Wireless Network Architecture Research Articles

A Comparative Analysis of F-RAN and C-RAN Architectures for Next-Generation Wireless Networks

Within the realms of software-defined networks (SDN), this study offers an in-depth analysis of F-RAN and C-RAN designs. It evaluates the essential characteristics, performance metrics, and deployment considerations of both architectures to enhance understanding of their capabilities. Additionally, the study explores the potential advantages and challenges of integrating F-RAN and C-RAN with SDN, offering insights for network architects and operators. By thoroughly comparing these architectures and analyzing their key features, performance metrics, and deployment considerations, the objective is to enhance comprehension of F-RANs and C-RANs within the framework of SDN. Additionally, it delves into examining the prospective advantages and obstacles linked with the combination of these architectural models with SDN, therefore providing network architects and operators with relevant insight. It serves to enrich the knowledge repository of SDN through meticulous analysis and furnishes pivotal insights for the deployment and refinement of network infrastructure.

Contribution-Based Resource Allocation for Effective Federated Learning in UAV-Assisted Edge Networks.

This paper considers UAVs as edge computing nodes and investigates a novel network resource allocation method for federated learning within a three-layer wireless network architecture containing cloud, edges (UAVs), and clients. To address the issue of fair bandwidth resource allocation among clients participating in federated learning, a contribution calculation strategy based on the Shapley value (SV) used as the weight for model aggregation is proposed. On this basis, a client selection and wireless resource allocation method based on model contribution is further designed. By reducing the training and aggregation frequency of the low-contribution clients during the asynchronous aggregation phase, the limited bandwidth resources are allocated to high-contribution clients, thus improving the convergence speed and accuracy of the global model. Simulation experiments demonstrate that the proposed method can significantly reduce the system delay and total energy consumption with gains between 15% and 50% while also improving the final accuracy of the global model by 0.3% and 2% on both short-term and long-term perspectives, respectively.

Generative Artificial Intelligence Using Machine Learning on Wireless Ad Hoc Networks

In this article, we discuss the use of Generative Artificial Intelligence (GenAI) to improve the efficiency and performance of access to wireless points located in various spaces and specific places, which allows interaction with wireless mesh networks and enables the use of mobile devices to access all types of information in internal environments. Furthermore, we propose the use of generative neural networks, which are one of the pillars of GenAI, since they use a methodology from the perspective of Machine Learning that allows analysis of a large amount of data and detection of certain types of patterns that help in the better placement of access points for improved reception and connectivity. Images (heat maps), access point locations, positioning points, and bandwidth are analyzed, allowing new information to be created. On the other hand, to understand and model the general architecture of the wireless Ad-Hoc network, we use two processes that are part of neural networks, such as Multilayer Perceptron (MLP), and the Radial Basis Function (RBF), which is a function of predictors or independent variables or input variables that allows the prediction error in the output variables of the wireless network architecture to be reduced. Using these two processes does help reduce blind spots in those internal places where the wireless signal does not reach, resulting in a signal drop. Improving internal scenarios with wireless Ad-Hoc networks is what is required for better functioning and performance of the network infrastructure.

Single-point calibration process based integrated electrical impedance analyzer for multi-selective gas detection

Impedance analysis is a powerful technique that has become increasingly important in various applications, it represents a leap forward in the field of electronic measurments and diagnostics. In this work, we present the development of miniaturized, multiplexed, and connected platform for impedance spectroscopy. Designed for online measurements and adapted to wireless network architectures, our platform has been tested and optimized to be used for multi-selective chemical organic sensor nodes. This compact and versatile circuit is built from low cost and low power consumption (250 mW) microelectronics components that achieve long duration operability (5 days and 16 h) without compromising on sensor measurement accuracy and precision. We used the well-known impedance network analyzer AD5933 (Analog Devices, Norwood, MA, USA) chip which can measure a spectrum of impedances in the range 5 kHz to 100 kHz. The proposed system is based on ESP32-C3 Microcontroller enabling the management of the AD5933 through its I2C interface. Our system benefits from two multiplexer components CD74HC4067 allowing calibration process and the interface of 15 conductimetric sensors with real time acquisition (less than 90 ms per acquisition). The system is capable of relaying information through the network for data analysis and storage. The paper describes the microelectronics design, the impedance response over time, the measurement’s sensitivity and accuracy and the testing of the platform with embedded chemical sensors for gas classification and recognition.

MURE: Multi-layer real-time livestock management architecture with unmanned aerial vehicles using deep reinforcement learning

In recent years, the combination of unmanned aerial vehicles (UAVs) and wireless sensor networks (WSNs) has gained popularity in livestock management (LM) due to energy constraints and network instability. Limited energy storage of sensor nodes (SNs) and the possibility of packet loss contribute to fast energy consumption and unstable networks, respectively. UAVs serve as relay nodes and data sinks, addressing these issues by temporarily storing data to reduce SN workload and establishing mobile nodes for network stability. We propose two innovations based on previous work: 1) We introduce a multi-layer wireless network architecture, categorizing UAVs into two layers based on their functions including data collection and data processing. This enhances task parallelization, bridging performance gaps among multiple UAVs; 2) We overcome the mobility limitation of SNs, considering their real-time movement in the network. Through deep reinforcement learning, UAVs learn to cooperatively locate moving SNs. This accounts for the inevitable mobility of livestock in the industry. Additionally, we simulate the environment and compare our approach to traditional methods, evaluating metrics such as collected data per timestep (DCPS), energy consumed per timestep (ECPS), and network stability (NS). Experimental results demonstrate that our method outperforms traditional approaches, achieving a data collecting gain of 4.84% and 8.20% compared to the methods without considering SN mobility or the multi-layer characteristics of WSNs, respectively. Under energy consumption limits, our method yields energy savings of 3.00% and 1.35% respectively. Furthermore, we extensively study and validate our method against other path planning algorithms, including genetic particle swarm optimization (GPSO), modified central force optimization (MCFO), and rapidly-exploring random trees (RRT). Our approach surpasses these methods in terms of data collecting efficiency and network stability.

Cognitive radio network architecture for GEO and LEO satellites shared downlink spectrum

The fixed spectrum assignment policy in the space sector and large constellations of Low Earth Orbit (LEO) satellites left little or no spectrum available for future LEO satellite communications services. Cognitive Radio (CR) technology enables spectrum sharing between primary and secondary users without limiting the transmission power, and thus is of great interest to commercial and defense entities. A number of Radio Environment Map (REM) techniques have been making Cognitive Radio Networks (CRNs) practical by constructing a comprehensive map of the CRN by utilizing multi-domain information from geolocation databases, characteristics of spectrum use, geographical terrain models, propagation environment, and regulations. In this paper, we investigate spectrum sharing for a network comprised of a Geostationary Orbit (GEO) and a LEO satellite with a multibeam antenna array. A CRN architecture of GEO and LEO satellites shared downlink spectrum is proposed and details are provided covering its architecture, REM structure and channel utilization data aggregation, as well as a frequency slot assignment mechanism.

A Deep Learning-Based Reliable Link Prediction Model for Achieving Traffic-Aware Routing in Mobile Ad-hoc Networks

One of the most promising wireless network architectures is the mobile ad-hoc network (MANET). Researchers have introduced enormous protocols for efficient routing, but it does not provide a reliable communication link for data transmission. Therefore, this research proposes a reliable link prediction-based traffic-aware deep learning routing protocol in MANET to maintain path stability and reliability to construct efficient routing. The reliable traffic-aware link prediction model used in this research is Fuzzy-based Deep Extreme [Formula: see text]-Learning (FDEQL) model. The fuzzy logic rule is used to compute the status of a wireless link to build stable and faster paths toward destinations. Traffic patterns can affect the efficiency of a node. So, to cope with the traffic pattern in MANET, the point-to-point (P2P) and end-to-end (E2E) traffic matrices are initially constructed. To evaluate whether the wireless link is reliable or not, the proposed approach utilizes fuzzy rules by considering essential parameters such as neighborhood overlap (NOVER), bipartivity index (BI), node mobility (NM), data rate (DR), received signal strength indicator (RSSI) and buffer occupancy (BO). The output is the [Formula: see text]-value for reliable link prediction. The performance of a proposed model is validated with other baseline methods based on various measures such as energy consumption, route failure, throughput, delay, packet delivery ratio (PDR), normalized routing load (NRL) and buffer occupancy by varying the mobility speed from 5 to 30[Formula: see text]m/sec, number of nodes and simulation time, respectively. At the mobility speed of 10[Formula: see text]m/sec, the proposed model has a delay of 0.08 sec, PDR of 99% and throughput of 1903.4[Formula: see text]kbps. The proposed model achieves a delay of 19.37[Formula: see text]msec, PDR of 96.22%, and throughput of 132.95[Formula: see text]kbps, respectively, for 30 nodes. If the simulation runs for 300 sec, the suggested model achieves a delay of 2.98 sec and a PDR of 0.946, respectively.

IRS-aided NOMA-based communication architecture for 6G wireless networks: An enhanced error-control and reliable data transmission

Intelligent reflecting surface (IRS) is a key technology for sixth-generation (6G) wireless communication to obtain high energy and spectral efficiency with a minimum cost. On the other hand, non orthogonal multiple access (NOMA) enhances the spectrum efficiency of the network. The combining schemes are implemented to obtain error-free data at the receiver. This paper proposes an IRS-aided NOMA-based packet combining (PC) and aggressive packet combining (APC) schemes-assisted communication architecture to enhance service quality and achieve sustainable and resilient connectivity among users for 6G wireless networks. In this paper, the mathematical model has been derived for the instantaneous achievable data rate, bit error rate, outage probability, and packet correction capability. The derived analytical expressions are validated through detailed Monte Carlo simulations. The obtained results show that the proposed schemes outperform the conventional NOMA with PC and NOMA with APC schemes and facilitate enhanced error control and reliable data transmission capabilities. Moreover, the proposed schemes have been compared with the IRS-aided orthogonal multiple access (OMA) schemes, where IRS-aided OMA schemes are considered benchmarks of the proposed schemes. It is presented that the proposed schemes are superior to the conventional OMA with PC and APC schemes and IRS-aided OMA with PC and APC schemes.

An Overview of Problems and Difficulties with ML in WSNs Protection

There are serious, mutually exclusive problems with resources and security in wireless sensor networks. As security complexity rises, battery consumption will follow suit. It is useless to rely on the security of common protocols like encryption and key management because of the limited capacity of wireless sensor networks and their dynamic architecture. Algorithms for machine learning are one of the proposed ways to combine judgment, awareness and observation to deliver intelligence services in this kind of network. Machine learning algorithms provide new issues related to training and the amount of data required for training. This paper neatly covers the architecture of wireless sensor networks as well as the security challenges they face. It also goes into the challenges and recommended fixes for improving sensors' ability to identify dangers, assaults, hazards, and suspicious activity via their ability to learn and grow on their own using machine learning techniques. This might be achieved by reducing the cost of WSNs across several domains. This research also addresses open issues with machine learning algorithms that are pertinent to adapting them to the properties of sensors inside that kind of network.

Wireless body area sensor networks based human activity recognition using deep learning

In the healthcare sector, the health status and biological, and physical activity of the patient are monitored among different sensors that collect the required information about these activities using Wireless body area network (WBAN) architecture. Sensor-based human activity recognition (HAR), which offers remarkable qualities of ease and privacy, has drawn increasing attention from researchers with the growth of the Internet of Things (IoT) and wearable technology. Deep learning has the ability to extract high-dimensional information automatically, making end-to-end learning. The most significant obstacles to computer vision, particularly convolutional neural networks (CNNs), are the effect of the environment background, camera shielding, and other variables. This paper aims to propose and develop a new HAR system in WBAN dependence on the Gramian angular field (GAF) and DenseNet. Once the necessary signals are obtained, the input signals undergo pre-processing through artifact removal and median filtering. In the initial stage, the time series data captured by the sensors undergoes a conversion process, transforming it into 2-dimensional images by using the GAF algorithm. Then, DenseNet automatically makes the processes and integrates the data collected from diverse sensors. The experiment results show that the proposed method achieves the best outcomes in which it achieves 97.83% accuracy, 97.83% F-measure, and 97.64 Matthews correlation coefficient (MCC).

A New Vision of Wireless Edge Caching Networks (WECNs): Issues, Technologies, and Open Research Trends

The future generation of wireless cellular system is expected to accommodate extensive latency-sensitive, tactus-aware, and data-intensive intelligent applications, thereby posing significant challenges on current network architectures. For meeting the challenges, wireless edge caching has been envisioned as a viable enabler. However, the underlying key issues therein, such as reliability, effectiveness, and the joint design, are significantly undervalued. The reliability issue refers to the data-corruption problem incurred by hardware impairments, software upgrades, and network congestion at edges. The effectiveness issue here expresses the unsatisfactory cache hit efficiency. Whilst the joint design aims to acquire the tradeoff among different metrics. In this article, we are dedicated to filling these research gaps. Therewith, we first brief the hierarchical wireless edge caching network architecture with proactive mechanisms. Then, we elaborate on the research roadways towards these critical issues and examine how fault-tolerant coding and bundle recommendation strategies can potentially enhance the reliability and the effectiveness of edge caching in future intelligent communication era. Moreover, the interplay between forgoing mentioned aspects is illuminated as well. A pair of case studies are exemplified to further demonstrate the validity of our devised versatile solutions. At last, the open research opportunities and future trends are identified.

Access Control, Key Management, and Trust for Emerging Wireless Body Area Networks.

Wireless Body Area Networks (WBANs) are an emerging industrial technology for monitoring physiological data. These networks employ medical wearable and implanted biomedical sensors aimed at improving quality of life by providing body-oriented services through a variety of industrial sensing gadgets. The sensors collect vital data from the body and forward this information to other nodes for further services using short-range wireless communication technology. In this paper, we provide a multi-aspect review of recent advancements made in this field pertaining to cross-domain security, privacy, and trust issues. The aim is to present an overall review of WBAN research and projects based on applications, devices, and communication architecture. We examine current issues and challenges with WBAN communications and technologies, with the aim of providing insights for a future vision of remote healthcare systems. We specifically address the potential and shortcomings of various Wireless Body Area Network (WBAN) architectures and communication schemes that are proposed to maintain security, privacy, and trust within digital healthcare systems. Although current solutions and schemes aim to provide some level of security, several serious challenges remain that need to be understood and addressed. Our aim is to suggest future research directions for establishing best practices in protecting healthcare data. This includes monitoring, access control, key management, and trust management. The distinguishing feature of this survey is the combination of our review with a critical perspective on the future of WBANs.

An endogenous intelligent architecture for wireless communication networks

AbstractThe challenges posed by the future wireless communication network, which will be a huge system with more complex structures, diverse functions, and massive communication ends, will be addressed by intelligent wireless communication technologies. These technologies are playing an increasingly important role in network architecture, computing architecture, resource allocation algorithm design, etc., thanks to the rapid development of artificial intelligence technologies, particularly the deep learning technologies, and their extensive application in various domains. In this paper, an endogenous intelligent architecture is developed to effectively clarify and understand in-depth the relationship among the factors by constructing wireless knowledge graph for the air interface transmission, the core network, as well as the network environment, and so on. Furthermore, the knowledge graph simultaneously reveals the structure and operation mechanism of the whole wireless communication networks. Cause tracing, intelligent optimization, and performance evaluation are sequentially implemented based on the knowledge graph, thus forming a complete closed-loop for endogenous intelligent wireless communication networks.

Chase–escape in dynamic device-to-device networks

AbstractWe feature results on global survival and extinction of an infection in a multi-layer network of mobile agents. Expanding on a model first presented in Cali et al. (2022), we consider an urban environment, represented by line segments in the plane, in which agents move according to a random waypoint model based on a Poisson point process. Whenever two agents are at sufficiently close proximity for a sufficiently long time the infection can be transmitted and then propagates into the system according to the same rule starting from a typical device. Inspired by wireless network architectures, the network is additionally equipped with a second class of agents able to transmit a patch to neighboring infected agents that in turn can further distribute the patch, leading to chase–escape dynamics. We give conditions for parameter configurations that guarantee existence and absence of global survival as well as an in-and-out of the survival regime, depending on the speed of the devices. We also provide complementary results for the setting in which the chase–escape dynamics is defined as an independent process on the connectivity graph. The proofs mainly rest on percolation arguments via discretization and multiscale analysis.

Internet of Things in healthcare: A survey on protocol standards, enabling technologies, WBAN architectures and open issues

This paper provides a state-of-art review on healthcare-IoT infrastructure, prominently focusing on protocol standards, enabling technologies, wireless body area network (WBAN) architectures and implementation issues. Internet of Things enchains the seamless healthcare devices from an actuate-sense-communicate (ASC) network in a proliferative channel in order to develop an operationally connected healthcare environment (OCHE). Sensors and actuators are the essential components of any IoT system that generates enormous data, and this data is communicated across the network concerning elementary statuses and stored in a distributed cloud platform. This paper begins by providing a brief horizontal overview of the IoT system. The enhanced technical details pertaining to healthcare scenarios such as WBAN architecture, layered healthcare IoT architecture, and enabling technologies are described in the forthcoming fragments of the paper. Authors have then provided a brief summary of the most anticipated protocol stacks and design issues that allows researchers and healthcare professionals to understand swiftly how the numerous protocols put together to attain desired functionalities without having to get through standards and RFCs. Authors have also explored recent state-of-art to identify some of the key challenges of the healthcare IoT domain and a short summary of each related research is presented. Moreover, the relation between Healthcare IoT and other disruptive technologies such as Blockchain and Big Data is being described. Finally, the authors explicated the detailed use-case scenarios to demonstrate how the numerous protocols and architectures presented in the paper could put together to attain desired healthcare services.

Design of the energy-balanced wireless sensor networks for 3D seismic exploration

Wireless Sensor Networks for 3D Seismic Exploration are large-scale and long-term networks to ensure high resolution. Energy balance is essential to avoid interruption of the whole network. In this paper, the hybrid wireless network architecture is designed for low-power monitoring system. An energy-balanced clustering method is developed to prolong network lifetime. The partial energy factor is introduced to optimize scheduling of cluster head nodes. An improved ant colony algorithm for energy-efficient clustering and routing network (IACA-EECR) is proposed to find optimal path. The results show the proposed architecture outperforms the existing platform. Extensive tests validate energy efficiency and network performance.

Security optimization method of high-power charging pile inter-group communication network under trusted taboo particle swarm optimization

In order to ensure the normal operation of the communication network in the event of a small number of charging pile failures, it is necessary to establish a stable communication network between the charging pile groups. In this case, it is necessary to improve the stability and viability of the communication network between the pile groups. Based on this, this paper proposes a security optimization method for high-power inter pile communication networks under trusted tabu particle swarm optimization. Using 6LoWPAN technology to optimize the wireless communication network architecture of charging piles to reduce the probability of communication network paralysis; design a neighborhood end-to-end communication strategy, and conduct research on lightweight key management of charging piles by building a three-tier architecture for the communication environment of charging piles. The trusted taboo particle swarm optimization algorithm optimizes the security of the communication network between high-power charging piles to ensure the security of the communication network. The experimental results show that after the optimization of the proposed method, the stability and invulnerability of the communication network between the charging pile groups have been effectively improved, and the method has a high convergence speed.

Internet of Things Energy Consumption Optimization in Buildings: A Step toward Sustainability

The internal components of a smart building interact through a compatible fabric and logic. A smart building integrates systems, structure, services, management, and their interrelationships to create a dynamic and cost-efficient environment. Smart buildings reduce the amount of cooling and heating load required to cool and heat spaces, thereby lowering operating costs and energy consumption without sacrificing occupant comfort. Smart structures are an Internet of Things (IoT) concern. The Internet of Things is a global network that virtualizes commonplace objects. The Internet of Things infuses non-technical objects with technology. IoT development has led to the creation of new protocols based on architectures for wireless sensor networks. Energy conservation extends the life and improves the performance of these networks, while overcoming the limitations of IoT node batteries. This research seeks to develop a data transmission model for routing IoT data in smart buildings. Utilization of intelligent object clustering and particle swarm optimization (PSO), chaotic particle swarm optimization (CPSO), and fractional chaotic order particle swarm optimization (FCPSO) optimization methods. Using the proposed algorithm to minimize energy consumption in the IoT is possible due to the algorithm’s ability to mitigate the problem by considering the number of parameters that can have a significant impact on performance, which is the goal of many optimization approaches.

A Centralized Multi-User Anti-Composite Intelligent Interference Algorithm Based on Improved Q-Learning

This paper proposes a central anti-jamming algorithm (CAJA) based on improved Q-learning to further solve the communication challenges faced by multi-user wireless communication networks in terms of external complex malicious interference. This will also reduce the dual factors restricting wireless communication quality, the impact of inter-user interference within the network, and the effect of external malicious interference on the communication system to improve multi-user wireless communication transmission. Firstly, a central base station that coordinates and allocates channels for users within the network is set up using multi-user wireless communication network architecture to constitute a centralized wireless communication network. Secondly, the multi-user system is modeled using the single-user Markov decision process in which the central base station is the main body. Finally, an improved Q-learning algorithm is used to improve overall system transmission income using the central base station, based on the network user number sequential decision action for avoiding external malicious interference. It is designed to avoid the impact of internal network interference on transmission performance during the early stage of communication, achieving overall system transmission income improvement. Simulation results show that in comparison to the existing multi-user independent Q-learning anti-jamming algorithm and the traditional orthogonal frequency-hopping scheme, the proposed algorithm significantly improves overall system transmission performance.

Integrating Terrestrial and Non-Terrestrial Networks: 3D Opportunities and Challenges

Integrating terrestrial and non-terrestrial networks has the potential of connecting the unconnected and enhancing the user experience for the already-connected, with technological and societal implications of the greatest long-term significance. A convergence of ground, air, and space wireless communications also represents a formidable endeavor for the mobile and satellite communications industries alike, as it entails defining and intelligently orchestrating a new 3D wireless network architecture. In this article, we present the key opportunities and challenges arising from this revolution by presenting some of its disruptive use cases and key building blocks, reviewing the relevant standardization activities, and pointing to open research problems. By considering two multi-operator paradigms, we also showcase how terrestrial networks could be efficiently re-engineered to cater for aerial services, or opportunistically complemented by non-terrestrial infrastructure to augment their current capabilities.