Resource allocation, user association and placement for uav-assisted communications

In the past few years, unmanned aerial vehicle (UAV)-assisted heterogeneous network has attracted significant attention due to its wide range of applications, such as disaster rescue and recovery, ground macro base station (MBS) traffic offloading, communications for temporary events, and data collection for further processing in Internet of Things (IoT). A UAV can act as a flying base station (BS) to quickly recover the communication coverage in the disaster area when the regular terrestrial infrastructure is malfunctioned. The UAV-assisted heterogeneous network can effectively provision line of sight (LoS) communication links and therefore can mitigate potential signal shadowing and blockage. The regulation relaxation and cost reduction of UAVs as well as communication equipment miniaturization make the practical deployment of highly mobile wireless relays more feasible than before. In fact, the 3GPP Rel-16 has included UAV-enabled wireless communications in the new radio standard, aiming to boost capacity and coverage of fifth generation (5G) wireless networks. However, the performance of UAV-assisted communications is greatly affected by the resource allocation scheme, user association policy and the UAV placement strategy. Also, the limited on-board energy and flight time of the UAV poses a great challenge on designing a robust and reliable UAV-enabled IoT network. To maximize the throughput in the UAV-assisted mobile access network, an optimization problem which determines the 3D UAV deployment and resource allocation in a given hotspot area under the constraints of user Quality of Service (QoS) requirements and total available resources is formulated. First, the primal problem is decomposed into two subproblems, i.e., the 3D UAV placement problem and the resource allocation problem. Second, a cyclic iterative algorithm which solves the two sub-problems separately and uses the output of one as the input of the other is proposed. An optimization problem that aims to minimize the average latency ratio of all users is formulated by determining the 3D location of the UAV, the user association and the bandwidth allocation policy between the MBS and the drone base station (DBS) with the constraint of each user’s QoS requirement and total available bandwidth. The formulated problem is a mixed integer non-convex optimization problem, a very challenging and difficult problem. To make formulated problem tractable, it is decomposed into two subproblems, i.e., the user association and bandwidth allocation problem and the 3D DBS placement problem. These two subproblems are alternatively optimized until no performance improvement can be further achieved. To address the challenge of limited on-board battery capacity and flight time, a tethered UAV (TUAV)-assisted heterogeneous network where the aerial UAV is connected with a ground charging station (GCS) through a tether is proposed. The objective of the formulated problem is to maximize the sum rate of all users by jointly optimizing the user association, resource allocation and placement of the GCSs and the aerial UAVs, constrained by each user’s QoS requirement and the total available resource. Since the primal problem is highly non-convex and non-linear and thus challenging to solve, it is decomposed into three subproblems, i.e., the TUAV placement problem, the resource allocation problem and the user association problem. Then, the three sub-problems are alternately and iteratively optimized by using the outputs of the first two as the input for the third. The future work comprises two parts. First, IoT devices usually are generally deployed at remote areas with limited battery capacities and computing power. Therefore, the generated data needs to be offloaded to a more powerful computing server for further processing. Unfortunately, the trajectory design in UAV data collection is generally NP-hard and difficult to obtain the optimal solution. Advances of machine learning (ML) provide a promising alternative approach to solve such problems that cannot be solved by traditional optimization methods. Hence, deep reinforcement learning (DRL) is proposed to be explored to obtain a near optimal solution. Second, the low earth orbit (LEO) satellite networks will revolutionize traditional communication networks with their promising benefits of service continuity, wide-area coverage, and availability for critical communications and emerging applications. However, the integration of LEO satellite networks and terrestrial networks will be another future research endeavor

Communication Aware UAV Swarm Surveillance Based on Hierarchical Architecture

Multi-agent unmanned aerial vehicle (UAV) teaming becomes an essential part in science mission, modern warfare surveillance, and disaster rescuing. This paper proposes a decentralized UAV swarm persistent monitoring strategy in realizing continuous sensing coverage and network service. A two-layer (high altitude and low altitude) UAV teaming hierarchical structure is adopted in realizing the accurate object tracking in the area of interest (AOI). By introducing the UAV communication channel model in its path planning, both centralized and decentralized control schemes would be evaluated in the waypoint tracking simulation. The UAV swarm network service and object tracking are measured by metrics of communication link quality and waypoints tracking accuracy. UAV swarm network connectivity are evaluated over different aspects, such as stability and volatility. The comparison of proposed algorithms is presented with simulations. The result shows that the decentralized scheme outperforms the centralized scheme in the mission of persistent surveillance, especially on maintaining the stability of inner UAV swarm network while tracking moving objects

Countering internet packet classifiers to improve user online privacy

Internet traffic classification or packet classification is the act of classifying packets using the extracted statistical data from the transmitted packets on a computer network. Internet traffic classification is an essential tool for Internet service providers to manage network traffic, provide users with the intended quality of service (QoS), and perform surveillance. QoS measures prioritize a network\u27s traffic type over other traffic based on preset criteria; for instance, it gives higher priority or bandwidth to video traffic over website browsing traffic. Internet packet classification methods are also used for automated intrusion detection. They analyze incoming traffic patterns and identify malicious packets used for denial of service (DoS) or similar attacks. Internet traffic classification may also be used for website fingerprinting attacks in which an intruder analyzes encrypted traffic of a user to find behavior or usage patterns and infer the user\u27s online activities. Protecting users\u27 online privacy against traffic classification attacks is the primary motivation of this work. This dissertation shows the effectiveness of machine learning algorithms in identifying user traffic by comparing 11 state-of-art classifiers and proposes three anonymization methods for masking generated user network traffic to counter the Internet packet classifiers. These methods are equalized packet length, equalized packet count, and equalized inter-arrival times of TCP packets. This work compares the results of these anonymization methods to show their effectiveness in reducing machine learning algorithms\u27 performance for traffic classification. The results are validated using newly generated user traffic. Additionally, a novel model based on a generative adversarial network (GAN) is introduced to automate countering the adversarial traffic classifiers. This model, which is called GAN tunnel, generates pseudo traffic patterns imitating the distributions of the real traffic generated by actual applications and encapsulates the actual network packets into the generated traffic packets. The GAN tunnel\u27s performance is tested against random forest and extreme gradient boosting (XGBoost) traffic classifiers. These classifiers are shown not being able of detecting the actual source application of data exchanged in the GAN tunnel in the tested scenarios in this thesis

AI/ML assisted Li-Fi communication systems for the future 6G communication systems

Η πανταχού παρούσα εξάπλωση της ασύρματης σύνδεσης κατά την τελευταία δεκαετία είχε ως αποτέλεσμα μια τεράστια αύξηση του όγκου της κίνησης και μια τεράστια ζήτηση, η οποία δημιούργησε μια αξιοσημείωτη πίεση στους πόρους του δικτύου που δεν μπορούν να διαχειριστούν εξαρχής λόγω της σπανιότητας του εύρους ζώνης. Επομένως; Η Optical Wireless Communication θεωρείται ως η αναδυόμενη λύση για τα τρέχοντα δίκτυα ραδιοφώνου, όπου λειτουργεί στην εκμετάλλευση του φωτός ως ασύρματος φορέας και έχει ταξινομηθεί ως φιλική προς το περιβάλλον τεχνολογία λόγω της βιωσιμότητας και του επιπέδου ασφάλειας. Το Light-Fidelity (LiFi) είναι το πιο πρόσφατο παράδειγμα της οπτικής ασύρματης επικοινωνίας όπου υπάρχουν νέα χαρακτηριστικά όπως π. Στο σύστημα έχουν εισαχθεί τεχνικές διαμόρφωσης πολλαπλών φορέων και τεχνολογίες πολλαπλής πρόσβασης. Αυτή η αναφορά παρουσιάζει τη διαδικασία σχεδιασμού ενός πομποδέκτη LiFi που χρησιμοποιεί το MATLAB. όπου όλα τα μέρη του συστήματος προσομοιώθηκαν για να μιμηθούν ένα σύστημα LiFi σε ένα εσωτερικό περιβάλλον που είναι ένα δωμάτιο με διαστάσεις 5 x 5 x 3 m. Ο πομποδέκτης έχει χαρακτηριστεί με χρήση οπτοηλεκτρονικών συσκευών περοβσκίτη λόγω της πολλά υποσχόμενης απόδοσής του όσον αφορά την εκπομπή φωτός και την ανίχνευση. Ωστόσο, έχει προκύψει σημαντικός όγκος θορύβου λόγω της φωτοανίχνευσης που έχει μετριαστεί με την εισαγωγή ενός ενισχυτή transimpedance μετά τον φωτοανιχνευτή και την εφαρμογή ενός μηχανισμού εκτίμησης καναλιών στην πλευρά του δέκτη. Τα ληφθέντα αποτελέσματα έδειξαν ότι το σχεδιασμένο σύστημα μπορεί να επιτύχει περίπου 3,5 Mbps με 25dB SNR και λιγότερο από 4x10^(-6) BER χρησιμοποιώντας 5 πομπούς με 1000 LED σε κάθε πομπό, χωρίς να λαμβάνεται υπόψη καμία εξωτερική πηγή θορύβου όπως ο θόρυβος περιβάλλοντος. Οι πιθανοί περιορισμοί για ένα τέτοιο σύστημα είναι οι προδιαγραφές των οπτοηλεκτρονικών συσκευών που περιλαμβάνουν, την επιφάνεια της συσκευής, το οπτικό πεδίο του φωτοανιχνευτή και τη γωνία μισής ισχύος του LED. Ωστόσο, τα συστήματα οπτικών ασύρματων επικοινωνιών είναι πιο ευέλικτα για βελτιστοποίηση και τα σχέδια μπορούν να τυποποιηθούν σύμφωνα με την ζητούμενη υπηρεσία και τη φύση του περιβάλλοντος λόγω της ποικιλίας των διαθέσιμων συσκευών με χαμηλό κόστος.The ubiquitous spread of the wireless connection during the last decade has resulted in a tremendous growth in the traffic volume and a huge demand, which created a remarkable pressure on the network’s resources that can’t be managed due to bandwidth scarcity in the first place. Therefore; Optical Wireless Communication is considered as the emerging solution for the current radio networks, where it works on exploiting light as a wireless carrier and it has been classified as eco-friendly technology due to its sustainability and safety level. Light-Fidelity (LiFi) is the most recent paradigm of the optical wireless communication where new features such as; multicarrier modulation techniques and multiple access technologies have been introduced to the system. This report presents the design process of a LiFi transceiver using MATLAB; where all system parts were simulated to imitate a LiFi system in an indoor environment which is a room with dimensions of 5 x 5 x 3m. The transceiver has been characterised using perovskite optoelectronic devices due to its promising performance in terms of light emission and detection. However, a considerable amount of noise has been resulted due to the photodetection that has been mitigated using inserting a transimpedance amplifier after the photodetector and implement a channel estimation mechanism at the receiver side. The obtained results have demonstrated that the designed system can achieve around 3.5Mbps with 25dB SNR and less then 4x10^(-6) BER using 5 transmitters with 1000 LED at each transmitter, without considering any external source of noise such as the ambient noise. The prospective limitations for such a system are the optoelectronic devices specs which include, the device’s surface area, the photodetector’s field of view, and the half power angle of the LED. However, the optical wireless communication systems are more flexible to be optimized and the designs can be standardized according to the requested service and the environment nature due to the variety of the available devices with low cost

Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures

The demographic shift of the population towards an increase in the number of elderly citizens, together with the sedentary lifestyle we are adopting, is reflected in the increasingly debilitated physical health of the population. The resulting physical impairments require rehabilitation therapies which may be assisted by the use of wearable sensors or body area network sensors (BANs). The use of novel technology for medical therapies can also contribute to reducing the costs in healthcare systems and decrease patient overflow in medical centers. Sensors are the primary enablers of any wearable medical device, with a central role in eHealth architectures. The accuracy of the acquired data depends on the sensors; hence, when considering wearable and BAN sensing integration, they must be proven to be accurate and reliable solutions. This book is a collection of works focusing on the current state-of-the-art of BANs and wearable sensing devices for physical rehabilitation of impaired or debilitated citizens. The manuscripts that compose this book report on the advances in the research related to different sensing technologies (optical or electronic) and body area network sensors (BANs), their design and implementation, advanced signal processing techniques, and the application of these technologies in areas such as physical rehabilitation, robotics, medical diagnostics, and therapy

Feature Papers of Drones - Volume I

[EN] The present book is divided into two volumes (Volume I: articles 1–23, and Volume II: articles 24–54) which compile the articles and communications submitted to the Topical Collection ”Feature Papers of Drones” during the years 2020 to 2022 describing novel or new cutting-edge designs, developments, and/or applications of unmanned vehicles (drones). Articles 1–8 are devoted to the developments of drone design, where new concepts and modeling strategies as well as effective designs that improve drone stability and autonomy are introduced. Articles 9–16 focus on the communication aspects of drones as effective strategies for smooth deployment and efficient functioning are required. Therefore, several developments that aim to optimize performance and security are presented. In this regard, one of the most directly related topics is drone swarms, not only in terms of communication but also human-swarm interaction and their applications for science missions, surveillance, and disaster rescue operations. To conclude with the volume I related to drone improvements, articles 17–23 discusses the advancements associated with autonomous navigation, obstacle avoidance, and enhanced flight plannin