Pietro Cassarà

Generalized Encoding CRDSA: Maximizing Throughput in Enhanced Random Access Schemes for Satellite

This work starts from the analysis of the literature about the Random Access protocols with contention resolution as Contention Resolution Diversity Slotted Aloha (CRDSA) and introduces a possible enhancement, named Generalized Contention Resolution Diversity Slotted Aloha (GE-CRDSA). The GE-CRDSA aims to improve the aggregated throughput when the system load is less than 50%, playing on the opportunity of transmitting an optimal combination of information and parity packets frame by frame.

Choosing an RSS device-free localization algorithm for Ambient Assisted Living

Device-free localization algorithms attract, among others, the attention of researchers working in the Ambient Assisted Living (AAL) scenarios, where the target user might not be able or willing to wear any devices. We concentrate on systems that exploit the Received Signal Strength indicator coming from wireless devices whose position is known, called anchors. In this paper we select and test the main device-free localization solutions and experimentally compare their performance using a smaller number of anchors than commonly found in the literature. We illustrate the procedure used to validate our comparing procedure and we give suggestions on usability in the application scenarios typical of AAL. To the best of our knowledge, this is the first direct comparison between different device-free algorithms using the same input data for all of them, and the first one that compares their performance with a varying number of anchors. Thanks to the characteristics of our comparison procedure, we can make suggestions about the more appropriate algorithms to use for different kinds of applications.

A Survey on Network Architectures and Applications for Nanosat and UAV Swarms

Nanosatellites and unmanned aerial vehicles are attracting more and more the interest of both industrial and research fields. They are low-cost and easy deployable items, therefore their use is expected to quickly grow in the next few years. This work proposes a survey on the network architectures and the applications for nanosatellite swarms and constellations, as well as for flying ad hoc networks, by characterizing distinctive features and issues yet to be resolved in order to take advantage from both technologies in a joint fashion.

How to Support the Machine Learning Take-Off: Challenges and Hints for Achieving Intelligent UAVs

Unmanned Aerial Vehicles (UAVs) are getting momentum. A growing number of industries and scientific institutions are focusing on these devices. UAVs can be used for a really wide spectrum of civilian and military applications. Usually these devices run on batteries, thus it is fundamental to efficiently exploit their hardware to reduce their energy footprint. A key aspect in facing the “energy issue” is the exploitation of properly designed solutions in order to target the energy- and hardware-constraints characterising these devices. However, there are not universal approaches easing the implementation of ad-hoc solutions for UAVs; it just depends on the class of problems to be faced. As matter of fact, targeting machine-learning solutions to UAVs could foster the development of a wide range of interesting application. This contribution is aimed at sketching the challenges deriving from the porting of machine-learning solutions, and the associated requirements, to highly distributed, constrained, inter-connected devices, highlighting the issues that could hinder their exploitation for UAVs.

Command and Control of UAV Swarms via Satellite

Unmanned Aerial Vehicles (UAVs) are attracting an increasing interest from both the industrial and the research fields, because of the large number of scenarios and applications that they can support. One of the big challenges of the next future is the use of UAV swarms, in order to exploit the advantages that coordinated actions of multiple drones can provide. In this work, we propose an analytical framework to evaluate the probability of a reliable command and control message delivery from a Ground Control Station to a UAV swarm via satellite, also exploiting intra-swarm gossiping.

Toward Decentralised Consensus and Offloading for Area Coverage in a Fleet of Drones

A precise and dynamic visual coverage of a given area is an essential task in many smart contexts, ranging from civil communities to military applications. Due to the last years advancement in hardware miniaturization and efficiency, area coverage is often performed with a combination of static and moving devices, such as unmanned aerial vehicles (drones). Drones are useful to cope with the highly unpredictability and dynamicity of environments, but require specific and efficient solutions toward and efficient area coverage. In this paper we proposes an initial work toward a drone-based approach for the task of area coverage. In particular, we focus our analysis on the following points: (i) decentralized consensus for movement planning, and (ii) the integration of cloud computing infrastructures and technologies for computation offloading, both for image analysis and movement planning.

Lessons learned on device free localization with single and multi channel mode

Indoor localization applications that involve Wireless Sensor Networks (WSNs) identify the target position by measuring the Received Signal Strength (RSS), the Time of Arrival (ToA), the Time Difference of Arrival (TDoA) or the Angle of Arrival (AoA). Of these, the most promising for low-cost applications are those based on measures of the RSS, which exploit the relationship between RSS and the distance, or more reliably the relation between the multi-path interference (shadowing) and the position of the target. These methods work with WSNs based on Wi-Fi, Bluetooth and ZigBee sensor technologies. In this paper we concentrate on device-free RSS-based indoor localization methods. These methods, which have generated much research interest in the last few years, are now starting to hit the market. Specifically, the purpose of this paper is to assess the performance improvements of a Variance-based Radio Tomographic Imaging technique, when scanning various radio channels with respect to using only one, the latter being the “minimum introduced interference” option. Moreover, in this paper we will discuss in which application scenario the multi-channel scanning technique is usable and appropriate. The experimental data used for target localization are captured by wireless sensors deployed in the localization area and the localization error metrics include the mean square error and percentiles of the error distribution. Specifically, we aim to study the localization error reduction obtained by using multiple ZigBee channels, with respect to using a single channel.