During advanced surveillance missions, Unmanned Aerial Vehicles (UAVs) usually require the execution of edge-assisted computer vision (CV) tasks. In multi-hop UAV networks, the successful transmission of these tasks to the edge is severely challenged due to severe bandwidth constraints, and the possible node failures. To address these critical challenges, we propose a novel A 2 - UAV framework that optimizes the number of correctly executed tasks at the edge. In stark contrast with existing art, we take an application-aware approach and formulate a novel Application-Aware Task Planning Problem ( A 2 - TPP ) to optimize routing, data pre-processing and target assignment for each UAV. Our formulation explicitly takes into account (i) the relationship between CV task accuracy and image compression for the classes of interest based on the available dataset, (ii) the target positions , (iii) the current energy/position of the UAVs, and (iv) the possible node failures. We demonstrate A 2 - TPP is NP-Hard and propose a polynomial-time algorithm to solve it efficiently. We extensively evaluate A 2 - UAV through simulation and real-world experiments using a testbed composed by four DJI Mavic Air 2 UAVs. Results on image classification show that A 2 - UAV attains on average around 38% more accomplished tasks w.r.t. the state of the art, with a 400% improvement in tasks-intensive scenarios. Moreover, we show that our framework is able to reconfigure the network in case of nodes failure.

Flying ad-hoc networks (FANETs) are a powerful tool for inspecting safety-critical scenarios, including post-disaster areas or military fields, where they ensure prompt area monitoring and fast detection of events of interest. However, wide area deployment of FANETs requires fast and reliable communications among devices and their base station to ensure prompt intervention upon detection of anomalies. Existing long-range communication technologies are inadequate to meet the data rate requirements and delay constraints of safety-critical applications. Previous solutions to enable ad-hoc communications in mobile networks also fall short of exploiting the controllable mobility of FANETs. To face this challenge, we formulate the connected deployment problem, where we require the FANET to dynamically create connected coverage formations to ensure multi-hop low-latency communications while performing the monitoring task. We show that addressing the above problem under the joint requirement of maximizing event coverage is NP-hard. We propose a joint Task Management and Routing Algorithm called TaMaRA, a polynomial-time solution based on a two-phase approximation of the problem. By means of extensive simulations and real field experiments we show that our approach outperforms existing solutions in terms of monitoring accuracy and system responsiveness.

Massive failures in communication networks result from natural disasters, heavy blackouts, and military and cyber attacks. After these events, an adequate network recovery plan is key to ensuring emergency-critical service restoration and preventing intolerable downtime and performance degradation. We tackle the problem of minimizing the time and number of interventions to sufficiently restore the communication network to support emergency services after large-scale failures. We propose Proton (Progressive RecOvery and Tomography-based mONitoring), an efficient algorithm for progressive recovery of emergency services. Unlike previous work, assuming centralized routing and complete network observability, Proton addresses the more realistic scenario in which the network relies on the existing routing protocols, and knowledge of the network state is partial and uncertain. Proton relies on Network Tomography for monitoring and acquiring information about the state of nodes and links. Simulation results on real topologies show that our algorithm outperforms previous solutions in terms of cumulative routed flow, repair costs and recovery time in static and dynamic failure scenarios.

Massive failures in communication networks are a consequence of natural disasters, heavy blackouts, military and cyber attacks. We tackle the problem of minimizing the time and number of interventions to sufficiently restore the communication network so as to support emergency services after large-scale failures. We propose PRoTOn (Progressive RecOvery and Tomography-based mONitoring), an efficient algorithm for progressive recovery of emergency services. Unlike previous work, assuming centralized routing and complete network observability, PRoTOn addresses the more realistic scenario in which the network relies on the existing routing protocols, and knowledge of the network state is partial and uncertain. Simulation results carried out on real topologies show that our algorithm outperforms previous solutions in terms of cumulative routed flow, repair costs and recovery time in both static and dynamic failure scenarios.

To perform advanced surveillance, Unmanned Aerial Vehicles (UAVs) require the execution of edge-assisted computer vision (CV) tasks. In multi-hop UAV networks, the successful transmission of these tasks to the edge is severely challenged due to severe bandwidth constraints. For this reason, we propose a novel A2-UAV framework to optimize the number of correctly executed tasks at the edge. In stark contrast with existing art, we take an application-aware approach and formulate a novel Application-Aware Task Planning Problem (A2-TPP) that takes into account (i) the relationship between deep neural network (DNN) accuracy and image compression for the classes of interest based on the available dataset, (ii) the target positions, (iii) the current energy/position of the UAVs to optimize routing, data pre-processing and target assignment for each UAV. We demonstrate A2-TPP is NP-Hard and propose a polynomial-time algorithm to solve it efficiently. We extensively evaluate A2-UAV through real-world experiments with a testbed composed by four DJI Mavic Air 2 UAVs. We consider state-of-the-art image classification tasks with four different DNN models (i.e., DenseNet, ResNet152, ResNet50 and MobileNet-V2) and object detection tasks using YoloV4 trained on the ImageNet dataset. Results show that A2-UAV attains on average around 38% more accomplished tasks than the state of the art, with 400% more accomplished tasks when the number of targets increase significantly. To allow full reproducibility, we pledge to share datasets and code with the research community.

Swarms of Unmanned Aerial Vehicles (UAVs) are a key technology to support communication in many harsh environments where fixed infrastructures (e.g., 5G) are disrupted or not available. However, the fast mobility and highly dynamic network topology pose unique challenges and require the development of novel multi-hop routing protocols. Previous work in this direction extends geographical protocols or adapts approaches designed for Mobile Ad-hoc NETworks (MANETs), rarely taking full advantage of UAV capabilities. In this paper, we introduce a novel data offloading approach, namely Stop & Offload, that exploits the device controllable mobility to facilitate network routing. The swarm of UAVs performs data offloading synchronously and recurrently. At fixed intervals of time, the swarm interrupts the sensing mission (Stop) and moves, as little as possible, to build a connected formation to the base station and offload the data (Offload). We provide both centralized solutions — assuming a long-range control channel — and a distributed solution — working in the absence of a control channel. By means of extensive simulations we show that our proposals outperform state-of-the-art solutions, decreasing the time taken to build a connected formation of about 45% and increasing the time spent on sensing of 10%. Additionally, we compared our protocol with various routing strategies and observe remarkable improvements, including a 50% reduction in average packet delay.

The fast and unconstrained mobility of Flying Ad-hoc NETworks (FANETs) brings about the need to develop solutions for packet routing in a highly dynamic topology scenario. Previous works in this direction aim at extending protocols designed for Mobile Ad-hoc NETworks (MANETs) to the more challenging domain of FANETs. Unlike previous approaches, we aim at exploiting the device controllable mobility to facilitate network routing. We propose MAD (Movement Assisted Delivery): a packet routing protocol specifically tailored for networks of aerial vehicles. MAD enables adaptive selection of the most suitable relay nodes for packet delivery, resorting to movement-assisted delivery upon need, which is supported by a reinforcement learning approach. By means of extensive simulations we show that MAD outperforms previous solutions in all the considered performance metrics including average packet delay, delivery ratio, and communication overhead, at the expense of a moderate loss in average device availability.

Many safety critical scenarios, including post-disaster areas, or military fields, require prompt area monitoring and fast detection of events of interest. Flying Ad-hoc Networks (FANETs) provide a powerful tool to search the area, and locate anomalies. Nevertheless, wide-area deployment of FANETs poses a number of challenges. Existing long range communication technologies are inadequate to meet the data rate and delay requirements of a safety critical application. To face this challenge, we formulate the connected deployment problem, where we require the FANET to create connected formations to ensure multi-hop low-latency communications while performing the monitoring task. We show that addressing the above problem with the aim of maximizing event coverage is NP-hard. We propose a polynomial time solution, called Greedy Connected Deployment (GCD), based on a two phase approximation of the problem. By means of extensive simulations and real field experiments, we show that our approach outperforms existing solutions to related problems, both in terms of monitoring accuracy and system responsiveness.