Francesco Betti Sorbelli

Asynchronous Corona Training Protocols in Wireless Sensor and Actor Networks

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.

Asynchronous training in wireless sensor networks

A scalable energy-efficient training protocol is proposed for massively-deployed sensor networks, where sensors are initially anonymous and unaware of their location. The protocol is based on an intuitive coordinate system imposed onto the deployment area which partitions the anonymous sensors into clusters. The protocol is 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 sink. Theoretical properties are stated under which the training of all the sensors is possible. Moreover, a worst-case analysis as well as an experimental evaluation of the performance is presented, showing that the protocol is lightweight and flexible.

Cooperative Training in Wireless Sensor and Actor Networks

Exploiting features of high density wireless sensor networks represents a challenging issue. In this work, the training of a sensor network which consists of anonymous and asynchronous sensors, randomly and massively distributed in a circular area around a more powerful device, called actor, is considered. The aim is to partition the network area in concentric coronas and sectors, centered at the actor, and to bring each sensor autonomously to learn to which corona and sector belongs. The new protocol, called Cooperative, is the fastest training algorithm for asynchronous sensors, and it matches the running time of the fastest known training algorithm for synchronous sensors. Moreover, to be trained, each sensor stays awake only a constant number of time slots, independent of the network size, consuming very limited energy. The performances of the new protocol, measured as the number of trained sensors, the accuracy of the achieved localization, and the consumed energy, are also experimentally tested under different network density scenarios.

Drone Path Planning for Secure Positioning and Secure Position Verification

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.

Localization with Guaranteed Bound on the Position Error using a Drone

In this paper, we study the sensor localization problem using a drone. Our goal is to localize each sensor in the deployment area ensuring a predefined localization precision, i.e., a bound on the position error, whatever is the drones altitude. We show how to guarantee a-priori the precision localization by satisfying few conditions. Such conditions are totally novel aspects that have not been considered in previous localization algorithms. In the new localization technique, we first determine the minimum ground distance that guarantees the predefined bound on the position error. According to that distance, a static path for the drone is designed. Then, the localization mission proceeds in two steps: Initially, the drone computes a rough estimation of the sensor position by using the first three distance measurements it can take greater than the minimum ground distance. Next the position is refined by employing three distance measurements that, in addition to the minimum ground distance, satisfy a specific geometric layout. In this way, the localization precision is guaranteed with just three measurements.

Precise Localization in Sparse Sensor Networks using a Drone with Directional Antennas

In this paper, we study a sensor localization technique that replaces fixed anchors with a drone equipped with directional antennas. Our goal is to localize each sensor in the deployment area achieving the localization precision explicitly required by the final user of the sensor networks. Due to the advent of directional antennas, we precisely localize applying a single trilateration for each sensor. To reach the desired precision, we force that each ground distance measured by the drone is greater than a minimum threshold and we set a suitable beamwidth for the directional antennas. Our proposed solution plans a static path for the drone and determines on it the measurements points such that all the sensors are measured at least three times from different antenna orientations. The performance of our solution, evaluated in terms of the static path length and of the achieved precision, is validated by simulation study. With respect to recently published results on sensor localization using flying anchors, our solution finds a shorter path (at least 30%), localizes all the nodes, and requires a single trilateration for each sensor.

Asynchronous training in SANET

Scalable energy-efficient training protocols are proposed for networks consisting of Sensors and Actors (SANET), 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 subsets of the sensor population 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, an experimental evaluation of the performance is presented, showing that the protocols are lightweight and flexible.

Range based algorithms for precise localization of terrestrial objects using a drone

Abstract In this paper we propose two algorithms, called Dir and Omni , for precisely localizing terrestrial objects, or more simply sensors, using a drone. Dir is based on the observation that, by using directional antennas, it is possible to precisely localize terrestrial sensors just applying a single trilateration. We extend this approach to the case of a regular omnidirectional antenna and formulate the Omni algorithm. Both Dir and Omni plan a static path for the drone over the deployment area, which includes a set of waypoints where distance measurements between the drone and the sensors are taken. Differently from previously proposed best-effort approaches, our algorithms prove that a guaranteed precision can be achieved by considering a set of waypoints, for each sensor, that are at a distance above a certain threshold and that surround the sensor with a certain layout. We perform extensive simulations to validate the performance of our algorithms. Results show that both approaches provide a comparable localization precision, but Dir exhibits a shorter path compared to Omni , being able to exploit the directional antennas.

Connectivity of a Dense Mesh of Randomly Oriented Directional Antennas Under a Realistic Fading Model

We study mesh networks formed by nodes equipped with directional antennas in a high node-density setting. To do so we create a random geometric graph with n nodes placed uniformly at random. The antenna at each node chooses a direction of orientation at random and edges are placed between pairs of nodes based on their distance from each other and their directions of orientation according to the gain function of the antennas. To model the directionality of the antennas we consider a realistic gain function where the signal fades away from the direction of orientation. We also consider an idealised function that concentrates the gain uniformly in a sector of angle