ieee transactions on knowledge and data engineering, vol

Efficient, Energy Conserving TransactionProcessing in Wireless Data Broadcast

SangKeun Lee, Chong-Sun Hwang, and Masaru Kitsuregawa, Member, IEEE

Abstract—Broadcasting in wireless mobile computing environments is an effective technique to disseminate information to a massive

number of clients equipped with powerful, battery operated devices. To conserve the usage of energy, which is a scarce resource, the

information to be broadcast must be organized so that the client can selectively tune in at the desired portion of the broadcast. In this

paper, the efficient, energy conserving transaction processing in mobile broadcast environments is examined with widely accepted

approaches to indexed data organizations suited for a single item retrieval. The basic idea is to share the index information on multiple

data items based on the predeclaration technique. The analytical and simulation studies have been performed to evaluate the

effectiveness of our methodology, showing that predeclaration-based transaction processing with selective tuning ability can provide a

significant performance improvement of battery life, while retaining a low access time. Tolerance to access failures during transaction

processing is also described.

Index Terms—Mobile computing, wireless data broadcast, access methods, indexing methods, performance evaluation.

Ç

1 INTRODUCTION

WITH the advent of a third generation wireless infra-structure and the rapid growth of wireless commu-

nication technology, such as Bluetooth and IEEE 802.11,mobile computing becomes possible. People with batterypowered mobile devices can access various kinds ofservices at any time any place. However, existing wirelessservices are limited by the constraints of mobile environ-ments such as narrow bandwidth, frequent disconnections,and limitations of the battery technology. Thus, mechan-isms to efficiently transmit information from the server to amassive number of clients have received considerableattention [1], [5], [15].

Wireless broadcasting is an attractive approach for

data dissemination in a mobile environment. Disseminat-

ing data through a broadcast channel allows simultaneous

access by an arbitrary number of mobile users and, thus,

allows efficient usage of scarce bandwidth. Due to this

scalability feature, the wireless broadcast channel has

been considered an alternative storage medium of the

traditional hard disks [1], [15]. Applications such as using

palmtops to access airline schedules, stock activities,

traffic conditions, and weather information on the road

are expected to become increasingly popular. It is noted,

however, that several mobile computers, such as desktops

and palmtops, use batteries of limited lifetime for their

operations and are not directly connected to any power

source. As a result, energy efficiency is a very important

issue to resolve before we can anticipate an even wideracceptability for mobile computers [13], [25], [34].

Among others, one viable approach to energy efficiencyis to use indexed data organization to broadcast data overwireless channels to mobile clients. Without any auxiliaryinformation on the broadcast channel, a client may have toaccess all objects in a broadcast cycle in order to retrieve thedesired data. This requires the client to listen to thebroadcast channel all the time, which is power inefficient.Air indexing techniques address this issue by precomputingsome index information and interleaving it with the data onthe broadcast channel, and many studies appear in theliterature [8], [12], [15], [41]. By first accessing the broadcastindex, the mobile client is able to predict the arrival time ofthe desired data. Thus, it can stay in the power saving modemost of the time, and tune into the broadcast channel onlywhen the requested data arrives. The drawback of thissolution is that broadcast cycles are lengthened due toadditional index information. As such, there is a trade-offbetween access and tuning time. In mobile broadcastenvironments, the following two parameters are of concern:

. Access time: the period of time elapsed from themoment a mobile client issues a query to themoment when the requested data items are receivedby the client.

. Tuning time: the period of time spent by a mobileclient staying active in order to retrieve therequested data items.

While access time measures the overhead of an indexstructure and the efficiency of data and index organizationon the broadcast channel, tuning time is frequently used toestimate the power consumption by a mobile client sincesending/receiving data is power dominant in a mobileenvironment [18].

In this paper, given the indexed data organizations suitedfor a single item retrieval, the issue of speedy, energy

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 18, NO. 9, SEPTEMBER 2006 1225

. S. Lee and C.-S. Hwang are with the College of Information andCommunication, Korea University, 5-1, Anam-dong, Seongbuk-gu, Seoul136-713, South Korea. E-mail: {yalphy, hwang}@korea.ac.kr.

. M. Kitsuregawa is with the Institute of Industrial Science, University ofTokyo, 4-6-1, Komaba, Meguro-ku, Tokyo, 1538505 Japan.E-mail: [email protected].

Manuscript received 1 June 2005; revised 11 Jan. 2006; accepted 9 May 2006;published online 19 July 2006.For information on obtaining reprints of this article, please send e-mail to:[email protected], and reference IEEECS Log Number TKDE-0218-0605.

1041-4347/06/$20.00 � 2006 IEEE Published by the IEEE Computer Society

efficient transaction processing is investigated in mobilebroadcast environments. To the best of our knowledge, theinvestigation of both time and energy efficient transactionprocessing at the same time has not been addressed before,although very little work [19], [36] has recently beenproposed to address power conservation in the context oftransaction processing. In our previous work [20], [21], apredeclaration-based query optimization was explored forefficient (in terms of access time) processing of wireless read-only transactions in mobile broadcast environments.Although the predeclaration technique has often been usedto avoid deadlocks in locking protocols [6], it has beenobserved that predeclaration has a novel property that eachread-only transaction can be processed efficiently. However,clients are continuously tuning in the broadcast channel andwaiting for the data of interests. This paper borrows the ideaof predeclaration that additionally helps augment theselective tuning ability and examines the efficient, energyconserving transaction processing in various indexed dataorganizations. In addition, tolerance to access failuresduring transaction processing is described. We conductboth an analysis and a simulation to evaluate the perfor-mance of the proposed scheme with respect to access andtuning time. The analysis and simulation studies demon-strate the proposed methodology improves system perfor-mance significantly. Notably, this study makes the firsteffort on speedy, energy efficient transaction processing inthe literature. The contribution of this study is five-fold:

. an access protocol for multiple items to supportwireless transactions,

. a predeclaration-based transaction processing withselective tuning ability,

. a mechanism to tolerate access failures during theselective tuning,

. an analytical cost model to derive the expectedaccess and tuning time of various transactionprocessing schemes in different index techniques,and

. both accuracy of the cost model and detailedperformance evaluation to provide a valuable in-sight on the decisive difference of the proposedmethod and the traditional ones.

With the rapid advances in wireless technologies, mobilecomputing systems are becoming widely available nowa-days. For instance, wireless data broadcast services havebeen available as commercial products for many years [38],[39]. In particular, the recent announcement of the smartpersonal objects technology (SPOT) by Microsoft [27] furtherhighlights the industrial interest in broadcast utilization forwireless data services. With a continuous broadcast net-work (called DirectBand Network) using FM radio sub-carrier frequencies, SPOT-based devices (e.g., PDAs andwatches) can continuously receive timely information. Thefast increase in such mobile applications justifies thetimeliness and importance of this study.

The remainder of this paper is organized as follows:Section 2 describes the background of our system modeland indexed data organizations. Section 3 presents theproposed access method in the context of predeclaration-based transaction processing. Section 4 describes how to

tolerate access failures during transaction processing.Section 5 develops analytical models to examine theeffectiveness of the proposed scheme, and Section 6 reportson the performance evaluation of ours and existingtechniques, which is based on both analysis and simulationresults in various indexed data organizations. Related workand the conclusion of the paper are in Sections 7 and 8,respectively.

2 PRELIMINARIES

2.1 Basics of Wireless Broadcasting

We briefly describe here the model of a mobile broadcastsystem, which is similar to the models in [1], [14], [15]. Thesystem consists of a data server and a number of mobileclients connected to the server through a low bandwidthwireless network. The server maintains the consistency of adatabase and reflects refreshment by updating transactionsbeing issued only on the server side. The correctnesscriterion in transaction processing adopted in this paper isserializability [6], which has been proven to be not expensiveto achieve in the work [20]. The server broadcasts data itemsin the database periodically to a number of clients, on acommunication channel which is assumed to have broad-casting capability. Clients will only receive the broadcastdata and fetch individual items (identified by a key) fromthe broadcast channel. However, updates to the items arereflected only between successive broadcasts. Hence, thecontent of the current version of the broadcast is completelydetermined before the start of broadcast of that version.

In our model, filtering is by simple pattern matching ofthe primary key. Clients will remain in doze mode most ofthe time and tune in periodically to the broadcast channel,in order to download the required data. Selective tuningwill require that the server, in addition to broadcasting thedata, also broadcasts index information that indicates thepoint of time in the broadcast channel when a particulardata item is broadcast. The broadcast channel is the sourceof all information to the client including data as well asindex.

Each data item in the database along with the associatedindex information will constitute a bcast, which will beorganized as a sequence of buckets. A bucket is the smallestlogical unit of a broadcast, and is a multiple of the packetsize. All buckets are of the same size. Both access andtuning time will be measured in terms of number of dataitems with the assumption that, without loss of generality,the size of a data item is identical to the size of a bucket.Similarly to the work [14], [15], the index information isorganized as a form of multileveled tree such that the toplevel index node has index information for the second levelindex nodes, and each node in the second level has indexinformation for the third level index nodes and so on.Finally, each node of the lowest level contains the pointer toan actual data. Pointers to specific buckets within the bcastwill be provided by specifying an offset from the bucketwhich holds pointer, to the bucket to which the pointerpoints to. The actual time of broadcast for such a bucket(from the current bucket) is the product of (offset -1) and thetime necessary to broadcast a bucket.

1226 IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 18, NO. 9, SEPTEMBER 2006

For brevity of presentation, it is assumed that each dataitem in the database appears once during one broadcastcycle, i.e., uniform broadcast [1]. We assume that the contentof the broadcast at each cycle is guaranteed to beconsistent.1 That is, the values of data items that arebroadcast during each cycle correspond to the state of thedatabase at the beginning of the cycle, i.e., the valuesproduced by all transactions that have been committed bythe beginning of the cycle. We also assume that each clientis equipped with enough local storage capacity, such asmain memory and hard disk, to hold all data items for itssingle transaction.2

2.2 Data Organization on the Broadcast Channel

In general, data organization techniques which seekoptimum in two-dimensional space of access and tuningtime are of importance. Being interleaved with data, theindex will provide a sequence of pointers which eventuallylead to the required data. To interleave data and index onthe wireless broadcast channel, Access opt, Tune opt, andð1;mÞ indexing techniques [14] are considered in this paper,which are illustrated in Fig. 1.

. Access_opt: This technique provides the best accesstime with a very large tuning time with respect to asingle item. The best access time is obtained when noindex is broadcast along with data items. The size ofthe entire broadcast is minimal in this way. Clientssimply tune into the broadcast channel and filter allthe data till the required data item is downloaded.

. Tune_opt: This technique provides the best tuningtime with a large access time with respect to a singleitem. The server broadcasts the index at thebeginning of each bcast. The client which needs theitem with primary key K, tunes into the broadcastchannel at the beginning of the next bcast to get theindex. It then follows the index pointers to the itemwith the required primary key. This method has theworst access time because clients have to wait till thebeginning of the next broadcast, even if the requireddata is just in front of them.

. ð1;mÞ indexing: In this index allocation method, theindex is broadcast m times during a single broadcastcycle. The whole index is broadcast preceding every1m fraction of the broadcast cycle. The first bucket ineach “index segment” (i.e., the set of contiguousindex buckets) has a tuple, with the first field as thelargest key value of the previous “data segment”(i.e., the set of data buckets broadcast betweensuccessive index segments) and the second field asthe offset to the beginning of the next bcast. Thistuple is to guide the users who have missed therequired item in the current bcast (called a data miss)and have to wait for the next bcast.

In case of Tune_opt and ð1;mÞ indexing, selective tuning isaccomplished by interleaving an index with the data itemsin the broadcast. The clients are only required to operate inactive mode when probing for the address of the index,traversing the index, and downloading the required data,while spending the waiting time in doze mode. Each entryof the index contains the pair (id, offset).

3 TIME AND ENERGY EFFICIENT TRANSACTION

PROCESSING

In previous work [20], [21], three predeclaration-basedtransaction processing schemes, called P (Predeclaration),PA (Predeclaration with Autoprefetching), and PA2 (PA/synchronous), are presented with Access_opt organization.The analysis and simulation-based studies showed that theyare able to greatly improve the access time of read-onlytransaction processing.

In this work, method P is adopted as our basic transactionprocessing approach, and is extended to integrate selectivetuning ability, since methods PA and PA2 work with theclient caching technique which is orthogonal to the issue inthe paper. Prior to proceeding, the usefulness of predeclara-tion-based transaction processing is explained briefly in thefollowing to help understand the basic behavior of pre-declaration-based transaction processing.

Predeclaration and Its Usefulness: The uniform broad-cast in Access_opt organization is illustrated in Fig. 2, wherethe server broadcasts a set of data items d0 to d6 in onebroadcast channel. Suppose that there is no updatingtransactions on the server side and a client transactionprogram starts its execution: IF ( d2 � 3) THEN read(d0)

ELSE read(d1). To show that the order in which atransaction reads data affects the access time of thetransaction, consider the traditional client transactionprocessing in Fig. 2a. Since both d0 and d1 precede d2 in

LEE ET AL.: EFFICIENT, ENERGY CONSERVING TRANSACTION PROCESSING IN WIRELESS DATA BROADCAST 1227

1. This requirement was also assumed in the previous work explicitly[26], [28], [29] or implicitly [33].

2. This limitation can be mitigated by attaching extra local hard diskdevices. This is valid due to the fact that, even though there exists no hopeto increase battery life, recent advances in the hardware indicate thatprocessing power and local storage capacities will be increased with areasonable cost [44].

Fig. 1. Data and index organization.

the bcast with respect to the client and access to data is

strictly sequential, the transaction has to read d2 first and

wait to read the value of d0 or d1. Thus, the access time of

the transaction is 11 in case d2 and d0 are accessed, or 12 in

case d2 and d1 are accessed. If, however, all data items that

will be accessed potentially by the transaction, i.e., {d0, d1,

d2}, are predeclared in advance, a client can hold all

necessary data items with a reduced response time of 6,

which is illustrated in Fig. 2b. Thus, the use of predeclara-

tion allows the necessary items to be retrieved in the order

they are broadcast, rather than in the order the requests are

issued.Method P has been originally presented with Access_opt

data organization in [20]. That is, without any form of

index, the client has to tune to the channel all the time

during the whole process of filtering. For realistic applica-

tions, however, this may be unacceptable as it requires the

client to be active for a long time, thereby consuming a

scarce battery resource. In the paper, we would rather

provide a selective tuning ability for method P , enabling the

client to become active only when data of interest is being

broadcast, in the context of Tune_opt and ð1;mÞ indexing

suited for a single item retrieval. In general, the access

protocol for Tune_opt and ð1;mÞ indexing involves the

following steps:

. Initial probe: The client tunes into the broadcastchannel and determines when the next nearest indexwill be broadcast. This is done by reading offset todetermine the address of the next nearest indexsegment. It then tunes into the power saving modeuntil the next index arrives.

. Index search: The client searches the index. It followsa sequence of pointers (i.e., selectively tunes into thebroadcast index) to locate the data of interest andfind out when to tune into the broadcast channel toget the desired data. It waits for the arrival of thedata in the power saving mode.

. Data retrieval: The client tunes into the channelwhen the desired data arrives and downloads thedata.

3.1 Access Protocol for Multiple Items

We first need to elaborate the access protocol for searchingand retrieving multiple items effectively. In predeclaration-based transaction processing, all data items are predeclaredprior to the actual processing and should be retrieved in theorder they appear from the broadcast channel. Thus, theclient is required to predict the arrival time of items byexploiting index information shown in the air. This can bedone as follows: Since the index provides a sequence ofpointers which eventually lead to the single required item,with the index information the client is able to sort all thepointers, which constitute the index information for multi-ple items of interest, in the order they appear on thechannel. This would result in a long sequence of interleavedpointers.

This idea is illustrated at the bottom of Fig. 3, where aclient transaction requires two items, d1 and d5, in the orderwith the current position d4. It is observed that someportion of index information, i.e., i0 and i2 in this specificexample, needs to be visited only once to retrieve the twodata items. This illustrates the reduction of tuning timewhich is possible in predeclaration-based transactionprocessing, compared to a straightforward approach, whichis shown in the top of Fig. 3, where individual, separateinitial probes and index search processes are performed foreach item.

3.2 Method P with Selective Tuning

Now, we describe the behavior of method P to achieveimprovement of tuning time, while retaining a low accesstime, with Tune opt or ð1;mÞ indexing techniques in mind.Let us define the predeclared readset of a transaction T ,denoted by Pre RSðT Þ to be a set of data items that T readspotentially. Each client processes T in three phases: 1) Pre-paration phase: It gets Pre RSðT Þ and constructs a sequenceof interleaved pointers to all the items in Pre RSðT Þ.2) Acquisition phase: It acquires data items in Pre RSðT Þfrom the periodic broadcast. During this phase, a clientadditionally maintains a set AcquireðT Þ of all data itemsthat it has acquired so far. 3) Delivery phase: It delivers dataitems to its transaction according to the order in which thetransaction requires data.

In particular, to construct a sequence of interleavedpointers to all the items in Pre RSðT Þ, the client is requiredto read the index at the beginning of the next broadcastcycle, instead of the next nearest index, irrespective ofTune opt or ð1;mÞ indexing techniques. This is mainlybecause, the initial probe step is made to be consistent withthe basic behavior of method P , in which the acquisitionphase starts at the beginning of next broadcast cycle due tothe ease of consistency maintenance [20], [21].

After obtaining the arrival time of the next broadcastcycle in the initial probe step, the client tunes in at thebeginning of the next broadcast cycle and examines theindex information which is broadcast by the server. On thebasis of the index information, the client locally constructs atuning sequence of pointers for all the items in Pre RSðT Þ.


Fig. 2. Usefulness of predeclaration-based transaction processing.

This tuning sequence is constructed by sorting pointers ofinterest in the order they appear on the channel. Based onthe generated tuning sequence, the client performs bothindex search and data retrieval steps accordingly.

With respect to consistency issue, since the content of thebroadcast at each cycle is guaranteed to be consistent, theexecution of each read-only transaction is clearly serializableif a client can fetch all data items within a single broadcastcycle. Now that all data items for its transaction are alreadyidentified and a sequence of interleaved pointers to the dataitems is constructed, the client is able to complete theacquisition phase within a single broadcast cycle.

More specifically, with Tune opt or (1;m) indexingtechniques, a client processes its transaction Ti as follows:

1. On receiving BeginðTiÞ {

get Pre RSðTiÞ by using preprocessor;

AcquireðTiÞ ¼ ;;tune into the current bucket on the broadcast

channel;

read the offset to determine the address of the

next broadcast cycle;

go into doze mode and tune in at the beginning of

the next broadcast cycle;

from the index segment construct a sequence of

interleaved pointers by sorting a sequence

of pointers to individual items in Pre RSðTiÞ;2. While (Pre RSðTiÞ 6¼ AcquireðTiÞ) {

for dj in Pre RSðTiÞ {

according to the sequence of interleaved

pointers tune in when djis broadcast and download dj;

put dh into local storage;

AcquireðTiÞ ( dj;

}

}

3. Deliver data items to Ti according to the order in which

Ti requires, and then commit Ti.

In case of Access opt, a client needs to retrieve items inPre RSðTiÞ from the beginning of a broadcast cycle byfiltering all the data appearing on the broadcast channel inactive mode until Pre RSðTiÞ is equal to AcquireðTiÞ, sincethere is no index information on the air (refer to [20]).

Theorem 1. Method P generates serializable execution of read-only transactions if the server broadcasts only serializable datavalues in each broadcast cycle.

Proof. It is straightforward from the fact that the data set readby each transaction is a subset of a single broadcast. tu

4 HANDLING ACCESS FAILURES IN TRANSACTION

PROCESSING

Access efficiency and energy consumption are majorconcerns in designing data access methods in transactionprocessing techniques, which have been covered so far.However, the robustness is also a very important issue in anerror-prone mobile environment [23]. The reason is that theaccess sequence of interleaved pointers induced by theindex structure should be followed exactly in the period ofthe data search. Otherwise, the entire search may fail. Manyfactors in the mobile environment may destroy the accesssequence. These factors include temporary disconnectionscaused by a weak connection and power insufficiency, dataloss caused by communication noises, and temporarysuspension caused by the processing of handoffs andlocation registration. The access failures can be detectedby adding the error-detecting code (for example, the paritycheck can be used to detect packet errors). An accessmethod should be able to tolerate these access failures.

By utilizing the index replication, the pioneering work[23] has presented three solutions to deal with access


Fig. 3. Access protocol for multiple items in Tune_opt.

failures for a single item retrieval in the context of

distributed indexing with entire path replication [14]:

. Reprobe: This simple solution is to restart the searchas long as a failure happens. The disadvantage ofthis method is that the efforts made before areuseless. This waste of effort is proportional to thefrequency of the failures.

. Reaccess: Another simple solution is to reaccess thebucket affected by failures to resume the data search.The defect of this method is that the waiting time forreaccessing the affected bucket in the next broadcastcycle is too long.

. Adaptive access method: This method makes use ofreplicated index in the same broadcast cycle insteadof waiting for the next broadcast cycle when areplicated index bucket fails to be retrieved. Duringa data search, a mobile client dynamically updatesthe search range [L, U], where L is the position of thenext bucket to visit in the access sequence and U isthe upper boundary of the segment containing thelast replicate of a replicated index bucket. After anaccess failure, the client resumes the data search bytrying to find a replicate of the bucket with theposition L.

Borrowing the rationale of using index replicates from

the work [23], we devise robust predeclaration-based

transaction processing mechanisms in an error-prone

ð1;mÞ indexing broadcast organization. To explain the

recovery behavior, Fig. 4 is used where the bucket second_i5

contains packet errors and is dropped while a mobile client

retrieves d17 and d25. Note that, unfortunately, the

methodology presented in [23] cannot be directly applicable

to the proposed predeclaration-based transaction proces-

sing mainly because:

. The method in [23] tries to find the next available indexreplicate within the search range for a failed index bucket.In our transaction processing, however, access sequenceinduced by one index bucket does not necessarily lead to a

single index bucket during the construction of a sequenceof interleaved pointers: Take the index pointers at i2 inFig. 4 as an example, which guides both i5 (for d17)and i6 (for d25) simultaneously in our approach.Thus, we cannot simply recover i5 and resume theaccess sequence by applying the approach [23], sincewe may miss the position of i6.

. The method in [23] focuses on the tolerance to accessfailure of a single item independently of other items. Inour transaction processing, however, the consistency issueamong multiple items should further be considered duringthe resumption procedure: The approach in [23] cannotguarantee the consistency of values of itemsaccessed by a transaction with access failures. Forinstance, one item may be retrieved normally fromthe current bcast, while another may be retrievedfrom the next bcast by a resumption process (i.e.,they can be retrieved from different broadcastcycles). Thus, we cannot simply apply the tolerancetechnique for access failures in [23] to our transac-tion processing.

The most straightforward solution to the access failureswithout distorting a sequence of interleaved pointers is torestart the search from scratch. In our example, the accesssequence is then changed to d9 (probe again), third_i0,third_i2, third_i5, third_i6, third_i11, third_i13, d17, d25.Although simple and effective, this approach loses theprevious search result. Here, we seek to design an accessmethod for predeclaration-based transaction processing,which uses the previous search result to continue anunfinished search.

In handling access failures in the proposed predeclara-tion-based transaction processing, notice that there alreadyexist replication in ð1;mÞ indexing for the “whole” index inthe same broadcast cycle. Further, we observe that theposition of the index bucket within an index segment is“identical” at all index segments. For example, i5 and i6always locate at the same sixth and seventh position,respectively, in each index segment. This motivates us tocontinue the search by using these replicates instead of justfinding the replicate of i5, which failed to be retrieved, byrecording the original position in the index segment. Inaddition, to cope with the consistency problem effectively,we design a robust predeclaration-based transaction pro-cessing with the following principle:

. All data items requested by a transaction should beretrieved within the same broadcast cycle.

Recall that, in our methodology for transaction proces-sing, the execution of a read-only transaction is clearlyserializable if a client can retrieve all data items within thesame broadcast cycle. To enforce the above principle, weneed to check a data miss (refer to Section 2.2 for itsdefinition) during the resumption process. That is, the clientchecks whether any item of interest never falls behind thecurrent position. If any data miss occurs, the client shouldwait for the beginning of the next broadcast cycle.Otherwise, it can resume the unfinished search by visitingthe original position for the failed index bucket and theremaining index buckets within the nearest index segment.


Fig. 4. The case of handling access failure in (1;m) indexing.

With the running example in Fig. 4, a client checks whethera data miss occurs for “every” item to be retrieved at theposition third_i0. After a failure at second_i5, since there isno data miss for d17 and d25 during the data miss check atthird_i0, the unfinished search is safely resumed by visitingindex replicates, third_i5 and third_i6, which can be done byrecording the original positions of second_i5 and second_i6during a normal search.

With the requirement that a client should record theoriginal positions of guided index buckets in an indexsegment throughout the search process, the above resump-tion process can be summarized as follows:

On downloading a bucket after a failure {

read the offset to determine the address of the

next index segment;

check whether a data miss occurs for every item

in Pre RSðTiÞ;if (no data miss occurs)

then tune in the original position(s) of the

index segment;

else {go into doze mode and tune in at the

beginning of the next broadcast cycle;

tune in the original position(s) of the

index segment; }

continue the construction of a sequence of

interleaved pointers by sorting a sequence

of pointers to individual items in Pre RSðTiÞ;conduct normal acquisition and delivery phase;

}

5 ANALYSIS

In this section, we develop analytical models to comparepredeclaration-based transaction processing method withselective tuning ability with two other methods which areslightly modified versions from ones proposed by [28], [35],[36]: invalidation and multiversion-based approach. Theperformance evaluation metrics are both access and tuningtime. We list two assumptions used in the performanceanalysis.

. Tuning time is directly proportional to the numberof downloaded buckets, by neglecting other compo-nents which use energy during data filtering andtransaction processing. In reality, mobile clients’features will be most constrained by the energyconsumption of their components (e.g., a processor,a network card, a GPS receiver, etc.) (refer to [16] fora specific data), while additional constraints stemfrom cost and size.

. We preclude the possibility of access failures such aspacket losses or errors for the sake of simplicity.

In wireless data broadcast, the performance of a singleclient read-only transaction for a given broadcast programis independent of the presence of other clients transactions.As a result, we will analyze the environment by consideringonly a single client. We examine both expected averageaccess and tuning time of transaction processing methods.We will derive the basic equation that describes theexpected average access time and tuning time, which ismeasured in number of data items broadcast by the server.The symbols and their meaning used throughout theanalysis are summarized in Table 1.

5.1 Method P

5.1.1 Access_opt

Access Time: For a database of size D items, accesss will, onaverage, be half the time between successive broadcasts ofthe data items, accesss ¼ D

2 . In method P , a transactionprocessing is divided into three phases: preparation,acquisition, and delivery phase. If the time required by aclient for each of three phases is expressed as PT , AT , andDT , respectively, the access time can be formulated by

accesst ¼ PT þAT þDT: ð1Þ

PT will, on average, be half of one broadcast cycle andDT is trivial, thus (1) can be reduced to

accesst ¼D

2þAT: ð2Þ

AT involves retrieving all the items in the predeclaredreadset in the order they appear on broadcast channel. The


TABLE 1Symbols and Their Meaning

retrieval time for the first item is accesss itself. The retrievaltime for the second item is half of the remaining bcast size,and the retrieval time for the next item is, in turn, half of theremaining bcast size, and so on. Thus, the expected AT for atransaction with op predeclared items is

AT ¼Xopi¼1

1

2

� �iD: ð3Þ

The expected average access time of method P istherefore computed as

accesst ¼1

2Dþ

Xopi¼1

1

2

� �iD: ð4Þ

Tuning Time: The average tuning time is equal to accesstime. This is because the client has to be in active modethroughout the period of access.

5.1.2 Tune_opt

Access Time: Probe wait, i.e., the average duration forgetting to the next index information, is ðDþIÞ

2 , whichcorresponds to PT in method P . With the similarreasoning to AT in Access_opt, bcast wait, i.e., the averageduration from the point the index information relevant tothe required transaction data items is encountered, to thepoint when the required items are downloaded, isPop

i¼1ð12ÞiðDþ IÞ, which again corresponds to AT in

method P . Since the access time is the sum of probe wait

(i.e., PT ) and bcast wait (i.e., AT ),

accesst ¼ðDþ IÞ

2þXopi¼1

1

2

� �iðDþ IÞ: ð5Þ

Tuning Time: Average tuning time for accessing asingle item, tunes, is kþ 2, where k is the number oflevels in the multileveled index tree, 1 for the initialprobe that gets a pointer to the beginning of the nextbcast, and 1 for the final probe to download the item.3

When the index tree is fully balanced, k ¼ dlognðDÞe andI ¼ 1þ nþ n2 þ � � � þ nk�1.

In a one-at-a-time access protocol fashion, the averagetuning time for accessing multiple items in a given transac-tion, tunet, is opðkþ 2Þ. However, in our method, multipledata items are retrieved with the help of a sequence ofinterleaved pointers. Therefore, in method P , tunet, is opðkþ2Þ minus num. of shared index buckets revisits throughout thewhole levels. For example, the root level in the index tree needsto be visited only once for multiple items. Since the expectednumber of shared index buckets revisits at level i is computedas maxð0; ðop � ðnum: of index buckets at level iÞÞÞ, the ex-pected total number of shared index buckets revisits at wholelevels are

Pk�1i¼0 maxð0; ðop � niÞÞ.

With the reasoning, the tuning time of method P is

tunet ¼ opðkþ 2Þ �Xk�1

i¼0

maxð0; ðop � niÞÞ: ð6Þ

5.1.3 ð1;mÞ indexing

Access Time: In general, the probe wait is 12 ðI þ D

mÞ when theclient tunes in at the broadcast of the next nearest indexsegment. In method P , however, the client tunes in at thebeginning of next broadcast cycle and, hence, the probe waitis 1

2 ðmI þDÞ. With the similar reasoning to Tune_opt, thebcast wait is

Popi¼1ð12Þ

iðmI þDÞ. Since the access time is thesum of probe wait and bcast wait,

accesst ¼1

2ðmI þDÞ þ

Xopi¼1

1

2

� �iðmI þDÞ: ð7Þ

Tuning Time: The average tuning time is equal to that inTune_opt. This is because, the client follows the same accessprotocol throughout the period of access.

5.2 Method InV

We describe below the procedure sketch of this method[28]. The key differences from ours are two-fold: 1) dataitems are retrieved in a one-at-a-time fashion and 2) theexecution of transactions may suffer from many restarts.

Invalidation-based Approach (InV ): This method in-validates (i.e., aborts) any transaction that reads data valuesthat correspond to different database states in order toensure the serializability of transactions. To achieve this, theserver broadcasts an invalidated data information. Eachclient maintains a set RSðT Þ for each active transaction T ,which includes all data items T has read so far. The clienttunes in at the beginning of each bcast to read theinvalidated data information. A transaction T is abortedand restarted if any item di 2 RSðT Þ is invalidated, i.e., if diis updated.

5.2.1 Access_opt

Access Time: With the use of InV, a client retrieves dataitems in a one-at-a-time fashion, i.e., only after retrievingone item from a bcast another request is issued. Let us firstconsider the case where there is no updates at the server sothe execution of transaction is always committed success-fully. The average response time for a single item is 1

2D.Then, the average response time of InV without updates atthe server, denoted as E, can be calculated by

E ¼ o

2D: ð8Þ

If data items are updated at the server, however, atransaction may be aborted and restarted several timesbefore it commits successfully. Fig. 5 shows the scenariothat its abort occurs i times before the successful execution.

The probability cmt that one execution of transactionwith InV leads to a successful commitment is smaller thanor equal to e��Dob

EDc. This is because, in order for a client to

commit its transaction successfully, o data items should not


3. In [14], the average tuning time is computed as kþ 1 with theknowledge of the beginning of the next bcast. In some cases, however, itmay be impossible for the client to have the knowledge due to, say,dynamic size of items.

Fig. 5. Scenario for method Inv.

be updated during at least DbEDc units (note that it receivesat least bEDc invalidated data information). Thus, theequation for access time (i.e., the difference between endand issue) is,

accesstðInV Þ ¼X1i¼0

Eð1� cmtÞi ¼ E

cmt: ð9Þ

Tuning Time: The average tuning time is equal to accesstime.

5.2.2 Tune_opt

Access Time: Probe wait is ðDþIÞ2 , bcast wait is ðDþIÞ2 , andaccess time for a single item, accesssðInV Þ, is ðDþ IÞ. Withthe similar reasoning to the derivation of access time inAccess_opt, the average access time of a transaction can becalculated by

accesstðInV Þ ¼ðDþ IÞocmt

: ð10Þ

Tuning Time: The average tuning time for a single item,tunesðInV Þ, is kþ 2. With the similar reasoning to thederivation of access time in Access_opt, the average tuningtime of a transaction with o items can be calculated by

tunetðInV Þ ¼ðkþ 2Þocmt

: ð11Þ


Access Time: Probe wait isðIþDmÞ

2 , bcast wait is ðmIþDÞ2 , and

access time for a single item, accesssðInV Þ, isððmþ1ÞIþð 1

mþ1ÞDÞ2 .

With the similar reasoning to the derivation of accesstime in Access_opt, the average access time of a transactioncan be calculated by

accesstðInV Þ ¼ððmþ 1ÞI þ ð 1

mþ 1ÞDÞo2cmt

: ð12Þ

Tuning Time: The first probe is the initial probe that getsa pointer to the next nearest index segment. Then, k probesare required for following the pointers in the index. Finally,one last probe is required for downloading the requireddata. Thus, the tuning time for a single item, tunesðInV Þ, iskþ 2. Therefore,

tunetðInV Þ ¼ðkþ 2Þocmt

: ð13Þ

5.3 Method MV

We below describe the procedure sketch of this method,which is modified from [28], [35], [36]. The key differencesfrom ours are two-fold: 1) data items are retrieved in a one-at-a-time fashion and 2) older versions of an item areincluded at the broadcast to increase the possibility ofsuccessful commitment.

Multiversion-based Approach (MV ): In method MV ,the server maintains and broadcasts multiple versions foreach item, instead of broadcasting the last committed valueonly. To maximize the commitment probability, the servermaintains a large number of old versions per item. Versionscorrespond to different values at the beginning of each

broadcast cycle and version numbers to the correspondingbroadcast cycle. On each client side, T reads the mostcurrent version for its first read operation, that is, theversion with the largest version number v0. For subsequentreads, T reads versions with the largest version numberssmaller than or equal to v0. If such a version exists, Tproceeds; otherwise, T is aborted. For a multiversionbroadcast organization, since a client is considered to accessdifferent data items of the same version, we adopt thevertical broadcast being justified as the best choice [35]. Thatis, a server broadcasts all data items having a particularversion number, then all data items having the next versionnumber and so on. In this case, the broadcast size and,consequently, the access time can be reduced by using somecompression scheme. The compressed scheme in [35]broadcasts a data value only if it is different from the datavalue of the previous version. For indexing scheme on oldversions, MV (1; 1) indexing was introduced at the work[36], where an index is broadcast at the beginning of eachbroadcast cycle. Naturally, this corresponds to Tune_optindex organization which will be analyzed in the Sec-tion 5.3.2. Rather than constructing an index structure on allthe separate versions presented at the work [36], however, itis assumed that each current item contains a pointer toconsequent old version repetitively in order to reduce theindex size.

5.3.1 Access_opt

Access Time: Since the average number of updated data

items during D is calculated by Nc ¼ Dð1� e��DÞ, and the

server maintains v old versions per data item, which is

assumed to be large such that a single execution of a

transaction leads to the successful commitment. The

increase for old versions on the bcast is therefore at least

vNc. Thus, the average response time for a transaction

accessing o items is o2 ðDþ vNcÞ, since a client retrieves data

items in a one-at-a-time fashion. Then, the average response

time can be calculated by

accesstðMV Þ ¼ o

2ðDþ vNcÞ: ð14Þ

Tuning Time: The average tuning time is equal to accesstime.

5.3.2 Tune_opt

Access Time: Probe wait is ðDþvNcþIÞ2 , bcast wait is ðDþvNcþIÞ

2 ,and access time for a single item, accesssðMV Þ, isðDþ vNc þ IÞ.

accesstðMV Þ ¼ ðDþ vNc þ IÞo: ð15Þ

Tuning Time: The probability currenti that the

retrieval execution of ith (1 � i � o) item with MV

involves current version of the item is smaller than or

equal to e��ðDþvNcþIÞbaccesstðMV ÞðDþvNcþIÞ c

ði�1Þo .

The probability current that the retrieval execution o items

with MV involves only current version of every item is

therefore smaller than or equal to e��ðDþvNcþIÞoðo�1Þ2 . The

expected number of access to old version is calculated by

old ¼ oð1� currentÞ: ð16Þ


Therefore,

tunetðMV Þ ¼ ðkþ 2Þðo� oldÞ þ kþ v

2

l mþ 2

� �ðoldÞ: ð17Þ


Access Time: The optimal number of index broadcasts so asto minimize the access time for the ð1;mÞ indexing, denotedas �m, is calculated by (according to the solution in [14])

�m ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðDþ vNcÞ

I

r: ð18Þ

Probe wait isðIþD�mÞ

2 , bcast wait is ð �mIþDÞ2 , and access time for

a single item, accesssðMV Þ, isðð �mþ1ÞIþð 1

�mþ1ÞðDþvNcÞÞ2 . Thus, the

average response time is calculated by

accesstðMV Þ ¼ðð �mþ 1ÞI þ ð 1

�mþ 1ÞðDþ vNcÞÞo2

: ð19Þ

Tuning Time: The probability currenti that the retrieval

execution of ith (0 � i � o) item with MV involves the

current version of the item that is smaller than or equal to

e��ðDþvNcþ �mIÞb accesstðMV ÞðDþvNcþ �mIÞc

ði�1Þo .

The probability current that the retrieval execution o items

with MV involves only current version of every item is

therefore smaller than or equal to e��ðDþvNcþ �mIÞb accesstðMV ÞðDþvNcþ �mIÞc

ðo�1Þ2 .

The expected number of access to the old version iscalculated by

old ¼ oð1� currentÞ: ð20Þ

Thus,

tunetðMV Þ ¼ ðkþ 2Þðo� oldÞ þ kþ v

2

l mþ 2

� �ðoldÞ: ð21Þ

6 PERFORMANCE EVALUATION

In this section, we evaluate the accuracy of the analyticalmodels, and compare access and tuning time performanceof predeclaration, invalidation, and multiversion-basedtransaction processing techniques. For all evaluations, weuse a relatively small number of database items so thatwireless data broadcast is made feasible, which is similarto the evaluation methodology in [14], [15], [28]. We alsouse a uniform data access to make it consistent with theuniform broadcast in the system model. We shall showthat the proposed methodology yields good energyconservation, i.e., a low tuning time, while retaining alow access time. The section is divided into three parts,focusing on the evaluation of the analytical model,comparison of access and tuning time performance ofthe three techniques, and detailed behavior of predeclara-tion-based approach, respectively.

Our evaluation environment is a broadcasting systemthat is similar to the QUOTREX system [14] where a stockmarket information of size 16� 104 Bytes is being broad-cast. The broadcast channel has a bandwidth of 10 Kbps.The bucket length is set to 128 bytes. Thus, there are1,250 buckets of data. Let n, the number of (primary-key pluspointer)s that can fit in a bucket, be 25. The index size is

53 buckets. It takes around 0.1 seconds to broadcast a singlebucket and 125 seconds to broadcast the whole database(with no index). For our evaluation purposes, the discretetime simulation package CSIM [32] is used to implement thesystem model of consideration throughout the paper. Forthe performance evaluation, an optimum m is computed tobe 5 according to the equation given in [14] with ð1;mÞindexing. The number of old versions v is set to 3; thus, anoptimum �m is computed to be 7 in multiversion-basedapproach. Further, the number of items appearing on atransaction program, denoted as op, is set to 1:5� o, where ois the number of items actually retrieved by a transaction.This is to consider the potential readset of predeclaration-based transaction processing for fair comparison (we havestudied the relative performance of the algorithms in a largerange of op and achieved a consistent relative performancebehavior shown in this section).

6.1 Analytical Model Accuracy

Our first goal is to show the accuracy of the analyticalmodel with respect to the simulation study. We evaluate theaccuracy of equations by using it to predict the access andtuning times of predeclaration, invalidation, and multi-version-based transaction processing techniques. Simula-tions were executed for 500,000 time units (one time unitcorresponds to the time taken to broadcast a single dataitem), and a simulation for a particular set of parameterswas repetitively tested 100 times to produce the simulatedaccess and tuning time. Think time between each transac-tion generation is set to half of the corresponding broadcastcycle for fair comparison.4 The actual access and tuningtime results were compared to the results estimated usingthe analytical model developed in this paper. Error (inpercentage) was used to assess the accuracy of estimationresults, which is measured as

Pi jsimulatedi � estimatedijP

i simulatedi;

where simulatedi (estimatedi) denotes the simulated (esti-mated) access and tuning time for the ith transaction.

Fig. 6 plots the error rate in the Tune_opt data organiza-tion, for access and tuning time, as a function of transactionsize (which ranges from 2 to 30) fixing an update rate to2� 10�4 (this value indicates that about 22 percent to26 percent of database items are updated in three indexingtechniques). The error rate of invalidation-based approachis rather high: average error ratio from 7 percent to55 percent. This is because our access and tuning timeanalysis of invalidation-based approach is based on thelower bound cmt probability (see Section 5.2.1). Consistentwith our reasoning, we observe that the simulated accessand tuning time of invalidation-based approach is nearlyalways larger than the estimated one. For predeclarationand multiversion-based techniques, however, our model isquite accurate in all cases: maximum error below 5 percent,confirming our access and tuning time derivation. The errorrates with respect to other parameters (e.g., update rate) or


4. It is observed that the average access time of method P gets smallerwith a close value of think time to 0 or a broadcast cycle, since most of thetime, the transaction is completed at the end part of a broadcast cycle.

data organization on broadcast channel (i.e., Access_opt andð1;mÞ indexing) are similar and omitted.

6.2 Performance Comparison

The performance comparison reported here is performed by

the analytical model of access and tuning time developed inSection 5. The analytical evaluation provides accurateoverall mean access and tuning time without having to

conduct multiple simulations to obtain small confidenceintervals.

Fig. 7 compares the expected access and tuning time as a

function of a transaction size, with the update rate being2� 10�4. As expected, the access time for the predeclara-tion-based approach remains almost constant irrespective of

the three index techniques. The graph demonstrates that thepredeclaration-based approach is an order of magnitudebetter than invalidation-based, multiversion-based one in

terms of access time for more than 10 operations. Forexample, for a workload with 10 operations (i.e., 15 opera-

tions with method P ), the use of method P improves accesstime to 9 percent (Access_opt), 4 percent (Tune_opt), 6 percent((1;m) indexing) of the access time in invalidation-based

approach, while improving to 18 percent (Access_opt),9 percent (Tune_opt), 15 percent ((1;m) indexing) of the

access time in multiversion-based approach. We see that forlong transactions (the number of accessed items is greaterthan 6 in our analysis), the access time of InV increasesrapidly. This is because a large value o decreases theprobability of a transaction’s commitment. As a result, atransaction suffers from many restarts until it commits. MVavoids this problem by making a client access old versionson each broadcast, thereby increasing the chance of atransaction’s commitment. We can observe that the perfor-mance of MV is less sensitive to the number of items thanInV . However, the increased size of broadcast affects theresponse time negatively in each broadcast. This explainswhy MV is inferior to InV for small data items. Turning tothe tuning time performance, all the approaches show anincreasing degradation of performance. As expected,method P outperforms InV by a wide margin when thenumber of items is more than 2. Comparing with MV , albeitP is involved with more items due to the potential readset,the tuning time is almost same. This explains the effective-ness of sharing index information in method P .

Fig. 8 shows the expected access and tuning time as afunction of an update rate, with the number of operationsbeing 6 (i.e., op ¼ 9). A higher update rate means a lowerprobability of successful commitment in InV . This explainswhy the access time of InV deteriorates so rapidly. Inparticular, if � > 2� 10�4, InV results in unacceptableperformance in terms of both access and tuning time. MValso degenerates as the update rate increases. This isbecause a higher update rate leads to the larger numberof updated items in the database, resulting in the largerbroadcast size. Unlike InV , however, with MV , a transac-tion can proceed and commit by reading appropriate oldversions of items which are on the air. This difference ofcommitment probability is the main reason why MV beatsInV for every range of update rates. With P , both accessand tuning times are not affected by the update ratesignificantly. As expected, it is observed that P is superiorto MV and InV in terms of access time for every range ofupdate rates. Although not observed in the figure, onlywhen a very small portion of database is updated and avery small number of items are retrieved (e.g., � < 1� 10�4

and o ¼ 2 in our analysis), the access time of P is slightlyworse than that of MV and InV , because up to this time


Fig. 6. Accuracy of the analytical model in Tune_opt.

Fig. 7. Performance comparison for varying transaction size.

Fig. 8. Performance comparison for varying update rate.

MV and InV have not degraded considerably and the

disadvantage from potential readset in P outweighs theadvantage from retrieving items in the order in which they

are broadcast. Turning to tuning time performance, the

tuning time with P and MV is almost the same, since thenumber of data retrieval for each method is not sensitive to

the update rate. As expected, both P and MV outperform

InV by a wide margin when � > 1� 10�4.

6.3 Some Scenarios and Practical Implications

In the following, we show some analytical results formethod P from scenarios [14]. Table 2 illustrates both access

and tuning times in the three indexing techniques for

various parameter settings, with the emphasis on Scenario 1to 3 for n ¼ 10. A simple scenario, the second column, is

described to exemplify different variants of index distribu-

tion in a database consisting of 243 data buckets with n ¼ 3.The bottom six rows denote both access and tuning time in

Access_opt, Tune_opt, ð1;mÞ indexing, respectively. As the

table illustrates, the tuning time in Tune_opt and ð1;mÞindexing are identical. The tuning time in Access_opt is very

large and very much higher than the other two. Both

Tune_opt and ð1;mÞ indexing always perform better thanAccess_opt in terms of tuning time. The most interesting

observation is that, method P with selective tuning ability

shows the most desirable performance in terms of bothaccess and tuning time in Tune_opt. This can be further

explained in the following practical implications.Consider the QUOTREX system again. Let the clients be

equipped with the Hobbit Chip (AT&T). The powerconsumption of the chip in doze mode is 50 �W and the

consumption in active mode is 250 mW . For simplicity, we

shall neglect other components which use energy duringdata filtering and transaction processing, and assume that

250 mW constitutes the total energy consumption.With Access_opt, the access time of transaction with size 6

predeclared items is 1,856 buckets, i.e., 185.6 seconds.Tuning time is also 1,856 buckets, i.e., the power consump-

tion is 185.6 sec � 250 mW = 46.400 Joules.

With Tune_opt, the access time of transaction with size 6predeclared items is 1,935 buckets, i.e., 193.5 seconds.Tuning time is 21 buckets, i.e., the power consumption is0:1 sec � ð21� 250þ 1; 914� 50� 10�3ÞmW ¼ 0:535 Joules.

The access time of transaction with 6 predeclareditems is 2,249 buckets, i.e., 224.9 seconds. The tuningtime is 21 buckets, i.e., the power consumption is0:1 sec� ð21� 250þ 2; 228� 50� 10�3ÞmW ¼ 0:536 Joules.

To sum up, in the Tune_opt case, the energy consumedper transaction issue is 87 times smaller than that ofAccess_opt. This is achieved by compromising on the accesstime which increases by just 4.25 percent, which is onlymarginal. Comparing with ð1;mÞ indexing, the powerconsumption is almost the same. However, the access timeimproves to 86 percent of the access time in ð1;mÞ indexing.

These practical implications are very interesting in that,if we want a low access time and also a low powerconsumption, then Tune_opt indexing is the best choice inthe context of predeclaration-based transaction processing.

In summary, the performance evaluation in this sectionconfirms the following:

. Our performance analysis is quite accurate, yieldingerror below 5 percent for method P and MV .

. The access time achieved by method P is substan-tially better than those of InV and MV approachesfor all indexing techniques of consideration.

. In terms of tuning time, both P and MV are almostequivalent albeit P retrieves more items than MV .

. For different indexing techniques, method P aug-mented with the selective tuning ability shows themost desirable performance with Tune_opt indexingin terms of both access and tuning time.

7 RELATED WORK

A lot of research effort [1], [2], [7], [10], [15], [24], [31], [41],[43] has focused on broadcast schedules in order to improveaccess time and/or tuning time, especially in the context of asingle item retrieval. For an error-prone mobile environ-ment where access failures are often occurred, an adaptiveaccess method, which is based on index replication, hasbeen suggested in [23]. In general, however, a client mayrequest multiple items simultaneously. Researchers havealso proposed algorithms for efficient broadcast schedulesfor multiple items [9] and range queries [42]. In this case,the access time may depend on the number of itemsrequested. Also, the client would expect to receive mutuallyconsistent versions of the requested items. This paperdiffers from the previous research on broadcast schedulesand tolerance to access failures in that it considers the casewhere clients issue wireless read-only transactions toretrieve consistent multiple items in a certain order, andpresents transaction processing methods to minimize theaverage access and tuning time.

Providing consistent data values to transactions has beenidentified as one of main issues in designing mechanismsfor wireless data broadcast [4], [13], [40], and severalapproaches to consistent and current data access despiteupdates in wireless data broadcast have been proposed inthe literature [3], [17], [22], [26], [28], [29], [33], [37]. The


TABLE 2Access and Tuning Time Result

validation protocols with dynamic adjustment of serial-ization order of transactions are proposed in [17], [22], [26].BUC (Broadcasted and Updated Cycles) control informa-tion for each item in wireless data broadcast is deployed in[3]. A control information matrix and a serialization graphtesting are used for concurrency checking in [33] and [29],respectively. A simple invalidation scheme is presented in[28]. To increase the number of read-only transactions thatare successfully processed despite updates at the server,multiversion schemes are employed in [28]. It has beenreported there that the time overhead induced by themultiple versions is smaller than the overall time lost foraborts and subsequent recoveries in the absence of multipleversions. Furthermore, multiple versions increase a client’stolerance to network disconnections that are common inwireless communications [30]. Recently, multiversion (1; 1)indexing where an index is broadcast once at the beginningof each broadcast cycle, is suggested [36] and the effect ofsome multiversion broadcast organization on the perfor-mance is reported [35]. The analysis and performanceevaluations in Sections 5 and 6 have shown that ourmethodology is superior to these schemes [28], [35], [36].Coping with updating transactions on mobile clients has notbeen examined in this paper.

With respect to consistency requirement, some weakconsistency criterion has often been adopted (e.g., updateconsistency [33] and quasi consistency [37]) to properly handleconsistency problem caused by updates at the server. Thiskind of approach is based on the belief that serializabilitywould be “expensive” to achieve for asymmetric commu-nication environments. This paper, however, has still stuckto serializability for the following reasons:

. We observed that serializability is not expensive toachieve in the proposed concurrency control techni-ques. This is in contrast to the argument that anypotential protocol for ensuring serializability wouldbe very expensive in broadcast environments, thusleading to poor performance [33]. Performanceevaluation presented in earlier work [20], [21]supports our argument.

. While the protocols based on the relaxed consistencyrequirements are useful in some applications, serial-izability may still be necessary to guarantee thecorrectness of some other applications such asmobile stock trading where a buy/sell trade willbe triggered to exploit the temporary pricingrelationships among stocks. From the trader’sperspective, the inability of maintaining serializabil-ity may lead to important financial consequences.For instance, if the users submitted multiple read-only transactions to communicate and compare theirquery results, they may be confused [11]. Further-more, it is very clear that serializability does nothamper the functionality of those applications wellsuited for weak criteria just if supported efficiently.

8 CONCLUSION

Both time and energy efficiency are crucial to mobilecomputing. To this end, instead of developing new indexeddata organizations, we have investigated an access protocol

for multiple items in the context of widely accepted

indexing techniques for a single item retrieval, and

integrated it with predeclaration-based transaction proces-

sing in wireless data broadcast. Although the uniform data

broadcast organization is considered in this paper, other

data broadcast schedules, like nonuniform broadcast [1] and

multiple channel [43] environments, can also be applied by

the same methodology developed in this paper. Tolerance

to access failures has also been investigated in the context of

transaction processing in this paper.The analysis and simulation results demonstrate the

advantages of the proposed approach. The performance

study indicates clearly that predeclaration-based transac-

tion processing with the selective tuning ability indeed is

capable of supporting transactions quite efficiently and it

outperforms the other techniques by far in terms of both

access and tuning time. Interestingly, in the context of

predeclaration-based transaction processing augmented

with the proposed access protocol, Tune_opt shows better

performance behavior than no index data organization and

ð1;mÞ indexing. This is rather contradictory to the traditional

belief that the optimum solution for tuning time is not

practical, since it has (unacceptable) very large access time

for a single item access. Currently, we are investigating the

extension of the proposed methodology for client caching/

prefetching techniques.

ACKNOWLEDGMENTS

This work was supported in part by Seoul R&BD Program

from Seoul Metropolitan Government, South Korea.

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SangKeun Lee received the BS, MS, and PhDdegrees in computer science and engineeringfrom Korea University, South Korea, in 1994,1996, and 1999, respectively. He was a recipientof the Japan Society for the Promotion ofScience (JSPS) Postdoctoral Fellowship in2000. Since 2003, he has been an assistant/associate professor in the College of Informationand Communication, Korea University, SouthKorea. His research interests include data

management in mobile/pervasive computing systems, location-basedinformation systems, XML databases, and data management in mobilead hoc networks.

Chong-Sun Hwang received the MS degree inmathematics from Korea University, SouthKorea, in 1970, and the PhD degree in statisticsand computer science from the University ofGeorgia in 1978. From 1978 to 1980, he was anassociate professor at South Carolina LanderState University. He is currently a full professorin the College of Information and Communica-tion at Korea University, South Korea. Since1995, he has been a dean in the Graduate

School of Computer Science and Technology at Korea University. Hisresearch interests include distributed systems, distributed algorithms,and mobile computing systems.

Masaru Kitsuregawa received the PhD degreefrom the University of Tokyo in 1983. He iscurrently a full professor and a director of theCenter for Information Fusion at the Institute ofIndustrial Science, the University of Tokyo inJapan. His current research interests coverdatabase engineering, Web archive/mining, ad-vanced storage system architecture, paralleldatabase processing/data mining, digital earth,transaction processing, etc. He is/was an as-

sociate editor of several international journals, such as the Very LargeDatabases Journal, DAPD (Distributed and Parallel Database) Journal,New Generation Computing Journal, and the IEEE Transactions onKnowledge and Data Engineering. He served as program cochair of theIEEE International Conference on Data Engineering (ICDE), 1999, andserved as general cochair of ICDE ’05 (Tokyo), PAKDD 2000, WAIM2002, APWEB 2006, etc. He served as a VLDB trustee and an ACMSIGMOD Japan Chapter chair. He is a fellow of the InformationProcessing Society of Japan and Information, Communication, En-gineering, Japan (IEICE) and he currently serves a director of the DataBase Society of Japan. He is a member of the IEEE and the IEEEComputer Society.


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