Delay- and Disruption Tolerant Networks (DTNs) are based on an overlay protocol and on the store-carry-forward paradigm. In practice, each DTN node can store information for a long time before forwarding it. DTNs are particularly suited to cope with the challenges imposed by the space environment. This paper is focused on routing in space DTNs, and in particular on contact graph routing (CGR) and its most representative enhancements, available in the literature, which are briefly surveyed in this work. Moreover, the applicability and the obtained performance of the DTN protocol stack and of the CGR have been evaluated by presenting results from real experimental experiences such as the Deep Impact Network experiment (employing the EPOXI space cruise), the JAXA jointly performed space link demonstrations with NASA (where the JAXA's GEO relay satellite called Data Relay Test Satellite has been used), the Space Data Routers European Project, and the pilot operation of a DTN implementation on the International Space Station (ISS).

Rehabilitation and elderly monitoring for active aging can benefit from Internet of Things (IoT) capabilities in particular for in-home treatments. In this paper, we consider two functions useful for such treatments: 1) activity recognition (AR) and 2) movement recognition (MR). The former is aimed at detecting if a patient is idle, still, walking, running, going up/down the stairs, or cycling; the latter individuates specific movements often required for physical rehabilitation, such as arm circles, arm presses, arm twist, curls, seaweed, and shoulder rolls. Smartphones are the reference platforms being equipped with an accelerometer sensor and elements of the IoT. The work surveys and compares accelerometer signals classification methods to enable IoT for the aforementioned functions. The considered methods are support vector machines (SVMs), decision trees, and dynamic time warping. A comparison of the methods has been proposed to highlight their performance: all the techniques have good recognition accuracies and, among them, the SVM-based approaches show an accuracy above 90% in the case of AR and above 99% in the case of MR.

The great availability of commercial drones has raised growing interest among people, since remotely piloted vehicles can be employed in numerous applications. The pervasive use of these devices has created many privacy and safety concerns, that need to be addressed by means of proper surveillance systems able to cope with such threats. In this paper we propose a WiFi statistical fingerprint based drone detection approach, which is capable of identifying nearby drone threats, even in the presence of malicious attacks. We present a performance analysis carried out through experimental tests, where our solution is able to achieve very good results in terms of correct recognitions in many real life scenarios, with a peak true positive rate of 96%.

Drone deployment has become crucial in a variety of applications, including solutions to traffic issues in metropolitan areas and highways. On the other hand, data collected via drones suffers from several problems, including a wide range of object scales, angle variations, truncation, and occlusion. To process and manipulate visual data from the drones, a variety of image processing algorithms have been employed, each with a distinct aim. Additionally, recent breakthroughs in the field of Artificial Intelligence, particularly deep learning, have attracted broad interest and are being applied to many domains in the framework of smart cities, including road traffic monitoring. The purpose of this study is to conduct a systematic review of drone-based traffic monitoring systems from a deep learning perspective. This work focuses on vehicle detection, tracking, and counting, since they are fundamental building blocks towards founding solutions for traffic congestion, flow rate and vehicle speed estimation. Additionally, drone-based datasets are examined, which face issues and problems caused by the diversity of features inherent of drone devices. The review analysis presented in this work summarizes the literature solutions provided and deployed so far and discusses future research trends in establishing a comprehensive traffic monitoring system in support of the development of smart cities.

Indoor localization of targets by using electromagnetic waves has attracted a lot of attention in the last few years. Thanks to the wide availability of electromagnetic sources deployed for various applications (e.g., WiFi), nowadays it is possible to perform this task by using low-cost mobile devices, such as smartphones. To this end, in order to achieve high positioning accuracy and reduce the computational resources used in the position estimation, fingerprinting approaches are usually employed. However, in this case, a time-consuming training phase, where a great number of measurements must be performed, is needed. In this letter, a novel approach, where the training data are obtained by means of finite-difference time-domain (FDTD) simulations of the electromagnetic propagation in the considered scenario, is presented. The performances of the method are assessed by means of experimental results in a real scenario.