341 research outputs found
A hybrid indoor localization solution using a generic architectural framework for sparse distributed wireless sensor networks
Indoor localization and navigation using wireless sensor networks is still a big challenge if expensive sensor nodes are not involved. Previous research has shown that in a sparse distributed sensor network the error distance is way too high. Even room accuracy can not be guaranteed.
In this paper, an easy-to-use generic positioning framework is proposed, which allows users to plug in a single or multiple positioning algorithms. We illustrate the usability of the framework by discussing a new hybrid positioning solution. The combination of a weighted (range-based) and proximity (range-free) algorithm is made. Roth solutions separately have an average error distance of 13.5m and 2.5m respectively. The latter result is quite accurate due to the fact that our testbeds are not sparse distributed. Our hybrid algorithm has an average error distance of 2.66m only using a selected set of nodes, simulating a sparse distributed sensor network. All our experiments have been executed in the iMinds testbed: namely at "de Zuiderpoort". These algorithms are also deployed in two real-life environments: "De Vooruit" and "De Vijvers"
Locating sensors with fuzzy logic algorithms
In a system formed by hundreds of sensors deployed
in a huge area it is important to know the position where every
sensor is.
This information can be obtained using several methods.
However, if the number of sensors is high and the deployment
is based on ad-hoc manner, some auto-locating techniques must
be implemented.
In this paper we describe a novel algorithm based on fuzzy
logic with the objective of estimating the location of sensors
according to the knowledge of the position of some reference
nodes.
This algorithm, called LIS (Localization based on Intelligent
Sensors) is executed distributively along a wireless sensor network
formed by hundreds of nodes, covering a huge area.
The evaluation of LIS is led by simulation tests. The result
obtained shows that LIS is a promising method that can easily
solve the problem of knowing where the sensors are located.Junta de Andalucía P07-TIC-0247
LIS: Localization based on an intelligent distributed fuzzy system applied to a WSN
The localization of the sensor nodes is a fundamental problem in wireless sensor networks.
There are a lot of different kinds of solutions in the literature. Some of them use external
devices like GPS, while others use special hardware or implicit parameters in wireless
communications.
In applications like wildlife localization in a natural environment, where the power available
and the weight are big restrictions, the use of hungry energy devices like GPS or hardware
that add extra weight like mobile directional antenna is not a good solution.
Due to these reasons it would be better to use the localization’s implicit characteristics in
communications, such as connectivity, number of hops or RSSI. The measurement related
to these parameters are currently integrated in most radio devices. These measurement
techniques are based on the beacons’ transmissions between the devices.
In the current study, a novel tracking distributed method, called LIS, for localization of
the sensor nodes using moving devices in a network of static nodes, which have no additional
hardware requirements is proposed.
The position is obtained with the combination of two algorithms; one based on a local
node using a fuzzy system to obtain a partial solution and the other based on a centralized
method which merges all the partial solutions. The centralized algorithm is based on the
calculation of the centroid of the partial solutions.
Advantages of using fuzzy system versus the classical Centroid Localization (CL)
algorithm without fuzzy preprocessing are compared with an ad hoc simulator made for
testing localization algorithms.
With this simulator, it is demonstrated that the proposed method obtains less localization
errors and better accuracy than the centroid algorithm.Junta de Andalucía P07-TIC-0247
A New RSSI-based Centroid Localization Algorithm by Use of Virtual Reference Tags
A good design of node location is critical for efficient
and effective wireless communications. This paper presents an
improved algorithm, in order to solve the low localization
accuracy caused by traditional centroid algorithm. The
improved algorithm combined with VIRE system and
traditional centroid algorithm. The VIRE algorithm is
introduced and the signal propagation model is utilized to
construct virtual reference tags in the location area. Simulation shows that this further developed algorithm has further improved the accuracy of positioning up to 35.12% compared
to the traditional centroid algorithm. It is concluded that this algorithm can further improve the locating accuracy in comparison with the original centroid algorithm
RSSI Based Indoor Localization for Smartphone Using Fixed and Mobile Wireless Node
Nowadays with the dispersion of wireless networks, smartphones and diverse related services, different localization techniques have been developed. Global Positioning System (GPS) has a high rate of accuracy for outdoor localization but the signal is not available inside of buildings. Also other existing methods for indoor localization have low accuracy. In addition, they use fixed infrastructure support. In this paper, we present a novel system for indoor localization, which also works well outside. We have developed a mathematical model for estimating location (distance and direction) of a mobile device using wireless technology. Our experimental results on Smartphones (Android and iOS) show good accuracy (an error less than 2.5 meters). We have also used our developed system in asset tracking and complex activity recognition
Enhancement of weighted centroid algorithm for indoor mobile non-cooperative localization system
Nowadays, indoor wireless localization is being challenged research by providing
high accuracy of location information. However lower processing time, resistant to
environmental condition, simple network topology are also become main concern. Noncooperative
localization based on RSSI allow the anchor nodes as the reference nodes
communicate directly to the target node by exchanging the location data. High sensitivity of
RSSI to the indoor environment, make difficulties in modelling propagation characteristic
called as PLE. Incompatibility the PLE value can influence to the estimated position result.
Weighted centroid localization (WCL) is feasible solution for RSSI-based that can obtain the
target node location just by RSSI and anchor nodes coordinate without PLE value and
estimated distance. While, the centroid determination of WCL give better estimation only to
the centralized position of target node between all anchor nodes position. Therefore, we
propose enhancement of WCL (eWCL) by replacing the weight based on RSSI with different
estimated distance from WCL calculation. The simulation result show that using eWCL can
reduce the error estimation around 60.42% compared to the WCL algorithm with 1.85 meters
MSE value. Then, compared to the cooperative localization based on trilateration algorithm
achieve 12.15% error estimation larger than eWCL at non-cooperative scheme
Localization Process for WSNs with Various Grid-Based Topology Using Artificial Neural Network
Wireless Sensor Network (WSN) is a technology that can aid human life by providing ubiquitous communication, sensing, and computing capabilities. It allows people to be more able to interact with the environment. The environment contains many nodes to monitor and collect data. Localizing nodes distributed in different locations covering different regions is a challenge in WSN. Localization of accurate and low-cost sensors is an urgent need to deploy WSN in various applications. In this paper, we propose an artificial automatic neural network method for sensor node localization. The proposed method in WSN is implemented with network-based topology in different regions. To demonstrate the accuracy of the proposed method, we compared the estimated locations of the proposed feedforward neural network (FFNN) with the estimated locations of the deep feedforward neural network (DFF) and the weighted centroid localization (WCL) algorithm based on the strength of the received signal index. The proposed FFNN model outperformed alternative methods in terms of its lower average localization error which is 0.056m. Furthermore, it demonstrated its capability to predict sensor locations in wireless sensor networks (WSNs) across various grid-based topologies
Localization Process for WSNs with Various Grid-Based Topology Using Artificial Neural Network
Wireless Sensor Network (WSN) is a technology that can aid human life by providing ubiquitous communication, sensing, and computing capabilities. It allows people to be more able to interact with the environment. The environment contains many nodes to monitor and collect data. Localizing nodes distributed in different locations covering different regions is a challenge in WSN. Localization of accurate and low-cost sensors is an urgent need to deploy WSN in various applications. In this paper, we propose an artificial automatic neural network method for sensor node localization. The proposed method in WSN is implemented with network-based topology in different regions. To demonstrate the accuracy of the proposed method, we compared the estimated locations of the proposed feedforward neural network (FFNN) with the estimated locations of the deep feedforward neural network (DFF) and the weighted centroid localization (WCL) algorithm based on the strength of the received signal index. The proposed FFNN model outperformed alternative methods in terms of its lower average localization error which is 0.056m. Furthermore, it demonstrated its capability to predict sensor locations in wireless sensor networks (WSNs) across various grid-based topologies
K-Means Fingerprint Clustering for Low-Complexity Floor Estimation in Indoor Mobile Localization
Indoor localization in multi-floor buildings is an important research
problem. Finding the correct floor, in a fast and efficient manner, in a
shopping mall or an unknown university building can save the users' search time
and can enable a myriad of Location Based Services in the future. One of the
most widely spread techniques for floor estimation in multi-floor buildings is
the fingerprinting-based localization using Received Signal Strength (RSS)
measurements coming from indoor networks, such as WLAN and BLE. The clear
advantage of RSS-based floor estimation is its ease of implementation on a
multitude of mobile devices at the Application Programming Interface (API)
level, because RSS values are directly accessible through API interface.
However, the downside of a fingerprinting approach, especially for large-scale
floor estimation and positioning solutions, is their need to store and transmit
a huge amount of fingerprinting data. The problem becomes more severe when the
localization is intended to be done on mobile devices which have limited
memory, power, and computational resources. An alternative floor estimation
method, which has lower complexity and is faster than the fingerprinting is the
Weighted Centroid Localization (WCL) method. The trade-off is however paid in
terms of a lower accuracy than the one obtained with traditional fingerprinting
with Nearest Neighbour (NN) estimates. In this paper a novel K-means-based
method for floor estimation via fingerprint clustering of WiFi and various
other positioning sensor outputs is introduced. Our method achieves a floor
estimation accuracy close to the one with NN fingerprinting, while
significantly improves the complexity and the speed of the floor detection
algorithm. The decrease in the database size is achieved through storing and
transmitting only the cluster heads (CH's) and their corresponding floor
labels.Comment: Accepted to IEEE Globecom 2015, Workshop on Localization and
Tracking: Indoors, Outdoors and Emerging Network
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