3,721 research outputs found
Selective AP-sequence Based Indoor Localization without Site Survey
In this paper, we propose an indoor localization system employing ordered
sequence of access points (APs) based on received signal strength (RSS). Unlike
existing indoor localization systems, our approach does not require any
time-consuming and laborious site survey phase to characterize the radio
signals in the environment. To be precise, we construct the fingerprint map by
cutting the layouts of the interested area into regions with only the knowledge
of positions of APs. This can be done offline within a second and has a
potential for practical use. The localization is then achieved by matching the
ordered AP-sequence to the ones in the fingerprint map. Different from
traditional fingerprinting that employing all APs information, we use only
selected APs to perform localization, due to the fact that, without site
survey, the possibility in obtaining the correct AP sequence is lower if it
involves more APs. Experimental results show that, the proposed system achieves
localization accuracy < 5m with an accumulative density function (CDF) of 50%
to 60% depending on the density of APs. Furthermore, we observe that, using all
APs for localization might not achieve the best localization accuracy, e.g. in
our case, 4 APs out of total 7 APs achieves the best performance. In practice,
the number of APs used to perform localization should be a design parameter
based on the placement of APs.Comment: VTC2016-Spring, 15-18 May 2016, Nanjing, Chin
MapSense: Mitigating Inconsistent WiFi Signals using Signal Patterns and Pathway Map for Indoor Positioning
The indoor positioning technology plays a significant role in the scenarios of the Internet of Things (IoT) which require indoor location context. In this paper, the WiFi signals under modern enterprise WiFi infrastructure, signal patterns between coexisting access points (APs) and signals’ correlation with indoor pathway map are investigated to address the problem of inconsistent WiFi signal observations. The sibling signal patterns (SSP) are defined for the first time and processed to generate Beacon APs which have higher confidence in positioning. The spatial signal patterns are used to bring the estimated location into a limited area through signal coverage constraint (SCC). A positioning scheme using SSP and SCC is proposed and shows improved positioning accuracy. The proposed scheme is fully designed, implemented and evaluated in a real-world environment, revealing its effectiveness and efficiency
Hapi: A Robust Pseudo-3D Calibration-Free WiFi-based Indoor Localization System
In this paper, we present Hapi, a novel system that uses off-the-shelf
standard WiFi to provide pseudo-3D indoor localization. It estimates the user's
floor and her 2D location on that floor. Hapi is calibration-free, only
requiring the building's floorplans and its WiFi APs' installation location for
deployment. Our analysis shows that while a user can hear APs from nearby
floors as well as her floor, she will typically only receive signals from
spatially closer APs in distant floors, as compared to APs in her floor. This
is due to signal attenuation by floors/ceilings along with the 3D distance
between the APs and the user. Hapi leverages this observation to achieve
accurate and robust location estimates. A deep-learning based method is
proposed to identify the user's floor. Then, the identified floor along with
the user's visible APs from all floors are used to estimate her 2D location
through a novel RSS-Rank Gaussian-based method. Additionally, we present a
regression based method to predict Hapi's location estimates' quality and
employ it within a Kalman Filter to further refine the accuracy. Our evaluation
results, from deployment on various android devices over 6 months with 13
subjects in 5 different up to 9 floors multistory buildings, show that Hapi can
identify the user's exact floor up to 95.2% of the time and her 2D location
with a median accuracy of 3.5m, achieving 52.1% and 76.0% improvement over
related calibration-free state-of-the-art systems respectively.Comment: Accepted for publication in MobiQuitous 2018 - the 15th International
Conference on Mobile and Ubiquitous Systems: Computing, Networking and
Service
Map-assisted Indoor Positioning Utilizing Ubiquitous WiFi Signals
The demand of indoor positioning solution is on the increase dramatically, and WiFi-based indoor positioning is known as a very promising approach because of the ubiquitous WiFi signals and WiFi-compatible mobile devices. Improving the positioning accuracy is the primary target of most recent works, while the excessive deployment overhead is also a challenging problem behind.
In this thesis, the author is investigating the indoor positioning problem from the aspects of indoor map information and the ubiquity of WiFi signals. This thesis proposes a set of novel WiFi positioning schemes to improve the accuracy and efficiency. Firstly, considering the access point (AP) placement is the first step to deploy indoor positioning system using WiFi, an AP placement algorithm is provided to generate the placement of APs in a given indoor environment. The AP placement algorithm utilises the floor plan information from the indoor map, in which the placement of APs is optimised to benefit the fingerprinting- based positioning. Secondly, the patterns of WiFi signals are observed and deeply analysed from sibling and spatial aspects in conjunction with pathway map from indoor map to address the problem of inconsistent WiFi signal observations. The sibling and spatial signal patterns are used to improve both positioning accuracy and efficiency. Thirdly, an AP-centred architecture is proposed by moving the positioning modules from mobile handheld to APs to facilitate the applications where mobile handheld doesn’t directly participate positioning. Meanwhile, the fingerprint technique is adopted into the AP-centred architecture to maintain comparable positioning accuracy. All the proposed works in this thesis are adequately designed, implemented and evaluated in the real-world environment and show improved performance
Quantifying Potential Energy Efficiency Gain in Green Cellular Wireless Networks
Conventional cellular wireless networks were designed with the purpose of
providing high throughput for the user and high capacity for the service
provider, without any provisions of energy efficiency. As a result, these
networks have an enormous Carbon footprint. In this paper, we describe the
sources of the inefficiencies in such networks. First we present results of the
studies on how much Carbon footprint such networks generate. We also discuss
how much more mobile traffic is expected to increase so that this Carbon
footprint will even increase tremendously more. We then discuss specific
sources of inefficiency and potential sources of improvement at the physical
layer as well as at higher layers of the communication protocol hierarchy. In
particular, considering that most of the energy inefficiency in cellular
wireless networks is at the base stations, we discuss multi-tier networks and
point to the potential of exploiting mobility patterns in order to use base
station energy judiciously. We then investigate potential methods to reduce
this inefficiency and quantify their individual contributions. By a
consideration of the combination of all potential gains, we conclude that an
improvement in energy consumption in cellular wireless networks by two orders
of magnitude, or even more, is possible.Comment: arXiv admin note: text overlap with arXiv:1210.843
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