916 research outputs found
Technologies and solutions for location-based services in smart cities: past, present, and future
Location-based services (LBS) in smart cities have drastically altered the way cities operate, giving a new dimension to the life of citizens. LBS rely on location of a device, where proximity estimation remains at its core. The applications of LBS range from social networking and marketing to vehicle-toeverything communications. In many of these applications, there is an increasing need and trend to learn the physical distance between nearby devices. This paper elaborates upon the current needs of proximity estimation in LBS and compares them against the available Localization and Proximity (LP) finding technologies (LP technologies in short). These technologies are compared for their accuracies and performance based on various different parameters, including latency, energy consumption, security, complexity, and throughput. Hereafter, a classification of these technologies, based on various different smart city applications, is presented. Finally, we discuss some emerging LP technologies that enable proximity estimation in LBS and present some future research areas
Inferring Room Semantics Using Acoustic Monitoring
Having knowledge of the environmental context of the user i.e. the knowledge
of the users' indoor location and the semantics of their environment, can
facilitate the development of many of location-aware applications. In this
paper, we propose an acoustic monitoring technique that infers semantic
knowledge about an indoor space \emph{over time,} using audio recordings from
it. Our technique uses the impulse response of these spaces as well as the
ambient sounds produced in them in order to determine a semantic label for
them. As we process more recordings, we update our \emph{confidence} in the
assigned label. We evaluate our technique on a dataset of single-speaker human
speech recordings obtained in different types of rooms at three university
buildings. In our evaluation, the confidence\emph{ }for the true label
generally outstripped the confidence for all other labels and in some cases
converged to 100\% with less than 30 samples.Comment: 2017 IEEE International Workshop on Machine Learning for Signal
Processing, Sept.\ 25--28, 2017, Tokyo, Japa
Advanced real-time indoor tracking based on the Viterbi algorithm and semantic data
A real-time indoor tracking system based on the Viterbi algorithm is developed. This Viterbi principle is used in combination with semantic data to improve the accuracy, that is, the environment of the object that is being tracked and a motion model. The starting point is a fingerprinting technique for which an advanced network planner is used to automatically construct the radio map, avoiding a time consuming measurement campaign. The developed algorithm was verified with simulations and with experiments in a building-wide testbed for sensor experiments, where a median accuracy below 2 m was obtained. Compared to a reference algorithm without Viterbi or semantic data, the results indicated a significant improvement: the mean accuracy and standard deviation improved by, respectively, 26.1% and 65.3%. Thereafter a sensitivity analysis was conducted to estimate the influence of node density, grid size, memory usage, and semantic data on the performance
Position Estimation of Robotic Mobile Nodes in Wireless Testbed using GENI
We present a low complexity experimental RF-based indoor localization system
based on the collection and processing of WiFi RSSI signals and processing
using a RSS-based multi-lateration algorithm to determine a robotic mobile
node's location. We use a real indoor wireless testbed called w-iLab.t that is
deployed in Zwijnaarde, Ghent, Belgium. One of the unique attributes of this
testbed is that it provides tools and interfaces using Global Environment for
Network Innovations (GENI) project to easily create reproducible wireless
network experiments in a controlled environment. We provide a low complexity
algorithm to estimate the location of the mobile robots in the indoor
environment. In addition, we provide a comparison between some of our collected
measurements with their corresponding location estimation and the actual robot
location. The comparison shows an accuracy between 0.65 and 5 meters.Comment: (c) 2016 IEEE. Personal use of this material is permitted. Permission
from IEEE must be obtained for all other uses, in any current or future
media, including reprinting/republishing this material for advertising or
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this work in other work
Collaborative Indoor Positioning Systems: A Systematic Review
Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing
steadily due to their potential to improve on the performance of their non-collaborative counterparts.
In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in
(collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is
being used. Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison. This paper presents a systematic review that gives a general view of the current CIPSs. A total of
84 works, published between 2006 and 2020, have been identified. These articles were analyzed and
classified according to the described system’s architecture, infrastructure, technologies, techniques,
methods, and evaluation. The results indicate a growing interest in collaborative positioning, and
the trend tend to be towards the use of distributed architectures and infrastructure-less systems.
Moreover, the most used technologies to determine the collaborative positioning between users are
wireless communication technologies (Wi-Fi, Ultra-WideBand, and Bluetooth). The predominant collaborative positioning techniques are Received Signal Strength Indication, Fingerprinting, and Time
of Arrival/Flight, and the collaborative methods are particle filters, Belief Propagation, Extended
Kalman Filter, and Least Squares. Simulations are used as the main evaluation procedure. On the
basis of the analysis and results, several promising future research avenues and gaps in research
were identified
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