of secret key generation within the lower layers of wireless networks, especially the link layer, proposes a novel information-theoretic SKG framework for the dynamic acquisition of symmetric secret keys, and responds to contemporary information security challenges by relying on information-theory principles rather than vulnerable mathematical relationships in the post-quantum period. A new cryptographic key can be generated using a straightforward method, and when it is combined (XORed) with the previous key, it creates a continuously changing secret for encryption and decryption. This approach enhances security because, as attackers attempt to break the encryption, the system generates fresh, dynamic keys, making it progressively more challenging for them to succeed. The research work in question integrates key renewal, or how often keys are updated (dynamic keys), with a security off-period. It introduces a framework for determining the best key refresh rate based on the anticipated rate at which keys might be compromised. Furthermore, the proposed framework is scalable, allowing new nodes to quickly join the existing network. The system was tested with multiple nodes equipped with IEEE 802.11 interfaces, which were set in monitor mode to capture frames at the link layer. Nodes map their on-time frames onto their Bloom filters. Nodes exchange these Bloom filters in a feedback mechanism. Nodes extract those frames from their .pcap files, which are present in all Bloom filters; these are common frames among all nodes. These frames are used to form a shared secret that is passed to HMAC Key Derivation Function by each node to acquire the final encryption key of the required length. The validation of this encryption key is performed using a simple challenge-response protocol; upon successful validation, encrypted communication begins. Otherwise, the key generation process is restarted.

Deep learning‐based spectrum sharing in next generation multi‐operator cellular networks

SummaryOwing to the exponential increase in wireless network services and bandwidth requirements, sharing the radio spectrum among multiple network operators seems inevitable. In wireless networks, enabling efficient spectrum sharing for resource allocation is quite challenging due to several random factors, especially in multi‐operator spectrum sharing. While spectrum sensing can be useful in spectrum‐sharing networks, the chance of collision exists due to the inherent unreliability of wireless networks, making operators reluctant to use sensing‐based mechanisms for spectrum sharing. To circumvent these issues, we utilize an alternative approach, whereby we propose an efficient spectrum‐sharing mechanism leveraging a spectrum coordinator (SC) in a multi‐operator spectrum‐sharing scenario assisted by deep learning (DL). In our proposed scheme, before the beginning of each timeslot, the base station of each operator transmits the number of required resources based on the number of packets in the base station's queue to SC. In addition, base stations also transmit the list of available channels to SC. After gathering information from all base stations, SC distributes this collected information to all the base stations. Each base station then utilizes the DL‐based spectrum‐sharing algorithm and computes the number of resources it can use based on the number of packets in its queue and the number of packets in the queues of other operators. Furthermore, by leveraging DL, each operator also computes the cost it must pay to other operators for using their resources. We evaluate the performance of the proposed network through extensive simulations. It is shown that the proposed DL‐based spectrum‐sharing mechanism outperforms the conventional spectrum allocation scheme, thus paving the way for more dynamic and efficient multi‐operator spectrum sharing.

Active reconfigurable intelligent surface‐aided multiple‐input‐multiple‐output radar detection in the presence of clutter

AbstractReconfigurable intelligent surface (RIS) refers to a two‐dimensional smart surface consisting of massive number of reflecting elements. Both active RIS (ARIS) and passive RIS (PRIS) are promising technologies that can adapt and reconfigure the wireless environment, enhancing the reliability and capacity of wireless networks. The authors focus on enhancing the detection ability of ARIS/PRIS‐aided multiple‐input‐multiple‐output radar system in the presence of signal‐dependent clutter. Constant‐envelope constraint is imposed on the sought radar waveforms to improve the practicability of the radar system. To tackle the resultant non‐convex problem, a cyclic method based on minorisation–maximisation and alternating direction method of multipliers is derived to iteratively optimise the receive filters, the transmit waveforms, and the RIS coefficients. The major finding is that the distance between the radar and the ARIS has little to no impact on the radar signal‐to‐interference‐plus‐noise ratio (SINR). For the PRIS‐aided radar system, a simplified model can significantly improve the operational efficiency and has little impact on the radar SINR. Numerous results verify the effectiveness of the proposed algorithm.

Dynamic Framing and Power Allocation for Real-Time Wireless Networks with Variable-Length Coding: A Tandem Queue Approach

Ensuring high reliability and low latency poses challenges for numerous applications that require rigid performance guarantees, such as industrial automation and autonomous vehicles. Our research primarily concentrates on addressing the real-time requirements of ultra-reliable low-latency communication (URLLC). Specifically, we tackle the challenge of hard delay constraints in real-time transmission systems, overcoming this obstacle through a finite blocklength coding scheme. In the physical layer, we encode randomly arriving packets using a variable-length coding scheme and transmit the encoded symbols by truncated channel inversion over parallel channels. In the network layer, we model the encoding and transmission processes as tandem queues. These queues backlog the data bits waiting to be encoded and the encoded symbols to be transmitted, respectively. This way, we represent the system as a two-dimensional Markov chain. By focusing on instances when the symbol queue is empty, we simplify the Markov chain into a one-dimensional Markov chain, with the packet queue being the system state. This approach allows us to analytically express power consumption and formulate a power minimization problem under hard delay constraints. Finally, we propose a heuristic algorithm to solve the problem and provide an extensive evaluation of the trade-offs between the hard delay constraint and power consumption.

Survey on 5G Physical Layer Security Threats and Countermeasures.

With the expansion of wireless mobile networks into both the daily lives of individuals as well as into the widely developing market of connected devices, communication is an increasingly attractive target for attackers. As the complexity of mobile cellular systems grows and the respective countermeasures are implemented to secure data transmissions, the attacks have become increasingly sophisticated on the one hand, but at the same time the system complexity can open up expanded opportunities for security and privacy breaches. After an in-depth summary of possible entry points to attacks to mobile networks, this paper first briefly reviews the basic principles of the physical layer implementation of 4G/5G systems, then gives an overview of possible attacks from a physical layer perspective. It also provides an overview of the software frameworks and hardware tool-software defined radios currently in use for experimenting with 4G/5G mobile networks, and it discusses their basic capabilities. In the final part, the paper summarizes the currently most promising families of techniques to detect illegitimate base stations-the machine-learning-based, localization-based, and behavior-based methods.

An Enhancement for Wireless Body Area Network Using Adaptive Algorithms

Wireless Body Area Networks (WBANs) are one of the most critical technologies for maintaining constant monitoring of patient’s health and diagnosing diseases. They consist of small, wearable wireless sensors transmitting signals. Within this vision, WBANs are not without unique difficulties, for instance, high energy consumption, heat from the sensor, and impaired data accuracy. This paper introduces adaptive algorithms combining Convolutional Neural Networks (CNNs) and dynamic threshold mechanisms to enhance the performance and energy efficiency of Wireless Body Area Networks. The study utilizes the MIB-BIH Arrhythmias dataset to improve the detection of arrhythmias. The results show a 10.53% improvement in battery life and a 5.62-fold enhancement in temperature management when sleep mode technology is applied. As a result, the model reached the average accuracy of ECG classification of 98% and a high level of selectivity and sensitivity to a normal type of heartbeat and quite satisfactory results in the classification of arrhythmia type of heartbeat.

Security Vulnerabilities of Wlan Protocols: A Review

This study reviews a significant number of known attacks with the security methods that are currently available for wireless local area networks (WLANs). After a thorough review of more than a dozen literature on the security issues affecting WLANs, it was found that the architecture of the WLAN itself has a vulnerability that hackers can exploit, especially the wired equivalent privacy (WEP) protocol. WLAN attacks are a combination of human and technology behavior. Human network compromise is still a serious danger to wireless technology, although new encryption technologies are being developed to counteract threats, they are not enough to address the issues at hand because hackers are constantly coming up with inventive ways to breach networks and undermine the standards. Therefore, it is advised to integrate more sophisticated and pervasive multidimensional AI algorithms at different wireless protocol layers as well as through certified ethical hacking experimentation standards in order to improve detection capabilities, identify unauthorized entry through their behaviors, and safeguard wireless connections and data. Also, Cyber security professionals should take note of this report and be proactive in their search for a sophisticated solution to the current issue.

Wireless Fidelity (Wi-Fi) Traffic Analysis: A Systematic Review

In the era of pervasive connectivity, Wireless Fidelity networks have become essential to modern communication systems, facilitating seamless wireless internet access and data transmission across miscellaneous devices. As the adoption of Wireless Fidelity technology continues, the complexity of monitoring and optimizing these networks also increases. This systematic review aims to enhance the existing knowledge on Wireless Fidelity traffic analysis tools by conducting a rigorous assessment of current tools, their functionalities, and areas of expertise. The study adopts the pre-recording systematic reviews and meta-analysis method and a total of thirty-three articles were carefully extracted and analysed using the chosen search technique. These articles were classified into two main categories: machine learning techniques and other techniques. The outcomes of the review can be divided into distinct parts. Firstly, between 2018 and 2022, there were no studies conducted in Malaysia regarding Wireless Fidelity traffic analysis tools. Secondly, machine learning techniques were found to be particularly effective in extracting valuable insights related to user preferences, content consumption habits, and prevailing service usage trends. As a result, the future work should focus on proposing a new model for an intelligent Wi-Fi traffic analysis tool based on machine learning techniques, to be implemented and validated in real environments.

A novel pathloss prediction and optimization approach using deep learning in millimeter wave communication systems

Millimeter wave communication systems design requires accurate pathloss prediction as well as optimization. Traditional pathloss models gives less accuracy therefore pathloss models using deep learning is alternative solution for traditional method. These methods overcome the time consuming pathloss prediction parameter prediction based on large dataset which is available for different frequencies. In this proposed work, for prediction of pathloss, Convolutional Neural Network (CNN) and for optimization of pathloss Particle Swarm Optimization (PSO) is proposed. Towards implementation of the same, outdoor scenario with two roads and one cross section is used and dataset is generated using DeepMIMO generic dataset. The simulation result shows that using optimization technique reduces pathloss and CNN give accurate prediction of 95.7 %. Furthermore, root mean square error value of 6.6 dB has been achieve which is less in comparison to alternative approaches designed for outdoor environments. The forthcoming development of multifrequency path loss models has sparked significant interest due to their forecasting ability to support higher frequency bands and address blockage scenarios within future wireless networks.

Sub-terahertz near field channel measurements and analysis with beamforming and Bessel beams

Sub-terahertz communications (100–300 GHz) are explored today as a candidate technology to enable extremely high-rate, low-latency data services and high-resolution sensing in beyond-fifth-generation (beyond-5G) wireless networks. However, these sub-terahertz wireless systems will often have to operate in the near field, where the signal propagation does not follow canonical far-field models, including the commonly used free space path loss equation. Instead, the signal propagation in the near field follows more complex patterns that are not well-captured with analytical far-field models standardized for 5G research. Moreover, state-of-the-art beamforming solutions exploited heavily in fourth-generation (4G) and 5G networks are notably less efficient in the near field. In this article, the near-field sub-terahertz channel is accurately measured and analyzed. In addition to state-of-the-art beamforming, the article also analyzes the sub-terahertz channel measurements when using near-field-specific Bessel beams that demonstrate fewer power fluctuations in the near field in addition to higher focusing gain. Novel distance-centric and angle-centric dependencies reported in this article may serve as a reference when developing next-generation channel models for sixth-generation (6G) and beyond-6G near-field sub-terahertz wireless systems.

Energy Minimization for IRS-Assisted SWIPT-MEC System.

With the rapid development of the internet of things (IoT) era, IoT devices may face limitations in battery capacity and computational capability. Simultaneous wireless information and power transfer (SWIPT) and mobile edge computing (MEC) have emerged as promising technologies to address these challenges. Due to wireless channel fading and susceptibility to obstacles, this paper introduces intelligent reflecting surfaces (IRS) to enhance the spectral and energy efficiency of wireless networks. We propose a system model for IRS-assisted uplink offloading computation, downlink offloading computation results, and simultaneous energy transfer. Considering constraints such as IRS phase shifts, latency, energy harvesting, and offloading transmit power, we jointly optimize the CPU frequency of IoT devices, offloading transmit power, local computation workload, power splitting (PS) ratio, and IRS phase shifts. This establishes a multi-variate coupled nonlinear problem aimed at minimizing IoT devices energy consumption. We design an effective alternating optimization (AO) iterative algorithm based on block coordinate descent, and utilize closed-form solutions, Dinkelbach-based Lagrange dual method, and semidefinite relaxation (SDR) method to minimize IoT devices energy consumption. Simulation results demonstrate that the proposed scheme achieves lower energy consumption compared to other resource allocation strategies.