Wireless Personal Communications最新文献

The Internet of Things (IoT) is a rapidly growing technology that has been generating increasing amounts of traffic from multiple devices. However, this growth in traffic has also created vulnerabilities that need to be addressed. To identify attacking traffic while preserving data, it is important to quickly process intrusive data. Federated learning is a popular solution for decentralized training that preserves data, but it can also be susceptible to federated poisoning attacks caused by malicious clients. This work proposes a clustering-based client selection strategy to identify malicious clients based on their run time, followed by a trigger-set-based encryption mechanism that verifies the authenticity of the clients. This approach allows unreliable clients with plain text-based gradients to be ignored by the global model. The methodology was evaluated using the IoT23 dataset, and its efficiency, robustness, false alarms, and ability to handle some of the poisoning attacks that occur due to tuning and pruning were verified. The LeNet and DeepCtrl algorithms were used to determine detection accuracy, and after the implementation of a watermarking strategy, the detection accuracy improved significantly. For the DeepCtrl classifier, the detection accuracy improved from 89.90 to 99.8%, while for the LeNet classifier, it improved from 86.21 to 96.54%. This proposed methodology can be a useful tool for identifying attacking traffic and improving the security of IoT networks.

With the advent of fifth generation (5G) mobile communication network slicing technology, the range of application scenarios is expanding significantly. For 5G to function well, it necessitates little delay, a fast rate of data transfer, and the ability to handle a large number of connections. This demanding service requires the allocation of resources in a dynamic manner, while maintaining a very high level of reliability in terms of Quality of Service (QoS).The applications like autonomous driving, telesurgery, etc. have stringent QoS demands and the present design of slices is not suitable for these services. Therefore, latency has been regarded as a crucial factor in the design of the slices. Conventional optimization algorithms often lack robustness and adaptability to dynamic environments, getting stuck in local optima and failing to generalize to varying conditions. Our solution utilizes Reinforcement Learning (RL) to allocate resources to the slices. The utilization of restricted resources can be optimized through the reconfiguration of slices. The ability of RL to acquire knowledge from the surroundings enables our solution to adjust to varying network conditions, enhance the allocation of resources and improve quality of service over a period of time for different network slices. This study introduces the Deep Actor Critic Reinforcement Learning- Network Slicing (DACRL-NS) technique, which utilizes Deep Actor Critic Reinforcement learning for efficient resource allocation to network slices. The objective is to achieve optimal throughput in the network. If the slices fail to meet the minimum criteria, they will be omitted from the allocation. With increasing training episodes, our Actor-Critic algorithm enhances average cumulative rewards and resource allocation efficiency, demonstrating continuous learning and improved decision-making.The simulated suggested system demonstrates an average throughput improvement of 8.92% and 16.36% with respect to the rate requirement and latency requirement, respectively. The data also demonstrate a 17.14% increase in the overall network throughput.

This study investigates the potential for significant enhancements in the gain and return loss of a microstrip antenna through the meticulous design of slits and a triangular slot in the radiation patch, coupled with precise dimensioning of these elements and careful selection of the feeding point. To this end, a microstrip patch antenna with multiple slits and a triangular slot was designed and simulated on an FR-4 with a dielectric constant (ε) of 4.4. In the initial configuration, the antenna exhibited a gain of 2.3 dB at 5.79 GHz for the maximum Eɵ at θ = 6º, with an S11 value of -16.5 dB. The antenna biasing and feed position were carefully optimized to achieve the desired performance improvements, in addition to meticulous design considerations for gain and return loss enhancements. The optimized antenna demonstrated a gain of 6.2 dB at 6 GHz for Eɵ_max at θ = 0º, along with an S11 value of -24.3 dB. The final optimized configuration was fabricated and subjected to rigorous laboratory measurements for validation. The proposed antenna demonstrated an S11 value of -18.6 dB, along with a corresponding gain of 5.4 dB for Eɵ_max at θ = 0º at 6 GHz. Furthermore, it was observed that the bandwidth increased by 330 MHz compared to its initial state. Finally, a comparative table is presented comparing the proposed antenna with other similar antennas found in the literature. Based on this table, it is evident that the gain enhancement can be achieved in a remarkable manner without altering the overall dimensions of the antenna and without the need for an expensive substrate with high permittivity.

Developing a robust Automatic Speech Recognition (ASR) system is a major challenge in speech signal processing research. These systems perform exceedingly well in clean environments. However, the performance of these systems is not acceptable when the spoken signal is corrupted by several environmental and other artificial noises. The efficiency of any ASR system depends on several factors such as size of the vocabulary, native language influences, transmission channel, emotional and health state of the speaker, age of the speaker, designed speech corpus, size of the dataset, training and testing strategy and its preprocessing and other challenges. It is well known fact that the presence of noise in speech signal degrades its perceptual quality and intelligibility and hence ASR system performance is also affected. So, in this paper Dabauchies Wavelet based time adaptive Bayes thresholding algorithm is proposed with a custom Wavelet Packet Decomposition and Reconstruction Tree. The proposed system performance is evaluated on the Private Kannada Dataset and TIMIT dataset. The results reveal the effectiveness of the proposed system in various SNR levels such as − 10, − 5, 0, 5, 10, 15, 20, 25 and 30 dB. The article begins with introductory insights on ASR, Physiological process of speech production and perception in Humans, ASR jorgans, the architecture of ASR, and barriers associated with the ASR design. The work also focus on dataset design, baseline speech enhancement methods. This work provides comprehensive review to Wavelet based speech enhancement approach to the research scholars pursuing research in the area of speech signal processing. 

Generalized frequency division multiplexing (GFDM) is a flexible block structured multicarrier scheme for next generation wireless systems featuring low out-of-band radiation and high spectrum efficiency. There are various approaches suggested for its analysis via simulations but testing in real time environments is necessary for its standardization. Traditional data aided methods of synchronization avoid the effect of egress noise in pilot preamble destroying its spectral advantage. To safeguard this advantage, preamble needs to be pulse shaped. The main contribution of this paper is the derivation of generalized maximum likelihood estimation of frequency and time offsets for receiver synchronization in GFDM systems, using the modified preamble by the application of matrix inversion lemma. The dependency of the choice of the filter on Cramer–Rao lower bound of frequency offset estimation is also emphasized. The performance of the system is analysed over additive white Gaussian noise and multipath channel environments. The authors carried out real time implementation of GFDM system using IEEE 802.11 short preamble in indoor environments by employing national instruments universal software radio peripheral 2953R boards as hardware platform which is interfaced with LABVIEW for practical validations of the results.

The visible light communication (VLC) technology has been gathering attention recently and as the emerging systems and appliances in this technology are still new, their behavior and performance issues must be taken into the consideration as much as possible. This paper aims to fully model the noise that is emanating from the usage of the Single-photon avalanche diodes (SPADs) in the VLC systems. This noise is either modeled with the sub-Poisson or Gaussian plus sub-Poisson distributions. These noise models are then applied to the diffusion adaptive networks to show the real-world impact of the VLC noisy links on their performances. The radio noisy link impacts have been investigated on the performance of the diffusion adaptive networks, however, the effects of the optical link noise on their performances is the contribution of this paper. Also, using the realistic and precise models for the optical noise is another novelty in this paper. The results show that the diffusion network can be implemented using VLC appliances and the existence of the SPAD noise will not hinder the convergence of this network.

Cloud technology is a utility where different hardware and software resources are accessed on pay-per-user ground base. Most of these resources are available in virtualized form and virtual machine (VM) is one of the main elements of visualization. In virtualization, a physical server changes into the virtual machine (VM) and acts as a physical server. Due to the large number of users sometimes the task sent by the user to cloud causes the VM to be under loaded or overloaded. This system state happens due to poor task allocation process in VM and causes the system failure or user tasks delayed. For the improvement of task allocation, several load-balancing techniques are introduced in a cloud but stills the system failure occurs. Therefore we hybrid modified artificial bee colony for improvement in task allocation of VM and minimizing time consummation in cloud datacenter like makespan, total processing time, response time of algorithms, response time of datacenter and degree of imbalance. The consequences of the proposed task-scheduling algorithm are matched with existing heuristic-based scheduling procedures. The experimental consequences demonstrate that our approach is efficient when it is compared with the existing algorithms and reduce 1.7% in time consumption for cloud datacenter.

The manuscript investigates the utilization of Koch curves and hexagonal ring-shaped geometry for an ultra-wideband fractal antenna, achieved through a combination of DGS, parasitic elements, and EC-SRR. The antenna's properties are investigated without and with DGS, Parasitic element, and EC-SRR in the hexagonal ring-shaped geometries for broader band characteristics. Simulation results show that the antenna designed without DGS resonates at five distinct frequencies, while with DGS, Parasitic element, and SRR, it resonates at six frequencies. An enhanced bandwidth of 10.42 GHz (99.43%) is revealed in the final antenna geometry, and the proposed antenna resonates at six frequencies, 3.4, 5.8, 8.5, 11, 14.2, and 15 GHz, with reflection coefficients of − 24.33, − 38.10, − 28.35, − 27.39, − 32.32, and − 15.93 dB, respectively. Combining a defected ground structure, parasitic element, and EC-SRR increases frequency bands and enhances the BW and reflection coefficient. With an overall dimension of 30 mm × 24 mm, the proposed antenna is suitable for wireless applications in the frequency ranges of 2.40–3.89 GHz and 5.33–15.75 GHz. The proposed antenna is fabricated, and the results are measured. It is found that simulated and measured results are in good agreement with each other.

These days, a significant portion of the solutions for vehicle Internet of things applications come from wireless sensor networks. This article uses cameras, radio-frequency identification, and ultrasonic sensors to address typical issues with vehicle technology, such as unlawful vehicle use inside a community, vehicle thefts, and vehicle accidents. It also addresses the issue of identifying vehicle pollution parameter values like carbon monoxide (CO) and carbon dioxide ((textrm{CO}_2)), providing information about the driver’s alcohol consumption, and verifying the driver’s eligibility (driving license). The driving license will be used to identify the driver. Deep learning algorithms, such as Multi-Task Cascaded Convolutional Neural Networks and facenet algorithms, can identify driving licenses. The proposed algorithm has an 92% accuracy rate in detecting the driver’s face. The proposed system is installed and demonstrated using Micro-controller, Micro-processor and other sensors in real time environment. The River Formation Dynamics based Multi-hop Routing Protocol for Vehicles (RFDMRPV) is used for communication between vehicles. Data collected from the sensors mounted in vehicles are communicated to server utilizing RFDMRPV for storing. Alert the driver, owner of the vehicle and other authorities depending on the acquired sensor results.

A four-port annular ring patch antenna with a symmetrical slotted partial ground structure for mid-band 5G/Bluetooth/Wi-Fi/ISM band/WLAN and IoT applications at an operating frequency of 2.5 GHz is proposed. The proposed geometry is fabricated over the FR-4 epoxy substrate with dielectric constant 4.4, thickness of the substrate 1.6 mm, and loss tangent of 0.02. Moreover, the complete electrical dimension of presented design is 0.62(lambda _{0} times ) 0.48(lambda _{0} times ) 0.013(lambda _{0}), where (lambda _{0}) is wavelength in the free space corresponding to the resonating frequency (i.e., 2.5 GHz). In the proposed work, for enhancing the isolation a vertical slotted strip is used in the middle of the ground plane. Furthermore, we also enhanced the MIMO parameters performances, such as a low envelope correlation coefficient (ECC) between antenna elements, good diversity gain (DG), low channel capacity loss (CCL), acceptable mean effective gain (MEG), and better total active reflection coefficient (TARC) in the operating band. Moreover, due to four elements the presented work provide the higher data rates, excellent throughput of the signals and improve communication reliability as well as signal strength. The values of different parameters obtained for the proposed structure are as follows; antenna realized gain of 4.65 dBi, total efficiency of 96%, ECC < 0.025, DG is 9.98 dB, CCL < 0.012 bits/s/Hz, and MEG lies in between (-) 3 dB and (-) 12 dB. Throughout the band, the maximum isolation achieved among all ports is < (-) 26.4 dB. The overall frequency range for which reflection coefficient < (-) 10 dB is 2.36 to 2.99 GHz.