Missing tags detection algorithm for radio frequency identification (RFID) data stream

RFID technology is a radio frequency identification services that provide a reader reading the information of items from the tags. Nowadays, RFID system is rapidly become more common in our live because it cheaper and smaller to be track, trace and identify the items. However, missing tag detection in RFID can occur due to RFID operating environment such as signal collisions and interferences. Missing tags also called as false negative reads is a tag that is present but it cannot be read by the nearby reader. The consequences of this problem can be enormous to business, as it will cause the system to report incorrect data due to an incorrect number of tags being detected. In fact, the performance of RFID missing tag detection is largely affected by uncertainty, which should be considered in the detecting process phase to minimize its negative impact. Thus in this research, an AC complement algorithm with hashing algorithm and Detect False Negative Read algorithm (DFR) is used to developed the Missing Tags Detection Algorithm (MTDA). AC complement algorithm was used to compare the different in each set of data. Meanwhile, DFR algorithm was used to identify the false negative read that present in the set of data. There are many approaches has been proposed to include Window Sub-range Transition Detection (WSTD), Efficient Missing-Tag Detection Protocol (EMD) and Multi-hashing based Missing Tag Identification (MMTI) protocol. This algorithm development has been guided by methodology in four stages. There stages including data preparation, simulation design, detecting false negative read strategy and performance measurement. MTDA can perform well in detecting false negative read with 100% detected in 3.25 second. This performance shows that the algorithm performs well in execution time in detecting false negative reads. In conclusion, it will give insight on the current challenges and open up to new solution to solve the problem of missing tag detection

RFID-Based Indoor Spatial Query Evaluation with Bayesian Filtering Techniques

People spend a significant amount of time in indoor spaces (e.g., office buildings, subway systems, etc.) in their daily lives. Therefore, it is important to develop efficient indoor spatial query algorithms for supporting various location-based applications. However, indoor spaces differ from outdoor spaces because users have to follow the indoor floor plan for their movements. In addition, positioning in indoor environments is mainly based on sensing devices (e.g., RFID readers) rather than GPS devices. Consequently, we cannot apply existing spatial query evaluation techniques devised for outdoor environments for this new challenge. Because Bayesian filtering techniques can be employed to estimate the state of a system that changes over time using a sequence of noisy measurements made on the system, in this research, we propose the Bayesian filtering-based location inference methods as the basis for evaluating indoor spatial queries with noisy RFID raw data. Furthermore, two novel models, indoor walking graph model and anchor point indexing model, are created for tracking object locations in indoor environments. Based on the inference method and tracking models, we develop innovative indoor range and k nearest neighbor (kNN) query algorithms. We validate our solution through use of both synthetic data and real-world data. Our experimental results show that the proposed algorithms can evaluate indoor spatial queries effectively and efficiently. We open-source the code, data, and floor plan at https://github.com/DataScienceLab18/IndoorToolKit