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EURASIP Journal on Wireless Communications and Networking
Journal Prestige (SJR): 0.301  Citation Impact (citeScore): 2 Number of Followers: 14  Open Access journalISSN (Print) 1687-1472 - ISSN (Online) 1687-1499 Published by SpringerOpen  [229 journals]
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- Routing protocols based on node selection for freely floating underwater
wireless sensor networks: a survey
Abstract: Abstract Recently, there has been an increasing interest in monitoring and exploring the underwater environment for scientific applications such as oceanographic data collection, marine surveillance, and pollution detection. Underwater acoustic sensor networks (UASN) have been proposed as the enabling technology to observe, map and explore the ocean. Due to the unique characteristics of underwater aquatic environment, which are low bandwidth, long propagation delays, and high energy consumption, the data forwarding process is very difficult. This paper presents a survey of the routing protocols for UASN. The addressed routing protocols are classified from a mobility point of view in freely floating underwater sensor networks. Indeed, managing the mobility of freely floating underwater sensors is one of the most critical constraints in the design of routing protocols. That is why we classify the routing protocols into “reliable data forwarding protocols” and “prediction-based data forwarding protocols.” In the first category, the proposed protocols mainly endure nodes’ mobility by continuously updating location information aiming at delivering the packets to the sink. In the second category, routing protocols try to rather master the nodes’ mobility by predicting the future nodes’ positions either based on a mobility model or on historical nodes’ positions using filtering techniques. We believe that our classification will help not only in deeply understanding the main characteristics of each protocol but also in investigating the evolution of research work evolution to provide energy-efficient data forwarding solutions for freely floating UASN.
PubDate: 2023-11-29
- Intelligent deep reinforcement learning-based scheduling in relay-based
HetNets
Abstract: Abstract We consider a fundamental file dissemination problem in a two-hop relay-based heterogeneous network consisting of a macro base station, a half-duplex relay station, and multiple users. To minimize the dissemination delay, rateless code is employed at the base station. Our goal is to find an efficient channel-aware scheduling policy at the half-duplex relay station, i.e., either fetch a packet from the base station or broadcast a packet to the users at each time slot, such that the file dissemination delay is minimized. We formulate the scheduling problem as a Markov decision process and propose an intelligent deep reinforcement learning-based scheduling algorithm. We also extend the proposed algorithm to adapt to dynamic network conditions. Simulation results demonstrate that the proposed algorithm performs very close to a lower bound on the dissemination delay and significantly outperforms baseline schemes.
PubDate: 2023-11-28
- Modified state activation functions of deep learning-based SC-FDMA channel
equalization system
Abstract: Abstract The most important function of the deep learning (DL) channel equalization and symbol detection systems is the ability to predict the user’s original transmitted data. Generally, the behavior and performance of the deep artificial neural networks (DANNs) rely on three main aspects: the network structure, the learning algorithms, and the activation functions (AFs) used in each node in the network. Long short-term memory (LSTM) recurrent neural networks have shown some success in channel equalization and symbol detection. The AFs used in the DANN play a significant role in how the learning algorithms converge. Our article shows how modifying the AFs used in the tanh units (block input and output) of the LSTM units can significantly boost the DL equalizer's performance. Additionally, the learning process of the DL model was optimized with the help of two distinct error-measuring functions: default (cross-entropy) and sum of squared error (SSE). The DL model's performance with different AFs is compared. This comparison is conducted using three distinct learning algorithms: Adam, RMSProp, and SGdm. The findings clearly demonstrate that the most frequently used AFs (sigmoid and hyperbolic tangent functions) do not really make a significant contribution to perfect network behaviors in channel equalization. On the other hand, there are a lot of non-common AFs that can outperform the frequently employed ones. Furthermore, the outcomes demonstrate that the recommended loss functions (SSE) exhibit superior performance in addressing the channel equalization challenge compared to the default loss functions (cross-entropy).
PubDate: 2023-11-27
- Channel state information-based wireless localization by signal
reconstruction
Abstract: Abstract Wireless localization technology has been widely used in indoor and outdoor fields. Channel estimation based on channel state information is a hot research topic in recent years. However, due to the interference of acquisition bandwidth, noise and Doppler effect, high-resolution channel estimation is a difficult problem. In this paper, the least squares estimate the amplitude of the signal subspace projection and estimate the time delay, using wireless channel state information to delay obey exponential distribution and magnitude obey normal distribution features, and reconstruction after the signal space and sampling to the Euclidean distance between the signal space, common as gradient optimization parameters, estimate the arrival time delay of high precision. The algorithm proposed in this paper filters out the noise interference in wireless communication and improves the accuracy of channel estimation through the method of least square and gradient optimization, which provides a feasible scheme for indoor wireless localization.
PubDate: 2023-11-06
- Research of 5G HUDN network selection algorithm based on Dueling-DDQN
Abstract: Abstract Due to the dense deployment and the diversity of user service types in the 5G HUDN environment, a more flexible network selection algorithm is required to reduce the network blocking rate and improve the user’s quality of service (QoS). Considering the QoS requirements and preferences of the users, a network selection algorithm based on Dueling-DDQN is proposed by using deep reinforcement learning. Firstly, the state, action space and reward function of the user-selected network are designed. Then, by calculating the network selection benefits for different types of services initiated by users, the analytic hierarchy process is used to establish the weight relationship between the different user services and the network attributes. Finally, a deep Q neural network is used to solve and optimize the proposed target and obtain the user’s best network selection strategy and long-term network selection benefits. The simulation results show that compared with other algorithms, the proposed algorithm can effectively reduce the network blocking rate while reducing the switching times.
PubDate: 2023-11-06
- Drone network for early warning of forest fire and dynamic fire quenching
plan generation
Abstract: Abstract Wildfires are one of the most frequent natural disasters which significantly harm the environment, society, and the economy. Transfer learning algorithms and modern machine learning tools can help in early forest fire prediction, detection, and dynamic fire quenching. A group of drones that has high-definition image processing and decision-making capabilities are used to detect the forest fires in the very early stage. The proposed system generates a fire quenching plan using particle swarm optimization technique and alerts the fire and rescue department for a quick action, thereby stop the forest fire at an early stage. Also, the drone network plays a major role to track the live status of forest fire and quenches the fire. ResNet, VGGNet, MobileNet, AlexNet, and GoogLeNet are used to detect the forest fire hazards. The experimental results prove that the proposed technique GoogLeNet-TL provides 96% accuracy and 97% F1 score in comparison with the state-of-the-art deep learning models.
PubDate: 2023-11-04
- A comprehensive study on the synchronization procedure in 5G NR with
3GPP-compliant link-level simulator
Abstract: Abstract The 5G New Radio synchronization procedure is the first step that the user must complete to access the mobile network. It mainly consists of the detection of the primary and secondary synchronization signals (PSS and SSS, respectively) and the decoding of the physical broadcast channel (PBCH). Our goal is to provide a comprehensive study of the synchronization procedure and investigate different techniques and approaches, through the implementation of a 5G New Radio-compliant simulator. Of significant interest is the investigation of impairments such as the fading channel, the frequency offset, and the delay spread. The results are provided in terms of detection probability for the PSS and SSS detection, and in terms of block error rate for the PBCH. From the data collected, there is evidence that choosing M-sequences for the PSS leads to an appreciably robust solution against frequency offset. The structure of the Gold sequences for SSS generation can be exploited to reduce the detection complexity, and different approaches can be chosen to improve reliability against delay spread. Moreover, the polar coding for 5G PBCH outperforms the former 4G coding technique, but they are still sensible to frequency offset. Finally, the simulator functionalities are validated through real captures of 5G signals.
PubDate: 2023-10-27
- Compact dual-port MIMO filtenna-based DMS with high isolation for C-band
and X-band applications
Abstract: Abstract A dual-port multiple-input multiple-output (MIMO) filtenna with minimal sizes of 80 × 45 mm2 is set up in this study. Each element in this MIMO filtenna is positioned orthogonally to the one next to it to improve isolation. For the MIMO element to achieve high-frequency selectivity and compact size, a frequency-reconfigurable filtenna that was created by fusing a band-pass filter and a monopole radiator was used. The suggested filtenna can switch between its C-band and X-band operating states with ease. On build the filtenna circuit, a band-pass filter based on defective microstrip structure is inserted to a circular monopole radiator. The developed filtenna operates in the C-band frequency range of 6.5–8 GHz and the X-band frequency range of 8–12 GHz. It is possible to use the X-band operating state for communication in a cognitive radio environment. Used as a decoupling structure, metamaterial structures can increase isolation to more than 40 dB across the bandwidth. The suggested MIMO filtenna system has an envelope correlation coefficient of 2.4e−6, a peak gain of 6 dBi, and an impedance bandwidth of 7.4–7.75 GHz. The MIMO filtenna is constructed and measured, and the findings of the measurement and simulation are in good agreement.
PubDate: 2023-10-25
- Efficient design of a wideband tunable microstrip filtenna for spectrum
sensing in cognitive radio systems
Abstract: Abstract This paper presents a novel design of a compact, wideband tunable microstrip filtenna system for effective spectrum sensing in cognitive radio (CR) applications. The proposed filtenna structure has a total bandwidth of \(1.63\,\text{GHz}\) and flexible frequency scanning design throughout the frequency range from \(1.93\,\text{ to }\,3.56\,\text{ GHz}\) with high selectivity and narrow bandwidths ranging from \(39.9\,\text{to}53\,\text{MHz}\) . Frequency tuning is accomplished electrically via integrating a varactor diode into the filtenna construction. The filtenna is realized on a Rogers TMM4 substrate with \(h=1.52\,\text{mm}\) thickness and relative dielectric constant of \({\varepsilon }_{r}=4.5\) with dimensions of \((25\times 35)\, {\text{mm}}^{2}\) . The obtained gain and efficiency of the filtenna ranged from \(0.7\) to \(2.26\,\text{dBi}\) and 49% to 60%, respectively, within the tuning range. Simple biasing circuitry, wideband operation, and compact planar structure are distinctive and appealing aspects of the design. For the manufactured prototypes, a significant level of agreement is found between the simulated and measured results in terms of scattering parameter \({\text{S}}_{11}\) and radiation patterns at different operating frequencies.
PubDate: 2023-10-23
- Correction: Fog computing network security based on resources management
PubDate: 2023-10-20
- Optimized design and application research of smart interactive screen for
wireless networks based on federated learning
Abstract: Abstract The rapid development of infinite networks and information technology has promoted the wide deployment and rapid growth of intelligent interactive devices. However, at the same time, touch interaction technology also faces many challenges such as lack of precision. This study combines federated learning with LayerGesture technology to optimize and design a touch interaction system with higher interaction accuracy and applies it to practice. The analysis results show that with the increase in the number of iterations of the federated model, the accuracy of the human–computer recognition interaction and the amount of information contained in it increases, and the accuracy curve reaches stability at about 2800 times and is at the optimal interaction adaptation. At this point, the loss function also decreases gradually, while the loss factor tends to 0, which verifies the stability of the optimized model. According to the participants’ interaction experience and experimental results, the optimized LayerGesture technique of the federated learning model has an average correctness rate of 90.4% and the lowest average selection time, while the average selection time of LayerGesture in the interaction area at the edge of the screen is 2510 ms and the average correctness rate is 93.60%, which is better than the Shift technique. In addition, the subjective survey results indicated that more participants favored the optimized LayerGesture technique. In summary, this paper’s joint learning algorithm contributes to the recognition effectiveness and efficiency of intelligent interactive systems.
PubDate: 2023-10-18
- Hybrid traffic security shaping scheme combining TAS and CQSF of
time-sensitive networks in smart grid
Abstract: Abstract The new intelligent factory introduces Time-Sensitive Network into industrial Ethernet to provide real time and deterministic guarantee communication for production system. Since the problem pertaining to data leakage or damage during transmission has increasingly become pronounced, security protection technology has been introduced, but this technology will bring about a delay in user response and a decline in the quality of service. Meanwhile, ensuring the deterministic mixed transmission of time-sensitive and large-bandwidth data traffic supported by the same switching device is a still challenging problem. Therefore, this study proposes a hybrid security scheduling scheme which combines Time-Aware Shaper and cycle specified queuing and forwarding (CSQF). Specifically, the mechanism first adopts various encryption methods for different traffic, and afterward, it reduces its resource occupation by adjusting the sampling period of the time-sensitive traffic. At the same time, it adopts CSQF to schedule the large-bandwidth data traffic, thereby improving the scheduling success rate. According to the experimental results, this scheme enhances network security and network scheduling success rate by up to 51%. The scheduling of mixed traffic in the Time-sensitive Network is realized securely and efficiently.
PubDate: 2023-10-12
- Physical layer security analysis of IRS-based downlink and uplink NOMA
networks
Abstract: Abstract In recent years, the development of intelligent reflecting surface (IRS) in wireless communications has enabled control of radio waves to reduce the detrimental impacts of natural wireless propagation. These can achieve significant spectrum and energy efficiency in wireless networks. Non-orthogonal multiple access (NOMA) technology, on the other hand, is predicted to improve the spectrum efficiency of fifth-generation and later wireless networks. Motivated by this reality, we consider the IRS-based NOMA network in the downlink and uplink scenario with a pernicious eavesdropper. Moreover, we investigated the physical layer security (PLS) of the proposed system by invoking the connection outage probability (COP), secrecy outage probability (SOP), and average secrecy rate (ASR) with analytical derivations. The simulation results reveal that (i) it is carried out to validate the analytical formulas, (ii) the number of meta-surfaces in IRS, transmit power at the base station, and power allocation parameters all play an essential role in improving the system performance, and (iii) it demonstrates the superiority of NOMA to the traditional orthogonal multiple access (OMA).
PubDate: 2023-10-11
- Design and development of multiband PIFA antenna for vehicular LTE/5G and
V2X communication
Abstract: Abstract This paper aims to introduce a custom-designed multiband planar inverted-F antenna (PIFA) suitable for automotive applications in LTE/5G schemes operating under 6 GHz, as well as Vehicle-to-Everything (V2X) communications. The PIFA antenna has a broad bandwidth capability, resonating from 950 MHz to 6 GHz. The proposed PIFA antenna is divided into three parts: the top, front, and back, resulting in a unique and effective antenna structure. The antenna is fabricated using a substrate made of FR4 material with a dielectric constant of 4.4. The whole measurements of the antenna are 54 × 38 × 25 mm3.The proposed PIFA antenna has been tested and has achieved a voltage standing wave ratio (VSWR) of less than 2 across the entire frequency range of 950 MHz to 6 GHz. Additionally, the maximum gain achieved by the antenna is 7.08 dBi at a frequency of 5.5 GHz, 6.81 dBi at 5.2 GHz, and 6.65 dBi at 5.9 GHz. The antenna also achieved a gain of 6.67 dBi at 3.8 GHz and a gain of 3.31 dBi at 1.7 GHz. Overall, this paper presents a well-designed and effective multiband PIFA antenna that is appropriate for use in vehicular applications. The antenna ability to cover a wide range of bandwidth and achieve high gain makes it an excellent candidate for use in LTE/5G systems and V2X communications.
PubDate: 2023-10-10
- RIS-enabled smart wireless environments: deployment scenarios, network
architecture, bandwidth and area of influence
Abstract: Abstract Reconfigurable intelligent surfaces (RISs) constitute the key enabler for programmable electromagnetic propagation environments and are lately being considered as a candidate physical-layer technology for the demanding connectivity, reliability, localisation, and sustainability requirements of next-generation wireless networks. In this paper, we first present the deployment scenarios for RIS-enabled smart wireless environments that have been recently designed within the ongoing European Union Horizon 2020 RISE-6G project, as well as a network architecture integrating RISs with existing standardised interfaces. We identify various RIS deployment strategies and sketch the core architectural requirements in terms of RIS control and signalling, depending on the RIS hardware architectures and respective capabilities. Furthermore, we introduce and discuss, with the aid of simulations and reflect array measurements, two novel metrics that emerge in the context of RIS-empowered wireless systems: the RIS bandwidth of influence and the RIS area of influence. Their extensive investigation corroborates the need for careful deployment and planning of the RIS technology in future wireless networks.
PubDate: 2023-10-10
- Auto scheduling through distributed reinforcement learning in SDN based
IoT environment
Abstract: Abstract The Internet of Things (IoT), which is built on software-defined networking (SDN), employs a paradigm known as channel reassignment. This paradigm has great potential for enhancing the communication capabilities of the network. The traffic loads may be scheduled more effectively with the help of an SDN controller, which allows for the transaction of matching channels via a single connection. The present techniques of channel reassignment, on the other hand, are plagued by problems with optimisation and cooperative multi-channel reassignment, which affect both traffic and routers. In this paper, we provide a framework for SDN–IoT in the cloud that permits multi-channel reassignment and traffic management simultaneously. The multi-channel reassignment based on traffic management is optimised via the use of a deep reinforcement learning technique, which was developed in this paper. We do an analysis of the performance metrics in order to optimise the throughput while simultaneously reducing the rate of packet loss and the amount of delay in the process. This is achieved by distributing the required traffic loads over the linked channels that make up a single connection.
PubDate: 2023-10-09
- Enhancing throughput using channel access priorities in frequency hopping
network using federated learning
Abstract: Abstract The data are sent by the nodes taking part in frequency hopping communications (FHC) utilising carrier frequencies and time slots that are pseudo-randomly assigned. Because of this, a high degree of protection against eavesdropping and anti-interference capabilities is provided. When using FHC in an environment, sharing time and frequency resources, avoiding collisions, and differentiating services are all made more complex as a result of this. A protocol for FHC that is based on dispersed wireless networks is presented by the authors of this research. It is a mechanism for multiple access control, which is prioritised and distributed. The ratio of empty channels metric can be found in the previous sentence. It is possible to provide priority in channel access by assigning different preset ratios of empty channel thresholds to the various traffic classes. Frames from frequency spread segments that have a partial collision are included as well. An analytical model is simulated for the analysis in terms of collision probability, transmission probability, and frame service time in order to carry out a theoretical examination of the performance of FHC. The objective of this inquiry is to determine how well FHC works. The analytical model has been proven correct by the exhaustive simulations as well as the theoretical findings. Cloud platforms are often used in the instruction of the most cutting-edge machine learning techniques of today, such as deep neural networks. This is done in order to take advantage of the cloud's capacity to scale elastically. In order to satisfy the criteria of these sorts of applications, federated learning, has been proposed as a distributed machine learning solution. This is done in order to fulfil the requirements of these kinds of applications. In federated learning (FL), even though everyone who uses the system works together to train a model, nobody ever shares their data with anybody else. Each user trains a local model with their own data, and then communicates the updated models with a FL server so that the data can be aggregated and a global model can be constructed. This process ensures that each user's model is unique. This process is repeated until a global model has been developed. This kind of training not only reduces the amount of network overhead that is necessary to transfer data to a centralised server, but it also safeguards the personal information of the users. Within the framework of this work, we looked at the feasibility of using the FL technique of learning on the many devices that are part of the dispersed network. On a centralised server, we conduct an analysis of the performance of the FL model by comparing its accuracy and the amount of time it takes to train using a range of various parameter value combinations. Additionally, the accuracy of these federated models may be made to reach a level that is comparable to that of the accuracy of central models.
PubDate: 2023-10-05
- Switching mode allocation in planning paths for vehicular network
communication
Abstract: Abstract Because of the increased mobility of vehicle users, it might be difficult to keep communication services in vehicle networks effective and dependable. Huge hurdles have been presented to vehicular networks as a result of the meteoric rise in the amount of data, which comes with the needs of high dependability and low latency. The deployment of access point servers at geographic locations that are closer to the vehicles in order to provide real-time service to applications that are based on the vehicles is one possible option. However, there is a limited amount of cache store space, and there is also a lack of a tractable access mode allocation method. As a result of these factors, it is very difficult to strike a compromise between the network transmission performance and fronthaul savings. Because the signal-to-interference-ratio (SIR) can be enhanced with switching mode in vehicular infrastructure, it may be possible to achieve higher levels of dependability. To serve all of the vehicles, the conventional allocation in vehicular network may not be sufficient on its own for two reasons: (1) the number of vehicles exceeds the number of paths, and (2) a vehicle may be located outside of the coverage path. Therefore, the implementation of switching mode allocation in vehicular communication is very necessary in order to increase the number of vehicles that can be supplied. In this paper, allocation using V2I, V2V, and V2X modes have been analyzed to provide dependable coverage for vehicles. These methods are used for communicating with other vehicles. In this paper, the numerical analysis has been performed such that SIR is optimized. In switching mode allocation, it has been shown that establishing a variable SIR threshold is helpful in achieving a path coverage that can be relied upon. It has been shown beyond a reasonable doubt that the coverage probability is likewise directly dependent on SIR thresholds. The theoretical analysis is verified, and it is confirmed that the suggested method is capable of achieving significant performance improvement in terms of coverage probability and data rate.
PubDate: 2023-10-02
- Blockchain managed federated learning for a secure IoT framework
Abstract: Abstract In this work, we present a blockchain-based federated learning (FL) framework that aims achieving high system efficiency while simultaneously addressing issues relating to data sparsity and the disclosure of private information. It is more efficient to build a number of smaller clusters rather than one big cluster for multiple networks. Blockchain-based FL is carried out in each cluster, with the model changes being compiled at the end of the process. Following that, the accumulated updates are swapped across the clusters, which, in practise, improves the updates that are accessible for each cluster. When compared to the extensive interactions that take place in blockchain-based FL, cluster-based FL only sends a limited number of aggregated updates across a substantial distance. This is in contrast to the extensive interactions that take place in blockchain-based FL. In order to conduct an analysis of our system, we have implemented the prototypes of both cluster and blockchain-based FL models. The findings of the experiments show that cluster-based FL model raise the accuracy goes upto 72.6%, and goes down to 11%. The loss goes upto 3.6 and goes down to 0.8. In addition, cluster-based FL model has the potential to hasten the convergence of the model, provided that the same quantity of data is input into it. The reason for this is due to the fact that during a training cycle, cluster-based FL model combines the computational resources of many different clusters.
PubDate: 2023-10-02
- A survey on cognitive radio network attack mitigation using machine
learning and blockchain
Abstract: Abstract Cognitive radio network is a promising technology to enhance the spectrum utilization and to resolve the spectrum scarcity issues. But the malicious users play havoc with the network during spectrum sensing and demean the network performance. It is mandatory to identify such malicious attacks and address it. There have been many traditional methods to mitigate the cognitive radio network attacks. In this paper, we have surveyed advanced attack mitigation techniques like machine learning, deep learning and blockchain. Thus, by detecting and addressing the malicious activities, the throughput and overall network performance can be improved.
PubDate: 2023-09-30
