In recent years, we have assisted with an impressive advance in augmented reality systems and computer vision algorithms, based on image processing and artificial intelligence. Thanks to these technologies, mainstream smartphones are able to estimate their own motion in 3D space with high accuracy. In this paper, we exploit such technologies to support the autonomous mobility of people with visual disabilities, identifying pre-defined virtual paths and providing context information, reducing the distance between the digital and real worlds. In particular, we present ARIANNA+, an extension of ARIANNA, a system explicitly designed for visually impaired people for indoor and outdoor localization and navigation. While ARIANNA is based on the assumption that landmarks, such as QR codes, and physical paths (composed of colored tapes, painted lines, or tactile pavings) are deployed in the environment and recognized by the camera of a common smartphone, ARIANNA+ eliminates the need for any physical support thanks to the ARKit library, which we exploit to build a completely virtual path. Moreover, ARIANNA+ adds the possibility for the users to have enhanced interactions with the surrounding environment, through convolutional neural networks (CNNs) trained to recognize objects or buildings and enabling the possibility of accessing contents associated with them. By using a common smartphone as a mediation instrument with the environment, ARIANNA+ leverages augmented reality and machine learning for enhancing physical accessibility. The proposed system allows visually impaired people to easily navigate in indoor and outdoor scenarios simply by loading a previously recorded virtual path and providing automatic guidance along the route, through haptic, speech, and sound feedback.

In this paper, we present a system that allows visually impaired people to autonomously navigate in an unknown indoor and outdoor environment. The system, explicitly designed for low vision people, can be generalized to other users in an easy way. We assume that special landmarks are posed for helping the users in the localization of pre-defined paths. Our novel approach exploits the use of both the inertial sensors and the camera integrated into the smartphone as sensors. Such a navigation system can also provide direction estimates to the tracking system to the users. The success of out approach is proved both through experimental tests performed in controlled indoor environments and in real outdoor installations. A comparison with deep learning methods has been presented.

Recent advances on WLAN technology have been focused mostly on boosting network capacity by means of a more efficient and flexible physical layer. A new concept is required at MAC level to exploit fully the new capabilities of the PHY layer. In this paper, we propose a contention mechanism based on Repeated Contentions (ReCo) in frequency domain. It provides a simple-to-configure, robust and short-term fair algorithm for the random contention component of the MAC protocol. The throughput efficiency of ReCo is not sensitive to the number of contending stations, so that ReCo does not require adaptive tuning of the access parameters for performance optimization. Efficiency and robustness is gained through the power of repeated contention rounds. We also apply the ReCo concept to the emerging IEEE 802.11ax standard, showing how it can boost performance of random access with respect to the current version of IEEE 802.11ax OFDMA Back-Off (OBO). Our proposal is supported by an experimental test-bed that realizes ReCo by means of simultaneous transmission and reception of short tones, which is feasible on top of programmable OFDM PHY layers.

In this paper we propose ReCo, a robust contention scheme for emerging wireless technologies, whose efficiency is not sensitive to the number of contending stations and to the settings of the contention parameters (such as the contention windows and retry limits). The idea is iterating a basic contention mechanism, devised to select a sub-set of stations among the contending ones, in consecutive elimination rounds, before performing a transmission attempt. Elimination rounds can be performed in the time or frequency domain, with different overheads, according to the physical capabilities of the nodes. Closed analytical formulas are given to dimension the number of contention rounds in order to achieve an arbitrary low collision probability. Simulation results and a real implementation for the time-domain solution demonstrate the effectiveness and robustness of this approach in comparison to IEEE 802.11 DCF.

We propose a solution for mitigating the performance impairments of CSMA/CA protocols in multi-hop topologies based on the dynamic adaptation of the contention process experienced by nodes in a wireless network. A distributed protocol is used to negotiate the channel airtime for a node as a function of the traffic requirements of its neighbourhood, taking into account bandwidth reserved for the control operations. A mechanism is provided for a node to tune its contention window depending on its allocated airtime. Different from previous schemes, a node's contention window is fixed in size unless the traffic requirements of its neighbourhood change. The scheme is implemented on legacy commercial 802.11 devices. Extensive experimental results, performed on the CREW European testbed, demonstrate the effectiveness of the approach.