The design and development of multi-agent based RFID middleware system for data and devices management

Thesis (D. Tech. (Electrical Engineering)) - Central University of technology, Free State, 2012Radio frequency identification technology (RFID) has emerged as a key technology for automatic identification and promises to revolutionize business processes. While RFID technology adoption is improving rapidly, reliable and widespread deployment of this technology still faces many significant challenges. The key deployment challenges include how to use the simple, unreliable raw data generated by RFID deployments to make business decisions; and how to manage a large number of deployed RFID devices. In this thesis, a multi-agent based RFID middleware which addresses some of the RFID data and device management challenges was developed. The middleware developed abstracts the auto-identification applications from physical RFID device specific details and provides necessary services such as device management, data cleaning, event generation, query capabilities and event persistence. The use of software agent technology offers a more scalable and distributed system architecture for the proposed middleware. As part of a multi-agent system, application-independent domain ontology for RFID devices was developed. This ontology can be used or extended in any application interested with RFID domain ontology. In order to address the event processing tasks within the proposed middleware system, a temporal-based RFID data model which considers both applications’ temporal and spatial granules in the data model itself for efficient event processing was developed. The developed data model extends the conventional Entity-Relationship constructs by adding a time attribute to the model. By maintaining the history of events and state changes, the data model captures the fundamental RFID application logic within the data model. Hence, this new data model supports efficient generation of application level events, updating, querying and analysis of both recent and historical events. As part of the RFID middleware, an adaptive sliding-window based data cleaning scheme for reducing missed readings from RFID data streams (called WSTD) was also developed. The WSTD scheme models the unreliability of the RFID readings by viewing RFID streams as a statistical sample of tags in the physical world, and exploits techniques grounded in sampling theory to drive its cleaning processes. The WSTD scheme is capable of efficiently coping with both environmental variations and tag dynamics by automatically and continuously adapting its cleaning window size, based on observed readings

VSMURF:A Novel Sliding Window Cleaning Algorithm for RFID Networks

Radio Frequency Identification (RFID) is one of the key technologies of the Internet of Things (IoT) and is used in many areas, such as mobile payments, public transportation, smart lock, and environment protection. However, the performance of RFID equipment can be easily affected by the surrounding environment, such as electronic productions and metal appliances. These can impose an impact on the RF signal, which makes the collection of RFID data unreliable. Usually, the unreliability of RFID source data includes three aspects: false negatives, false positives, and dirty data. False negatives are the key problem, as the probability of false positives and dirty data occurrence is relatively small. This paper proposes a novel sliding window cleaning algorithm called VSMURF, which is based on the traditional SMURF algorithm which combines the dynamic change of tags and the value analysis of confidence. Experimental results show that VSMURF algorithm performs better in most conditions and when the tag’s speed is low or high. In particular, if the velocity parameter is set to 2 m/epoch, our proposed VSMURF algorithm performs better than SMURF. The results also show that VSMURF algorithm has better performance than other algorithms in solving the problem of false negatives for RFID networks

A Review on Missing Tags Detection Approaches in RFID System

Radio Frequency Identification (RFID) system can provides automatic detection on very large number of tagged objects within short time. With this advantage, it is been using in many areas especially in the supply chain management, manufacturing and many others. It has the ability to track individual object all away from the manufacturing factory until it reach the retailer store. However, due to its nature that depends on radio signal to do the detection, reading on tagged objects can be missing due to the signal lost. The signal lost can be caused by weak signal, interference and unknown source. Missing tag detection in RFID system is truly significant problem, because it makes system reporting becoming useless, due to the misleading information generated from the inaccurate readings. The missing detection also can invoke fake alarm on theft, or object left undetected and unattended for some period. This paper provides review regarding this issue and compares some of the proposed approaches including Window Sub-range Transition Detection (WSTD), Efficient Missing-Tag Detection Protocol (EMD) and Multi-hashing based Missing Tag Identification (MMTI) protocol. Based on the reviews it will give insight on the current challenges and open up for a new solution in solving the problem of missing tag detection

The Challenges and Issues Facing the Deployment of RFID Technology

Griffith Sciences, School of Information and Communication TechnologyFull Tex

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

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