RFID and RFID Data Processing.ppt

Handling RFID Data

Zhang Yelei

April 9, 2023

04/09/23Handling RFID Data2

Presentation Outline

RFID Introduction Data Processing Data Integration Questions



RFID Introduction What is RFID Standards System architecture Applications Challenges

Data Processing Data Integration Questions


What is RFID (1)

RFID: Radio Frequency Identification A new method/technology of remotely

storing and retrieving data Need to use RFID readers and tags

A little historical information First applied in World War II by UK First paper exploring RFID “Communication

by Means of Reflected Power” by Harry Stockman published in 1948

Not until 1990s it was widely deployed


What is RFID (2)Roy Want, RFID: a Key to Automate Everything


What is RFID (3)

RFID Tags (transponder) Low cost, not application-specific Operate on frequencies ranging from

100KHz to beyond 2.5 GHz Type: passive, semi-passive, active Majority are write-once/read-only, others

offer r/w capability Readability influenced by factors like

frequency, environment, tag position, antenna direction etc. *

• * Christian Floerkemeier and Matthias Lampe’s cards playing experiment illustrates some sources of errors in tag reading.


What is RFID (4)

RFID ReadersPortable or fixedUse serial port (RS232) or network

interface/protocol (wired/ wireless connection) to communicate with computers

Radio could be software defined


Related Standards

Standards about frequencies and communication Identification cards and related areas: ISO/IEC 10536,

ISO/IEC 14443 … Automatic identification and data capture technologies:

ISO/IEC 15961, ISO/IEC 15962… Conformance: ISO/IEC 18046, ISO/IEC 18047 ETSI, ERO

Standards about the data format on tags EPCGlobal: focuses on the standardization of the data

format EPC, electronic product code (64,96,256 bits long), is

now the internationally accepted item-level code.


System Architecture

tagtag

tagtag

tagtag

tag

Reader Reader Reader

Savant Savant

Central Information Server

Savant: mapping low-level data stream from readers to a more manageable form, cleaning data, supporting simple queries and installed standard queries;

Central IS: provide high-level services that are easier for application to use.


Applications (1)

Business applications Transport and logistics Supply chain management Agriculture

Government applications Defense and security Library systems

Consumer applications Personal welfare and safety Sports and leisure Shopping and dining out Smart homes

……


Applications (2)

EPCGlobal NetworkA method of using RFID to share

information in the global supply chain 5 components

EPC (Electronic Product Code), ID system, EPC middleware, Discovery services, EPC information services (EPC IS)


Applications (3)

Source: Sun and RFID


Challenges

Reducing tag costs Global standards

Frequency of tags and readers Other specifications…

IT infrastructure Data processing: handling large amount of

stream data online, effecient use of storage, network bandwidth, and so on.

Integration: with databases, data warehouses and enterprise applications

Security issues ……



RFID Introduction Data Processing

Challenges of Handling Data Stream DSMS vs DBMS Projects Problems Solutions

Data Integration Questions


Challenges of Handling Data Stream What is the data stream

A potentially unbounded sequence of tuples (transactional data stream and measurement data stream*)

Data is continuous, infinite Most operations should be done online without interrupting

data stream. Data recovery could also be a serious problem

Computational resources are limited Real-time data stream requires efficient data handling

Complex queries need to be performed nearly real-time

*AT&T Labs-research, Data Stream Query Processing


DSMS vs. DBMS

DSMS DAHP model Deals with tuple sequences Complex queries executed real-time and online Database updated frequently Query persistent, plan adaptive, answer approximate

DBMS HADP model Deals with tuple sets Complex queries usually executed offline Database relatively stable Query transient, plan fixed, answer exact


Research Projects

Aurora (supports cq, ad-hoc query, and materialized view) Aims to better support monitoring applications

Borealis (distributed SPE, QoS based techniques) A distributed stream processing engine based on Aurora

and Medusa TelegraphCQ (focuses on hybrid, ad-hoc query)

Intends to handle large streams of continuous queries over high-volume, hightly-variable data streams

PSoup (focuses on both ad-hoc and continuous query) A query processor that supports both streaming data and

streaming query STREAM

A general purpose DSMS prototype GigaScope, Hancock, Nile, TinyDB, COUGAR ……


Problems

Data models Window operations Query languages Query processing System optimization


Solutions (1): Data Models

Relation-based Models Aurora: stream type ( TS, A1,…, An) PSoup STREAM: a stream S is an unbounded bag

of pairs <s,т>, a relation R is a time-varying bag of tuples

Object-based Models COUGAR and Tribeca: data types are

associated with methods


Solutions (2): Window Operations Why?

Time/ordering is a very important aspect of streaming data;

Data processing is still based on a finite data set. How to define?

Window can be time-based or tuple-based, or partitioned sliding window.

Types Fixed, snapshot Landmark Sliding


Solutions (3): Query Languages

Relation-based Languages CQL used by STREAM: select * from S1

[Rows 1000], S2 [Range 2 Minutes] where S1.A=S2.A and S1.A>10

Object-based Languages * COUGAR: select R.s.getTemperature() from

R where R.floor=3 and $every(60) Others (Procedural Languages ??) *

Aurora: 7 new operators like “map”, “resample” are defined

* Golab and Ozsu, Data Stream Management Issues ----- A Survey


Solutions (4): Query Processing

Use connection points to cach streaming data (Aurora) TelegraphCQ use OSCAR for the trade-off of

quality and size of the data (from the disk) Attach data queues with operators

In Aurora, queue is managed by successors’ pointers.

Shared modules among different queries In STREAM, synopses is replaced by stub

and store to reduce redundancy.


Solutions (5): System Optimization Data gathering

Run-time statistics are gathered Inserting, combining, reordering operators Train scheduling, superbox scheduling (like

batch operation) Load shedding

Static analysis and delay-based dynamic analysis for overload detection

By dropping tuples, or by value-based tuple filtering



RFID Introduction Data Processing Data Integration

Research gapDesign considerations

Questions


Research Gap

Academic research Focuses on issues like processing ability,

efficient deployment, antenna design, and so on.

Lack of the emphasis on the effective interaction with data warehouses and high-level applications.

Enterprise IS and data warehouse Emerged in 1980s, intend to deal with

discrete, aggregated data, not continuous, real-time, single-item data.


Design considerations

Manage data storage Which data should be saved and where Eliminate redundant data Handle historical data

Query data Study business scenarios Identify typical on-site queries and data warehouse

queries Real-time processing

Design of triggers Link real-time events with business processes. (for

example, BPEL process and web service)



RFID Introduction Data Processing Data Integration Questions