Urban delivery by Unmanned Aerial Vehicles (UAVs), or drones, is a promising logistics solution. While a dominant vision involves drones navigating autonomously through complex open airspace, this approach demands advanced perception and control capabilities. In contrast, this work explores an alternative vision: low-altitude drone flight along obstacle-free existing road networks, leveraging digital maps. To assess the feasibility of this approach, we investigate the indirect risks associated with drone failures, specifically, the risk that a falling drone causes a traffic-related accident. We show that, under dry conditions, the score risk meets the aviation-grade safety expectations (usually 10−6fatal injuries per flight hour) under any traffic level when vehicle speeds are low or moderate (below 50km/h), and under low traffic conditions (1 car every 100 seconds) when the vehicle speed is above 70 km/h. We also analyze the impact of contextual factor such as wet road conditions, and nighttime driving into the risk score: at low speed (30 km/h), the safety aviation requirements are always met. These findings represent a first step toward establishing the potential safety of road-aligned drone navigation in urban environments.

Unmanned Aerial Vehicles (UAVs) are increasingly permitted to operate within Visual Line of Sight (VLoS) under EU and US regulations. However, Beyond Visual Line of Sight (BVLoS) operations remain restricted, with waivers or certifications required. Extended Visual Line of Sight (EVLoS) offers a transitional solution, involving trained observers to assist pilots when visibility is obstructed. We propose enhancing EVLoS by integrating ground infrastructure, specifically city cameras and wireless communication networks already available on the ground, to replace human observers and enable BVLoS capabilities. Fixed and mobile cameras track drones to ensure regulatory compliance, while real-time data transmission via communication networks provides indirect oversight. The approach increases operational range, reliability, and redundancy through multi-hop connectivity. We introduce the Minimum Latency Problem (MLP), a UAV multi-trajectory optimization problem where UAVs are constantly tracked and monitored through ground antennas and city cameras, mimicking the human observers in EVLoS. Our goal is to minimize communication latency while ensuring that the number of antennas used for coverage is minimum. We prove MLP is$N P$-hard and propose an algorithm to solve it. Experiments on synthetic data demonstrate the effectiveness of our approach in matching coverage and latency requirements.

In this paper, we investigate the problem of deploying the minimum number of Unmanned Aerial Vehicles (UAVs) and determining their flying tours to collect data from all Internet of Things (IoT) sensors. We study this problem in a scenario with neighborhoods where a UAV can collect data from an IoT sensor if the distance between them is less than the wireless communication range of the IoT sensor. Since UAVs are powered by batteries with a limited amount of energy, we assume that the total energy consumed during the flying tour of each UAV is bounded by a given budget. We present the Minimum rooted drone Deployment Problem with Neighborhoods (MDPN), which is NP-hard, and propose two approximation algorithms for the single-depot case, where one of them is a bi-criteria approximation algorithm that returns a solution whose tour’s cost is violated by a factor of 1 + ε. Furthermore, we extend these two algorithms to the multi-depot scenario. Finally, we evaluate our algorithms in three different scenarios: the ideal one where the communication range is a circle and the data transfer rate is constant, and two more realistic scenarios where we introduce some degree of irregularity in the communication range and a non-constant rate in data transfer.

Unmanned aerial vehicles (UAVs) are crucial in various civilian applications, especially in Beyond Visual Line of Sight (BVLoS) operations. However, current regulations restrict BVLoS flights to specific corridors for safety reasons. This paper investigates the Drone Path Scheduling Problem (DPSP) whose goal is to assign time slots to each UAV by considering the corridors that UAVs need to traverse, and their starting time slot, in order to reach their destination such that the maximum slot for which all UAVs accomplished their mission is minimized. Time slots guarantee that each UAV accesses a corridor at a unique time, preventing multiple UAVs from using the same corridor simultaneously. We propose the Rec and the Heap-Based algorithms for unidirectional paths, demonstrating their optimality. For bidirectional paths, we offer sub-optimal solutions using Heap-Based and a 2-approximation algorithm called Bi-Alg. Furthermore, we present an Integer Linear Programming (ILP) formulation to optimally solve DPSP on unidirectional and bidirectional paths. Performance evaluations show the efficacy and scalability of our proposed algorithms compared to the ILP formulation.

We consider Internet of Things (IoT) sensors deployed inside an area to be monitored. Drones can be used to collect the data from the sensors, but they are constrained in energy and storage. Therefore, all drones need to select a subset of sensors whose data are the most relevant to be acquired, modeled by assigning a reward. We present an optimization problem called Multiple-drone Data-collection Maximization Problem (MDMP) whose objective is to plan a set of drones' missions aimed at maximizing the overall reward from the collected data, and such that each individual drone's mission energy cost and total collected data are within the energy and storage limits, respectively. We optimally solve MDMP by proposing an Integer Linear Programming based algorithm. Since MDMP is NP-hard, we devise suboptimal algorithms for single- and multiple-drone scenarios. Finally, we thoroughly evaluate our algorithms on the basis of random generated synthetic data.

Drones have become increasingly popular in a variety of fields, including agriculture, emergency response, and package delivery. However, most drone operations are currently limited to within Visual Line of Sight () due to safety concerns. Flying drones Beyond Visual Line of Sight () broadens to new challenges and opportunities, but also requires new technologies and regulatory frameworks to ensure that the drone is constantly under the control of a remote operator. In this work, we propose a novel graph-based multi-layer framework that closely resembles real-world scenarios and challenges in order to plan drone operations. Our framework includes layers of constraints such as ground risk, cellular network infrastructure, and obstacles, at different heights. From the multi-layer structure, a graph is constructed whose edges are weighted with a dependability score that takes into account the information of the layers, allowing efficient path planning of missions, using algorithms such as Dijkstra’s. Since the built graph can be really large, we also propose lighter graph-based corridors by considering only a limited portion of the original graph. Through extensive experimental evaluation on a real dataset, we demonstrate the effectiveness of our framework in solving the (), which can be efficiently solved by applying the Dijkstra’s algorithm.

Unmanned Aerial Vehicles (UAVs) or drones are increasingly used for urban applications like traffic monitoring and construction surveys. Autonomous navigation allows drones to visitwaypointsand accomplishactivitiesas part of theirmission. A common activity is to hover and observe a location using on-board cameras. Advances in Deep Neural Networks (DNNs) allow such videos to be analyzed for automated decision making. UAVs also host edge computing capability for on-board inferencing by such DNNs. To this end, for a fleet of drones, we propose a novelMission Scheduling Problem ()that co-schedules the flight routes to visit and record video at waypoints, and their subsequent on-board edge analytics. The proposed schedule maximizes the data capture and computing utilities from the activities while meeting the activity deadlines, and the energy and computing constraints. We first prove that is NP-hard and then optimally solve it by formulating a mixed integer linear programming (MILP) problem. Next, we design five time-efficient heuristic algorithms that provide sub-optimal but fast solutions that are empirically competitive with the optimal solution. Evaluation of these five schedulers using real drone traces demonstrate utility–runtime trade-offs under diverse workloads.

The use of drones for collecting information and detecting bugs in orchards covered by nets is a challenging problem. The nets help in reducing pest damage, but they also constrain the drone’s flight path, making it longer and more complex. To address this issue, we model the orchard as an aisle-graph, a regular data structure that represents consecutive aisles where trees are arranged in straight lines. The drone flies close to the trees and takes pictures at specific positions for monitoring the presence of bugs, but its energy is limited, so it can only visit a subset of positions. To tackle this challenge, we introduce the Single-drone Orienteering Aisle-graph Problem (SOAP), a variant of the orienteering problem, where likely infested locations are prioritized by assigning them a larger profit. Additionally, the drone’s movements have a cost in terms of energy, and the objective is to plan a drone’s route in the most profitable locations under a given drone’s battery. We show that SOAP can be optimally solved in polynomial time, but for larger orchards/instances, we propose faster approximation and heuristic algorithms. Finally, we evaluate the algorithms on synthetic and real datasets to demonstrate their effectiveness and efficiency.

In this work, we investigate the impact of the wind in a drone-based delivery system. For the first time, to the best of our knowledge, we adapt the trajectory of the drone to the wind. We consider a truck-drone tandem delivery system. The drone actively reacts to the wind adopting the “most tailwind” trajectory available between the truck’s path and the delivery. The truck moves on a predefined route and carries the drone close to the delivery point. We propose the Minimum-energy Drone-trajectory Problem (MDP) which aims, when the wind affects the delivery area, at planning minimum-energy trajectories for the drone to serve the customers starting from and returning to the truck. We then propose two algorithms that optimally solve MDP under two different routes of the truck. We also analytically study the feasibility of sending drones with limited battery to deliver packages. Finally, we first numerically compare our algorithms on randomly generated synthetic and real data, and then we evaluate our model simulating the drone’s flight in the BlueSky simulator.

In this work, we investigate the use of drones in a delivery scenario formed by two contiguous areas. In one area the drones can freely fly on straight lines between any two locations (Euclidean metric), while in the other one the drones must follow the open space above the roads (Manhattan metric). We model this delivery scenario as a Euclidean-Manhattan-Grid (EM-grid). Given a set of customers to be served in an EM-grid, the objective is to find the distribution point (DP) for the drone that minimizes the overall traveled distance, considering that the drone has to do multiple round trips to/from the DP. In our view, the DP is optimized with respect to the set of customers and its computation must be light because it needs to be recomputed every time the set of customers varies. Accordingly, we define the Single Distribution Point Problem (SDPP) and devise sub-optimal time-efficient algorithms for solving it. We numerically compare the cost of our sub-optimal solutions with that of an optimal solution computed with a brute-force approach. Finally, using the BlueSky open air simulator, we compare the cost of our best solution with the cost of a solution that serves the costumers from a fixed DP, like the location of a delivery company’s depot. The fixed DP can perform very poorly for some customer instances, while our solution is highly adaptive and reduces the time and the distance covered by the drone.

In a disaster response management (DRM) scenario, communication and coordination are limited, and absence of related infrastructure hinders situational awareness. Unmanned aerial vehicles (UAVs) or drones provide new capabilities for DRM to address these barriers. However, there is a dearth of works that address multiple heterogeneous drones collaboratively working together to form a flying ad-hoc network (FANET) with air-to-air and air-to-ground links that are impacted by: (i) environmental obstacles, (ii) wind, and (iii) limited battery capacities. In this paper, we present a novel environmentally-aware and energy-efficient multi-drone coordination and networking scheme that features a Reinforcement Learning (RL) based location prediction algorithm coupled with a packet forwarding algorithm for drone-to-ground network establishment. We specifically present two novel drone location-based solutions (i.e., heuristic greedy, and learning-based) in our packet forwarding approach to support application requirements. These requirements involve improving connectivity (i.e., optimize packet delivery ratio and end-to-end delay) despite environmental obstacles, and improving efficiency (i.e., by lower energy use and time consumption) despite energy constraints. We evaluate our scheme with state-of-the-art networking algorithms in a trace-based DRM FANET simulation testbed featuring rural and metropolitan areas. Results show that our strategy overcomes obstacles and can achieve 81-to-90% of network connectivity performance observed under no obstacle conditions. In the presence of obstacles, our scheme improves the network connectivity performance by 14-to-38% while also providing 23-to-54% of energy savings in rural areas; the same in metropolitan areas achieved an average of 25% gain when compared with baseline obstacle awareness approaches with 15-to-76% of energy savings.

In this paper, we consider the problem of using a drone to collect information within orchards in order to detect bugs. An orchard can be modeled as an aisle-graph, which is a regular data structure formed by consecutive aisles where trees are arranged in a straight line. For monitoring the presence of bugs, a drone flies close to the trees and takes videos and/or pictures that will be analyzed offline. As the drone’s energy is limited, only a subset of locations in the orchard can be visited with a fully charged battery. Those places that are most likely to be infested should be selected to promptly detect the parasite. We study the budgeted constrained position selection problem in the orchard from an algorithmic point of view. We present the Single-drone Orienteering Aisle-graph Problem (SOAP), a variant of the well-known orienteering problem where the finite resource is the drone’s battery. We first show that SOAP can be optimally solved for aisle-graphs in polynomial time. However, the optimal solution is not efficient for large orchards. Then, we propose two efficient heuristics that work even for large (orchard) instances. After a thorough analysis of the proposed solutions, we evaluate their performance by simulation experiments on both synthetic and real data sets.

Localizing ground devices (GDs) is an important requirement for a wide variety of applications, such as infrastructure monitoring, precision agriculture, search and rescue operations, to name a few. To this end, unmanned aerial vehicles (UAVs) or drones offer a promising technology due to their flexibility. However, the distance measurements performed using a drone, an integral part of a localization procedure, incur several errors that affect the localization accuracy. In this paper, we provide analytical expressions for the impact of different kinds of measurement errors on the ground distance between the UAV and GDs. We review three range-based and three range-free localization algorithms, identify their source of errors, and analytically derive the error bounds resulting from aggregating multiple inaccurate measurements. We then extend the range-free algorithms for improved accuracy. We validate our theoretical analysis and compare the observed localization error of the algorithms after collecting data from a testbed using ten GDs and one drone, equipped with ultra wide band (UWB) antennas and operating in an open field. Results show that our analysis closely matches with experimental localization errors. Moreover, compared to their original counterparts, the extended range-free algorithms significantly improve the accuracy.

In this article, we study the orienteering aisle-graph single-access problem (OASP), a variant of the orienteering problem for a robot moving in a so-called single-access aisle graph, i.e., a graph consisting of a set of rows that can be accessed from one side only. Aisle graphs model, among others, vineyards or warehouses. Each aisle-graph vertex is associated with a reward that a robot obtains when it visits the vertex itself. As the energy of the robot is limited, only a subset of vertices can be visited with a fully charged battery. The objective is to maximize the total reward collected by the robot with a battery charge. We first propose an optimal algorithm that solves the OASP in O (m 2n 2) time for aisle graphs with a single access consisting of m rows, each with n vertices. With the goal of designing faster solutions, we propose four greedy suboptimal algorithms that run in at most O(mn\(m + n)) time. For two of them, we guarantee an approximation ratio of 1 2(1-1 e), where e is the base of the natural logarithm, on the total reward by exploiting the well-known submodularity property. Experimentally, we show that these algorithms collect more than 80% of the optimal reward.

Unmanned aerial vehicles or drones provide new capabilities for disaster response management (DRM). In a DRM scenario, multiple heterogeneous drones collaboratively work together forming a flying ad-hoc network (FANET) instantiated by a ground control station. However, FANET air-to-air and air-to-ground links that serve critical application expectations can be impacted by: (i) environmental obstacles, and (ii) limited battery capacities. In this paper, we present a novel obstacle-aware and energy-efficient multi-drone coordination and networking scheme that features a Reinforcement Learning (RL) based location prediction algorithm coupled with a packet forwarding algorithm for drone-to-ground network establishment. We specifically present two novel drone location-based solutions (i.e., heuristic greedy, and learning-based) in our packet forwarding approach to support heterogeneous drone operation as per application requirements. These requirements involve improving connectivity (i.e., optimize packet delivery ratio and end-to-end delay) despite environmental obstacles, and improving efficiency (i.e., by lower energy use and time consumption) despite energy constraints. We evaluate our scheme by comparing it with state-of-the-art networking algorithms in a trace-based DRM FANET simulation testbed. Results show that our strategy overcomes obstacles and can achieve between 81-90% of network connectivity performance observed under no obstacle conditions. With obstacles, our scheme improves network connectivity performance by 14-38 % while also providing 23-54% of energy savings.

Unmanned Aerial Vehicles (UAVs) or drones are increasingly used for urban applications like traffic monitoring and construction surveys. Autonomous navigation allows drones to visit waypoints and accomplish activities as part of their mission. A common activity is to hover and observe a location using on-board cameras. Advances in Deep Neural Networks (DNNs) allow such videos to be analyzed for automated decision making. UAVs also host edge computing capability for on-board inferencing by such DNNs. To this end, for a fleet of drones, we propose a novel Mission Scheduling Problem (MSP) that co-schedules the flight routes to visit and record video at waypoints, and their subsequent on-board edge analytics. The proposed schedule maximizes the utility from the activities while meeting activity deadlines as well as energy and computing constraints. We first prove that MSP is NP-hard and then optimally solve it by formulating a mixed integer linear programming (MILP) problem. Next, we design two efficient heuristic algorithms, jsc and vrc, that provide fast sub-optimal solutions. Evaluation of these three schedulers using real drone traces demonstrate utility-runtime trade-offs under diverse workloads.

The use of drones can be a valuable solution for the problem of delivering goods for many reasons. In fact, they can be efficiently employed in time-critical situations when there is a traffic jam on the roads, to serve customers in hard-to-reach places, or simply to expand the business. However, due to limited battery capacities and the fact that drones can serve a single customer at a time, a drone-based delivery system (DBDS) aims to minimize the drones’ energy usage for completing a route from the depot to the customer and go back to the depot for new deliveries. In general, the shortest delivery route could not be the optimal choice since external factors like the wind (which varies with time) can affect energy consumption. Previous work has mainly considered simplified DBDSs assuming architectures with a single drone and with static costs on paths. Moreover, in these non-centralized architectures, the drones themselves compute the routes on the fly employing their onboard processing resources, making this choice costly. In this paper we develop a centralized system for computing energy-efficient time-varying routes for drones in a multi-depot multi-drone delivery system. Specifically, we propose a novel centralized parallel algorithm called Parallel Shortest Route Update (PSRU) that, over time, updates the drones’ delivery routes avoiding the whole recomputation from scratch. A comprehensive evaluation proves that PSRU is up to 4. 5x faster than the state-of-the-art algorithms.

In this paper, we study the feasibility of sending drones to deliver goods from a depot to a customer by solving what we call the Mission-Feasibility Problem (MFP). Due to payload constraints, the drone can serve only one customer at a time. To this end, we propose a novel framework based on time-dependent cost graphs to properly model the MFP and tackle the delivery dynamics. When the drone moves in the delivery area, the global wind may change thereby affecting the drone's energy consumption, which in turn can increase or decrease. This issue is addressed by designing three algorithms, namely: (i) compute the route of minimum energy once, at the beginning of the mission, (ii) dynamically reconsider the most convenient trip towards the destination, and (iii) dynamically select only the best local choice. We evaluate the performance of our algorithms on both synthetic and real-world data. The changes in the drone's energy consumption are reflected by changes in the cost of the edges of the graphs. The algorithms receive the new costs every time the drone flies over a new vertex, and they have no full knowledge in advance of the weights. We compare them in terms of the percentage of missions that are completed with success (the drone delivers the goods and comes back to the depot), with delivered (the drone delivers the goods but cannot come back to the depot), and with failure (the drone neither delivers the goods nor comes back to the depot).

In this paper, we study the Orienteering Aislegraphs Single-column Problem (OASP), which is a variant of the route planning problem for an entity/robot moving along a specific aisle-graph consisting of a set of rows connected via just one column at one endpoint of the rows. Such constrained aislegraph may model, for instance, a vineyard or warehouse, where each vertex is assigned with a reward that a robot gains when visiting it for accomplishing a task. As the robot is energy limited, it must visit a subset of vertices before going back to the depot for recharging, while maximizing the total reward gained. It is known that the OASP for constrained aisle-graphs composed by m rows of length n is polynomially solvable in O(m2n2) time, which can be prohibitive for graphs of large dimensions. With the goal of designing more time efficient solutions, we propose four algorithms that iteratively build the solution in a greedy manner. These solutions take at most O(mn (m + n)) time, thus improving the optimal solution by a factor of n. Experimentally, we show that these algorithms collect more than 80% of the optimum reward. For two of them, we also guarantee an approximation ratio of 1/2(1 - 1/e)on the reward function by exploiting the submodularity property, where e is the base of the natural logarithm.

In this paper, we consider the Constant-cost Orienteering Problem (COP) where a robot, constrained by a limited travel budget, aims at selecting a path with the largest reward in an aisle-graph. The aisle-graph consists of a set of loosely connected rows where the robot can change lane only at either end, but not in the middle. Even when considering this special type of graphs, the orienteering problem is known to be intractable. We optimally solve in polynomial time two special cases, COP-FR where the robot can only traverse full rows, and COP-SC where the robot can access the rows only from one side. To solve the general COP, we then apply our special case algorithms as well as a new heuristic that suitably combines them. Despite its light computational complexity and being confined into a very limited class of paths, the optimal solutions for COP-FR turn out to be competitive in terms of achieved rewards even for COP. This is shown by means of extended simulations performed on both real and synthetic scenarios. Furthermore, our new heuristic for the general case outperforms state-of-art algorithms, especially for input with highly unbalanced rewards.

We study the possibility of using drones to implement an automated picking system in a warehouse. We imagine a warehouse divided into two contiguous areas: in one area, the drone moves according to the Euclidean distance, while in the other area, the drone moves according to the Manhattan distance. For each customer-order (CO), the automated picking system is in charge of gathering the items requested in the CO to a predefined location where the cart of the drone is positioned. For each item of the order, the drone flies to the location where the item is stored, grasps it, and brings it back to its cart. Our goal is to find the position of the drone's cart that minimizes the sum of the distances traversed by the drone to pick-up all the items of the CO. We propose algorithms to find such a location when the items to be collected are in Euclidean and Manhattan areas. We can prove a √2-approximation factor for our solutions. Moreover, we compare the efficiency of the automated picking system employing a drone with that of a traditional picking system employing a worker that pushes a cart, and we find under which conditions the drone can be more efficient.

We introduce a drone delivery system for the "last-mile" logistics of small parcels. The system serves mixed delivery areas modeled as EMs. The shortest path between two destinations of an EM concatenates the Euclidean-and Manhattan-distance metrics. The drone's mission consists in one delivery for each destination of the grid, and, due to the strict payload constraint, the drone returns to the warehouse after each delivery. Our goal is to set the drone's warehouse in the delivery area so as the sum of the distances between the locations to be served and the warehouse is minimized. We exactly solve the problem proposing an algorithm that takes logarithmic time in the length of the Euclidean side of the EM. We also devise two approximate solutions that select the warehouse among a constant number of vertices of the EM. Such solutions are almost as good as the exact solution and we prove a √2-approximation bound in the worst case. Finally, we propose an exact solution for the two warehouse problem in a Manhattan grid, and an approximate solution for EMs.

In this work, we evaluate the accuracy of the Drone Range-Free (DRF) localization algorithm presented in the literature on a simple test-bed built using the Decawave Ultra Wide Band (UWB) Sensors Kit MDEK1001 and a customary drone. DRF localizes an IoT device at the intersection of the perpendicular bisectors of two chords of its receiving disk. Despite its simplicity and elegance, DRF poses great challenges in a real implementation on the field because the device's receiving disk is in reality far from a perfect circle. We solve this problem by relaxing the range-free assumption and by discovering almost perfect inner-circles in the IoT-device receiving disk using the ability of the MDEK1001 sensors of taking distance measurements. With a set of simplified experiments that aim to localize a single antenna, we show that, using the inner-circle method, we significantly improve on the localization accuracy without loosing the DRF simplicity and elegance. The accuracy of the new inner-circle method, along with that of a simplified range-based multilateration method, is also proved by localizing three antennas posed at the vertices of a pre-determined triangle.

Unmanned Aerial Vehicles (UAVs) have enormous potentials for several important applications, such as search and rescue and structural health monitoring. An important requirement for these applications is the ability to accurately localize objects, such as sensors or ``smart-things'', equipped with wireless communication capability. However, most previous works in this area neglect the unavoidable errors that are involved in the localization process, thus resulting in poor performance in practice. In this paper, for the first time, we express the measurement error on the ground as a function of the rolling, altitude, and instrumental precision provided by the hardware on the drone. We takeaway two lessons from this analysis: to limit the ground error (i) all the waypoints used to measure the same node must be at a sufficiently large ground distance from the node itself, and (ii) they must not be collinear among themselves nor with the node. We validate the error expressions derived analytically through real experiments using the 3DR Solo Drone.

The localization of devices is a key ingredient of Internet of Things (IoT). However, localization requires deploying many anchor nodes that are nodes whose location is known a-priori. Anchor nodes are expensive and their utilization may be unfeasible in some cases, such as in search-and-rescue operations. In this work, we propose a range-free localization algorithm that replaces the anchor nodes with an off-the-shelf drone. During the mission, the drone scans the deployment area and regularly broadcasts a beacon consisting of the current drone's position projected on the ground. The sensors simply listen to the drone until they hear three special beacons and, after that, they locally compute their position. Our algorithm is able to ensure any user-defined localization precision just varying the distance betweenthe beacons. Differently from the other range-based localization algorithms proposed for drones, our algorithm guarantees the localization precision without requiring any specific hardware technology, except the ability to communicate. Since our algorithm does not take any measure, the height of the drone only affect the receiving area of the sensor. Due to the simplicity of the interaction between the drone and the sensors during the algorithm, this solution can localize very high dense networks, even using a slightly shorter drone's trajectory than the previous algorithms.

Many dependable systems rely on the integrity of the position of their components. In such systems, two key problems are secure localization and secure location verification of the components. Researchers proposed several solutions, which generally require expensive infrastructures of several fixed stations (anchors) with trusted positions. In this paper, we explore the approach of replacing all the fixed anchors with a single drone that flies through a sequence of waypoints. At each waypoint, the drone acts as an anchor and securely determines the positions. This approach completely eliminates the need for many expensive anchors. The main challenge becomes how to find a convenient path for the drone to do this for all the devices. The problem presents novel aspects, which make existing path planning algorithms unsuitable. We propose LocalizerBee, VerifierBee, and PreciseVerifierBee: three path planning algorithms that allow a drone to respectively measure, verify, and verify with a guaranteed precision a set of positions in a secure manner. They are able to securely localize all the positions in a generic deployment area, even in the presence of drone control errors. Moreover, they produce short path lengths and they run in a reasonable processing time.

Exploiting high density features of wireless sensor networks represents a challenging issue. In this context, anonymous, asynchronous and randomly distributed sensors are considered along with few devices, called actors, which are more powerful than sensors in terms of energy and transmission capabilities. The paper proposes a new distributed training protocol for coarse-grain localization purposes in high density environments. The aim is to auto-organize the sensors with respect to a virtual infrastructure centered at actors and constituted of concentric rings divided into sectors. Analytical study as well as experiments on the proposed protocol are provided. The obtained results show under which theoretical and practical settings the training process can be performed in a fast and high quality way with respect to the granularity of the required localization and the energy consumption.

Scalable energy-efficient training protocols are proposed for wireless networks consisting of sensors and a single actor, where the sensors are initially anonymous and unaware of their location. The protocols are based on an intuitive coordinate system imposed onto the deployment area, which partitions the sensors into clusters. The protocols are asynchronous, in the sense that the sensors wake up for the first time at random, then alternate between sleep and awake periods both of fixed length, and no explicit synchronization is performed between them and the actor. Theoretical properties are stated under which the training of all the sensors is possible. Moreover, both worst-case and average case analyses of the performance, as well as an experimental evaluation, are presented showing that the protocols are lightweight and flexible.