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.

In this demo, we showcase the collaborative effort that was put into bringing an offloading protocol for UAVs, Stop & Offload [1], on real-world hardware. The process includes multiple stages, from hardware configuration and testing in a simulated environment, to the final deployment in the field. The protocol enhances patrolling missions by improving coordination and data offloading among drones. We address the challenges of translating this protocol into a real hardware implementation using DJI Drones, and provide a detailed walkthrough of the process, from simulation to deployment. Our solution utilizes the DJI PSDK [2] libraries, ROS2 [5], and the Gazebo [4] simulator to ensure a secure and strong implementation.

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.

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.

Aerial drones are increasingly used to perform monitoring tasks in a large number of applications. Current solutions to trajectory planning rely on perfect knowledge of ongoing events requiring inspection. Nevertheless, in many scenarios the events’ time and position can only be estimated with someuncertainty. Unlike previous work, we consider critical scenarios where a squad of drones is required to autonomously inspect an area of interest underuncertaintyof time and location of target events. The main goal of the squad is to ensure maximum coverage of event monitoring with minimum average inspection delay. With no initial knowledge, the drones share their local observations of the environment and apply the Parzen-Rosenblatt approach to manage a dynamic probabilistic map of ongoing events. This map is integrated into a virtual force approach for a joint solution to distributed dynamic trajectory planning and collision avoidance. Through extensive simulations and real-field experiments, we compare our proposal againstAC-GAP, a state-of-art solution for UAVs, andSweep, a sweep-based algorithm for multiple robots. We show that our proposal discovers new events 30-40$\%$faster than the other algorithms, and outperforms them in terms of percentage of visited events and inspection delay, under a wide variety of scenarios.

In most of the developing countries, the economy is largely based on agriculture. The poor availability of skilled personnel and of appropriate supporting infrastructure, make crop fields vulnerable to the outbreak of plant diseases, possibly due to spreading viruses and fungi, or to adverse environmental conditions, such as drought. The mobile application PlantVillage Nuru provides an invaluable tool for early detection of plant diseases and sustainable food production. A mobile device endowed with Nuru is a powerful mobile sensor: it analyzes plant images and uses an AI engine to recognize health issues. In this article, we propose a crowdsensing framework, where Nuru is adopted at large scale in the farmer population. We tackle the device deployment problem, where device mobility is only partially controllable, mostly in an indirect manner, through incentives. We propose two problem formulations, and related algorithms, to minimize the number of required smartphones while providing sufficient geographical coverage. We study the proposed models in simulated as well as real scenarios, showing that they outperform the current solutions in terms of monitoring accuracy and completeness, with lower cost. Then, we describe the testbed implementation, confirming the applicability of the proposed crowdsensing framework in a real scenario in Kenya.

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.

Millions of RFID tags are pervasively used all around the globe to inexpensively identify a wide variety of everyday-use objects. One of the key issues of RFID is that tags cannot use energy-hungry cryptography, and thus can be easily cloned. For this reason, radio fingerprinting (RFP) is a compelling approach that leverages the unique imperfections in the tag's wireless circuitry to achieve large-scale RFID clone detection. Recent work, however, has unveiled that time-varying channel conditions can significantly decrease the accuracy of the RFP process. Prior art in RFID identification does not consider this critical aspect, and instead focuses on custom-tailored feature extraction techniques and data collection with static channel conditions. For this reason, we propose the first large-scale investigation into RFP of RFID tags with dynamic channel conditions. Specifically, we perform a massive data collection campaign on a testbed composed by 200 off-the-shelf identical RFID tags and a software-defined radio (SDR) tag reader. We collect data with different tag-reader distances in an over-the-air configuration. To emulate implanted RFID tags, we also collect data with two different kinds of porcine meat inserted between the tag and the reader. We use this rich dataset to train and test several convolutional neural network (CNN)-based classifiers in a variety of channel conditions. Our investigation reveals that training and testing on different channel conditions drastically degrades the classifier's accuracy. For this reason, we propose a novel training framework based on federated machine learning (FML) and data augmentation (DAG) to boost the accuracy. Extensive experimental results indicate that (i) our FML approach improves accuracy by up to 48%; (ii) our DAG approach improves the FML performance by up to 19% and the single-dataset performance by 31%. To the best of our knowledge, this is the first paper experimentally demonstrating the efficacy of FML and DAG on a large device population. To allow full replicability, we are sharing with the research community our fully-labeled 200-GB RFID waveform dataset, as well as the entirety of our code and trained models, concurrently with our submission.

Recent years have witnessed the design and development of several smart devices that are wireless and battery-less. These devices exploit RFID backscattering-based computation and transmissions. Although singular devices can operate efficiently, their coexistence needs to be controlled, as they have widely varying communication requirements, depending on their interaction with the environment. The design of efficient communication protocols able to dynamically adapt to current device operation is quite a new problem that the existing work cannot solve well. In this article, we propose a new communication protocol, called ReLEDF, that dynamically discovers devices in smart buildings and their active and nonactive status and when active their current communication behavior (through a learning-based mechanism) and schedules transmission slots (through an Earliest Deadline First-- (EDF) based mechanism) adapt to different data transmission requirements. Combining learning and scheduling introduces a tag starvation problem, so we also propose a new mode-change scheduling approach. Extensive simulations clearly show the benefits of using ReLEDF, which successfully delivers over 95% of new data samples in a typical smart home scenario with up to 150 heterogeneous smart devices, outperforming related solutions. Real experiments are also conducted to demonstrate the applicability of ReLEDF and to validate the simulations.

Fleets of cooperative drones are a powerful tool in monitoring critical scenarios requiring early anomaly discovery and intervention. Due to limited energy availability and application requirements, drones may visit target points in consecutive trips, with recharging and data offloading in between. To capture timeliness of intervention and prioritize early coverage, we propose the new notion of Weighted Progressive Coverage, which is based on the definition of time dependent weights. Weighted progressive coverage generalizes classic notions of coverage, as well as a new notion of accumulative coverage specifically designed to address trip scheduling. We show that weighted progressive coverage maximization is NP-hard and propose an efficient polynomial algorithm, called Greedy and Prune (GaP), with guaranteed approximation. By means of simulations we show that GaP performs close to the optimal solution and outperforms a previous approach in all the considered performance metrics, including coverage, average inspection delay, energy consumption, and computation time, in a wide range of application scenarios. Through prototype experiments we also confirm the theoretical and simulation analysis, and demonstrate the applicability of our algorithm in real scenarios.

Has now become more important than ever to guarantee an always present connectivity to users, especially in emergency scenarios. However, in case of a disaster, network infrastructures are often damaged, with consequent connectivity disruption, isolating users when are more in need for information and help. Drones may supply with a recovery network, thanks to their capabilities to provide network connectivity on the fly. However, users typically need special devices or applications to reach these networks, reducing their applicability and adoption.

We consider the problem of assigning tasks and related trajectories to a fleet of drones, in critical scenarios requiring early anomaly discovery and intervention. Drones visit target points in consecutive trips, with recharging and data offloading in between. We propose a novel metric, called weighted coverage, which generalizes classic notions of coverage, as well as a new notion of accumulative coverage which prioritizes early inspection of target points. We formulate an ILP problem for weighted coverage maximization and show its NP-hardness. We propose an efficient polynomial algorithm with guaranteed approximation. By means of simulations we show that our algorithm performs close to the optimal solution and outperforms a previous approach in terms of several performance metrics, including coverage, average inspection delay, energy consumption, and computation time, under a wide range of application scenarios.

With the ever-growing usage of batteries in the IoT era, the need for more eco-friendly technologies is clear. RF-powered computing enables the redesign of personal computing devices in a battery-less manner. While there has been substantial work on the underlying methods for RF-powered computing, practical applications of this technology has largely been limited to scenarios that involve simple tasks. This paper demonstrates how RFID technology, typically used to implement object identification and counting, can be exploited to realize a battery-free smart home. In particular, we consider the coexistence of several battery-free devices, with different transmission requirements-periodic, event-based, and real-time-and propose a new adaptive and quick-to-learn MAC protocol, called APT-MAC, which dynamically collects information from devices without requiring any a priori knowledge of the environment. Extensive simulations clearly show the benefits of using APT-MAC, which is able to successfully deliver 97.7% of new data samples in complex scenarios, including several high traffic demanding devices, such as joysticks and cameras.

In this paper we address configuration and management issues of smart homes. Current platforms requires the user to deal with several management inconvenience problems, such as increasing devices, operating between devices, and using new devices. From a user perspective, system configuration and management are major issues: ordinary consumers want to use systems performing minimal configuration. To address this issue, we propose a platform, composed of a web application and Software Defined Network (SDN). While the user interacts with an easy-to-use interface on a smart device, the app automatically generates and installs SDN rules. Our platform, besides facilitating configuration and management, results more efficient --- up to 4 times faster --- and reliable --- able to operate even in case of no connection with the cloud --- than current solutions.

This demo presents our experiences in developing a joystick for videogames that uses RFID backscattering for battery-free operation. Specifically, we develop a system to gather data from a wireless and battery-less joystick, named JoyTag, while it interacts with a videogame console. Our system enables consumers to use JoyTag at every moment without caring about charging.

We present a distributed, integrated medium access control, scheduling, routing and congestion/rate control protocol stack for cognitive radio ad hoc networks (CRAHNs) that dynamically exploits the available spectrum resources left unused by primary licensed users, maximizing the throughput of a set of multi-hop flows between peer nodes. Using a network utility maximization (NUM) formulation, we devise a distributed solution consisting of a set of sub-algorithms for the different layers of the protocol stack (MAC, flow scheduling and routing), which result from a natural decomposition of the problem into sub-problems. Specifically, we show that: 1) The NUM optimization problem can be solved via duality theory in a distributed way, and 2) the resulting algorithms can be regarded as the CRAHN protocols. These protocols combine back-pressure scheduling with a CSMA-based random access with exponential backoffs. Our theoretical findings are exploited to provide a practical implementation of our algorithms using a common control channel for node coordination and a wireless spectrum sensor network for spectrum sensing. We evaluate our solutions through ns-2 MIRACLE-based simulations. Our results show that the proposed protocol stack effectively enables multiple flows among cognitive radio nodes to coexist with primary communications. The CRAHN achieves high utilization of the spectrum left unused by the licensed users, while the impact on their communications is limited to an increase of their packet error rate that is below 1 percent.

We consider the problem of efficient and fast identification of mobile tags in RFID networks. So far only a few works have addressed identification of mobile tags, and in very specific scenarios (i.e., tags placed on a moving conveyor). In this paper we address more general scenarios, involving tags that are free to move and may stay in the reader range for very short time (e.g., a few seconds), making their identification a real challenge for the reader. We propose a protocol, called PrIME (for Priority-based tag Identification in Mobile Environments), that is based on a probabilistic model and performs continuous reading cycles during which tags may enter and leave the system at any time. Through extensive ns2-based simulations we show that PrIME is very efficient, as it is able to identify 98-99% of mobile tags and to reduce the identification delay drastically with respect to other protocols.

In this paper we investigate interference cancellation to faster identify tags in RFID networks. We explore how interference cancellation can be applied to ALOHA and tree-based identification schemes, its limitations, the extent of achievable improvements, and the overhead incurred to obtain effective gains. Analytical and simulation results show that for an ALOHA-based scheme interference cancellation allows us to identify nearly 23% of tags without directly interrogating them. This speeds up tag identification (over 20% faster) while producing little overhead. For a tree-based scheme nearly 50% of the tags are identified by exploiting interference cancellation, resulting in an improvement of the identification rate of over 20%. Finally, we propose an enhancement of the tree-based scheme with interference cancellation that achieves a further identification speed up of 50%.

One of the major challenges in the use of Radio Frequency-based Identification (RFID) on a large scale is the ability to read a large number of tags quickly. Central to solving this problem is resolving collisions that occur when multiple tags reply to the query of a reader. To this purpose, several MAC protocols for passive RFID systems have been proposed. These typically build on traditional MAC schemes, such as aloha and tree-based protocols. In this paper, we propose a new performance metric by which to judge these anticollision protocols: time system efficiency. This metric provides a direct measure of the time taken to read a group of tags. We then evaluate a set of well-known RFID MAC protocols in light of this metric. Based on the insights gained, we propose a new anticollision protocol, and show that it significantly outperforms previously proposed mechanisms.

Recently RFID technology has made its way into end-user applications, enabling automatic item identification without requiring line of sight. In particular passive tags provide a promising, low cost and energy-efficient solution for inventory applications. However, their large-scale adoption strictly depends on the efficiency of the identification process. A major challenge is how to arbitrate channel access so that all tags are able to answer the reader inquiries and identify themselves over time. This paper stems from the observation that a variety of anti-collision protocols for RFIDs have been proposed in the literature. However, a thorough simulation comparison among them and a clear identification of the mechanisms resulting in better end- to-end performance is lacking. The objective of our work has been to fill this gap. This paper presents the results of a detailed ns2-based comparative evaluation of representatives of all the classes of anti-collision protocols so far proposed. Simulation results show that end-to-end performance of the different classes of protocols in terms of metrics such as the time needed for tags identification differ significantly over what previously found by experiments which only focused on the number of reading cycles for tag identification. Our thorough performance evaluation has highlighted that different solutions are to be used in different application scenarios and that decreasing the collisions (rather than idle times) is the way to go to further improve anti-collision protocols performance.

The emergent commercial use of techniques for Radio Frequency-based IDentification of different items (RFID) requires the investigation and testing of collision resolution mechanisms for the efficient and correct communication between the system reader and the tags labeling the items that need to be identified. Several MAC protocols have been proposed to resolve collisions in RFID networks. A recent solution, named Tree Slotted Aloha (TSA), has been shown to outperform previous ones with respect to the time it takes for identifying all tags, and the total number of bits transmitted to complete the identification process. However, almost half of the time needed by TSA for identifying tags is spent in collisions. This depends on TSA operation and in particular on the way TSA estimates the number of colliding tags. We have observed that in the case of realistically large networks, TSA highly underestimates this number, with non-negligible impact on the protocol performance. In this paper, we propose a Dynamic Tree Slotted Aloha (Dy TSA) protocol that exploits the knowledge acquired during ongoing readings to refine the estimation of the number of colliding tags. In so doing, Dy TSA adapts the length of the following reading cycles to the actual number of tags still requiring identification. Through ns2-based simulations we show that the proposed method is effective for tag identification and results in significantly improved performance over TSA. Specifically, the length of the identification process is up to 20% lower than that of TSA. Furthermore, the amount of transmitted bits needed for identifying all tags decreases up to 30%.

Abstract — Data transfer in ad hoc environments shows poor network performance due to frequent link breakages and route failures. Selfish and malicious nodes may further deteriorate nodes communication, having a strong impact on transport layer protocols such as TCP, which are highly sensitive to packet losses. Although these misbehaviors have similar effects on the network functioning (i.e. packets are dropped), they are separately addressed by the research community. This paper provides a comprehensive method to improve the performance and reliability (performability) of nodes commu-nication in presence of faults, selfish and malicious behavior. Specifically, we propose and evaluate a novel forwarding policy that is based on multi-path routing and considers nodes reliability and routes length in forwarding decisions. We investigate through simulations how this mechanism improves the performability of TCP data transfers. In particular, we show that the simultaneous use of multiple paths yields higher throughput and continuous network connectivity when compared to single path forwarding. This has been verified in case of both fault conditions and intentional nodes misbehavior. I.