This paper presents the state-of-the-art and reviews the state-of-research of acoustic sensors used for a variety of navigation and guidance applications on air and surface vehicles. In particular, this paper focuses on echolocation, which is widely utilized in nature by certain mammals (e.g., cetaceans and bats). Although acoustic sensors have been extensively adopted in various engineering applications, their use in navigation and guidance systems is yet to be fully exploited. This technology has clear potential for applications in air and surface navigation/guidance for intelligent transport systems (ITS), especially considering air and surface operations indoors and in other environments where satellite positioning is not available. Propagation of sound in the atmosphere is discussed in detail, with all potential attenuation sources taken into account. The errors introduced in echolocation measurements due to Doppler, multipath and atmospheric effects are discussed, and an uncertainty analysis method is presented for ranging error budget prediction in acoustic navigation applications. Considering the design challenges associated with monostatic and multi-static sensor implementations and looking at the performance predictions for different possible configurations, acoustic sensors show clear promises in navigation, proximity sensing, as well as obstacle detection and tracking. The integration of acoustic sensors in multi-sensor navigation systems is also considered towards the end of the paper and a low Size, Weight and Power, and Cost (SWaP-C) sensor integration architecture is presented for possible introduction in air and surface navigation systems.

This paper presents a user evaluation of seven sonification methods in two-dimensional (2D) manual micro-guidance tasks, which can be used as building blocks for spatialized audio in Mixed and Virtual Reality to model next-generation guidance aids for the Blind and Visually Impaired (BVI). The methods were tested in comparable interactive sonifications of 2D positions in a series of hand-navigation assessments with BVI and blindfolded sighted users, to validate the different approaches in environments without any visual feedback. Results highlighted that alternation and spatiality can be useful resources in sonified guidance, and that users accustomed to faster-than-regular audio speed replay tend to have more precise performances, while musical literacy only had a performance effect on methods highly dependent on aural skills. Ultimately, this work corroborates the notion that sonification may help BVI users perform better in day-to-day manual micro-guidance tasks such as retrieving items from a pantry, handling kitchen appliances, and properly discarding trash.

The Internet of Things represents a technology revolution transforming the current environment into a ubiquitous world, whereby everything that benefits from being connected will be connected. Despite the benefits, the privacy of these things becomes a great concern and it is imperative to apply privacy preservation techniques to IoT data collection. One such technique is called data obfuscation in which data is deliberately modified to blur the sensitive information. The current obfuscation techniques, however, focus on the privacy of published datasets. The high connectivity and distributed nature of IoT, opens up the possibility of privacy compromise before obfuscation can take effect, and therefore privacy enforcement should be deployed at earlier stages. Classical privacy treatments are too restrictive for IoT, where coarser/finer data details should be revealed. Motivated by these challenges, we propose a framework for privacy preservation in IoT environments that is capable of multi-granular obfuscation by enforcing context-driven disclosure policies. Then, we customize our framework for a smart vehicle system and make use of data stream watermarking to protect privacy. To address possible concerns about additional performance overhead, we show the burden to be very lightweight, thus validating the suitability of ubiquitous use of our framework for IoT settings.

The aim of this paper is to present a service for blind and people with low vision to assist them to cross the street independently. The presented approach provides the user with significant information such as detection of pedestrian crossing signal from any point of view, when the pedestrian crossing signal light is green, the detection of dynamic and fixed obstacles, predictions of the movement of fellow pedestrians and information on objects which may intersect his path. Our approach is based on capturing multiple frames using a depth camera which is attached to a user's headgear. Currently a testbed system is built on a helmet and is connected to a laptop in the user's backpack. In this paper, we discussed efficiency of using Speeded-Up Robust Features (SURF) algorithm for object recognition for purposes of blind people assistance. The system predicts the movement of objects of interest to provide the user with information on the safest path to navigate and information on the surrounding area. Evaluation of this approach on real sequence video frames provides 90% of human detection and more than 80% for recognition of other related objects.