Wireless Networks最新文献

Abstract

The identification of the presence of primary user enhances the spectrum efficiency in cognitive radio (CR). The studies suggested that the existence of malicious user adversely affects the system performances; especially the primary user emulation attack (PUEA) has a greater influence in spectrum sensing on the CR network. Moreover, the detection of PUEA is a challenging and complex task and involves constructive design with sensing algorithm. In this study, a support vector machine (SVM) along with energy vectors is designed to improve the spectrum sensing mechanism. The presented approach integrates the SVM with the Bayesian optimization algorithm (BOA) in which SVM aims to detect the malicious user by randomly selecting the primary and secondary users. The BOA aims to optimize the hyperparameters of the SVM, thereby improving the detection performances and maximizes the algorithms convergence speed. The experimental analysis illustrate that the presented approach predicts the PUEA with 98% accuracy and reduces the average node power is 9.7. Moreover, the results demonstrated that the system performance does not vary on implementing it with the large-scale CR network. Finally, the system performances are compared and evaluated with existing techniques in terms of accuracy, and average noise power.

Abstract

Cognitive radio networks (CRNs) offer a promising solution for improving spectrum utilization. However, ensuring quality of service (QoS) for heterogeneous secondary users (SUs) during spectrum handoff, particularly under high primary network traffic, poses challenges. This study develops a Markov-based analytical model to evaluate the gain of a non-switching spectrum handoff technique using a hybrid interweave-underlay spectrum access strategy, considering sensing errors. The proposed model assesses the effects of the hybrid spectrum access method for prioritized traffic across multiple SU classes, aiming to meet QoS requirements for delay-sensitive traffic. The study examines the CRN’s short-term behavior and realistic queueing scenarios by analyzing the system’s transient dynamics. Different spectrum access methods are compared for evaluation purposes. The analysis focuses on evaluating the effectiveness of the enhanced hybrid spectrum access scheme compared to individual interweave and hybrid interweave-underlay spectrum access strategies in terms of QoS provisioning for heterogeneous SUs. The results demonstrate increased throughput and improved spectrum utilization with the suggested scheme, affirming its suitability for satisfying QoS requirements for both delay-sensitive and delay-tolerant users.

The unmanned aerial vehicle (UAV) is considered a promising auxiliary relay for enhancing network coverage due to its easy deployment, agility, and maneuverability. In order to overcome the obstruction of towering buildings and the limitation of relaying energy, this paper proposes a half-duplex decode-and-forward UAV-assisted relaying network based on simultaneous wireless information and power transfer scheme. The power splitting (PS) is implemented at the UAV relay to address the challenge of energy limitation by separating information and energy from the radio frequency signal. We also study the impact of hardware impairments on transmitters, which can actually degrade communication performance. We derive both an exact expression and asymptotic analysis for the outage probability (OP) with energy harvesting and hardware impairments. In addition, we formulate an optimization form to find the optimal PS ratio and minimize the outage probability. Due to the nonconvex nature of this optimization problem, we perform a series of transformations to reformulate it into a convex optimization form. Experimental simulations validate our theoretical results. Specifically, optimal altitude for UAV is about 300 m and relatively constant in different conditions. The OP of our work is nearly 0.33 smaller than that of AF relaying in a given situation. Moreover, we provide practical guidance for the design and deployment of the UAV-assisted communication by exploring the effects of UAV’s altitude, hardware impairments level, and power splitting ratio on the outage probability.

Abstract

This article proposes an electrically small, probe-fed Ultra-Wideband (UWB) monopole antenna on a slotted truncated ground plane for breast cancer detection. The physical footprint of the proposed antenna element is 33 mm × 35 mm × 0.5 mm. This element is designed on the low-cost FR4 Epoxy substrate with a thickness of 0.5 mm. The proposed antenna has an electrical size of 0.33λ × 0.35λ × 0.005λ at the lowest frequency of operation; the radiator offers an impedance bandwidth of 8.34 GHz, which implies a fractional bandwidth of 115.5%. A compact dual-polarized antenna topology with two orthogonally placed probe-fed monopole antennas is proposed to achieve polarization diversity. The impedance characteristics of the individual radiating elements are maintained in spite of the electrical proximity of the radiators. The numerically computed and experimentally measured results of dual-polarized electrically small UWB antenna agree quite well. The proposed dual-polarized antenna topology is investigated for its utility in breast cancer detection in simulation.

Mobile communications systems are affected by what is known as fading, which is a well-known problem largely studied for decades. The direct consequence of fading is the complete loss of signal (or a large decrease of the received power). Rayleigh fading is a reasonable model for wireless channels although, Nakagami-m distribution seems better suited to fitting experimental data. In this paper we obtain the Nakagami-m distribution as a composite (mixture) of the Rayleigh distribution, a result which as far as we know it has not been shown in the literature. This representation of the Nakagami-m distribution facilitates computations of the average BER (Bit Error Rate) for DPSK (Differential Phase Shift Keying) and MSK (Minimum-Shift Keying) modulations for this distribution and higher moments of them, which is of great applicability to modeling wireless fading channels. Furthermore, a simple, not depending on any special function, apart of the Gamma function, bivariate version of the Nakagami-m distribution is also proposed as a special case of the multivariate version which is also presented. The proposed composite distribution is simulated through the standard procedure of summation of phasors, and results for the new closed-form measures for the MSK modulation are also shown. From that it is clear that the alternative formulation of the Nakagami-m distribution allows for easier modeling of fading fading-shadowing wireless channels through the new explicit second order statistics metrics. is well suited for modelling fading-shadowing wireless channels.

In this paper, we compute the throughput of wireless communications using Reconfigurable Intelligent Surfaces (RIS) when the source harvests energy using a solar panel. Harvesting duration is also optimized to enhance the performance of wireless communications when RIS is used. We derive the statistics of the Signal to Noise Ratio (SNR). We show that the SNR is the product of a Gaussian and a chisquare random variables (r.v.). We consider solar energy harvesting for Rayleigh channels.

Abstract

In today's world, the Internet of Things (IoT) plays a major role to interconnect all the devices and improve the overall Quality of Life (QoL) for people. The main concern among IoT systems revolve around three pillars namely security, confidentiality, and privacy owing to the sensitive nature of the data being transmitted and processed byIoT devices. Traditional cryptographic approaches address these concerns by ensuring the authenticity and confidentiality of IoT systems. However, the majority of IoT devices are resource-constrained, which implies that they operate under significant resource constraints such as limited computational power, constrained battery life, physical compactness, and restricted memory capacity. To this end, Lightweight cryptography (LWC) offers methods specifically designed to accommodate the limitations of resource-constrained IoT devices. This work establishes the role of light weight cryptography for such resource constrained IoT networks in terms of security perspectives. In this work, we explore the security vulnerabilities of IoT systems and the associated lightweight cryptographic methods highlighting four components namely lightweight block ciphers, lightweight stream ciphers, hash functions, and Elliptic Curve Cryptography. The work further discusses the role of LWC and reviews the recent advancements in different sectors of IoT such as smart city, industries, healthcare, smart grids, and agriculture. Finally, several open research directions are highlighted in order to guide future LWC and IoT researchers.

The group of connected small “Bio-sensor nodes (BSNs)” is employed in various parts of the human body that is called “Wireless body area networks (WBAN)”. It helps to recognize health-related data and to monitor the readings of blood pressure, “Electro-Cardiogram (ECG)”, heartbeat rate, “Electro-Myography (EMG)”, and glucose levels in the blood of the human body to know the real-time health. Many applications and research areas use the WBAN, like sports, social welfare, medical field, and entertainment. For WBAN, the major backbone is the BSNs, generally known as “Sensor nodes (SNs)”. Based on the small size of the SNs, they have basic resources. High energy is consumed when there is heavy data transmission. When all the energy is drained, that leads to the death of some SN. Routing is the data transfer method from the main source to the sink nodes. The minimum number of SNs is the efficient routing in the data transmission process, resulting in maximum energy consumption. Hence, an energy-efficient routing scheme is implemented with heuristic approaches to conserve more energy in the WBAN. To perform routing effectively, the Cluster Head (CH) needs to be selected initially. In this work, the optimal selection of the CH is carried out using a hybrid Red piranha and egret swarm algorithm (RPESA). Once the CH is optimally selected, the optimal routing is implemented using the RPESA algorithm. The data transmitted using this optimal routing scheme is then utilized for disease diagnosis using an Adaptive dilated cascaded recurrent neural network (ADC-RNN). The parameters in the ADC-RNN technique are optimally selected using the same RPESA algorithm. The classified disease outcome was obtained from ADC-RNN. The suggested heuristic-based energy-efficient routing approach for WBAN and the deep learning-based disease detection model was implemented, and its function was validated by differentiating it with other existing schemes.

As wireless networks continue to advance, virtual reality (VR) transmission over wireless connections is progressively transitioning from concept to practical application. Although this technology can significantly enhance the VR user experience, its development bottleneck lies in the computing capacity of devices and transmission latency. Considering the limited computational resources of VR devices for rendering tasks, multi-access edge computing (MEC) servers are introduced to provide powerful computing capabilities. To cope with transmission latency, reconfigurable intelligent surface (RIS) enhances links between base stations (BSs) and users. Based on these two technologies, we propose a RIS-assisted VR streaming model, where BSs are equipped with MEC servers to assist data rendering. Firstly, the user association, power control, and RIS phase shift optimization problems in the VR transmission system are jointly modeled and analyzed, establishing a long-term minimization of the interaction delay model. Secondly, by modeling the optimization problem as a Markov decision process (MDP), a joint optimization framework based on multi-agent deep reinforcement learning (MADRL) is proposed. In this framework, we have separately designed two dedicated algorithms for discrete and continuous variables. Furthermore, multiple agents can provide feedback based on user experience and cooperate with each other to improve the joint strategy. Finally, the performance and superiority of the proposed solution and algorithm are validated through simulation experiments in different application scenarios.

In this article, a human activity recognition system based on Wi-Fi signal strength variation (SSV) has been proposed. This strategy is built by exploiting the known fact that radio signal significantly reacts when it interfaces with the human body by causing fading and shadowing effects. Different irregularities in the received signal strength indicator (RSSI) propagation patterns indicate individual human activities. In the proposed method, utilizing the received RSSIs from various access points (APs) of known locations to the smartphone carried by a human, first, the position of the human is localized with the distances utilizing half the number of APs’ based on the strong RSSI values. Then, using the strongest RSSIs of the nearest AP, the activity of the human is recognized using the changing signal strengths. To accurately measure the monotonic distances by the RSSI values, the regression analysis technique (RAT) is used in the path loss model (PLM) to mitigate error significantly. Besides, to classify human activities, we calculate the deviation between any human activity and no human. Moreover, we arrange all activities in a successive order. With this infrastructure, we can develop a system where both human localization and activity recognition can be done within a single setup, which not only detects the position of a person on the floor but also produces the health condition of each person staying on the floor. In the existing methods, wirable devices are used to detect human activities, which creates irritations when they have to carry some heavy electronic device attached to their body. Moreover, these devices are expensive. On the other hand, channel state-based solutions have some advantages over wirable systems, but this technology does not support in major smartphones. So, in this work, to overcome such challenges, we have focused on an RSSI-based framework that does not need to wear electronic devices on the body as well as supports every smartphone. So, with a simple setup, the system can be operated. Our system can successfully recognize at most five activities simultaneously for the presence of the same humans in the experimental indoor premises. Such an approach enhances the interactions in intelligent healthcare systems.