Effective Aggregation and Querying of Probabilistic RFID Data in a Location Tracking Context

RFID applications usually rely on RFID deployments to manage high-level events such as tracking the location that products visit for supply-chain management, localizing intruders for alerting services, and so on. However, transforming low-level streams into high-level events poses a number of challenges. In this paper, we deal with the well known issues of data redundancy and data-information mismatch: we propose an on-line summarization mechanism that is able to provide small space representation for massive RFID probabilistic data streams while preserving the meaningfulness of the information. We also show that common information needs, i.e. detecting complex events meaningful to applications, can be effectively answered by executing temporal probabilistic SQL queries directly on the summarized data. All the techniques presented in this paper are implemented in a complete framework and successfully evaluated in real-world location tracking scenarios

Enchancing RFID data quality and reliability using approximate filtering techniques

Radio Frequency Identification (RFID) is an emerging auto-identification technology that uses radio waves to identify and track physical objects without the line of sight. While delivering significant improvements in various aspects, such as, stock management and inventory accuracy, there are serious data management issues that affect RFID data quality in preparing reliable solutions. The raw read rate in real world RFID deployments is often in the 60-70% range and naturally unreliable because of redundant and false readings. The redundant readings result in unnecessary storage and affect the efficiency of data processing. Furthermore, false readings that focused on false positive readings generated by cloned tag could be mistakenly considered as valid and affects the final results and decisions. Therefore, two approaches to enhance the RFID data quality and reliability were proposed. A redundant reading filtering approach based on modified Bloom Filter is presented as the existing Bloom Filter based approaches are quite intricate. Meanwhile, even though tag cloning has been identified as one of the serious RFID security issue, it only received little attention in the literature. Therefore we developed a lightweight anti-cloning approach based on modified Count- Min sketch vector and tag reading frequency from e-pedigree in observing identical Electronic Product Code (EPC) of the low cost tag in local site and distributed region in supply chain. Experimental results showed, that the first proposed approach, Duplicate Filtering Hash (DFH) achieved the lowest false positive rate of 0.06% and the highest true positive rate of 89.94% as compared to other baseline approaches. DFH is 71.1% faster than d-Left Time Bloom Filter (DLTBF) while reducing amount of hashing and achieved 100% true negative rate. The second proposed approach, Managing Counterfeit Hash (MCH) performs fastest and 25.7% faster than baseline protocol (BASE) and achieved 99% detection accuracy while DeClone 64% and BASE 77%. Thus, this study successfully proposed approaches that can enhance the RFID data quality and reliability

Approximate filtering of redundant RFID data streams in mobile environment

U zadnje vrijeme RFID tehnologija (Radio Frequency Identification Technology) se naveliko rabi u mnogim aplikacijama kao što su nadgledanje i praćenje objekta, zahvaljujući jedinstvenim značajkama kao što su beskontaktna, brza i simultana identifikacija više ciljeva. Međutim, zbog interferencije faktora okoline i potrebe za detekcijom u realnom vremenu, podaci koje su RFID čitači prikupili često su puni redundancije, a to može smanjiti učinkovitost obrade RFID aplikacijskih servera, pa čak rezultirati i donošenjem krivih zaključaka. Stoga je neophodno potrebno filtrirati redundantne podatke u RFID sustavima prije nego se prenesu do naprednijih aplikacija. U svrhu podržavanja aproksimativnog filtriranja RFID nizova podataka u mobilnom okruženju, u radu se pokušava analizirati mehanizam za učinkovito redundantno filtriranje modelom kliznog prozora. Najprije se daje razvoj aplikacije RFID nizova podataka i arhitektura RFID sustava utemeljeni na međusoftveru. Zatim se predlaže vremensko-prostorni Bloom filtar utemeljen na kliznim prozorima koji proširuje niz podataka s jednom dimenzijom u standardnom Bloom filtru na filtar s dvije dimenzije, pohranjujući i čitača IDs-a i promatrane vremenske oznake originalnih promatranih stavki. U međuvremenu, kako bi se osigralo da se lažno pozitivna brzina ne poveća zbog toga što se popunio prostor filtra, predlažemo strategiju slučajnog nestajanja za brisanje zastarjelih elemenata. Relativno učestale pogreške predloženog filtra, uključujući lažno pozitivne i lažno negativne, teorijski se analiziraju. Eksperimentalni rezultati pokazuju da predloženi filtar može učinkovito filtrirati vremenski redundantne podatke te uspješno locirati RFID objekte.Recently, RFID technology has been widely used in many applications such as object monitoring and tracing due to the unique features such as non-contact, automatic, fast and multi-target identification simultaneously. However, because of the interference of environmental factors and the requirement of real-time detection, the data collected by the RFID readers are often full of redundancy, which may reduce the processing efficiency of RFID application servers, even lead to making false decisions. Therefore, it is of definite necessity to filter the redundant data in RFID systems before transmitting them to the upper applications. In order to support approximate filtering of RFID data streams in mobile environment, this paper intends to study effective redundant filtering mechanism in the sliding window model. Firstly, we introduce the application background of RFID data streams and the RFID system architecture based on middleware. Then, we propose a temporal-spatial Bloom filter based on sliding windows, which extends the one-dimension array in the standard bloom filter to a two-dimension array, storing both reader IDs and the observed timestamps of original observation items. Meanwhile, in order to guarantee the false positive rate does not increase due to the reason that the space of the filter becomes full, we suggest a random decay strategy for deleting the expired elements. The error rates of the suggested filter, including false positives and false negatives, are analysed in theory. Experimental results show that the suggested filter can filter time redundant data effectively and has a good performance to deal with location movement of RFID objects

RFID Data Reliability Optimiser Based on Two Dimensions Bloom Filter

Radio frequency identification (RFID) is a flexible deployment technology that has been adopted in many applications especially in supply chain management. RFID system used radio waves to perform wireless interaction to detect and read data from the tagged object. However, RFID data streams contain a lot of false positive and duplicate readings. Both types of readings need to be removes to ensure reliability of information produced from the data streams. In this paper, a single approach, which based on Bloom filter was proposed to remove both dirty data from the RFID data streams. The noise and duplicate data filtering algorithm was constructed based on bloom filter. There are two bloom filters in one algorithm where each filter holds function either to remove noise data and to recognize data as correct reading from duplicate data reading. Experimental results show that our proposed approach outperformed other existing approaches in terms of data reliability

RFID data reliability optimizer based on two dimensions bloom filter

Radio Frequency Identification (RFID) is a flexible deployment technology that has been adopted in many applications especially in supply chain management. It provides several features such as to monitor, to identify and to track specific item hidden in a large group of objects in a short range of time. RFID system uses radio waves to perform wireless interaction to detect and read data from the tagged object. However, RFID data streams contain a lot of false positive and duplicate readings. Both types of readings need to be removed to ensure reliability of information produced from the data streams. A small occurrence of false positive can change the whole information, while duplicate readings unnecessarily occupied storage and processing resources. Many approaches have been proposed to remove false positive and duplicate readings, but they are done separately. These readings exist in the same data stream and must be removed using a single mechanism only. In this thesis, an efficient approach based on Bloom filters was proposed to remove both noisy and duplicate data from the RFID data streams. The noise and duplicate filter algorithm was constructed based on bloom filter. There are two bloom filters in one algorithm where each filter holds function either to remove noise data and to recognize data as correct reading from duplicate data reading. In order to test the algorithm, synthetic data was generated by using Poisson distribution. The simulation results show that our proposed approach outperformed other existing approaches in terms of data reliability

Improving Group Integrity of Tags in RFID Systems

Checking the integrity of groups containing radio frequency identification (RFID) tagged objects or recovering the tag identifiers of missing objects is important in many activities. Several autonomous checking methods have been proposed for increasing the capability of recovering missing tag identifiers without external systems. This has been achieved by treating a group of tag identifiers (IDs) as packet symbols encoded and decoded in a way similar to that in binary erasure channels (BECs). Redundant data are required to be written into the limited memory space of RFID tags in order to enable the decoding process. In this thesis, the group integrity of passive tags in RFID systems is specifically targeted, with novel mechanisms being proposed to improve upon the current state of the art. Due to the sparseness property of low density parity check (LDPC) codes and the mitigation of the progressive edge-growth (PEG) method for short cycles, the research is begun with the use of the PEG method in RFID systems to construct the parity check matrix of LDPC codes in order to increase the recovery capabilities with reduced memory consumption. It is shown that the PEG-based method achieves significant recovery enhancements compared to other methods with the same or less memory overheads. The decoding complexity of the PEG-based LDPC codes is optimised using an improved hybrid iterative/Gaussian decoding algorithm which includes an early stopping criterion. The relative complexities of the improved algorithm are extensively analysed and evaluated, both in terms of decoding time and the number of operations required. It is demonstrated that the improved algorithm considerably reduces the operational complexity and thus the time of the full Gaussian decoding algorithm for small to medium amounts of missing tags. The joint use of the two decoding components is also adapted in order to avoid the iterative decoding when the missing amount is larger than a threshold. The optimum value of the threshold value is investigated through empirical analysis. It is shown that the adaptive algorithm is very efficient in decreasing the average decoding time of the improved algorithm for large amounts of missing tags where the iterative decoding fails to recover any missing tag. The recovery performances of various short-length irregular PEG-based LDPC codes constructed with different variable degree sequences are analysed and evaluated. It is demonstrated that the irregular codes exhibit significant recovery enhancements compared to the regular ones in the region where the iterative decoding is successful. However, their performances are degraded in the region where the iterative decoding can recover some missing tags. Finally, a novel protocol called the Redundant Information Collection (RIC) protocol is designed to filter and collect redundant tag information. It is based on a Bloom filter (BF) that efficiently filters the redundant tag information at the tag’s side, thereby considerably decreasing the communication cost and consequently, the collection time. It is shown that the novel protocol outperforms existing possible solutions by saving from 37% to 84% of the collection time, which is nearly four times the lower bound. This characteristic makes the RIC protocol a promising candidate for collecting redundant tag information in the group integrity of tags in RFID systems and other similar ones

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

Big data analytics for large-scale wireless networks: Challenges and opportunities

© 2019 Association for Computing Machinery. The wide proliferation of various wireless communication systems and wireless devices has led to the arrival of big data era in large-scale wireless networks. Big data of large-scale wireless networks has the key features of wide variety, high volume, real-time velocity, and huge value leading to the unique research challenges that are different from existing computing systems. In this article, we present a survey of the state-of-art big data analytics (BDA) approaches for large-scale wireless networks. In particular, we categorize the life cycle of BDA into four consecutive stages: Data Acquisition, Data Preprocessing, Data Storage, and Data Analytics. We then present a detailed survey of the technical solutions to the challenges in BDA for large-scale wireless networks according to each stage in the life cycle of BDA. Moreover, we discuss the open research issues and outline the future directions in this promising area