Performance Analysis of WebRTC-based Video Streaming over Power Constrained Platforms

This work analyses the use of the WebRTC framework on resource-constrained platforms. WebRTC is a consolidated solution for real-time video streaming, and it is an appealing solution in a wide range of application scenarios. We focus our attention on those in which power consumption, size and weight are of paramount importance because of size, weight and power requirements, such as the use case of unmanned aerial vehicles delivering real-time video streams overWebRTC to peers on the ground. The testbed described in this work shows that the power consumption can be reduced by changing WebRTC default settings while maintaining comparable video quality

Drone Base Station Trajectory Management for Optimal Scheduling in LTE-Based Sparse Delay-Sensitive M2M Networks

Providing connectivity in areas out of reach of the cellular infrastructure is a very active area of research. This connectivity is particularly needed in case of the deployment of machine type communication devices (MTCDs) for critical purposes such as homeland security. In such applications, MTCDs are deployed in areas that are hard to reach using regular communications infrastructure while the collected data is timely critical. Drone-supported communications constitute a new trend in complementing the reach of the terrestrial communication infrastructure. In this study, drones are used as base stations to provide real-time communication services to gather critical data out of a group of MTCDs that are sparsely deployed in a marine environment. Studying different communication technologies as LTE, WiFi, LPWAN and Free-Space Optical communication (FSOC) incorporated with the drone communications was important in the first phase of this research to identify the best candidate for addressing this need. We have determined the cellular technology, and particularly LTE, to be the most suitable candidate to support such applications. In this case, an LTE base station would be mounted on the drone which will help communicate with the different MTCDs to transmit their data to the network backhaul. We then formulate the problem model mathematically and devise the trajectory planning and scheduling algorithm that decides the drone path and the resulting scheduling. Based on this formulation, we decided to compare between an Ant Colony Optimization (ACO) based technique that optimizes the drone movement among the sparsely-deployed MTCDs and a Genetic Algorithm (GA) based solution that achieves the same purpose. This optimization is based on minimizing the energy cost of the drone movement while ensuring the data transmission deadline missing is minimized. We present the results of several simulation experiments that validate the different performance aspects of the technique

5G and beyond networks

This chapter investigates the Network Layer aspects that will characterize the merger of the cellular paradigm and the IoT architectures, in the context of the evolution towards 5G-and-beyond, including some promising emerging services as Unmanned Aerial Vehicles or Base Stations, and V2X communications

Reliable and Secure Drone-assisted MillimeterWave Communications

The next generation of mobile networks and wireless communication, including the fifth-generation (5G) and beyond, will provide a high data rate as one of its fundamental requirements. Providing high data rates can be accomplished through communication over high-frequency bands such as the Millimeter-Wave(mmWave) one. However, mmWave communication experiences short-range communication, which impacts the overall network connectivity. Improving network connectivity can be accomplished through deploying Unmanned Ariel Vehicles(UAVs), commonly known as drones, which serve as aerial small-cell base stations. Moreover, drone deployment is of special interest in recovering network connectivity in the aftermath of disasters. Despite the potential advantages, drone-assisted networks can be more vulnerable to security attacks, given their limited capabilities. This security vulnerability is especially true in the aftermath of a disaster where security measures could be at their lowest. This thesis focuses on drone-assisted mmWave communication networks with their potential to provide reliable communication in terms of higher network connectivity measures, higher total network data rate, and lower end-to-end delay. Equally important, this thesis focuses on proposing and developing security measures needed for drone-assisted networks’ secure operation. More specifically, we aim to employ a swarm of drones to have more connection, reliability, and secure communication over the mmWave band. Finally, we target both the cellular 5Gnetwork and Ad hoc IEEE802.11ad/ay in typical network deployments as well as in post-disaster circumstances

Machine Learning for Unmanned Aerial System (UAS) Networking

Fueled by the advancement of 5G new radio (5G NR), rapid development has occurred in many fields. Compared with the conventional approaches, beamforming and network slicing enable 5G NR to have ten times decrease in latency, connection density, and experienced throughput than 4G long term evolution (4G LTE). These advantages pave the way for the evolution of Cyber-physical Systems (CPS) on a large scale. The reduction of consumption, the advancement of control engineering, and the simplification of Unmanned Aircraft System (UAS) enable the UAS networking deployment on a large scale to become feasible. The UAS networking can finish multiple complex missions simultaneously. However, the limitations of the conventional approaches are still a big challenge to make a trade-off between the massive management and efficient networking on a large scale. With 5G NR and machine learning, in this dissertation, my contributions can be summarized as the following: I proposed a novel Optimized Ad-hoc On-demand Distance Vector (OAODV) routing protocol to improve the throughput of Intra UAS networking. The novel routing protocol can reduce the system overhead and be efficient. To improve the security, I proposed a blockchain scheme to mitigate the malicious basestations for cellular connected UAS networking and a proof-of-traffic (PoT) to improve the efficiency of blockchain for UAS networking on a large scale. Inspired by the biological cell paradigm, I proposed the cell wall routing protocols for heterogeneous UAS networking. With 5G NR, the inter connections between UAS networking can strengthen the throughput and elasticity of UAS networking. With machine learning, the routing schedulings for intra- and inter- UAS networking can enhance the throughput of UAS networking on a large scale. The inter UAS networking can achieve the max-min throughput globally edge coloring. I leveraged the upper and lower bound to accelerate the optimization of edge coloring. This dissertation paves a way regarding UAS networking in the integration of CPS and machine learning. The UAS networking can achieve outstanding performance in a decentralized architecture. Concurrently, this dissertation gives insights into UAS networking on a large scale. These are fundamental to integrating UAS and National Aerial System (NAS), critical to aviation in the operated and unmanned fields. The dissertation provides novel approaches for the promotion of UAS networking on a large scale. The proposed approaches extend the state-of-the-art of UAS networking in a decentralized architecture. All the alterations can contribute to the establishment of UAS networking with CPS

Architecture design for disaster resilient management network using D2D technology

Huge damages from natural disasters, such as earthquakes, floods, landslide, tsunamis, have been reported in recent years, claiming many lives, rendering millions homeless and causing huge financial losses worldwide. The lack of effective communication between the public rescue/safety agencies, rescue teams, first responders and trapped survivors/victims makes the situation even worse. Factors like dysfunctional communication networks, limited communications capacity, limited resources/services, data transformation and effective evaluation, energy, and power deficiency cause unnecessary hindrance in rescue and recovery services during a disaster. The new wireless communication technologies are needed to enhance life-saving capabilities and rescue services. In general, in order to improve societal resilience towards natural catastrophes and develop effective communication infrastructure, innovative approaches need to be initiated to provide improved quality, better connectivity in the events of natural and human disasters. In this thesis, a disaster resilient network architecture is proposed and analysed using multi-hop communications, clustering, energy harvesting, throughput optimization, reliability enhancement, adaptive selection, and low latency communications. It also examines the importance of mode selection, power management, frequency and time resource allocation to realize the promises of Long-term Evolution (LTE) Device to Device (D2D) communication. In particular, to support resilient and energy efficient communication in disaster-affected areas. This research is examined by thorough and vigorous simulations and validated through mathematical modelling. Overall, the impact of this research is twofold: i) it provides new technologies for effective inter- and intra-agency coordination system during a disaster event by establishing a stronger and resilient communication; and ii) It offers a potential solution for stakeholders such as governments, rescue teams, and general public with new informed information on how to establish effective policies to cope with challenges before, during and after the disaster events

Unmanned Aerial Vehicle-Enabled Mobile Edge Computing for 5G and Beyond

The technological evolution of the fifth generation (5G) and beyond wireless networks not only enables the ubiquitous connectivity of massive user equipments (UEs), i.e., smartphones, laptops, tablets, but also boosts the development of various kinds of emerging applications, such as smart navigation, augmented reality (AR), virtual reality (VR) and online gaming. However, due to the limited battery capacity and computational capability such as central processing unit (CPU), storage, memory of UEs, running these computationally intensive applications is challenging for UEs in terms of latency and energy consumption. In order to realize the metrics of 5G, such as higher data rate and reliability, lower latency, energy reduction, etc, mobile edge computing (MEC) and unmanned aerial vehicles (UAVs) are developed as the key technologies of 5G. Essentially, the combination of MEC and UAV is becoming more and more important in current communication systems. Precisely, as the MEC server is deployed at the edge network, more and more applications can benefit from task offloading, which could save more energy and reduce round trip latency. Additionally, the implementation of UAV in 5G and beyond networks could play various roles, such as relaying, data collection, delivery, SWIFT, which can flexibly enhance the QoS of customers and reduce the load of network. In this regard, the main objective of this thesis is to investigate the UAV-enabled MEC system, and propose novel artificial intelligence (AI)-based algorithms for optimizing some challenging variables like the computation resource, the offloading strategy (user association) and UAVs’ trajectory. To achieve this, some of existing research challenges in UAV-enabled MEC can be tackled by some proposed AI or DRL based approaches in this thesis. First of all, a multi-UAV enabled MEC (UAVE) is studied, where several UAVs are deployed as flying MEC platform to provide computing resource to ground UEs. In this context, the user association between multiple UEs and UAVs, the resource allocation from UAVs to UEs are optimized by the proposed reinforcement learning-based user association and resource allocation (RLAA) algorithm, which is based on the well known Q-learning method and aims at minimizing the overall energy consumption of UEs. Note that in the architecture of Q-learning, a Q-table is implemented to restore the information of all state and action pairs, which will be kept updating until the convergence is obtained. The proposed RLAA algorithm is shown to achieve the optimal performance with comparison to the exhaustive search in small scale and have considerable performance gain over typical algorithms in large-scale cases. Then, in order to tackle the more complicated problems in UAV-enabled MEC system, we first propose a convex optimization based trajectory control algorithm (CAT), which jointly optimizes the user association, resource allocation and trajectory of UAVs in the iterative way, aiming at minimizing the overall energy consumption of UEs. Considering the dynamics of communication environment, we further propose a deep reinforcement learning based trajectory control algorithm (RAT), which deploys deep neural network (DNN) and reinforcement learning (RL) techniques. Precisely, we apply DNN to optimize the UAV trajectory with continuous manner and optimize the user association and resource allocation based on matching algorithm. It performs more stable during the training procedure. The simulation results prove that the proposed CAT and RAT algorithms both achieve considerable performance and outperform other traditional benckmarks. Next, another metric named geographical fairness in UAV enabled MEC system is considered. In order to make the DRL based approaches more practical and easy to be implemented in real world, we further consider the multi agent reinforcement learning system. To this end, a multi-agent deep reinforcement learning based trajectory control algorithm (MAT) is proposed to optimize the UAV trajectory, in which each of UAV is instructed by its dedicated agent. The experimental results prove that it has considerable performance benefits over other traditional algorithms and can flexibly adjusts according to the change of environment. Finally, the integration of UAV in emergence situation is studied, where an UAV is deployed to support ground UEs for emergence communications. A deep Q network (DQN) based algorithm is proposed to optimize the UAV trajectory, the power control of each UE, while considering the number of UEs served, the fairness, and the overall uplink data rate. The numerical simulations demonstrate that the proposed DQN based algorithm outperforms the existing benchmark algorithms

Marine biodiversity assessments using aquatic internet of things

While Ubiquitous Computing remains vastly applied in urban environments, it is still scarce in oceanic environments. Current equipment used for biodiversity assessments remain at a high cost, being still inaccessible to wider audiences. More accessible IoT (Internet of Things) solutions need to be implemented to tackle these issues and provide alternatives to facilitate data collection in-the-wild. While the ocean remains a very harsh environment to apply such devices, it is still providing the opportunity to further explore the biodiversity, being that current marine taxa is estimated to be 200k, while this number can be actually in millions. The main goal of this thesis is to provide an apparatus and architecture for aerial marine biodiversity assessments, based on low-cost MCUs (Microcontroller unit) and microcomputers. In addition, the apparatus will provide a proof of concept for collecting and classifying the collected media. The thesis will also explore and contribute to the latest IoT and machine learning techniques (e.g. Python, TensorFlow) when applied to ocean settings. The final product of the thesis will enhance the state of the art in technologies applied to marine biology assessments.A computação ubíqua é imensamente utilizada em ambientes urbanos, mas ainda é escassa em ambientes oceânicos. Os equipamentos atuais utilizados para o estudo de biodiversidade são de custo alto, e geralmente inacessíveis para o público geral. Uma solução IoT mais acessível necessita de ser implementada para combater estes problemas e fornecer alternativas para facilitar a recolha de dados na natureza. Embora o oceano seja um ambiente severo para aplicar estes dispositivos, este fornece mais oportunidades para explorar a biodiversidade, sendo que a taxa de marinha atual é estimada ser 200 mil, mas este número pode estar nos milhões. O objetivo principal desta tese é fornecer um aparelho e uma arquitetura para estudos aéreos de biodiversidade marinha, baseado em microcontroladores low-cost e microcomputadores. Adi cionalmente, este aparelho irá fornecer uma prova de conceito para coletar e classificar a mídia coletada. A tese irá também explorar e contribuir para as técnicas mais recentes de machine learn ing (e.g. Python, TensorFlow) quando aplicadas num cenário de oceano. O produto final desta tese vai elevar o estado da arte em tecnologias aplicadas a estudos de biologia marinha

Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking

The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out

Blockchain in Agriculture: A PESTELS Analysis JAVIER

Blockchain (BC) represents a disruptive technology that has been extensively used to ensure immutability of digital transactions. Starting as an underlying mechanism in the digital currency sector, it has been applicable in a wide range of sectors and application domains. Agriculture represents a sector of significance for overall sustainability challenges that is benefiting from digitalisation and technological evolution and the enforcement of Industry 4.0 paradigm shift towards precision agriculture. Introduction of Internet of Things, and Cyber-Physical Systems increase overall complexity, with Big Data analysis and Machine Learning technologies paving the way for innovative applications. BC appears to be a promising technology for agriculture introducing new mechanisms for tracing of products and overall agricultural Supply Chain management from the farm to the fork. The authors perform a review of 152 scientific works, providing a concise summary for each and extracting current challenges and open issues for the application of BC in agriculture. By synthesizing their findings, they perform a state of the art analysis along the PESTELS framework. A large number of challenges including technological ones, create big research potential for the evolution of the area.SUSTAINABLE Project, funded by the European Union’s Horizon 2020 Research and Innovation Program, through the Marie Skłodowska-Curie-Research and Innovation Staff Exchange (RISE) under Grant 10100770