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

ABSTRACT  Radio Frequency Identification(RFID) tags has been proposed for use in novel ways for hundreds ofapplications. RFIDs hold the promise of revolutionizing business processes.

This paper focuses on how RFID Technology can be used to solve problems facedby public transport in metropolitan cities of the country. Automated trackingof buses can be used to provide useful estimates of arrival times and enhancecommuter convenience. There are, however, formidable hurdles in the way ofwidespread RFID deployment. From a systems perspective, we highlight andexplore the problem of data capturing, storage and retrieval and how Event,Condition and Action (ECA) rulesdeveloped for active databases can help us in managing the huge number ofevents generated each day.

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We also discuss how the collected data can be usedto predict bus movement timings in order to provide better service. Keywords:  RFID, ECA, Data Management, Forecasting, PublicTransportation. 1 INTRODUCTION  Radio Frequency Identification (RFID) tagshave emerged as a key technology for real-time asset tracking.

It is anautomated identification technology that allows for non-contact reading 1 ofdata 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 ofidentifying an item. One of the major advantages of RFIDs over bar codes isthat it is a non-line-of-sight technology – thus every item need not be handledmanually for reading. In addition, RFID readers can read tags even when theyare hidden. The bus system is one of the largest in transportationmedium all around the India. Often the buses are overcrowded.

As a resultcommuters usually spend long hours at bus stops waiting. The bus arrivals at aparticular stop are stochastic variables thanks to traffic congestion. Thisunpredictability can be partly alleviated by deploying a bus tracking andreporting system. There are a couple of ways to address this problem; oneapproach is to use the Global Positioning System (GPS) and another is throughthe use of RFIDs. In this paper, we propose a solution usingRFID technology    and present issuesrelated to its deployment. In section 2 we briefly introduce RFIDtechnology and its components. Section 3 explains the solution for the proposedproblem using RFID Technology. In sub-section 4.

1 & 4.2 we pose thechallenges of handling huge number of events and discuss how ECA rules 16 canbe used in a distributed manner to handle event explosion. Section 5 provides aframework for using the collected data in predicting arrival times for buses atdifferent stops.  Figure 1. Radio Frequency Identification 2 COMPONENTS of RFID System RFID system comprises of RFID tag, RFIDtransceiver, servers, and middleware and application software. The RFID tag isa low functionality microchip with an antenna connected to the item to betracked, or identified, and stores the unique identification number of theitem. These chips transform the electromagnetic energy of radio-frequencysignals/queries from a RFID reader/transceiver to respond by sending backinformation they enclose. The readers communicate with the tags forreading/writing the information stored on them as well as update the serverswhich may be standalone or networked.

Readers may be fixed or mobile. Finally,a computer hosting a specific RFID application pilots the reader and processesthe data it sends. Figure 2. RFID System Components RFID Tags can be Active or Passive. ActiveRFID tags (beacons) are powered by an internal battery which is used to powerICs and generate the outgoing signal. They are typically read/write type andthe size of memory used varies according to application requirements. The batterysupplied power of an active tag gives it a longer read range, but such tagshave large size, higher cost, and a limited operational life.

Passive RFID tags operate without anexternal power source. They use the operating power generated from the reader. Electricalcurrent induced in the antenna by the incoming radio frequency signal providesenough power for the CMOS integrated circuit in the tag to power up andtransmit a response. The absence of battery makes them lighter than activetags, less expensive, and offers a virtually unlimited operational lifetime.But they have shorter read range than active tags and require a higher-poweredreader. Passive tags are typically Read-only and are programmed with a uniqueset of data (usually 32 to 128 bits) that cannot be modified.The data transmitted by the tag mayprovide identification or location information, or specifics about the producttagged, such as price, color, date of purchase, etc.

The interrogator, anantenna packaged with a transceiver and decoder, emits a signal activating theRFID tag so it can read and write data to it. When an RFID tag passes throughthe electromagnetic zone, it detects the reader’s activation signal. The readerdecodes the data encoded in the tag’s integrated circuit (silicon chip) and thedata is passed to the host computer.RFID system can also be distinguished byfrequency range. Low-frequency (30 KHz to 500 KHz) systems have short readingrange and lower system costs.

They are most commonly used in security access,asset tracking, and animal identification applications. High-frequency (850 MHzto 950 MHz and 2.4 GHz to 2.5 GHz) systems, offering long read range (greaterthan 90 feet) and high reading speeds, are used for applications such astracking 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 RFIDsfollowed by an introduction to the GPS based solution.  3.

1 RFID basedapproach  Each bus could have an RFID tag affixed toit while the readers are conveniently mounted at intersections, lamp posts orbus stops. The crucial information associated with a tag is the specific busnumber, the capacity of the bus, the route number currently plying and thetermination point (for example, during non-peak hours a bus may terminate at adepot before its usual terminating point). Tag readers continually monitorpassing buses and transfer this information in real-time to a central computer.A commuter with access to a cell phonecould subscribe to the following service from the mobile network provider. Thesubscriber may enter his destination stop, D, (and optionally location ofnearest bus stop) on his cell phone in the comfort of his home. The system willinform him of the relevant buses closest to him and expected arrival times ofthese buses.

The above service can be provided by themobile network operator. The provider contacts a central computer to obtain theset of buses traveling to D through the closest bus stop to the customer. Thislist is obtained in sorted order and could possibly be filtered or enhanced insome way depending on the preferences of the customer. For example, if thereare several bus stops in proximity to the commuter, information on relevantarrivals at all these bus stops can be provided.

The provider can providecustomized service to each subscribing commuter for a small fee. This service can be used for multiplepurposes to locate and control bus movement in the metro city. For example, inthe event of an accident causing traffic congestion on a particular road, thebuses leading to the road can be informed. In some cases, the routes of the buscan be changed temporarily and accordingly bus driver can be informed viawireless network. Or if it is found that a particular bus was stuck in trafficand that has led to a smaller gap with the next bus, the bus driver of the nextbus can be informed to slow down to increase the gap. Many such applicationscan be thought of based on such an RFID application. Before describing some of the researchchallenges in deploying such a system, we first describe an alternatetechnology that can also achieve similar objectives.       3.

2 GPS– based approach  A GPS tracking system uses GPS (GlobalPositioning System) 14 to determine the location of a vehicle, person, or petand to record the position at regular intervals in order to create a track fileor 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 bereported in real-time; using either web browser based tools or customizedsoftware’s. More often, GPS receivers are used for navigation, positioning,time dissemination, and other research.

Research projects 15 include usingGPS signals to measure atmospheric parameters. Though GPS based systems are widely usedin the developed countries, there exists some serious limitations of thistechnology in developing countries like India. Firstly the coverage of GPSsystem in developing countries is not as wide. Secondly, effectiveimplementation 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, roadinfrastructure is almost static. However in the developing world metros (e.g.Mumbai, Delhi, Chennai, Hyderabad), new roads are being constantlybuilt and layout of old roads is frequently changed. This will requireremapping of roads at regular intervals.

On the other hand with RFID systemsnew roads and change of old roads will require just reinstalling few RFIDscanners or changes in the positions of these scanners.  4 ResearchChallenges of RFID based Solutions  There are many technical challengesassociated with deployment of RFIDs. For example, there are problems with falseor missing reads as a result of radio waves being easily distorted, detected,absorbed, or interfered with. There are a number of system-level challengessuch as determining the number, type and placement of readers. In this paper weprimarily focus on the challenges related to data management which deals withcapturing, storing and querying RFID data.  4.1 Data management problem  BEST runs over 335 bus routes in Mumbai13.

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 30minutes. Overall, there are around 3380 buses in B.E.

S.T. which carry around45,00,000 passengers everyday. BEST buses, on many routes run for 21hours (from 4:00AM to 1:00AM) a day. So the number of trips along a route willbe 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 estimatethe 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 toderive a meaningful real-time decision is a challenging task. The aboveestimates occur in the case when readers are placed at bus stops and depots andwhen only BEST buses are taken into consideration. If the data is captured notonly from bus stops but also from several traffic lights to get intermediateinformation between two bus stops the number of events will further increase.This situation will be exacerbated if other kinds of traffic movements such astaxis, trucks are also monitored. Managing such high volume of events andgenerated data poses the challenges to applications as well as back-enddatabases. This data is often redundant and needs to be filtered/cleaned andconsolidated in order to occupy less space in database. In doing so, care mustbe taken that no useful information is lost.

Researchers in the database community havepresented techniques and models for warehousing as well as cleaning/filteringRFID data. EPC-IS 9 and PML Core 10 are the RFID system standardizationefforts by auto-ID center. 9 Summarizes the data characteristics, models dataas events and provides some reference relation to represent data. DynamicRelationship ER (DRER) presented in 17 is an expressive temporal data modelwhich enables support for basic queries for tracking and monitoring RFID taggedobjects. A simple observation that objects move together in initial stagesbring a couple of more proposals. Hu et al. 12 used bitmap data type tocompress the information corresponding to objects that move together.

RFID-Cuboids 11 are a new warehousing model that preserves object transitionswhile providing significant compression and path-dependent aggregates. FlowCube11 is a method to construct a warehouse of commodity flows. Some of these aresimple representations of various relationships in the Relational DBMS. 4.2 Real-time Decision Making  In this sub-section we describe how theapplication of Event-Condition-Action (ECA) framework can address some of thereal-time event management issues. Assume that we record each instance ofreader-tag interaction with the help of a tuple: {object_epc, location,timestamp}.

Here, object_epc is Electronic Product Code used to uniquelyidentify an object (the bus and the route), location denotes the place wherethe interaction took place (say in some bus stop), and  timestamp denotes the time at which theinteraction took place. Figure 3: Data Aggregation & Partitioning inDatabases Each event is characterized by certain dimensions liketime of scan, location of the reader, etc. Similarly, the conditions andactions also have some dimensions.

Consider an example event that the distancebetween two consecutive buses is below a certain threshold. This can beexpressed 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}  CONDITION= {e2.l2 = e1.

l1 + 1 AND e2.t2 < e1.t1 +     threshold AND e1.route1 = e2.route2}      ACTION = {Notify bus driver of e2.bus1 to slow} It is possible to dynamically add, deleteand modify such rules without interrupting the system. Moreover such ECA ruleswill be invoked in real-time by a distributed ECA framework. These ECA rulescan also be used to do several data management tasks – data cleaning, dataaggregation and prediction.

 4.3 Scalability As theamount of data generated by RFID system is enormous, scalability of theproposed system is an important aspect. A couple of techniques can be employedin order to make our system scalable.     4.3.1Data Aggregation:  In many scenarios, granularity of recentlygenerated data is more important as opposed to old data. So we can havemulti-layered data architecture with high granularity recent data at the topand consolidated, less granular data towards the bottom. If for example, thedata on individual bus movement is required for immediate action or foranalysis over a day.

However beyond few days, the historical data of individualbuses will be of much less interest. Rather it will be more interesting to knowthe aggregated overall bus movement data. Following this, in our proposed RFIDsystem, the historical data will be aggregated and stored.

This is avery good example for layered architecture for data storage on basis ofaggregation. But this assumption may not always hold true. Sometimes, specialevents might take place making it necessary to violate this generalizedstructure. E.g., for future application it will be logical to study themovement pattern of individual buses on the day of the Mumbai bomb-blast(7/11). Such analysis can be used to effectively plan for disbursement oftraffic in case of a future terrorist attack.

So, individual bus movement dataneeds to be kept for that particular day. It is possible to describe such dataaggregation rules based on the proposed ECA framework.  4.3.2Event Explosion:  Each timethe tag is scanned a tuple is produced indicating location of object and timeof scan. This tuple directly or indirectly leads to an event (assume filteringof tuples is not done).

So, the number of events generated will be proportionalto the number of objects which need to be tracked or monitored. This on anaverage is large. We can have a centralized event manager which identifies allthe events in the systems, checks for conditions associated with it, evaluatesthese conditions and finally fires events.

This event manager will also beoverloaded and will become the bottleneck for our system. Thesolution is to distribute the job of the event manager. The whole purposebehind this distribution is to capture events as close as possible to itsgeneration point and execute corresponding action if the conditions defined aretrue. As an example, consider the ECA “when a bus arrives at bus stop A, if aprevious bus of the same route has left A, beyond a threshold time then informthe bus driver to increase the speed”. This ECA can be executed locally withinthe RFID scanner or a nearby computer to which the RFID scanner is connected.The event need not be propagated to the centralized system.

Such a system willreduce the number of events to be handled at any single location. To achievethe optimal execution of RFID ECA rules, the system should be able to specifythe location of event detection, condition checking and action handling.  5 Turning Datainto Valuable           Information Considerthat a customer has queried the system at time t to obtain an estimate of theearliest arrival time of Bus # 252 at stop S3. Figure 2 shows the locations oftwo instances of Bus # 25 closest to the customer. The closer of the two bussesis between stops S1 and S2.

To estimate the arrival time of this bus at S3, weneed to forecast the following times:  ·           Time to completehop h1 and ·           Times to traversehops h2 and h3.  Figure 4: Estimating the earliest busarrival time Theestimated arrival time of this bus at S3 is t plus the sum of the above delays.We next attempt to answer the customer’s query by formulating it as a problemin the domain of forecasting theory. Let  be thetime taken to traverse the hth hop on bus route r given that the busstarts from its depot at time = ti. The variable i is the busdeparture index – it corresponds to the ith departure of Bus # r sincebus arrival records were maintained. ?ˆ denotes an estimate of delay.

Specifically  denotesthe time, estimated at time t, to traverse the hth hop on bus route r giventhat the bus starts from its depot at time = ti. Estimatedtimes can best be analyzed by considering two categories of days – normal orregular days (these are typically working days in the week) and “other” days(these include week-ends and other holidays.) Let Tr,h denote the time series:Note thatt1, t2, t3 . . . are the departure times fromthe depot of the 1st, 2nd, 3rd buses, etc.plying on route r.

We clarify that, if there are n departures per day, then thefirst n terms of the series, Tr,h correspond to thedepartures on Day 1, the next n departures are those on Day 2 and so on. Forsimplicity, we include only regular days in the series. We expect to minecertain patterns from such series. In particular for a given series (and hencea particular hop on a specific route, r).·        Values ofsuccessive terms in the series would tend to be close. ·        The hop delay at 5:00 am (early morning, low-trafficload) would not be a good indicator of hop delay during evening rush hour (sayat 6:00 pm).

On the otherhand, hop delay at 6:00 pmyesterday may be a better predictor of today’s hop delay at 6:00 pm. Observation2 suggests a form of seasonality with period n in the time series (where n isthe number of departures of Bus # r on any given regular day).    Figure 5: Relating delay in traversingthe 3rd hop to corresponding delays in previous trips. We thussee that the traversal time for hop h on route r may be estimated with the aidof previous terms in the series, Tr,h. This, however, maynot always be the case. Consider, for example, a road accident or other suchevent that creates congestion at a certain point in the road network.

Allroutes that converge at that point may also be affected. This means that hopdelay estimates should also factor fresh information concerning hop delaysencountered on other routes that intersect the route of interest to ourcustomer.  6. Conclusion PracticalRFID systems are involved in real time tracking and monitoring of events.

Thesystem performs appropriate actions in response to events based on certainconditions. It is natural to consider the use of the Event, Condition andAction (ECA) framework to address event management issues. Since the number ofevents captured by many RFID systems is very large, clever filtering andaggregation techniques should be employed. We are currently engaged in thedevelopment of a RFID rule based management system using existing RFIDmiddleware from Sun.

Transportationis a fertile area for deployment of RFID-based systems. Tracking of buses andother vehicles in crowded metros could greatly benefit Commentswho could plan their trips to avoid long delays at bus stops. In this paper, weuse of RFIDs for bus tracking using readers strategic location. Event such asarrival of buses can be used to generate useful information such as earliestarrival time of a bus on a given stop. By informing a commuter about busarrival times the commuter can save valuable waiting time. WeFormulated the goal of estimating bus arrival times as atechnique developed for seasonal time series as well as regression analysis.