8,635 research outputs found
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
Channel Sounding for the Masses: Low Complexity GNU 802.11b Channel Impulse Response Estimation
New techniques in cross-layer wireless networks are building demand for
ubiquitous channel sounding, that is, the capability to measure channel impulse
response (CIR) with any standard wireless network and node. Towards that goal,
we present a software-defined IEEE 802.11b receiver and CIR estimation system
with little additional computational complexity compared to 802.11b reception
alone. The system implementation, using the universal software radio peripheral
(USRP) and GNU Radio, is described and compared to previous work. By overcoming
computational limitations and performing direct-sequence spread-spectrum
(DS-SS) matched filtering on the USRP, we enable high-quality yet inexpensive
CIR estimation. We validate the channel sounder and present a drive test
campaign which measures hundreds of channels between WiFi access points and an
in-vehicle receiver in urban and suburban areas
JamLab: Augmenting Sensornet Testbeds with Realistic and Controlled Interference Generation
Radio interference drastically affects the performance of sensor-net communications, leading to packet loss and reduced energy-efficiency. As an increasing number of wireless devices operates on the same ISM frequencies, there is a strong need for understanding and debugging the performance of existing sensornet protocols under interference. Doing so requires a low-cost flexible testbed infrastructure that allows the repeatable generation of a wide range of interference patterns. Unfortunately, to date, existing sensornet testbeds lack such capabilities, and do not permit to study easily the coexistence problems between devices sharing the same frequencies. This paper addresses the current lack of such an infrastructure by using off-the-shelf sensor motes to record and playback interference patterns as well as to generate customizable and repeat-able interference in real-time. We propose and develop JamLab: a low-cost infrastructure to augment existing sensornet testbeds with accurate interference generation while limiting the overhead to a simple upload of the appropriate software. We explain how we tackle the hardware limitations and get an accurate measurement and regeneration of interference, and we experimentally evaluate the accuracy of JamLab with respect to time, space, and intensity. We further use JamLab to characterize the impact of interference on sensornet MAC protocols
Occupancy Estimation Using Low-Cost Wi-Fi Sniffers
Real-time measurements on the occupancy status of indoor and outdoor spaces
can be exploited in many scenarios (HVAC and lighting system control, building
energy optimization, allocation and reservation of spaces, etc.). Traditional
systems for occupancy estimation rely on environmental sensors (CO2,
temperature, humidity) or video cameras. In this paper, we depart from such
traditional approaches and propose a novel occupancy estimation system which is
based on the capture of Wi-Fi management packets from users' devices. The
system, implemented on a low-cost ESP8266 microcontroller, leverages a
supervised learning model to adapt to different spaces and transmits occupancy
information through the MQTT protocol to a web-based dashboard. Experimental
results demonstrate the validity of the proposed solution in four different
indoor university spaces.Comment: Submitted to Balkancom 201
Advanced indoor localisation based on the Viterbi algorithm and semantic data
In this work a real-time indoor localisation system based on the Viterbi algorithm is developed. This Viterbi principle is used in combination with semantic data to improve the accuracy: i.e., the environment of the object that is being tracked and an adjustable maximum speed. The developed algorithm was verified by simulations and with experiments in a building-wide testbed for sensor and WiFi experiments. 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 26.4% and 63.9%
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