Localization in Wireless Sensor Networks with Range Measurement Errors

2009 ◽  
Author(s):  
Gulnur Selda Kuruoglu ◽  
Melike Erol ◽  
Sema Oktug
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Measurement Errors ◽  
Wireless Sensor ◽  
Range Measurement

A Novel Method for Data Calibration in Wireless Sensor Networks

2013 ◽  
Vol 347-350 ◽  
pp. 1068-1073
Author(s):  
Wei Min Qi ◽  
Jie Xiao
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Measurement Errors ◽  
Information Technologies ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Network Data ◽  
Error Elimination ◽  
Calibration Algorithm ◽  
Data Calibration

In order to provide efficient and suitable services for users in a ubiquitous computing environment, many kinds of context information technologies have been researched. Wireless sensor networks are among the most popular technologies providing such information. Therefore, it is very important to guarantee the reliability of sensor data gathered from wireless sensor networks. However there are several factors associated with faulty sensor readings which make sensor readings unreliable. The research put forward classifying faulty sensor readings into sensor faults and measurement errors, then propose a novel in-network data calibration algorithm which includes adaptive fault checking, measurement error elimination and data refinement. The proposed algorithm eliminates faulty readings as well as refines normal sensor readings and increase reliability. The simulation study shows that the in-network data calibration algorithm is highly reliable and its network overhead is very low compared to previous works.

scholarly journals Performance Analysis of Distributed Estimation for Data Fusion Using a Statistical Approach in Smart Grid Noisy Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 567 ◽  
Author(s):  
Chatura Seneviratne ◽  
Patikiri Arachchige Don Shehan Nilmantha Wijesekara ◽  
Henry Leung
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Smart Grid ◽  
Communication Networks ◽  
Smart Grids ◽  
Measurement Errors ◽  
Power Grid ◽  
Distributed Estimation ◽  
Wireless Sensor ◽  
Grid Infrastructures

Internet of Things (IoT) can significantly enhance various aspects of today’s electric power grid infrastructures for making reliable, efficient, and safe next-generation Smart Grids (SGs). However, harsh and complex power grid infrastructures and environments reduce the accuracy of the information propagating through IoT platforms. In particularly, information is corrupted due to the measurement errors, quantization errors, and transmission errors. This leads to major system failures and instabilities in power grids. Redundant information measurements and retransmissions are traditionally used to eliminate the errors in noisy communication networks. However, these techniques consume excessive resources such as energy and channel capacity and increase network latency. Therefore, we propose a novel statistical information fusion method not only for structural chain and tree-based sensor networks, but also for unstructured bidirectional graph noisy wireless sensor networks in SG environments. We evaluate the accuracy, energy savings, fusion complexity, and latency of the proposed method by comparing the said parameters with several distributed estimation algorithms using extensive simulations proposing it for several SG applications. Results prove that the overall performance of the proposed method outperforms other fusion techniques for all considered networks. Under Smart Grid communication environments, the proposed method guarantees for best performance in all fusion accuracy, complexity and energy consumption. Analytical upper bounds for the variance of the final aggregated value at the sink node for structured networks are also derived by considering all major errors.

scholarly journals An Efficient Hybrid RSS-AoA Localization for 3D Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2121 ◽  
Author(s):  
Thu L. N. Nguyen ◽  
Tuan D. Vy ◽  
Yoan Shin
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Measurement Errors ◽  
Dimensional Space ◽  
Weighted Least Squares ◽  
Three Dimensional ◽  
Path Loss ◽  
Wireless Sensor ◽  
Angle Of Arrival ◽  
Localization Scheme

Wireless sensor networks (WSNs) enable many applications such as intelligent control, prediction, tracking, and other communication network services, which are integrated into many technologies of the Internet-of-Things. The conventional localization frameworks may not function well in practical environments since they were designed either for two-dimensional space only, or have high computational costs, or are sensitive to measurement errors. In order to build an accurate and efficient localization scheme, we consider in this paper a hybrid received signal strength and angle-of-arrival localization in three-dimensional WSNs, where sensors are randomly deployed with the transmit power and the path loss exponent unknown. Moreover, in order to avoid the difficulty of solving the conventional maximum-likelihood estimator due to its non-convex and highly complex natures, we derive a weighted least squares estimate to estimate jointly the location of the unknown node and the two aforementioned channel components through some suitable approximations. Simulation results confirm the effectiveness of the proposed method.

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.

Experiences in Data Processing and Bayesian Filtering Applied to Localization and Tracking in Wireless Sensor Networks

2009 ◽  
pp. 406-429
Author(s):  
Junaid Ansari ◽  
Janne Riihijärvi ◽  
Petri Mähönen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Processing ◽  
Measurement Errors ◽  
Wireless Sensor ◽  
Bayesian Filtering ◽  
Design Decisions ◽  
Non Linear Systems ◽  
Non Gaussian ◽  
Localization And Tracking

The authors discuss algorithms and solutions for signal processing and filtering for localization and tracking applications in Wireless Sensor Networks. Their focus is on the experiences gained from implementation and deployment of several such systems. In particular, they comment on the data processing solutions found appropriate for commonly used sensor types, and discuss at some length the use of Bayesian filtering for solving the tracking problem. They specifically recommend the use of particle filters as a flexible solution appropriate for tracking in non-linear systems with non-Gaussian measurement errors. They also discuss in detail the design of some of the indoor and outdoor tracking systems they have implemented, highlighting major design decisions and experiences gained from test deployments.

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