Performance-based evaluation of RFID-based indoor location sensing solutions for the built environment

This paper proposes Kalman-filter drift removal (DR) and Heron-bilateration location estimation (LE) to significantly reduce the received signal strength index (RSSI) drift, localization error, computational complexity, and deployment cost of conventional radio frequency identification (RFID) indoor positioning systems without any sacrifice of localization granularity and accuracy. By means of only one portable RFID reader as the targeted device and only one pair of active RFID tags as the border-deployed landmarks, this paper develops a real-time portable RFID indoor positioning device and cost-effective scalable RFID indoor positioning infrastructure, based on Kalman-filter DR, Heron-bilateration LE, and four novel preprocessing/postprocessing techniques. Experimental results reveal that the proposed Kalman-filter DR method is faster and better to converge the distance measurement (DM) error than conventional probability/statistics in terms of various relative distances under certain RSSI drift effect condition, and the proposed Heron-bilateration LE method is also faster and better to converge the LE error than conventional proximity pattern matching and trilateration in terms of three or more landmarks under certain DM error condition. On the other hand, a portable RFID indoor positioning device is smoothly implemented on an Android smartphone platform attached with a portable Bluetooth-based RFID reader.

Real-Time RFID Indoor Positioning System Based on Kalman-Filter Drift Removal and Heron-Bilateration Location Estimation

With the advances in information and communication technologies, wireless sensor networks has made Ambient Intelligence (AmI) applications possible that can monitor the situation around the persons or objects and give certain responses for their needs. The location awareness is an important technology for AmI applications. The advantages of ZigBee wireless sensor networks such as low cost, high scalability, high availability and supporting dynamic routing topology make ZigBee more suitable for indoor location system. In this research, we propose a ZigBEe-bAsed indoor loCatiON (ZigBEACON) system for the AmI applications. The proposed approach is based on the k-nearest neighbor algorithm. According to the Received Signal Strength Indication's (RSSI) path loss distribution, the RSSI values are defined into four classes. The signals that belong to different classes will be adjusted by the different ratio and will be referred to as weighted RSSI. The use of weighted RSSI can effectively choose the p-nearest reference nodes. Finally, the position of mobile node would be derived by calculating the coordinates of pnearest reference nodes. Comparing the results with that of ZigBee-based LANDMARC system, our approach has 29% improvement on average error distance. The approach not only improves the accuracy, but also provides less calculation complexity than other improvement methods of LANDMARC. The ZigBEACON approach is an adequate solution to the indoor location system for AmI applications.

ZigBee-based indoor location system by k-nearest neighbor algorithm with weighted RSSI

Radio Frequency Identification (RFID) technology has been widely used in many application domains. How to apply RFID technology to develop an Indoor Positioning System (IPS) has become a hot research topic in recent years. LANDMARC approach is one of the first IPSs by using active RFID tags and readers to provide location based service in indoor environment. However, major drawbacks of the LANDMARC approach are that its localization accuracy largely depends on the density of reference tags and the high cost of RFID readers. In order to overcome these drawbacks, two localization algorithms, namely weighted path loss (WPL) and extreme learning machine (ELM), are proposed in this paper. These two approaches are tested on a novel cost-efficient active RFID IPS. Based on our experimental results, both WPL and ELM can provide higher localization accuracy and robustness than existing ones.

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An RFID indoor positioning system by using weighted path loss and extreme learning machine

In recent years, developing Indoor Positioning System (IPS) has become an attractive research topic due to the increasing demands on Location-Based Service (LBS) in indoor environment. Several advantages of Radio Frequency Identification (RFID) Technology, such as anti-interference, small, light and portable size of RFID tags, and its unique identification of different objects, make it superior to other wireless communication technologies for indoor positioning. However, certain drawbacks of existing RFID-based IPSs, such as high cost of RFID readers and active tags, as well as heavy dependence on the density of reference tags to provide the LBS, largely limit the application of RFID-based IPS. In order to overcome these drawbacks, we develop a cost-efficient RFID-based IPS by using cheaper active RFID tags and sensors. Furthermore, we also proposed three localization algorithms: Weighted Path Loss (WPL), Extreme Learning Machine (ELM) and integrated WPL-ELM. WPL is a centralized model-based approach which does not require any reference tags and provides accurate location estimation of the target effectively. ELM is a machine learning fingerprinting-based localization algorithm which can provide higher localization accuracy than other existing fingerprinting-based approaches. The integrated WPL-ELM approach combines the fast estimation of WPL and the high localization accuracy of ELM. Based on the experimental results, this integrated approach provides a higher localization efficiency and accuracy than existing approaches, e.g., the LANDMARC approach and the support vector machine for regression (SVR) approach.

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Platform and Algorithm Development for a RFID-Based Indoor Positioning System

This paper aims to locate a tag position inside the building by using low cost device with minimal location error. The enhancement approach with Received Signal Strength Indication (RSSI) technique is proposed to achieve the target. In this paper, after introducing the existing LANDMARC system, we try to construct the relationship between RSSI and distance from Reader to Tag by experiment. Finally, an enhanced approach to reach higher accuracy of indoor localization is proposed. The proposed enhancement approach utilizes the strategy of putting the tracking tag and its k nearest reference tags in a set, then repeating to calibrate the target coordinate by the error corrections which obtained by members of this set. It is a recursive data processing approach. The calibration will continue until the tracking tag’s coordinate tend to be a stable value. Simulation and experimental comparison are also shown here to prove that the accuracy of localization is improved.

An Enhanced Approach of Indoor Location Sensing Using Active RFID

This paper aims to locate the target position by using RFID Readers and tags with minimum error. After investigating the existing fingerprinting location technique, we find that the distribution of RSSI-value at each sample point is fluctuant even in the same position. Therefore, we propose an enhanced algorithm to the existing deterministic algorithms. The proposed enhancement approach collects ranges of possible RSSI-values from either sample point or target object to build the probability model, which is different from original algorithm that only saves one single RSSI-value at each point into a database, then several sets of RSSI-values are got for further location by using permutation and combination. Unlike the existing techniques, more than one locations are computed and the final location is the weighted average of those computed locations. simulation results and experimental comparisons are shown, in order to prove the accuracy of location estimation is improved.

Xiaolei Wang

Papers

An Enhanced Approach of Indoor Location Sensing Using Active RFID

An Enhanced Location Estimation Approach Based on Fingerprinting Technique