scholarly journals Outlier-Detection-Based Indoor Localization System for Wireless Sensor Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Yu-Chi Chen ◽  
Jyh-Ching Juang
Keyword(s):  
Outlier Detection ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Signal Strength ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Localization Problem ◽  
Detection Scheme ◽  
Localization System ◽  
Detection Techniques

The paper exploits the outlier detection techniques for wireless-sensor-network- (WSN-) based localization problem and proposes an outlier detection scheme to cope with noisy sensor data. The cheap and widely available measurement technique—received signal strength (RSS)—is usually taken into account in the indoor localization system, but the RSS measurements are known to be sensitive to the change of the environment. The paper develops an outlier detection scheme to deal with abnormal RSS data so as to obtain more reliable measurements for localization. The effectiveness of the proposed approach is verified experimentally in an indoor environment.

scholarly journals Outlier Detection for IoT devices in Indoor Situating Framework using Machine Learning Techniques and Comparison

2021 ◽  
Vol 309 ◽  
pp. 01024
Author(s):  
M. Sri Vidya ◽  
G. R. Sakthidharan
Keyword(s):  
Machine Learning ◽  
Outlier Detection ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Detection Techniques ◽  
Learning Techniques ◽  
Global Positioning ◽  
Iot Devices

Internet of Things connects various physical objects and form a network to do the services for sensing the physical things without any human intervention. They compute the data, retrieve the data by the network connections made through IoT device components such as Sensors, Protocols, Address, etc., The Global Positioning System (GPS) is used for localization in outer areas such as roads, and ground but cannot be used for Indoor environment. So, while using Indoor Environment, finding or locating an object is not possible by GPS. Therefore by using IoT devices such as Wi-Fi routers in Indoor Environment can localize the objects. It can be done by using Received Signal Strengths (RSSs) from a Wi-Fi router. But by using RSSs in Wi-Fi, there are disturbances, reflections, interferences are caused. By using Outlier detection techniques for localization can identify the objects clearly without any interruptions, noises, and irregular signal strengths. This paper produces research about Indoor Situating Environment and various techniques already used for localization and form the effective solution. The several methods used are compared and form a result to make the further computation in the Indoor Environment. The Comparison is done in order to find the effective and more accurate Machine Learning algorithms used for Indoor Localization.

Why General Outlier Detection Techniques Do Not Suffice for Wireless Sensor Networks

2010 ◽  
pp. 136-158 ◽  
Author(s):  
Yang Zhang ◽  
Nirvana Meratnia ◽  
Paul Havinga
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Outlier Detection ◽  
Environmental Effects ◽  
Measurement Errors ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Detection Techniques ◽  
General Data ◽  
Faulty Sensors

Raw data collected in wireless sensor networks are often unreliable and inaccurate due to noise, faulty sensors and harsh environmental effects. Sensor data that significantly deviate from normal pattern of sensed data are often called outliers. Outlier detection in wireless sensor networks aims at identifying such readings, which represent either measurement errors or interesting events. Due to numerous shortcomings, commonly used outlier detection techniques for general data seem not to be directly applicable to outlier detection in wireless sensor networks. In this chapter, the authors report on the current stateof- the-art on outlier detection techniques for general data, provide a comprehensive technique-based taxonomy for these techniques, and highlight their characteristics in a comparative view. Furthermore, the authors address challenges of outlier detection in wireless sensor networks, provide a guideline on requirements that suitable outlier detection techniques for wireless sensor networks should meet, and will explain why general outlier detection techniques do not suffice.

scholarly journals An Indoor Localization System Using Residual Learning with Channel State Information

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 574
Author(s):  
Chendong Xu ◽  
Weigang Wang ◽  
Yunwei Zhang ◽  
Jie Qin ◽  
Shujuan Yu ◽  
...  
Keyword(s):  
Channel State Information ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Location Based Services ◽  
Channel State ◽  
State Information ◽  
Localization Accuracy ◽  
Localization System ◽  
Increasing Demand ◽  
Degradation Problem

With the increasing demand of location-based services, neural network (NN)-based intelligent indoor localization has attracted great interest due to its high localization accuracy. However, deep NNs are usually affected by degradation and gradient vanishing. To fill this gap, we propose a novel indoor localization system, including denoising NN and residual network (ResNet), to predict the location of moving object by the channel state information (CSI). In the ResNet, to prevent overfitting, we replace all the residual blocks by the stochastic residual blocks. Specially, we explore the long-range stochastic shortcut connection (LRSSC) to solve the degradation problem and gradient vanishing. To obtain a large receptive field without losing information, we leverage the dilated convolution at the rear of the ResNet. Experimental results are presented to confirm that our system outperforms state-of-the-art methods in a representative indoor environment.

scholarly journals A Research Cluster based Scheme for Node Positioning in Indoor Environment

2019 ◽  
Vol 8 (6S) ◽  
pp. 79-83
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Indoor Environment ◽  
Cluster Head ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Location Estimation ◽  
Wireless Sensor ◽  
Localization Error ◽  
Network Efficiency

Location estimation in Wireless Sensor Network (WSN) is mandatory to achieve high network efficiency. Identifying the positions of sensors is an uphill task as monitoring nodes are involved in estimation and localization. Clustered Positioning for Indoor Environment (CPIE) is proposed for estimating the position of the sensors using a Cluster Head (CH) based mechanism. The CH estimates the number of neighbor nodes in each floor of the indoor environment. It sends the requests to the cluster members and the positions are estimated based on the Received Signal Strength Indicators (RSSIs) from the members of the cluster. The performance of the proposed scheme is analyzed for both stable and mobile conditions by varying the number of floors. Experimental results show that the propounded scheme offers better network efficiency and reduces delay and localization error

scholarly journals Outlier Detection Using Improved Support Vector Data Description in Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4712
Author(s):  
Pei Shi ◽  
Guanghui Li ◽  
Yongming Yuan ◽  
Liang Kuang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Relative Density ◽  
Outlier Detection ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description

Wireless sensor networks (WSNs) are susceptible to faults in sensor data. Outlier detection is crucial for ensuring the quality of data analysis in WSNs. This paper proposes a novel improved support vector data description method (ID-SVDD) to effectively detect outliers of sensor data. ID-SVDD utilizes the density distribution of data to compensate SVDD. The Parzen-window algorithm is applied to calculate the relative density for each data point in a data set. Meanwhile, we use Mahalanobis distance (MD) to improve the Gaussian function in Parzen-window density estimation. Through combining new relative density weight with SVDD, this approach can efficiently map the data points from sparse space to high-density space. In order to assess the outlier detection performance, the ID-SVDD algorithm was implemented on several datasets. The experimental results demonstrated that ID-SVDD achieved high performance, and could be applied in real water quality monitoring.

scholarly journals MuCHLoc: Indoor ZigBee Localization System Utilizing Inter-Channel Characteristics

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1645 ◽  
Author(s):  
Ryota Kimoto ◽  
Shigemi Ishida ◽  
Takahiro Yamamoto ◽  
Shigeaki Tagashira ◽  
Akira Fukuda
Keyword(s):  
Large Scale ◽  
Indoor Localization ◽  
Single Channel ◽  
Sensor Nodes ◽  
Localization Problem ◽  
Sensor Localization ◽  
Localization Accuracy ◽  
Localization System ◽  
Channel Diversity ◽  
Radio Signals

The deployment of a large-scale indoor sensor network faces a sensor localization problem because we need to manually locate significantly large numbers of sensors when Global Positioning System (GPS) is unavailable in an indoor environment. Fingerprinting localization is a popular indoor localization method relying on the received signal strength (RSS) of radio signals, which helps to solve the sensor localization problem. However, fingerprinting suffers from low accuracy because of an RSS instability, particularly in sensor localization, owing to low-power ZigBee modules used on sensor nodes. In this paper, we present MuCHLoc, a fingerprinting sensor localization system that improves the localization accuracy by utilizing channel diversity. The key idea of MuCHLoc is the extraction of channel diversity from the RSS of Wi-Fi access points (APs) measured on multiple ZigBee channels through fingerprinting localization. MuCHLoc overcomes the RSS instability by increasing the dimensions of the fingerprints using channel diversity. We conducted experiments collecting the RSS of Wi-Fi APs in a practical environment while switching the ZigBee channels, and evaluated the localization accuracy. The evaluations revealed that MuCHLoc improves the localization accuracy by approximately 15% compared to localization using a single channel. We also showed that MuCHLoc is effective in a dynamic radio environment where the radio propagation channel is unstable from the movement of objects including humans.

scholarly journals Outlier Detection in Wireless Sensor Networks Using Model Selection-Based Support Vector Data Descriptions

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4328 ◽  
Author(s):  
Zhan Huan ◽  
Chang Wei ◽  
Guang-Hui Li
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Model Selection ◽  
Outlier Detection ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Quality Data ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data

Wireless sensor networks (WSNs) are often deployed in harsh and unattended environments, which may cause the generation of abnormal or low quality data. The inaccurate and unreliable sensor data may increase generation of false alarms and erroneous decisions, so it’s very important to detect outliers in sensor data efficiently and accurately to ensure sound scientific decision-making. In this paper, an outlier detection algorithm (TSVDD) using model selection-based support vector data description (SVDD) is proposed. Firstly, the Toeplitz matrix random feature mapping is used to reduce the time and space complexity of outlier detection. Secondly, a novel model selection strategy is realized to keep the algorithm stable under the low feature dimensions, this strategy can select a relatively optimal decision model and avoid both under-fitting and overfitting phenomena. The simulation results on SensorScope and IBRL datasets demonstrate that, TSVDD achieves higher accuracy and lower time complexity for outlier detection in WSNs compared with existing methods.

scholarly journals A Systematic Literature Review on Outlier Detection in Wireless Sensor Networks

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 328 ◽  
Author(s):  
Mahmood Safaei ◽  
Shahla Asadi ◽  
Maha Driss ◽  
Wadii Boulila ◽  
Abdullah Alsaeedi ◽  
...  
Keyword(s):  
Literature Review ◽  
Outlier Detection ◽  
Systematic Literature Review ◽  
Machine Learning Algorithms ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Distributed Data ◽  
Detection Techniques ◽  
Distributed Data Processing ◽  
Spatially Distributed

A wireless sensor network (WSN) is defined as a set of spatially distributed and interconnected sensor nodes. WSNs allow one to monitor and recognize environmental phenomena such as soil moisture, air pollution, and health data. Because of the very limited resources available in sensors, the collected data from WSNs are often characterized as unreliable or uncertain. However, applications using WSNs demand precise readings, and uncertainty in data reading can cause serious damage (e.g., health monitoring data). Therefore, an efficient local/distributed data processing algorithm is needed to ensure: (1) the extraction of precise and reliable values from noisy readings; (2) the detection of anomalies from data reported by sensors; and (3) the identification of outlier sensors in a WSN. Several works have been conducted to achieve these objectives using several techniques such as machine learning algorithms, mathematical modeling, and clustering. The purpose of this paper is to conduct a systematic literature review to report the available works on outlier and anomaly detection in WSNs. The paper highlights works conducted from January 2004 to October 2018. A total of 3520 papers are reviewed in the initial search process. Later, these papers are filtered by title, abstract, and contents, and a total of 117 papers are selected. These papers are examined to answer the defined research questions. The current paper presents an improved taxonomy of outlier detection techniques. This will help researchers and practitioners to find the most relevant and recent studies related to outlier detection in WSNs. Finally, the paper identifies existing gaps that future studies can fill.

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