ABSTRACT Event, Condition and Action (ECA) rules developed for



 Radio Frequency Identification
(RFID) tags has been proposed for use in novel ways for hundreds of
applications. RFIDs hold the promise of revolutionizing business processes.
This paper focuses on how RFID Technology can be used to solve problems faced
by public transport in metropolitan cities of the country. Automated tracking
of buses can be used to provide useful estimates of arrival times and enhance
commuter convenience. There are, however, formidable hurdles in the way of
widespread RFID deployment. From a systems perspective, we highlight and
explore the problem of data capturing, storage and retrieval and how Event,
Condition and Action (ECA) rules
developed for active databases can help us in managing the huge number of
events generated each day. We also discuss how the collected data can be used
to predict bus movement timings in order to provide better service.

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Keywords:  RFID, ECA, Data Management, Forecasting, Public




Radio Frequency Identification (RFID) tags
have emerged as a key technology for real-time asset tracking. It is an
automated identification technology that allows for non-contact reading 1 of
data making it attractive in verticals such as manufacturing, warehousing,
retail 2, 3, logistics, pharmaceuticals 4, health care 5 and security.
RFID systems are foreseen as replacement to the legacy bar code system of
identifying an item. One of the major advantages of RFIDs over bar codes is
that it is a non-line-of-sight technology – thus every item need not be handled
manually for reading. In addition, RFID readers can read tags even when they
are hidden.

The bus system is one of the largest in transportation
medium all around the India. Often the buses are overcrowded. As a result
commuters usually spend long hours at bus stops waiting. The bus arrivals at a
particular stop are stochastic variables thanks to traffic congestion. This
unpredictability can be partly alleviated by deploying a bus tracking and
reporting system. There are a couple of ways to address this problem; one
approach is to use the Global Positioning System (GPS) and another is through
the use of RFIDs.

In this paper, we propose a solution using
RFID technology    and present issues
related to its deployment.

In section 2 we briefly introduce RFID
technology and its components. Section 3 explains the solution for the proposed
problem using RFID Technology. In sub-section 4.1 & 4.2 we pose the
challenges of handling huge number of events and discuss how ECA rules 16 can
be used in a distributed manner to handle event explosion. Section 5 provides a
framework for using the collected data in predicting arrival times for buses at
different stops.


Figure 1. Radio Frequency Identification




RFID system comprises of RFID tag, RFID
transceiver, servers, and middleware and application software. The RFID tag is
a low functionality microchip with an antenna connected to the item to be
tracked, or identified, and stores the unique identification number of the
item. These chips transform the electromagnetic energy of radio-frequency
signals/queries from a RFID reader/transceiver to respond by sending back
information they enclose. The readers communicate with the tags for
reading/writing the information stored on them as well as update the servers
which may be standalone or networked. Readers may be fixed or mobile. Finally,
a computer hosting a specific RFID application pilots the reader and processes
the data it sends.


Figure 2. RFID System Components


RFID Tags can be Active or Passive. Active
RFID tags (beacons) are powered by an internal battery which is used to power
ICs and generate the outgoing signal. They are typically read/write type and
the size of memory used varies according to application requirements. The battery
supplied power of an active tag gives it a longer read range, but such tags
have large size, higher cost, and a limited operational life.

Passive RFID tags operate without an
external power source. They use the operating power generated from the reader. Electrical
current induced in the antenna by the incoming radio frequency signal provides
enough power for the CMOS integrated circuit in the tag to power up and
transmit a response. The absence of battery makes them lighter than active
tags, less expensive, and offers a virtually unlimited operational lifetime.
But they have shorter read range than active tags and require a higher-powered
reader. Passive tags are typically Read-only and are programmed with a unique
set of data (usually 32 to 128 bits) that cannot be modified.

The data transmitted by the tag may
provide identification or location information, or specifics about the product
tagged, such as price, color, date of purchase, etc. The interrogator, an
antenna packaged with a transceiver and decoder, emits a signal activating the
RFID tag so it can read and write data to it. When an RFID tag passes through
the electromagnetic zone, it detects the reader’s activation signal. The reader
decodes the data encoded in the tag’s integrated circuit (silicon chip) and the
data is passed to the host computer.

RFID system can also be distinguished by
frequency range. Low-frequency (30 KHz to 500 KHz) systems have short reading
range and lower system costs. They are most commonly used in security access,
asset tracking, and animal identification applications. High-frequency (850 MHz
to 950 MHz and 2.4 GHz to 2.5 GHz) systems, offering long read range (greater
than 90 feet) and high reading speeds, are used for applications such as
tracking fast moving vehicles and automated toll collection. However,
high-frequency RFID systems incur higher system costs.


3 Proposed RFID based Solution


We first propose a solution based on RFIDs
followed by an introduction to the GPS based solution.


3.1 RFID based


Each bus could have an RFID tag affixed to
it while the readers are conveniently mounted at intersections, lamp posts or
bus stops. The crucial information associated with a tag is the specific bus
number, the capacity of the bus, the route number currently plying and the
termination point (for example, during non-peak hours a bus may terminate at a
depot before its usual terminating point). Tag readers continually monitor
passing buses and transfer this information in real-time to a central computer.

A commuter with access to a cell phone
could subscribe to the following service from the mobile network provider. The
subscriber may enter his destination stop, D, (and optionally location of
nearest bus stop) on his cell phone in the comfort of his home. The system will
inform him of the relevant buses closest to him and expected arrival times of
these buses.

The above service can be provided by the
mobile network operator. The provider contacts a central computer to obtain the
set of buses traveling to D through the closest bus stop to the customer. This
list is obtained in sorted order and could possibly be filtered or enhanced in
some way depending on the preferences of the customer. For example, if there
are several bus stops in proximity to the commuter, information on relevant
arrivals at all these bus stops can be provided. The provider can provide
customized service to each subscribing commuter for a small fee.

This service can be used for multiple
purposes to locate and control bus movement in the metro city. For example, in
the event of an accident causing traffic congestion on a particular road, the
buses leading to the road can be informed. In some cases, the routes of the bus
can be changed temporarily and accordingly bus driver can be informed via
wireless network. Or if it is found that a particular bus was stuck in traffic
and that has led to a smaller gap with the next bus, the bus driver of the next
bus can be informed to slow down to increase the gap. Many such applications
can be thought of based on such an RFID application.

Before describing some of the research
challenges in deploying such a system, we first describe an alternate
technology that can also achieve similar objectives.


     3.2 GPS
– based approach


A GPS tracking system uses GPS (Global
Positioning System) 14 to determine the location of a vehicle, person, or pet
and to record the position at regular intervals in order to create a track file
or log of activities. The recorded data can be stored within the tracking unit,
or it may be transmitted to a central location, or Internet-connected computer,
using a cellular modem, 2-way radio, or satellite. This allows the data to be
reported in real-time; using either web browser based tools or customized
software’s. More often, GPS receivers are used for navigation, positioning,
time dissemination, and other research. Research projects 15 include using
GPS signals to measure atmospheric parameters.

Though GPS based systems are widely used
in the developed countries, there exists some serious limitations of this
technology in developing countries like India. Firstly the coverage of GPS
system in developing countries is not as wide. Secondly, effective
implementation of a GPS system will require mapping the roads to the GPS system.
Such mapping so far does not exist for metro cities in India. In the developed world, road
infrastructure is almost static. However in the developing world metros (e.g.
Mumbai, Delhi, Chennai, Hyderabad), new roads are being constantly
built and layout of old roads is frequently changed. This will require
remapping of roads at regular intervals. On the other hand with RFID systems
new roads and change of old roads will require just reinstalling few RFID
scanners or changes in the positions of these scanners.


4 Research
Challenges of RFID based Solutions


There are many technical challenges
associated with deployment of RFIDs. For example, there are problems with false
or missing reads as a result of radio waves being easily distorted, detected,
absorbed, or interfered with. There are a number of system-level challenges
such as determining the number, type and placement of readers. In this paper we
primarily focus on the challenges related to data management which deals with
capturing, storing and querying RFID data.


4.1 Data management problem


BEST runs over 335 bus routes in Mumbai
13. The average time between two buses on a route is about 15 minutes. But,
due to traffic congestion and peak crowds, the maximum time may exceed 30
minutes. Overall, there are around 3380 buses in B.E.S.T. which carry around
45,00,000 passengers everyday.

BEST buses, on many routes run for 21
hours (from 4:00AM to 1:00AM) a day. So the number of trips along a route will
be 84 trips (21 x 60 /15), on an average. Thus, with 84 trips per bus route,
and an estimated average number of bus stops per route = 17, we could estimate
the number of events that will be generated in this scenario as below:

84 trips x 335 routes x 17 stops = 4,78,380 events

Processing and relating so many events to
derive a meaningful real-time decision is a challenging task. The above
estimates occur in the case when readers are placed at bus stops and depots and
when only BEST buses are taken into consideration. If the data is captured not
only from bus stops but also from several traffic lights to get intermediate
information between two bus stops the number of events will further increase.
This situation will be exacerbated if other kinds of traffic movements such as
taxis, trucks are also monitored.

Managing such high volume of events and
generated data poses the challenges to applications as well as back-end
databases. This data is often redundant and needs to be filtered/cleaned and
consolidated in order to occupy less space in database. In doing so, care must
be taken that no useful information is lost.

Researchers in the database community have
presented techniques and models for warehousing as well as cleaning/filtering
RFID data. EPC-IS 9 and PML Core 10 are the RFID system standardization
efforts by auto-ID center. 9 Summarizes the data characteristics, models data
as events and provides some reference relation to represent data. Dynamic
Relationship ER (DRER) presented in 17 is an expressive temporal data model
which enables support for basic queries for tracking and monitoring RFID tagged
objects. A simple observation that objects move together in initial stages
bring a couple of more proposals. Hu et al. 12 used bitmap data type to
compress the information corresponding to objects that move together.
RFID-Cuboids 11 are a new warehousing model that preserves object transitions
while providing significant compression and path-dependent aggregates. FlowCube
11 is a method to construct a warehouse of commodity flows. Some of these are
simple representations of various relationships in the Relational DBMS.


4.2 Real-time Decision Making


In this sub-section we describe how the
application of Event-Condition-Action (ECA) framework can address some of the
real-time event management issues.

Assume that we record each instance of
reader-tag interaction with the help of a tuple: {object_epc, location,
timestamp}. Here, object_epc is Electronic Product Code used to uniquely
identify an object (the bus and the route), location denotes the place where
the interaction took place (say in some bus stop), and  timestamp denotes the time at which the
interaction took place.

Figure 3: Data Aggregation & Partitioning in


Each event is characterized by certain dimensions like
time of scan, location of the reader, etc. Similarly, the conditions and
actions also have some dimensions. Consider an example event that the distance
between two consecutive buses is below a certain threshold. This can be
expressed in the ECA form as:

EVENT e1 = {location =
l1, timestamp = t1, epc = {route1, bus1}}

EVENT e2 = {location =
l2, timestamp = t2, epc = {route2, bus2}}

EVENT e3 = {e1 AND e2}

= {e2.l2 = e1.l1 + 1 AND e2.t2