Jae Sung Choi

Localization Systems Using Passive UHF RFID

Since gathering spatial information of objects often provides a large number of extended functions in RFID based applications, many different localization systems have been developed in recent years. Determining and estimating the physical location of tagged objects in an interrogating area is known as localization. In this paper, we study properties of passive UHF RFID systems such as the relationship between distance and RSSI (Received Signal Strength Indictor), performance variations among the same type of passive tags, and readabilities of tags. We propose a received signal strength based Localization Algorithm using passive UHF RFID system. The localization uses the k-nearest neighbor algorithm to estimate the physical position of the target tag. To improve the accuracy of the passive tag attached object location, the properties and characteristics are applied to the localization algorithm. According to the analysis of experimentation, our proposed approach shows over 34% improvement compared with the k-Nearest Neighbor algorithm with the use of single reader and a single antenna, and 13 reference tags.

Passive UHF RFID-Based Localization Using Detection of Tag Interference on Smart Shelf

In this paper, we present a novel radio-frequency identification (RFID) smart shelf that accurately locates tagged objects using standard passive UHF RFID tags. This standard-based commercial off-the-shelf approach provides significant advantages over custom HF RFID and other near-field RFID approaches, including reduced tag costs, minimal infrastructure costs, and simple operation. In order to achieve accurate location sensing of objects sitting on the shelf, we utilize a novel localization algorithm that utilizes detected changes in a tags readability to infer the presence of neighboring tags. According to our experimentation results, with a single RFID reader antenna for two wooden shelves of size 91 cm × 152 cm, our smart-shelf system estimates nine box-level object locations with an average error of just 18.48 cm, which is a 71% improvement in accuracy compared with the previously published k nearest neighbor (KNN) algorithm.

Sensor Data Fusion Using DSm Theory for Activity Recognition under Uncertainty in Home-Based Care

Reliable contextual information of remotely monitored patients should be generated to prevent hazardous situations and to provide pervasive services in home-based care. This is difficult for several reasons. First, low level data obtained from heterogeneous sensors have different degrees of uncertainty. Second, generated contexts can be corrupted or conflicted even if they are acquired by simultaneous operations. In this paper, we utilize Dezert-Smarandache Theory (DSmT) as an evidence fusion approach to reduce ambiguous or imperfect information then to get higher belief levels in the data fusion process of contextual information. To analyze the improvement of DSmT fusion process, we compare DSmT with Dempster-Shafer Theory (DST) using PCR5 rule of combination and Dempsters rule of combination respectively.

A Static Evidential Network for Context Reasoning in Home-Based Care

In home-based care, reliable contextual information of remotely monitored patients should be generated by correctly recognizing the activities to prevent hazardous situations of the patient. It is difficult to achieve a higher confidence level of contextual information for several reasons. First, low-level data from multisensors have different degrees of uncertainty. Second, generated contexts can be conflicting, even though they are acquired by simultaneous operations. We propose the static evidential fusion process (SEFP) as a context-reasoning method. The context-reasoning method processes sensor data with an evidential form based on the Dezert-Smarandache theory (DSmT). The DSmT approach reduces ambiguous or conflicting contextual information in multisensor networks. Moreover, we compare SEFP based on DSmT with traditional fusion processes such as Bayesian networks and the Dempster-Shafer theory to understand the uncertainty analysis in decision making and to show the improvement of the DSmT approach compared to the others.

A classification and modeling of the quality of contextual information in smart spaces

Reliable contextual information should be generated to provide pervasive services to the occupant in smart spaces. This is difficult for several reasons. First, the number of ways to describe an event or an object is unlimited and there is no standard regarding granularity of context information in context classification schemes. Second, the quality of a given piece of contextual information is not guaranteed by uncertainty. In this paper, we propose a pragmatic context classification and a generalized context modeling scheme based on sensor fusion techniques. To make a pragmatic context classification, we introduce two approaches, “occupant-centered pragmatic approach” and “relation-dependency” approach. To improve the quality of given contextual information by reducing uncertainty, we introduce “state-space based sensor fusion modeling” as a generalized context modeling. Finally, we show an example within the applied scenario as an evidential network.

Robust and Dynamic Bin Slotted Anti-Collision Algorithms in RFID System

In this paper, we present the Dynamic Bin Slotted memoryless anti-collision algorithm (DBS) and the Robust Dynamic Bin Slotted memoryless anti-collision algorithm (RDBS) (which is a countermeasure to the strong and weak tag problems). They are based upon a bin slot tree algorithm. Both algorithms combine deterministic and probabilistic approaches to improve performance. We suggest a simple estimator to estimate the number of tags in an interrogation zone that is optimized for bin-slot tree algorithms. Our performance evaluation shows that DBS and RDBS surpass other existing bin slotted algorithms. According to our simulation results, the total identification time of the DBS algorithm for all tags is reduced by 57.28% for 300 tags compared to the conventional Bin Slotted Algorithm (BSA). Moreover, under the strong-weak tag problem, the RDBS algorithm reduces over 29.59% of the total number of PingID commands and 3.51% of the identification time for maximum 300 tags over the Bin-slotted Hybrid Search Algorithm, which is the best reported variation of BSA.

Investigation of impact factors for various performances of passive UHF RFID system

In this paper, we present causes of variation in performance for passive UHF RFID tags with empirical results in two different environments: practical conditions and an anechoic chamber. We study the critical causes of RSSI ambiguity, such as a posture of tag, and variations among uniform tags. Moreover, in passive UHF RFID systems, Tag-to-Tag interferences affect performance of passive RF tags. When two tags are located close each other, an adjacent tag influences the other tags. This causes increase or decrease of the backscattering communication budgets. According to our empirical results, compared with non-interfered backscattering signal strength in an anechoic chamber, tag-to-tag interference affects the reader received signal strength, such as 5.8dB of excess decrease and 2.5dB of increase, depending on the distance between two tags. We present a new model of backscattered signal strength for passive UHF RFID system under tag-to-tag interference. The variation of excess power volume by the interference depends on an interference coefficient. In order to analyze the impacts of tag-to-tag interference, we show empirical results in the anechoic chamber, then, we model the change of the backscattered signal strength using the second order under-damped system for different tag-to-tag distances and angles.

Trilateration based multi-robot localization under anchor-less outdoor environment

In mobile multi-robot, estimation of robot location is a fundamental and critical issue to provide efficient management of given tasks for the robot systems. We propose the ideal method to estimate location information of multiple mobile robots without stationary anchors. According to our simulation result, the proposed localization algorithm decreases maximal 34.55% of computational overhead compared with the traditional trilateration based localization.

Reducing localization ambiguity of immobile passive UHF RFID tagged physical objects

Location sensing of physical objects is one of critical issues in many applications. Passive UHF Radio Frequency Identification (RFID) technique provides an efficient solution because of its low cost for installation and easy identification of the tagged physical objects. In this paper, we research on the localization problem using passive UHF RFID systems. We discuss theoretical and practical characteristics of a passive UHF RFID system. We propose novel algorithm to minimize the number of ambiguous tag points against a single RSSI value from a target tag and increase accuracy of location estimation in 2D grid space. Because of a single Received Signal Strength Indicator (RSSI) can be related to multiple points in the monitoring area due to signal propagation when we use the RSSI based localization technique. According to the analysis of our experiment results, our proposed approach shows over 50% improvement compared with the conventional k-Nearest Neighbor algorithm in a test frame, and 23.24cm of estimation error with a high granularity for localization of box level items with 4 different positions in the immobile applied application such as a smart shelf.

A dynamic evidential network for multisensor context reasoning in home-based care

In home-based care, reliable contextual information of remotely monitored patients should be generated to recognize activities and to identify hazardous situations of the patient. This is difficult for several reasons. First, low level data obtained from multisensor have different degrees of uncertainty. Second, generated contexts can be conflicting even if they are acquired by simultaneous operations. And last, context reasoning over time is difficult for temporal changes in sensory information. In this paper, we propose the dynamic evidential fusion approach as a context reasoning method in home-based care. The proposed approach processes the generated contexts with Dynamic Evidential Network (DEN), which is composed of the combination of Dezert-Smarandache Theory (DSmT) and Markov Chain (MC). The DSmT reduces ambiguous or conflicting contextual information and the MC processes the association and correlation of sensory information that may change based on time series. Finally, we compare the dynamic evidential fusion approach with the static evidential fusion approach for analyzing the improvement of the dynamic evidential fusion approach.