Research on Data Filtering Technology Based on the RFID Middleware
in the Internet of Things
Kun Huang 1, Bing-wu Liu 2, Jun-tao Li 3
1
Graduate Division, Beijing Wuzi University, Beijing, 101149, China
Graduate Division, Beijing Wuzi University, Beijing, 101149, China
3
School of Information, Beijing Wuzi University, Beijing, 101149, China
(1huang__kun@126.com, 2Liubingwu@bwu.edu.cn, 3 Lijuntao@bwu.edu.cn)
2
Abstract-Middleware technology is becoming a hot topic
in research due to its important position in the application of
radio frequency identification (RFID). This paper first
introduced the architecture of the Internet of Things, as well
as the important position of the RFID middleware, and then
summarizes the existing data filtering methods and
characteristics of several filters. At last build the initial form
of a combination of filters, take smart shelves for example.
Keywords-RFID middleware, Internet of Things, Data
filtering, Smart shelves
I.
INTERNET OF THINGS
Internet of things, abbreviated as IOT, is a network
which through radio frequency identification (RFID) ,
infrared sensors, global positioning system, laser scanners
and other information sensing device, make articles
connect with internet to realize information exchange and
communication according to the agreed agreement ,in
order to achieve intelligent identification, location,
tracking, monitoring, and management. Internet of Things
is "the Internet connects material objects". It has two
meanings [1]: first, the core and foundation of the Internet
of Things is still the Internet, it is the extension and
expansion of the Internet network. Second, its client-side
spread and extended to any goods and articles for
information exchange and communication.
The application of the Internet of Things is bound to
produce vast amounts of sensory data, how to collect,
store and process this mass data in real time is a problem.
In addition, a number of sensory devices are provided by
different hardware vendors, and now China has not yet
formed a uniform standard in interface and protocol
-aware layer. How can complex equipment be seamlessly
integrated into the existing system? How to carry out a
unified monitoring and management for the sensory
devices? How to seamlessly integrate new equipment into
the Internet? These are big challenges to the development
of Internet and which is the responsibility of the Internet
of Things application middleware have to shoulder. In
this paper the Internet of Things middleware mainly
refers to the RFID middleware [2].
___________________________
Financial supported by: Beijing Natural Science Foundation Project
(Class B) (Key Project of Beijing Municipal Education Commission
Science and Technology Development Plan), Research on Intelligent
Logistics System Based on Internet of Things Technology,
(NO.:KJ201210037037)
II.
RFID MIDDLEWARE
RFID is the abbreviation for "radio frequency
identification", which is a non-contact automatic
identification and data collection technology [3]. The
application of RFID technology is expanding rapidly after
the 1990s. From 2000 year to the present, RFID product
range has been significantly enriched and costs is
becoming lower and lower, a variety of new applications
are emerging.
RFID System includes RFID hardware and
application support software. Hardware part is made up of
electronic tags and readers. Electronic tags are data
carriers, which is divided into passive RFID label,
half-passive RFID tags and active RFID tag. Passive
electronic tag extracts the radio frequency energy radiate
by the reader as its working power and transmits the label
information to the reader; Semi-passive tags and active
tags powered by battery. RFID middleware, known as the
nerve center of the RFID systems[4], is the most important
part of the RFID software system, it directly face mass
data collected by hardware, filter the data and submit to
the high-level application software after effective
packaging. Nowadays, research on RFID middleware is
mainly concentrated on how to filter the vast amounts of
data, the redundancy, and exploit the useful information
[5]
.The functions of RFID middle ware are shown as
Figure1.
Application
software systems
Application
software systems
Interfaces and protocols
RFID Middleware
Interfaces and protocols
RFID reader
RFID reader
Radio Frequency Identification
Tags
FigureI.
Tags
RFID Middleware diagram
III. DATA FILTERING
The original data collected from the underlying
hardware is enormous, yet truly meaningful to the user is
not so much. If redundant data is not filtered out, it will
bring three aspects of the burden:
(1) Burden on network bandwidth due to the transfer
of large amounts of data;
(2) Burden on Data processor due to the needs of
handling large amounts of data;
(3) Burden on data storage due to the database need to
store large extra amounts of data.
Middleware receive data from RFID reader, there will
exist some redundant information and also wrong
information. So it is necessary to filter the data, this is
also its important feature, filter's purpose is to eliminate
redundant data, eliminate "useless" information and
transmit "useful" information application.
Redundant data that middleware needs to filter out
including:
(1)In a short term the same RFID readers duplicate
reported the same data. When detecting node location,
fixed node information duplicate reported; nodes are
repeated tested when the node goes into and out of an
area.
(2)Neighboring readers report the same data. Readers
have a missing rates, it has a relationship with the
placement of the antenna, the distance from readers and
the texture. Typically to ensure the read rates, there may
place more than one reader in the same place. More than
one reader report the monitored articles may generate
repeated data.
(3)In addition to the above issues, many users might
also want to get some information for a particular node,
the information for new node and disappeared node or
just some special node.
When users use the data, what expected is minimized
redundancy and gets accurate data close to the demands,
it's up to middleware to solve this problem. Solution for
redundant information is to set filters for processing in
middleware. According to the requirements of different
systems, it need to set different types of filters; according
to some of the redundant data listed above, current filter
can be summed up in the following three ways:
(1)Weight filter
The data we collect often produce a significant
amount of redundant data, filter can eliminate the
redundant data[6].For filter, we adopt a filtering algorithm
as follows: assumes that the data middleware acquired
can be expressed as (ReaderID, NodeID, Timestamp),
wherein ReaderID is the ID of RFID reader, NodeID
identifying RFID node ID,Timestamp represents the
node's read time. In the filtering process, the data will be
put into a Hash table, use NodeID as hash table keys. We
define a time interval, when the reader read a new node
data, check if there are nodes with the same data in the
Hash table. If there are nodes with the same data and the
read time lag between the nodes is less than Timelnterval,
then consider the node as a repeat reading data that
should be filtered. Meanwhile update the node data’s read
time in the hash table. If there are nodes with the same
data and the read time lag between the nodes is greater
than Timelnterval, the node data is considered to be the
new node data that need be output. Meanwhile update the
node data’s read time in the hash table. If it does not exist,
then insert it into a Hash table and output the node data.
(2)Event filter
In this paper, middleware mainly filter three kinds of
nodes: new node, left node and currently active node. A
new node means the node appears this time that never
appear before. Left node is the node that has appeared
several times before but does not occur in the PersistTime.
Current active node represents a new node and the node
appeared last time and occurs in the PersistTime once
again.
For event filter [7], we adopt a filtering algorithm as
follows: assume that nodes data’s format is (ReaderID,
NodeID, Timelnterval). In the filtering process, put the
currently active node data into a Hash tables, regard
Node1D as Hash table key word. Set up another two
queues, hold new node data and node data that have
already left. When you are reading a node, checking
whether the Hash table exist the same node, if it does not
exist, then insert it into the Hash table and the new label
queue. If it exists, then updates the node’s reading time in
the Hash table. Traverse the Hash tables, put the node that
does not update in PersistTime into the queue of nodes
that has already left, and then remove it [8].
(3)Invalid RFID Data filter
In practical applications, data filters also have other
requirements rather than data redundancy filtering. Due to
instable signal or other interference factors, the RFID tags
of items which are on the shelves can not be detected in
each reader cycle; or when the customer pushed his cart
next to shelf, merchandises within the cart be read by the
readers in the shelf, this is invalid RFID data.
Identify the occasional data and erase it through
program is the key to invalid RFID data suppression.
Algorithm presented here uses a threshold [9], each label’s
report is given a certain amount of weight, reduce the
weight of labels that do not appear. When the label value
is be above or below the threshold, triggering the
appropriate label [10]. This algorithm is described as
follows:
1) Defines the label’s add up value after each
appearance as valueStep;
2) Defines the threshold that triggers the tag’s
appearance as fapp;
3) Defines the threshold that triggers the tag’s
disappearance as fdis;
4) Defines the label status’s field as detectStatus;
5) If the label appears, its weight adds valueStep;
6) If the label does not appear, its weight value will
reduce 1;
7) Label weight value greater than or equal to fapp,
detectStatus =false , the label appearance event is
triggered, generate a label appear records and then reset
detectStatus to true；
8) Label weight value less than or equal to fdis,
detectStatus =true, the label disappearance event is
triggered, generate a label disappear records, and reset
detectStatus to false;
In the algorithm above the threshold can be set
according to needs, because an invalid RFID data
occurrences less, it is difficult to appear above fapp or
below fdis, it can effectively inhibit invalid RFID data.
IV. FURTHER ANALYSIS OF THE DATA FILTER
In specific application, in order to ensure the
effectiveness of the information uploaded to the
upper-layer service interfaces, the three type filters above
are used in combination to improve the filters accuracy.
Following part sets the intelligent shelf in supermarket
which is popular in intelligent logistics system as an
example to illustrate the combination of a smart filter.
Figure 2 shows the filtering process of smart filter:
Reader
Eliminate redundant
Filter
Invalid RFID
Filter
Discard
Feedback
Event Filter
Discard
Discard
Supermarket application
software system
Tag data stream
Supermarket manage database
FigureII.
Flow chart of data filtering
By smart shelves, administrator can monitor shelf
articles in real time. First, when there is repeating entry of
articles already exists on the shelf, weight filter eliminate
redundant information. Second, administrators need to
know when a new product put on the shelves or what
merchandise is bought by customers, then event filter
work. Third, when the articles customs get from another
shelves passing the shelf, RFID reader may read this
product information and regard this product as a new
product, so an error occurs, invalid RFID filter has its
place.Thus, RFID middleware use triple filter
combination to filter out redundant information
effectively before the label information be passed to
upper-layer application software and reduce the burden
on the system.
V.
CONCLUSION
This paper study the basic knowledge of RFID
middleware in the internet of things, points out the
importance of RFID middleware in the internet of things.
Through research on the different features of the existing
filtering technology and the example of smart shelves,
this paper discussed the availability of the combinations
of filters. Hope this paper can contribute to the further
development of data filtering technology based on the
RFID middleware.
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