548 research outputs found
Body attenuation and path loss exponent estimation for RSS-based positioning in WSN
The influence of the human body in antenna systems has significant impact in the received signal strength (RSS) of wireless transmissions. Accounting for body effect is generally considered as being able to improve position estimation based on RSS measurements. In this work we perform several experiments with a wireless sensor network, using a sensor node equipped with an inertial measurement unit (IMU), in order to obtain the relative orientation between the sensor node and multiple anchor nodes. A model of the RSS attenuation induced by the body was created using experimental measurements in a controlled environment and applied to a real-time positioning system. A path loss exponent (PLE) estimation method using RSS information from neighbor anchors was also implemented and evaluated. Weighted centroid localization (WCL) algorithm was the positioning method used in this work. When the sensor node was placed on the user’s body, accounting for body effect produced negligible improvements (6%) in the best-case scenario and consistently degraded accuracy under real conditions, whether the node was placed on the user’s body (in the order of 3%), 10 cm away (from 14% to 35%) or 20 cm away from the body (from 42% to 105%) for results in the 70th percentile. The PLE estimation method showed improvements (in the order of 11%) when the sensor node is further away from the body. Results demonstrate that the distance between sensor node and the body has an extremely important influence on the accuracy of the position estimate.This work has been supported by FCT (Fundação para a Ciência e Tecnologia) in the scope of the project UID/EEA/04436/2013. Helder D. Silva is supported by FCT under the grant SFRH/BD/78018/2011info:eu-repo/semantics/publishedVersio
On localisation with robust power control for safety critical wireless sensor networks
A hybrid methodology is proposed for use in low power, safety critical wireless sensor network applications, where quality-of-service orientated transceiver output power control is required to operate in parallel with radio frequency-based localization. The practical implementation is framed in an experimental procedure designed to track a moving agent in a realistic indoor environment. An adaptive time synchronized approach is employed to ensure the positioning technique can operate effectively in the presence of dataloss and where the transmitter output power of the mobile agent is varying due to power control. A deterministic multilateration-based positioning approach is adopted and accuracy is improved by filtering signal strength measurements overtime to account for multipath fading. The location estimate is arrived at by employing least-squares estimation. Power control is implemented at two separate levels in the network topology. First, power control is applied to the uplink between the tracking reference nodes and the centralized access point. A number of algorithms are implemented highlighting the advantage associated with using additional feedback bandwidth, where available, and also the need for effective time delay compensation. The second layer of power control is implemented on the uplink between the mobile agent and the access point and here quantifiable improvements in quality of service and energy efficiency are observed. The hybrid paradigm is extensively tested experimentally on a fully compliant 802.15.4 testbed, where mobility is considered in the problem formulation using a team of fully autonomous robots.A hybrid methodology is proposed for use in low power, safety critical wireless sensor network applications, where quality-of-service orientated transceiver output power control is required to operate in parallel with radio frequency-based localization. The practical implementation is framed in an experimental procedure designed to track a moving agent in a realistic indoor environment. An adaptive time synchronized approach is employed to ensure the positioning technique can operate effectively in the presence of dataloss and where the transmitter output power of the mobile agent is varying due to power control. A deterministic multilateration-based positioning approach is adopted and accuracy is improved by filtering signal strength measurements overtime to account for multipath fading. The location estimate is arrived at by employing least-squares estimation. Power control is implemented at two separate levels in the network topology. First, power control is applied to the uplink between the tracking reference nodes and the centralized access point. A number of algorithms are implemented highlighting the advantage associated with using additional feedback bandwidth, where available, and also the need for effective time delay compensation. The second layer of power control is implemented on the uplink between the mobile agent and the access point and here quantifiable improvements in quality of service and energy efficiency are observed. The hybrid paradigm is extensively tested experimentally on a fully compliant 802.15.4 testbed, where mobility is considered in the problem formulation using a team of fully autonomous robots
Filtering Effect on RSSI-Based Indoor Localization Methods
Indoor positioning systems are used to locate and track objects in an indoor environment. Distance estimation is done using received signal strength indicator (RSSI) of radio frequency signals. However, RSSI is prone to noise and interference which can greatly affect the accuracy performance of the system. In this paper Internet of Things (IoT) technologies like low energy Bluetooth (BLE), WiFi, LoRaWAN and ZigBee are used to obtain indoor positioning. Adopting the existing trilateration and positioning algorithms, the Kalman, Fast Fourier Transform (FFT) and Particle filtering methods are employed to denoise the received RSSI signals to improve positioning accuracy. Experimental results show that choice of filtering method is of significance in improving the positioning accuracy. While FFT and Particle methods had no significant effect on the positioning accuracy, Kalman filter has proved to be the method of choice in for BLE, WiFi, LoRaWAN and ZigBee. Compared with unfiltered RSSI, results showed that accuracy was improved by 2% in BLE, 3% in WiFi, 22% in LoRaWAN and 17% in ZigBee technology for Kalman filtering method
A Novel Path Prediction Strategy for Tracking Intelligent Travelers
There are various technologies for positioning and tracking of intelligent travelers such
as wireless local area networks (WLAN). However, the loss of actual positioning data is
a common problem due to unexpected disconnection between tracking references and
the traveler. Disconnection of the mobile terminal (MT) from the access points (AP) in
WLAN-based systems is the example case of the problem. While enhancement of the
physical system itself can reduce the risk of disconnections, complementary algorithms
provide even more robustness in localization and tracking of the traveler.
This research aims to develop a novel path prediction system which could keep track of
the traveler during temporary shortage of actual positioning data. The system takes the
advantage of the past trajectory information to compensate for the missing information
during disconnections. A novel decision support system (DSS) is devised with the
ability of learning decisional as well as kinematical behaviors of intelligent travelers. The system is then used in path prediction mode for reconstructing the missing parts of
the trajectory when actual positioning data is unavailable.
An ActivMedia Pioneer robot navigating under fuzzy artificial potential fields (APF)
and blind-folded human subjects are the two types of intelligent travelers. The reactive
motion of robots and path planning strategies of the blinds are similar in that both of
them locally acquire knowledge and explore the space based on route-like spatial
cognition. It is proposed and shown that route-like intelligent motion is based on a
combination of decisional and kinematical factors. The system is designed in such a way
to integrate these two types of motion factors using causal inference mechanism of the
fuzzy cognitive map (FCM). The FCM nodes are a novel selection of kinematical
factors. Genetic algorithm (GA) is then used to train the FCM to be able to replicate the
decisional behaviors of the intelligent traveler.
Experimental works show the capabilities of the developed DSS in human path
prediction using both simulated and actual WLAN-based positioning dataset. Locational
error is set to be limited to 1 m which is suitable for wireless tracking of human subjects
with up to 10% improvement compared to the most related works. Both simulation and
actual experiments were also carried out on the Pioneer platform. The accuracy in
prediction of robot trajectory was obtained about 83% with considerable improvement
compared to the recent methods. Apart from the positioning algorithm of this
dissertation, there are several applications of this DSS to other areas including assistive
technology for the blind and human-robot interaction
ConservationBots: Autonomous Aerial Robot for Fast Robust Wildlife Tracking in Complex Terrains
Today, the most widespread, widely applicable technology for gathering data
relies on experienced scientists armed with handheld radio telemetry equipment
to locate low-power radio transmitters attached to wildlife from the ground.
Although aerial robots can transform labor-intensive conservation tasks, the
realization of autonomous systems for tackling task complexities under
real-world conditions remains a challenge. We developed ConservationBots-small
aerial robots for tracking multiple, dynamic, radio-tagged wildlife. The aerial
robot achieves robust localization performance and fast task completion times
-- significant for energy-limited aerial systems while avoiding close
encounters with potential, counter-productive disturbances to wildlife. Our
approach overcomes the technical and practical problems posed by combining a
lightweight sensor with new concepts: i) planning to determine both trajectory
and measurement actions guided by an information-theoretic objective, which
allows the robot to strategically select near-instantaneous range-only
measurements to achieve faster localization, and time-consuming sensor rotation
actions to acquire bearing measurements and achieve robust tracking
performance; ii) a bearing detector more robust to noise and iii) a tracking
algorithm formulation robust to missed and false detections experienced in
real-world conditions. We conducted extensive studies: simulations built upon
complex signal propagation over high-resolution elevation data on diverse
geographical terrains; field testing; studies with wombats (Lasiorhinus
latifrons; nocturnal, vulnerable species dwelling in underground warrens) and
tracking comparisons with a highly experienced biologist to validate the
effectiveness of our aerial robot and demonstrate the significant advantages
over the manual method.Comment: 33 pages, 21 figure
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Wireless indoor localisation within the 5G internet of radio light
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonNumerous applications can be enhanced by accurate and efficient indoor localisation using wireless
sensor networks, however trade-offs often exist between these two parameters. In this thesis, realworld
and simulation data is used to examine the hybrid millimeter wave and Visible Light
Communications (VLC) architecture of the 5G Internet of Radio Light (IoRL) Horizon 2020 project.
Consequently, relevant localisation challenges within Visible Light Positioning (VLP) and asynchronous
sampling networks are identified, and more accurate and efficient solutions are developed.
Currently, VLP relies strongly on the assumed Lambertian properties of light sources.
However, in practice, not all lights are Lambertian. To support the widespread deployment of VLC
technology in numerous environments, measurements from non-Lambertian sources are analysed to
provide new insights into the limitations of existing VLP techniques. Subsequently, a novel VLP
calibration technique is proposed, and results indicate a 59% accuracy improvement against existing
methods. This solution enables high accuracy centimetre level VLP to be achieved with non-
Lambertian sources.
Asynchronous sampling of range-based measurements is known to impact localisation
performance negatively. Various Asynchronous Sampling Localisation Techniques (ASLT) exist to
mitigate these effects. While effective at improving positioning performance, the exact suitability of
such solutions is not evident due to their additional processes, subsequent complexity, and increased
costs. As such, extensive simulations are conducted to study the effectiveness of ASLT under variable
sampling latencies, sensor measurement noise, and target trajectories. Findings highlight the
computational demand of existing ASLT and motivate the development of a novel solution. The
proposed Kalman Extrapolated Least Squares (KELS) method achieves optimal localisation
performance with a significant energy reduction of over 50% when compared to current leading ASLT.
The work in this thesis demonstrates both the capability for high performance VLP from non-
Lambertian sources as well as the potential for energy efficient localisation for sequentially sampled
range measurements.Horizon 202
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