UAV-enabled wireless-powered Iot wireless sensor networks

Future massive internet of thing (IoT) networks will enable the vision of smart cities, where it is anticipated that a massive number of sensor devices, in the order of tens of millions devices, ubiquitously deployed to monitor the environment. Main challenges in such a network are how to improve the network lifetime and design an e cient data aggregation process. To improve the lifetime, using low-power passive sensor devices have recently shown great potential. Ambient backscattering is a novel technology which provides low-power long-range wireless communication expanding the network lifetime signi cantly. On the other hand, in order to collect the sensed data from sensor devices deployed over a wide area, unmanned aerial vehicles (UAVs) has been considered as a promising technology, by leveraging the UAV's high mobility and line-of-sight (LOS) dominated air-ground channels. The UAV can act as data aggregator collecting sensed data from all sensors. In this thesis, we consider medium-access control (MAC) policies for two sensor data collection scenarios. First, the objective is to collect individual sensor data from the eld. The challenge in this case is to determine how a large number of sensors should access the medium so that data aggregation process performed in a fast and reliable fashion. Utilizing conventional orthogonal medium access schemes (e.g., time-division vi multiple access (TDMA) and frequency-division multiple access (FDMA)), is highly energy consuming and spectrally ine cient. Hence, we employ non-orthogonal multiple access (NOMA) which is envisaged as an essential enabling technology for 5G wireless networks especially for uncoordinated transmissions. In Chapter 2, we develop a framework where the UAV is used as a replacement to conventional terrestrial data collectors in order to increase the e ciency of collecting data from a eld of passive backscatter sensors, and simultaneously it acts as a mobile RF carrier emitter to activate backscatter sensors. In the MAC layer, we employ uplink power-domain NOMA scheme to e ectively serve a large number of passive backscatter sensors. Our objective is to optimize the path, altitude, and beamwidth of the UAV such that the network throughput is maximized. In Chapter 3, we consider the scenario where there are a separate data collector and RF carrier emitter such that the former is a gateway on the ground and the latter is a single UAV hovering over the eld of backscatter sensors. Secondly, we consider a case where only a function of sensed data is of interest rather than individual sensor values. A new challenge arises where the problem is to design a communication policy to improve the accuracy of the estimated function. Recently, over-the-air computation (AirComp) has emerged to be a promising solution to enable merging computation and communication by utilizing the superposition property of wireless channels, when a function of measurements are desired rather than individual in massive IoT sensor networks. One of the key challenges in AirComp is to compensate the e ects of channel. Motivated by this, in Chapter 4, we propose a UAV assisted communication framework to tackle this problem by a simple to implement sampling-then-mapping mechanism

Mobility-assisted Over-the-Air Computation for Backscatter Sensor Networks

Future intelligent systems will consist of a massive number of battery-less sensors, where quick and accurate aggregation of sensor data will be of paramount importance. Over-the-air computation (AirComp) is a promising technology wherein sensors concurrently transmit their measurements over the wireless channel, and a reader receives the noisy version of a function of measurements due to the superposition property. A key challenge in AirComp is the accurate power alignment of individual transmissions, addressed previously by using conventional precoding methods. In this paper, we investigate a UAVenabled backscatter communication framework, wherein UAV acts both as a power emitter and reader. The mobility of the reader is leveraged to replace the complicated precoding at sensors, where UAV first collects sum channel gains in the first flyover, and then, use these to estimate the actual aggregated sensor data in the second flyover. Our results demonstrate improvements of up to 10 dB in MSE compared to that of a benchmark case where UAV is incognizant of sum channel gains.Comment: 4 Pages, 3 figure

UAV data collection over NOMA backscatter networks: UAV altitude and trajectory optimization

The recent evolution of ambient backscattering technology has the potential to provide long-range and low-power wireless communications. In this work, we study the unmanned aerial vehicle (UAV)-assisted backscatter networks where the UAV acts both as a mobile power transmitter and as an information collector. We aim to maximize the number of successfully decoded bits in the uplink while minimizing the UAV's flight time by optimizing its altitude. Power-domain NOMA scheme is employed in the uplink. An optimization framework is presented to identify the trade-off between numerous network parameters, such as UAV's altitude, number of backscatter devices, and backscatter coefficients. Numerical results show that an optimal altitude is computable for various network setups and that the impact of backscattering reflection coefficients on the maximum network throughput is significant. Based on this optimal altitude, we also show that an optimal trajectory plan is achievable

Self-Evolving Integrated Vertical Heterogeneous Networks

6G and beyond networks tend towards fully intelligent and adaptive design in order to provide better operational agility in maintaining universal wireless access and supporting a wide range of services and use cases while dealing with network complexity efficiently. Such enhanced network agility will require developing a self-evolving capability in designing both the network architecture and resource management to intelligently utilize resources, reduce operational costs, and achieve the coveted quality of service (QoS). To enable this capability, the necessity of considering an integrated vertical heterogeneous network (VHetNet) architecture appears to be inevitable due to its high inherent agility. Moreover, employing an intelligent framework is another crucial requirement for self-evolving networks to deal with real-time network optimization problems. Hence, in this work, to provide a better insight on network architecture design in support of self-evolving networks, we highlight the merits of integrated VHetNet architecture while proposing an intelligent framework for self-evolving integrated vertical heterogeneous networks (SEI-VHetNets). The impact of the challenges associated with SEI-VHetNet architecture, on network management is also studied considering a generalized network model. Furthermore, the current literature on network management of integrated VHetNets along with the recent advancements in artificial intelligence (AI)/machine learning (ML) solutions are discussed. Accordingly, the core challenges of integrating AI/ML in SEI-VHetNets are identified. Finally, the potential future research directions for advancing the autonomous and self-evolving capabilities of SEI-VHetNets are discussed.Comment: 25 pages, 5 figures, 2 table

A Case Study of the Impact of Parental Diseases on the Probability of Hypertension Using Data Mining Techniques

Introduction: Hypertension is one of the most common health problems. As it has a major impact on other serious diseases such as cardiovascular diseases and strokes, and due to not having any specific symptoms, it is known as a silent killer. Therefore, proper diagnosis, control, and treatment of hypertension is crucial in health care systems and will indeed prevent the development of the other diseases affected by. Studies have shown a strong connection between some diseases of parents and the probability of having hypertension in children. Method: In this study, using data collected from two health centers in Ardabil province and applying data mining techniques and Rapid Miner software, the impact of parental diseases on the probability of hypertension in children was investigated. Results: The results indicated that using paternal medical history on hypertension, premature coronary artery disease, hypercholesterolemia, and hyperthyroidism has a significant role in predicting hypertension in children. Moreover, the performance of the predictive model, developed using the collected samples and data mining techniques, was enhanced by 5%. Conclusion: Some diseases of parents, especially hypertension, increase the risk of hypertension in children. However, individual characteristics are the major determinants of this complication

FLSTRA: Federated Learning in Stratosphere

We propose a federated learning (FL) in stratosphere (FLSTRA) system, where a high altitude platform station (HAPS) facilitates a large number of terrestrial clients to collaboratively learn a global model without sharing the training data. FLSTRA overcomes the challenges faced by FL in terrestrial networks, such as slow convergence and high communication delay due to limited client participation and multi-hop communications. HAPS leverages its altitude and size to allow the participation of more clients with line-of-sight (LOS) links and the placement of a powerful server. However, handling many clients at once introduces computing and transmission delays. Thus, we aim to obtain a delay-accuracy trade-off for FLSTRA. Specifically, we first develop a joint client selection and resource allocation algorithm for uplink and downlink to minimize the FL delay subject to the energy and quality-of-service (QoS) constraints. Second, we propose a communication and computation resource-aware (CCRA-FL) algorithm to achieve the target FL accuracy while deriving an upper bound for its convergence rate. The formulated problem is non-convex; thus, we propose an iterative algorithm to solve it. Simulation results demonstrate the effectiveness of the proposed FLSTRA system, compared to terrestrial benchmarks, in terms of FL delay and accuracy.Comment: Submitted to IEEE for possible publicatio

UAV Data Collection over NOMA Backscatter Networks: UAV Altitude and Trajectory Optimization

The recent evolution of ambient backscattering technology has the potential to provide long-range and low-power wireless communications. In this work, we study the unmanned aerial vehicle (UAV)-assisted backscatter networks where the UAV acts both as a mobile power transmitter and as an information collector. We aim to maximize the number of successfully decoded bits in the uplink while minimizing the UAV's flight time by optimizing its altitude. Power-domain NOMA scheme is employed in the uplink. An optimization framework is presented to identify the trade-off between numerous network parameters, such as UAV's altitude, number of backscatter devices, and backscatter coefficients. Numerical results show that an optimal altitude is computable for various network setups and that the impact of backscattering reflection coefficients on the maximum network throughput is significant. Based on this optimal altitude, we also show that an optimal trajectory plan is achievable

In vitro antimicrobial activities of metabolites from vaginal Lactobacillus strains against Clostridium perfringens isolated from a woman's vagina

Background: More than 50 different species of bacteria may live in a woman's vagina, with lactobacilli being the predominant microorganism found in healthy adult females. Lactobacilli are relevant as a barrier to infection and are important in the impairment of colonization by pathogens, owing to competitive adherence to adhesion sites in the vaginal epithelium and their capacity to produce antimicrobial compounds. Methods: The aim of the present study was to demonstrate the inhibitory capability of Lactobacillus metabolites against Clostridium perfringens, an anaerobic Gram-positive bacterium. These bacteria were isolated from vaginal swabs by using culture-dependent approaches, and the bacteriostatic effect of Lactobacillus metabolites, extracted from different isolates, was assessed using a modified E test. Results: Among the 100 vaginal swabs, 59 (59%) samples showed the presence of Lactobacillus strains and only one sample contained C. perfringens. Lactobacillus metabolites demonstrated the significant potency of in vitro activity against C. perfringens, with minimal inhibitory concentration values ranging from 15.6 μg/mL to 31.2 μg/mL. Conclusion: This study suggests that women without vaginal Lactobacillus strains may be susceptible to nonindigenous and potentially harmful microorganisms

Depression, Anxiety, and Stress Among Patients with COVID-19: A Cross-Sectional Study

© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. Aim: Patients with confirmed COVID-19 infection can develop several psychological consequences. Epidemiological data on mental health and psychological disorder inpatients infected with COVID-19 pneumonia are not available in Iranian patients. The purpose of this study was to evaluate the anxiety, stress, and depression of patients with COVID-19. Material and Methods: This cross-sectional survey was conducted in 2020. All confirmed patients with COVID-19 were included in the study by census sampling. Assessment of depression, stress, and anxiety was performed using the DASS-21 questionnaire. All statistical analyses were performed using R version 3.5.1. Results: The questionnaires were completed by 221 patients with COVID-19 infection (204 males, 17 females). The mean age was 45.90 ± 7.73 years. Our results indicated that the mean scores of depression and anxiety were at “extremely severe” levels, while stress levels were “severe.” The prevalence of “extremely severe” symptoms of depression and anxiety was 54.29% and 97.29%, respectively. The prevalence of severe stress was 46.61%. Conclusion: In this study, patients infected with COVID-19 reported severe and extremely severe experience psychological distress. Further studies should focus on the combined use of psychological and molecular biomarker testing to increase accuracy. Overall, the findings demonstrate the necessity of special intervention programs for the confirmed patients with emerging infectious disease COVID-19 to promote mental health needs