Towards High Precision End-to-End Video Streaming from Drones using Packet Trimming

The emergence of a number of network communication facilities such as Network Function Virtualization (NFV), Software Defined Networking (SDN), the Internet of Things (IoT), Unmanned Aerial Vehicles (UAV), and in-network packet processing, holds a potential to meet the low latency, high precision requirements of various future multimedia applications. However, this raises the corresponding issues of how all of these elements can be used together in future networking environments, including newly developed protocols and techniques. This paper describes the architecture of an end-to-end video streaming platform for video surveillance, consisting of a UAV network domain, an edge server implementing in-network packet trimming operations with the use of Big Packet Protocol (BPP), utilization of Scalable Video Coding (SVC) and multiple video clients which connect to a network managed by an SDN controller. A Virtualized Edge Function at the drone edge utilizes SVC and in communication with the Drone Control Unit to manage the transmitted video quality. Experimental results show the potential that future multimedia applications can achieve the required high precision with the use of future network components and the consideration of their interactions

DeepBrain: Experimental Evaluation of Cloud-Based Computation Offloading and Edge Computing in the Internet-of-Drones for Deep Learning Applications

This article belongs to the Special Issue Time-Sensitive Networks for Unmanned Aircraft SystemsUnmanned Aerial Vehicles (UAVs) have been very effective in collecting aerial images data for various Internet-of-Things (IoT)/smart cities applications such as search and rescue, surveillance, vehicle detection, counting, intelligent transportation systems, to name a few. However, the real-time processing of collected data on edge in the context of the Internet-of-Drones remains an open challenge because UAVs have limited energy capabilities, while computer vision techniquesconsume excessive energy and require abundant resources. This fact is even more critical when deep learning algorithms, such as convolutional neural networks (CNNs), are used for classification and detection. In this paper, we first propose a system architecture of computation offloading for Internet-connected drones. Then, we conduct a comprehensive experimental study to evaluate the performance in terms of energy, bandwidth, and delay of the cloud computation offloading approach versus the edge computing approach of deep learning applications in the context of UAVs. In particular, we investigate the tradeoff between the communication cost and the computation of the two candidate approaches experimentally. The main results demonstrate that the computation offloading approach allows us to provide much higher throughput (i.e., frames per second) as compared to the edge computing approach, despite the larger communication delays.info:eu-repo/semantics/publishedVersio

Evaluation of Flying Ad Hoc Network Topologies, Mobility Models, and IEEE Standards for Different Video Applications

Nowadays, drones became very popular with the enhancement of the technological progress of moving devices with a connection to each other, known as Flying Ad Hoc Network (FANET). It is used in most worldwide necessary life scenarios such as video recording, search and rescue, military missions, moving items between different areas, and many more. This leads to the necessity to evaluate different network strategies between these flying drones, which are essential to improve their quality of performance in the field. Several challenges must be addressed to effectively use FANET, to provide stable and reliable transmission for different types of data during vast changing topologies, such as different video sizes, different types of mobility models, recent Wireless Fidelity standards, types of routing protocols used, security problems, and many more. In this paper, a fully comprehensive analysis of FANET will be done to evaluate and enhance these challenges that concern different video types, mobility models, and IEEE 802.11n standards for best performance, by measuring throughput, retransmission attempt, and delay metrics. The result shows that Gauss–Markov mobility model gives the highest result using Ad Hoc On-Demand Vector and lowest delay, whereas for retransmission attempts, 2.4 GHz frequency has the lowest as it can reach more coverage area than 5 GHz

Supporting UAVs with Edge Computing: A Review of Opportunities and Challenges

Over the last years, Unmanned Aerial Vehicles (UAVs) have seen significant advancements in sensor capabilities and computational abilities, allowing for efficient autonomous navigation and visual tracking applications. However, the demand for computationally complex tasks has increased faster than advances in battery technology. This opens up possibilities for improvements using edge computing. In edge computing, edge servers can achieve lower latency responses compared to traditional cloud servers through strategic geographic deployments. Furthermore, these servers can maintain superior computational performance compared to UAVs, as they are not limited by battery constraints. Combining these technologies by aiding UAVs with edge servers, research finds measurable improvements in task completion speed, energy efficiency, and reliability across multiple applications and industries. This systematic literature review aims to analyze the current state of research and collect, select, and extract the key areas where UAV activities can be supported and improved through edge computing

Design and management of image processing pipelines within CPS: Acquired experience towards the end of the FitOptiVis ECSEL Project

Cyber-Physical Systems (CPSs) are dynamic and reactive systems interacting with processes, environment and, sometimes, humans. They are often distributed with sensors and actuators, characterized for being smart, adaptive, predictive and react in real-time. Indeed, image- and video-processing pipelines are a prime source for environmental information for systems allowing them to take better decisions according to what they see. Therefore, in FitOptiVis, we are developing novel methods and tools to integrate complex image- and video-processing pipelines. FitOptiVis aims to deliver a reference architecture for describing and optimizing quality and resource management for imaging and video pipelines in CPSs both at design- and run-time. The architecture is concretized in low-power, high-performance, smart components, and in methods and tools for combined design-time and run-time multi-objective optimization and adaptation within system and environment constraints

Routing and video streaming in drone networks

PhDDrones can be used for several civil applications including search and rescue, coverage, and aerial imaging. Newer applications like construction and delivery of goods are also emerging. Performing tasks as a team of drones is often beneficial but requires coordination through communication. In this thesis, the communication requirements of video streaming drone applications based on existing works are studied. The existing communication technologies are then analyzed to understand if the communication requirements posed by these drone applications can be met by the available technologies. The shortcomings of existing technologies with respect to drone applications are identified and potential requirements for future technologies are suggested. The existing communication and routing protocols including ad-hoc on-demand distance vector (AODV), location-aided routing (LAR), and greedy perimeter stateless routing (GPSR) protocols are studied to identify their limitations in context to the drone networks. An application scenario where a team of drones covers multiple areas of interest is considered, where the drones follow known trajectories and transmit continuous streams of sensed traffic (images or video) to a ground station. A route switching (RS) algorithm is proposed that utilizes both the location and the trajectory information of the drones to schedule and update routes to overcome route discovery and route error overhead. Simulation results show that the RS scheme outperforms LAR and AODV by achieving higher network performance in terms of throughput and delay. Video streaming drone applications such as search and rescue, surveillance, and disaster management, benefit from multicast wireless video streaming to transmit identical data to multiple users. Video multicast streaming using IEEE 802.11 poses challenges of reliability, performance, and fairness under tight delay bounds. Because of the mobility of the video sources and the high data-rate of the videos, the transmission rate should be adapted based on receivers' link conditions. Rate-adaptive video multicast streaming in IEEE 802.11 requires wireless link estimation as well as frequent feedback from multiple receivers. A contribution to this thesis is an application-layer rate-adaptive video multicast streaming framework using an 802.11 ad-hoc network that is applicable when both the sender and the receiver nodes are mobile. The receiver nodes of a multicast group are assigned with roles dynamically based on their link conditions. An application layer video multicast gateway (ALVM-GW) adapts the transmission rate and the video encoding rate based on the received feedback. Role switching between multiple receiver nodes (designated nodes) cater for mobility and rate adaptation addresses the challenges of performance and fairness. The reliability challenge is addressed through re-transmission of lost packets while delays under given bounds are achieved through video encoding rate adaptation. Emulation and experimental results show that the proposed approach outperforms legacy multicast in terms of packet loss and video quality