mobile broadcast services with mimo antennae in 4g

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Mobile broadcast services with MIMO antennae in 4G wireless networks Agustinus Borgy Waluyo & David Taniar & Wenny Rahayu & Bala Srinivasan Received: 28 June 2010 / Revised: 10 January 2011 Accepted: 24 January 2011 # Springer Science+Business Media, LLC 2011 Abstract The deployment of wireless data broadcast to empower mobile information services as a resource-conserving means offers significant benefits due to the scalability feature offered by the technology. In this paper, we present a novel and holistic data broadcast management approach in 4 G wireless networks with multiple-input multiple- output (MIMO) antennae. The proposed scheme consists of three elements, namely: (i) broadcast ordering; (ii) Global indexing; and (iii) merging data structure. With the integration of these elements, we expect to obtain substantial efficiency for mobile computing clients when retrieving data on-air. We have experimentally evaluated the performance of the proposed model including comparison with the relevant schemes. The results from the experiments affirm the effectiveness of our proposed approach in respect to minimizing query access time and conserving energy utilization of the clients. Keywords mobile broadcast services . mobile data management . broadcast-based information services 1 Introduction With the constant, growing use of portable wireless devices (i.e. mobile phones, smart phones or Personal Digital Assistants) followed by the advancement of wireless network World Wide Web DOI 10.1007/s11280-011-0113-9 A. B. Waluyo : D. Taniar (*) : B. Srinivasan Clayton School of Information Technology, Monash University, Melbourne, Australia e-mail: [email protected] A. B. Waluyo e-mail: [email protected] B. Srinivasan e-mail: [email protected] W. Rahayu Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Australia e-mail: [email protected]

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Page 1: Mobile Broadcast Services With MIMO Antennae in 4G

Mobile broadcast services with MIMO antennae in 4Gwireless networks

Agustinus Borgy Waluyo & David Taniar &

Wenny Rahayu & Bala Srinivasan

Received: 28 June 2010 /Revised: 10 January 2011Accepted: 24 January 2011# Springer Science+Business Media, LLC 2011

Abstract The deployment of wireless data broadcast to empower mobile informationservices as a resource-conserving means offers significant benefits due to the scalabilityfeature offered by the technology. In this paper, we present a novel and holistic databroadcast management approach in 4 G wireless networks with multiple-input multiple-output (MIMO) antennae. The proposed scheme consists of three elements, namely:(i) broadcast ordering; (ii) Global indexing; and (iii) merging data structure. With theintegration of these elements, we expect to obtain substantial efficiency for mobilecomputing clients when retrieving data on-air. We have experimentally evaluated theperformance of the proposed model including comparison with the relevant schemes. Theresults from the experiments affirm the effectiveness of our proposed approach in respect tominimizing query access time and conserving energy utilization of the clients.

Keywords mobile broadcast services . mobile data management . broadcast-basedinformation services

1 Introduction

With the constant, growing use of portable wireless devices (i.e. mobile phones, smartphones or Personal Digital Assistants) followed by the advancement of wireless network

World Wide WebDOI 10.1007/s11280-011-0113-9

A. B. Waluyo :D. Taniar (*) : B. SrinivasanClayton School of Information Technology, Monash University, Melbourne, Australiae-mail: [email protected]

A. B. Waluyoe-mail: [email protected]

B. Srinivasane-mail: [email protected]

W. RahayuDepartment of Computer Science and Computer Engineering, La Trobe University,Melbourne, Australiae-mail: [email protected]

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technologies, providing reliable and yet efficient information services has never been moreimportant. A large number of online information services are now available for public use,and due to the paradigm shift from stationary to mobile computing, it is natural that themajority of users will be mobile clients. Although mobility provides flexibility for users toengage in activities while travelling and staying connected to the network, the environmentis completely different from the traditional setting. Their mobility is constrained in anenvironment that has inherently limited resources [21, 41]. Therefore, it will be appropriatefor the content providers to help users operate in the most efficient manner.

Wireless data broadcasts are the most effective and efficient data dissemination methodto a large number of mobile/wireless clients. Public information that will benefit frombroadcast technology includes stock exchange updates, traffic conditions, election results,tourist services, weather forecasts, flight schedules, and marketing activities to name a few.Data broadcasts (radio programs and TV programs) are not new and have been in frequentuse for some time. However, in the past, these were mainly concerned with analogue datatransmission. The behavior of broadcast-based information services is unidirectional, whichmeans the server disseminates a set of data periodically to a multiple number of users. Withthis mechanism, the requests from the clients are not known a priori. Users are required toselect only the desired channel. When clients are disconnected from the network duringquery processing, they can simply repeat the process when they reconnect, withoutworrying about the large amount of power consumption required to send the request back tothe server, as is the case with the traditional client-server application. Therefore, databroadcast is a very promising mechanism for information delivery services in a mobileenvironment.

With data broadcast, public information can be efficiently communicated to the generalpublic. Nowadays, most business processes can be done online through web servicesdeployment [43] or so called e-business. The support from broadcast technology will enableusers to efficiently request information related to the services. Let us consider the followingscenario in Figure 1. In this example, the user is a traveler, who wants to go on a holiday.She has decided to stay in a particular hotel and take a certain airline. Thus, she will beperforming activities involving two web services, namely hotel reservation and airlineservices. The user knows that sometimes, the hotel and airline offer special deals inmagazines or newspapers where a discounted rate can be obtained on presenting a voucheror promotion number. Thus, she wants to check if a special rate or a package deal iscurrently being offered. In days gone by, she would have needed to perform this tedioustask by physically checking the publicity or making a call to the hotel and the airline forinformation on the special offer, which sometimes cannot be obtained unless the person hasthe promotion number. However, in today’s technological age, she can visit the websites ofthe respective newspaper or magazine to find the special offer of the hotel/airline. As theuser may not know when the offer was published, or if there is an offer at all, she may haveto search in the archives. Consequently, this will only not consume resources (i.e. powerand bandwidth) but also a substantial amount of time, which is particularly precious in themobile environment.

These problems can be eased by broadcast technology. The hotels/airlines that have aspecial offer or package may register with the broadcast provider and disseminate theinformation to mobile users periodically. This means that mobile users simply need to listento the broadcast channel and specify the criteria for their selection. When the informationthat satisfies the criteria arrives in the channel, it will be retrieved by the device. At thesame time, users are able to do other activities without having to spend time on searching.After the information is obtained, the user is able to make the reservation through the

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associated web services and specify the promotion number to obtain a special rate.Similarly, when the user has no preference regarding a hotel or airline, she can simply tunein to the broadcast channel to find the ones offering a discounted rate.

Although it is promising, data broadcast services cause performance degradation whenthe number of data items being broadcast increases. This is due to the typical linear accessof broadcast data items in the wireless network [41]. The increasing number of broadcastitems causes mobile clients to wait for a substantial amount of time before receiving thedesired data item. Consequently, the advantages of data broadcast diminish.

In view of the ongoing proliferation of wireless communication technology, this papercorrelates with the fourth-generation wireless system (4G) that leads to the deployment ofmultiple-input multiple-output (MIMO) antennae in the mobile device. This advancedtechnology allows clients to set up multiple connections to a base station and listen tomultiple broadcast channels at once. It is noteworthy that there are two differentperspectives for MIMO systems [11]. Firstly, the nature of the MIMO system allows usto enhance the fading statistics of the received signal by virtue of multiple available replicasover independent fading channels. As a result, the reliability of the communication link canbe improved, the outage probability can be decreased, and the effects of multi-path fadingcan be reduced by sending the same signal through parallel and independent fadingchannels [23]. The second perspective is referred to as spatial multiplexing [23]. In thisperspective, the efficiency of the spectral can be increased by transmitting differentinformation streams on multiple parallel spatial channels associated with the transmitterantennaes. The receiver terminal should be equipped with at least the same number ofreceiver antennaes as the number of parallel channels generated by the transmitter in orderto separate the individual streams. This paper is more concerned with the latter concept ofthe MIMO antennae and uses this as the underlying infrastructure of the proposed method.

Hotel Airline

Listening tothe channel

Retrieving thedesired information

Web-Services

Hotel ReservationServices

PaymentValidationServices

Local QueryProcessing

Airline Services

Registering Ads

Requesting services

Broadcast Channel

Content provider/Broker

Figure 1 Data broadcast in conjunction with web services: an overview.

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1.1 Scope and contributions

The earlier example shows that data broadcast is most advantageous when used inconjunction with mobile services (i.e. web services) to support real-world applications (i.e.e-commerce, entertainment, healthcare [39]) especially when in a resource-constrainedwireless environment. Wireless broadcasting can be considered as a supporting elementwithin the mobile information services framework. Therefore, as it is deployed in thecontext of mobile information services, we refer to this data broadcast as mobile broadcastservices. However, in order to achieve the full potential of the mobile broadcast services, itis crucial to incorporate a data management scheme. The data management role is mainly toaddress the resource scarcity in mobile environments [10].

This paper is especially concerned with 4 G wireless systems as the underlyingcommunication technology for the mobile clients to perform online activities while roamingabout in regions. Although we use the term ‘data item’ in this paper as a physicalrepresentation of the data, data broadcast is not restricted to this as it can also be extendedto disseminate types of services on the web [49, 50]. Mobile clients, upon receivinginformation from the mobile broadcast services, (depending on the application type), mayrequest other services based on the information that has been obtained. All these servicescan be performed under a scalable technological platform such as web access (i.e. WirelessApplication Protocol or WAP [33]).

Common metrics to estimate the cost of data access in the mobile broadcast environmentare [10]: (i) Access time: Elapse time from the time a request is initiated until all data items ofinterest are received; and (ii) Tuning time: Amount of time spent by the client listening for thedesired broadcast data items. Tuning time comprises two modes: active and doze mode.Active mode is when the client listens to the channel for the desired data item which is costlyto power consumption, while doze mode is when clients simply turn to a power saving mode.Generally, the amount of power consumption is directly related to the tuning time.

Overall, the main objective of this paper is to present a novel and holistic mobilebroadcast management scheme consisting of: (i) broadcast ordering; (ii) Global indexing;and (iii) merging data structures in mobile 4 G networks with MIMO antennae. The aim ofthis approach is to minimize the query access time and tuning time of the client whenobtaining broadcast information in multiple channel 4 G wireless environments. Ourbroadcast ordering scheme is designed to strategically organize the order of the data itemsin the channel in the most effective way possible, considering the access patterns of theusers. A Global indexing scheme, which is modified from our earlier report in paralleldatabase processing [32], offers important features that benefit mobile users in accessingmultiple broadcast channels due to its partitioning strategy. Finally, our new data structureis devised to absorb the impact of having an index directory in the broadcast channel, whichtypically increases the broadcast cycle. With the merging data structure, the query accesstime of the client will not be much affected by the existence of the index. The integration ofthese three elements forming a single holistic data broadcast approach in mobile 4 Gnetworks with MIMO antennae distinguishes this paper.

1.2 Structure of the paper

The remaining sections in this paper are organized as follows. Section II presents the relatedwork in data broadcast management, indexing and scheduling. Section III describes somepreliminaries on data broadcast and indexing, which is then followed by the proposed databroadcast model in section IV. Section V discusses the experimental performance of the

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proposed approach as compared to the existing methods. The results of the experiments andanalysis are also provided in this section. Finally, section VI concludes the paper.

2 Related work

Despite the growing trends in mobile information services, it is unfortunate that mobileenvironment experiences inherent resource constraints such as short-life batteries, limitedstorage restriction, frequency of disconnection, narrow bandwidth capacity, and asymmetriccommunications costs and bandwidth [9, 10, 15, 41]. Thus, efficient data retrieval is anessential aspect in mobile environments.

Deploying data broadcast allows mobile clients to obtain information without the need totransmit a request to the server. Mobile clients simply filter their desired data on the fly[10]. Furthermore, data broadcast is able to serve any number of queries, and the queryperformance is not affected by the number of mobile clients in a cell [41]. Strategies tobroadcast data through wireless channels have been effective in addressing the efficiencyand scalability issue [2, 41, 42].

2.1 Broadcast scheduling

Acharya et al. [1] proposed a data scheduling method, called broadcast disks. In broadcastdisks, data items are partitioned into several groups. These groups are broadcasted atdifferent broadcast periods, based on the access frequency of the data items. The groupswith higher access frequency have shorter broadcast periods. Hence, it reduces the averageaccess times of the data items. However, this method only concerns a single broadcastchannel environment.

Prabhakara et al. [25] presented a broadcast ordering scheme where hot items or themost frequently accessed data items are broadcast more often than cold items. This is one ofthe most cited data broadcast models and is the one to which most other data broadcastmodels are compared. This will be used for our comparison in section 4. The authorsfurther improve the work by allocating the hot items to the fastest bandwidth in decreasingorder. The number of items in the channel is specified by a given temperature thresholdvalue. Tran et al. [34] proposed a broadcast ordering scheme by considering differentbandwidths possessed by each channel and the size of data items is varied. However, theseapproaches do not consider the relationship of one data item to the other. Therefore, theseschemes may not be so effective for multiple data item retrieval.

Huang and Chen [7, 8] proposed several algorithms to identify the most effectiveorganization of broadcast data items. They were concerned with a broadcast dataorganization scheme in the context of multiple broadcast channels. They presented ananalytical model to calculate query access time, and employed a Genetic Algorithm to findthe best organization of broadcast data items. However, they did not apply an indexingscheme in the broadcast program. This situation may lead to wasteful power consumptionas mobile clients need to keep listening to the channel and filtering the data items until thedesired ones arrive in the channel.

2.2 Broadcast indexing

The broadcast indexing technique is designed to minimize the amount of time the clientlistens to or tunes into the channel, thereby reducing power consumption [10]. The tree-

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indexing structure is first introduced in [9]. It is later expanded and modified in [10]whereby clustering, non-clustering, and multiple index methods were discussed. Tree-indexscheduling for (1, m), and distributed and combined message structures for single indexchannel were studied in [44]. A signature-based indexing technique was proposed by Leeand Lee [13] and a hybrid index technique, made up of an index tree and signature wasproposed by Hu, Lee and Lee [6]. The combination of index and data items scheduling inmultiple channels was reported in [16].

Another indexing technique is the Exponential index [45]. Exponential indexing isassociated with an index table that allows the index pointers to be partitioned into apredefined base value. With this scheme, mobile clients are able to tune into the channel atany broadcast point and the index items in each bucket will guide the clients to the right indexbucket. However, exponential indexing incurs a large index bucket size due to a number ofreplications of the identifier and and pointers to data items which are equal in number to theentries in the index table. For a very large data set, the exponential index incurs an extendedlatency delay due to the fast increment in the size of the index with the number of items.

2.3 Multiple channel scheduling

Other literature has proposed broadcasting schemes in multiple channel environments [38,40]. Their typical assumption is that each client is equipped with only one antennae (or oneretrieving capability). Each time, the antennae can establish communication with onechannel. However, one connection results in narrow bandwidth and small throughput,which is a crucial physical constraint for wireless networks especially when consideringQuality of Service (QoS). The Fourth-Generation Wireless Communication System (4G)has managed to partially solve this issue by applying multiple-input multiple-output(MIMO) technology, which allows different data streams to be received simultaneouslyfrom a limited number of antennae [22, 36]. With this, the client is able to communicatewith multiple channels at the same time and reduces the query processing time significantly.It is strongly expected that 4 G will replace the current Third-Generation Network System(3 G) in the near future [11].

Work which is closely related to this paper was reported by Lee et al. in [14]. In thisliterature, the authors proposed data scheduling for hybrid on-demand and broadcast modeof operation with time constraints in the multi channel/receivers environment. The wirelessinfrastructure design to achieve this was not clearly described, but the concept of retrievingdata streams from multiple channels simultaneously is similar to this paper. However, inthis literature, the authors mainly focused on the data scheduling issue in the hybridenvironment. This is different from our paper whereby we attempt to support data broadcastservices by introducing a holistic broadcast management approach with the aim ofminimizing the query access time and battery consumption of the mobile users.

2.4 Web services on air

Web services are considered the most significant e-commerce technologies, and in order tosupport wireless-oriented services, mobile services (m-services) have emerged as a newgeneration of web services [50]. There have been several attempts to promote mobileservices on the web such as those reported in [4, 17–19, 24, 28, 29, 37, 49, 50], and ofthese, Yang and Bouguettaya [49, 50] proposed the utilization of the broadcast paradigm toperiodically disseminate the available m-services over a wireless channel. Clients listen tothe channel, identify the m-service of interest, and download it to the mobile host for local

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execution. The broadcast information about the m-services includes registry, description,code, and data to mobile clients. However, these existing papers are only concerned withthe broadcast model and access to support m-services in a wireless environment. To addressthe access and tuning time issue, they adopt the relevant data management scheme from theliterature. Our proposed holistic approach in this paper aims to improve the underlying databroadcast management scheme, and this can be easily extended to perform in the same waywithin the context of m-services over broadcast channels.

3 Preliminaries

In this section, we present a brief preliminary overview of data broadcasting and indexing,which serves as the basis of the model of this paper.

3.1 Data broadcast architecture

The data broadcast architecture is shown in Figure 2. This architecture reflects a holisticdata broadcast system, which incorporates the three elements of our proposed approach inthis paper. In this model, the server periodically broadcasts data items according to apredetermined broadcast program. When a user submits a query to his or her mobile device,the mobile device will retrieve the required data item from the broadcast channel byreferring to the index information.

As depicted in Figure 2, the server contains six entities namely: (i) Broadcast ProgramGenerator/Scheduler; (ii) Index Generator; (iii) Message Structure; (iv) Access Patterns;(v) Database; and (vi) Server Cache. The mechanism of this broadcast system can bedescribed as follows:

– The server retrieves the database items from the data repository, which aresubsequently processed by the broadcast program generator.

– The broadcast program generator is responsible for the ordering and allocation of dataitems in the broadcast channels. To obtain optimum placement of broadcast data items,

Broadcast Server

Wireless Channel

Listening and retrievingthe desired data items

from the channel

AccessProfiles

Database

Broadcast ProgramGenerator/Scheduler

(Section 4A)

Index Generator(Section 4B)

Message Structure(Section 4C)

ServerCache

Transceiver

Figure 2 The architecture of the broadcast model and its correlation with the contribution of the paper aspointed out by the section number in the box.

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the broadcast program generator needs to be informed about the query access patternsof mobile clients. Correspondingly, the broadcast data is obtained from the database.This is where the data broadcast ordering scheme takes place.

– Having determined the broadcast order, the broadcast program generator sends theinformation to the index generator.

– The index generator constructs the index based on the information from the broadcastprogram generator. The index generator in this paper incorporates the Global indexstructuring scheme.

– Following the final broadcast program and the index structure, we will implement anew broadcast structure. This message/packet structure is designed to minimize theconsequence of having index information in the broadcast channel.

– Finally, a broadcast program with an index directory following the defined messagestructure is obtained and forwarded to the server cache. The server cache holds theinformation for broadcast in a periodic basis. One complete transmission of a broadcastprogram is called a broadcast cycle.

– When data items in the database are updated, the new values and the relevantidentification will be sent over to the server cache immediately. As such, the new datawill be made directly available to mobile clients.

– The update on the access profiles and the broadcast data can be carried out within apre-determined interval.

As stated earlier, we consider data broadcast technology in 4 G networks with MIMOantennae; hence the data retrieval mechanism in this scheme can be described as follows:

– Mobile client tunes into one or more channels (i.e. can be of all channels).– Mobile client follows the index pointer to the right index key. The pointer may lead to

another channel that contains the relevant index. This pointer can be translated into atime value, which indicates when the data item will arrive in the channel. Whilewaiting for the index to arrive, mobile clients can switch to a sleep/power saving mode.

– Mobile client tunes back in to the channel that has the right index key, which points tothe desired data item. Mobile client tunes in to the relevant channel, and switches backto power saving mode while waiting for the data item to arrive.

– Mobile client switches back to active mode just before the desired data item arrives,and retrieves the information.

In our model, each antenna of the mobile client is independent to each other, and they arecontrolled by the processing unit of the mobile device. All antennas are set to an active modeonly when the user initiates a query. When an index pointer to the relevant index key and dataitem are found, the processing unit assigns an antenna to the associated broadcast channel andfollows the index pointer accordingly. All other antennas are switched to a sleep mode. Whilewaiting for the index key/data item to arrive in the channel, the associated antenna may alsoturn into a sleep mode and turn back on only when the required index key/data item is aboutto arrive in the respected channel. In the event of multiple data items query where the data ofinterest are located across different broadcast channels, more than one antenna may beassigned to the relevant channels to obtain the data simultaneously.

3.2 Index broadcast

The index in a mobile data broadcast indicates when the requested data will arrive in thechannel, which is quite different from the traditional database system whereby the index

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points to a certain location memory of the data. The significance of the index in a databroadcast lies in its ability to help clients reduce the tuning time by providing accurateinformation for a client to tune in to at the appropriate time for the required data [41]. Toincorporate this scheme, some form of directory is broadcast along with the data. Theclients are to obtain the index directory from the channel and use it in subsequent reads.The index enables mobile clients to conserve energy by switching to “doze” mode and backto “active mode” when the data is about to be broadcast.

In general, the broadcast index scheme causes a trade-off between the client’s tuningtime and the query access time. The consequence of minimizing one of them is the increaseof the other. For instance, to minimize the query access time is to reduce the length ofbroadcast cycles. However, the tuning time will be large since the client has to listen toevery single data that arrives in the channel. The index broadcast is able to address this byproviding auxiliary information on when the desired data item will arrive in the channel andthe client may turn in to sleep mode while waiting for the data. This will reduce the client’stuning time but the existence of the index increases the length of the broadcast cycle, whichconsequently affects the query access time.

In this paper, the index structure is designed following a tree-indexing based. The tree-index serves as a pointer to guide the client to the relevant index and each index containsthe exact time when the data will be broadcast [10].

Definition 1 Query access time is measured from the time a client starts to probe into thebroadcast channel to the time they receive all the desired data items. Suppose that databaseD contains n data items, so that D = {d1, d2,…, dn}. For the sake of simplicity, we assumethat the data packets are of equal size. Let’s say that each data packet is broadcastcontinuously, hence the waiting time for each packet can be associated with time, ti. Thequery access time, At, will be the time it first listens to the channel, t1 to the time it receives

the desired data item, tj, which can be represented as: At :¼ if j > i;Pj

i¼1ti;Pn

i¼1ti

� �

. If the

desired data item is missing, the client has to wait for it to arrive in next cycle.

Example 1 The client is interested in data item #8, and begins to probe into the channelfrom the beginning of the broadcast cycle. Assuming the data items are broadcast in a

sequential order, the At will beP8

i¼1ti.

Definition 2 With the presence of the index broadcast, the client does not have to listen tothe broadcast channel continuously, as the index provides information to the client on whenthe data item of interest will arrive in the channel. Thus, the client is able to turn to sleepmode while waiting for the data item, and switch back on when the data is about to arrive.Let’s assume the data packet for the data and index item are of similar structure and size. Aset of index broadcast I = (i_root, i_1,…i_m), and this index node points to the D = {d1,d2,…, dn}. The index segment is always broadcast ahead of the data items. The tuning time,Tt, can be measured as follows: Tt :¼ if t1 ! ti root; ti root þ b� ti pointer

� �þ tdata� �

;�

ti root þ 2 b� ti pointer

� �þ tdata� �g;where b denotes the number of index level to traverse.

Example 2 Let’s say m=15, and one index root at the beginning of the broadcast cycle, sothe number of data packets in the index segment is 16. Correspondingly, n=15. The client isto obtain data item #8, and starts to probe into the channel in the beginning of the broadcastcycle. Assuming b=1, the Tt for this case will be t1+ t8+ t24.

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3.3 Assumption

In this paper, it is assumed the packet size for each data item and bandwidth of eachchannel is uniform. The data packet is dispatched and received in a sequential mannerfollowing the wireless characteristic [41]. Therefore, all bursts of a transmission are thesame size, which allows time-slicing bursts of data, and eventually enables the mobile clientto estimate when the requested data item will arrive in the channel. The start times of thetransmission of each data packet in all available broadcast channels are synchronized, i.e.the transmission of the data packet begins simultaneously and the time interval of each datapacket is fixed. This allows mobile clients to continue receiving a service withoutdisruption when moving across a cell boundary [5, 20].

4 Proposed method: A novel and holistic mobile broadcast management

In this section, we are describing each of the elements in the proposed broadcast approach,which consists of: (i) broadcast ordering scheme; (ii) Global index; and (iii) merging datastructure. The interconnection between these elements in the broadcast model wasdiscussed earlier in section 3.

4.1 Broadcast ordering scheme

The proposed ordering scheme is concerned with one or more data items’ query. Anexample of applications in this context includes a situation where the mobile client wants toreceive ads for more than one hotel at the same time (i.e. to list the hotels ads underMarriott Group) or when the client wants to retrieve ads for all five star hotels concurrently.To accommodate this type of request, the relationship between one data item and the otherswill be considered. In this case, it is essential to obtain information about the behavior ofmobile users in accessing the broadcast information. To find the query access patterns ofmobile clients, either one of the following mechanisms may be incorporated. One is tocollect the access information from each mobile client at regular intervals, and the second isto determine the access information from the behavior of offline or desktop users. It can beassumed that stationary users and wireless users have similar access patterns. Once thequery pattern information is received by the broadcast server, the statistics will be compiledand the broadcast organization scheme will be implemented.

The broadcast ordering scheme is executed by the broadcast program generator. Let D ={d1, d2… dm}, be a set of data items to be broadcast by the server from the database, and Dt

be the number of items to broadcast. Likewise, let Q be a set of queries {Q1,Q2,…,Qn}, andQt be the number of queries pattern. In this case, it is assumed that the data item has anequal size and the order of the retrieval can be arbitrary, which means if any of the dataitems required by Qi arrive in the channel, they will be retrieved first. Each query, Qi,accesses a number of data items di, where di ∈ D. The broadcast channel is indicated by C,and the length of the broadcast cycle in a channel is given by BC. Let the broadcastschedule be denoted as S = {dx, dy,….dz}. Similarly, the broadcast program for each channelis defined by SC.

There are three stages involved in this scheme. The first stage is to list the data items in asequential order based on the access frequency. The second stage is to analyze therelationship between data items and calculate the access frequency of each pair of dataitems, according to the given query patterns. The final stage is to order the pairs of data

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items based on the value of the access frequency in descending order. Subsequently, theyare placed into multiple broadcast channels. A detailed mechanism of this scheme isdescribed as follows.

Firstly, the data items are ordered based on the popularity of the item in each query. Thepopularity of data items is decided by the access frequency of each data item given by eachquery in Qt. Subsequently, the data items will be sorted in decreasing order of accessfrequency (f(x)). The result will be temporarily stored in ST1, and incorporated into the finalbroadcast order S after the second stage is finalized. The illustration and algorithm ofthis stage is given in Figures 3 and 4, respectively. The complexity of this algorithm isO(N • M), where N is the number of data sets (Dt) and M is the number of queries (Qt).

Secondly, the relationship between one data item and the other is studied. The accessfrequency of each data item in relation to other data items is assessed from each query in Q.This stage is required in order to group the relevant data items close to each other. Thus, thenumber of calculations required for this purpose will be Dt� Dt�1ð Þ

2 , where Dt is the number ofdata items in D.

In this stage, each data item needs to be firstly analyzed for its relationship with otherdata items based on Q. The calculation is done on a sequential basis. i in Figure 5 reads thedata item number. This process iterates until each data item has been counted. A singleprocess includes a two-way relationship of the data. Each data item is counted for itsrelationship with the next sequence of the data item from the given Qi. The double pointedarrows indicate the two-way relationship of the two data. In this case, D = {d1, d2, d3, d4,d5}. Starting from d1, it is analyzed for its relationship with the subsequent data items,namely: d2, d3, d4, d5. This process continues sequentially until m-1 data item. After all dataitems have been analyzed, they are sorted in descending order based on the accessfrequency with other data items, and subsequently stored in ST2. Each allocation involvestwo data items. If there is duplication of data items within ST2, then the data items with thehighest ST2 value are kept and the rest are removed. Figure 6 shows the algorithm forthe second stage. The complexity of this algorithm is equivalent to O(N2 • M), where N isthe number of data sets (Dt) and M is the number of queries (Qt).

The final stage is to combine the output of the first and second stages (ST1 and ST2) toform a single broadcast order S. The allocation begins by moving data items in ST2 into Salso in descending order. This process continues and only ceases when the access frequencyof one is left to be processed. Subsequently, the result from ST1 is obtained, and it is thenanalyzed against S. The duplicate data items in ST1 that existed in S will be discarded. Theleft over data items are placed into S in non-increasing order. The broadcast order is thenassigned over multiple wireless channels. A mobile client with x antennae enables it toretrieve data using at most x processes in parallel. However, the number of antennae that amobile client may have is still limited due to the size of mobile devices. A typical numberof antennae for mobile handsets is three [36].

Assuming each client is able to listen to three channels simultaneously, the broadcastcycle is split into three channels (indicated by BC). With this assumption, the data items inS are partitioned sequentially across each channel. As such, clients are able to obtain thedata items in a much more efficient manner. An illustration of this final stage and its

d f(x ) d f(x-1 ) d f(x -2 ) d f(x-3 ) d f(x- 4 ) d f(x -5 ) … DataSegments

Stage 1:

Figure 3 Stage 1: illustration.

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associated algorithm is given in Figures 7 and 8, respectively. The complexity of algorithm3 is equivalent to O(N), where N is the (ST2 maxð Þ þ ST1 maxð Þ þ S maxð Þ).

4.2 Global indexing

Once the broadcast order is finalized, the index can be constructed following the schedulein the broadcast program. Our Global index strategy is designed based on B+ -tree structure[31]. It consists of non-leaf nodes, and leaf nodes. A leaf node is the bottom-most index thatconsists of up to k keys, where each key points to actual data items and each node has onenode pointer to a right-hand side neighboring leaf node. Unlike leaf nodes, non-leaf nodesmay consist of up to k keys and k+1 pointers to the nodes on the next level on the treehierarchy (i.e. child nodes). All child nodes, which are on the left-hand side of the parentnode have key values less than or equal to the key of their parent node. On the other hand,keys of child nodes on the right-hand side of the parent node are greater than the key oftheir parent node. When being broadcast, each physical pointer to the neighboring leaf nodeas well as actual data items are replaced by a time value, which indicates when the leafnode or data item will be broadcast. The index pointer often specifies the packet number ofthe data item in the broadcast channel. As the packet size for each data item and its inter-arrival rate are uniform, the estimated time for the data item to arrive in the channel can bemeasured accordingly.

Let’s consider our simple scenario to broadcast hotel promotion rates. In this example,there are 30 hotels in total to broadcast following the order from the broadcast program

d id i 2 d i 4

d i 1

d i 3

QueryPatterns

Stage 2 :

Figure 5 Stage 2: an illustration.

Figure 4 Stage 1: algorithm.

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generator, and similar to our earlier assumption, the number of antennae is limited to 3, as isthe number of parallel channels to which the mobile client can listen simultaneously. Assuch, each data channel will contain 10 hotel ads. In this case, we use a table consisting ofrecords of IDs, hotel name, special package ads, and promotion/voucher numbers.

The number of channels used for index-tree partitioning is the same number as thechannels. The index-tree structure is partitioned into 3, based on the ID attribute. The ID isused as the index-partitioning attribute. With 3 channels, the index-partitioning attributewill be the highest ID over 3 index channels. The result is index channel 1 holds data IDs

{d i, d i+ 4} { d i, d i+2} {d i+ 1, d i+2} {d i+ 1, d i+3} { d i+ 2, d i+3} {d i+ 2,d i+ 5}

f(6 ) f(5 ) f(4 ) f(3 ) f(2 ) f(1 )

Broadcast Channel(Ch 1)

d i d i+4 d i+2 d i+ 1 d i+3 d i+5

d i d i+ 2 d i+3

Broadcast Channel(Ch 2)

d i+4 d i+ 1 d i+ 5

(a) Each pair of data items is ordered based on the access frequency

(b) The organisation of data items is finalised

(c) The broadcast program for multi-channelling is generated

Stage 3 :

Figure 7 Final stage: an illustration with two broadcast channels.

Figure 6 Stage 2: algorithm.

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between 1 to 37, index channel 2 holds data IDs between 38 to 67, and the rest go to indexchannel 3. Having determined the index-partitioning attribute, it is then necessary to apply atraversal algorithm to assign the index nodes into the index channels.

A post order traversal algorithm is exercised in order to allocate the index nodes to thedesignated index channel. The post order traversal for the Global index is depicted inFigure 9 and the associated algorithm is shown in Figure 10.

The Global Index Post Order traversal algorithm traverses the index bucket rather thanthe index nodes. The algorithm analyses each index node within each bucket prior toallocating the index bucket to the relevant index channel, based on the index-partitioningattributes. Figure 11 shows the Global index construction and restructuring process startingfrom the initial index tree. Notice from Figure 11 that the fifth leaf node (35, 40, 44) of the

Figure 8 Final stage: algorithm.

Index construction (top-down order from leaf to right)

15 17 22 23 25 27 28 30 31 35 40 44 22 28 31 25

45 46 50 53 54 55 56 57 63 66 67 72 76 78 81 82 85 99

50 63 78 82 55 67 44

o 44 o 53 54 o 55

o 50 o 56 o 57 o 63 o 72 o 76 o 78 o 31

27 28 o 66 o 67 o 81 o 82

85 o99

22 50 63

44

25

28 31 78 82

55 67

15 o 17

o 23 o 25

o o

o 30

o 35 o 40

o 45 o 46

o

o o

o 22 o

Figure 9 Post order traversal – Global index.

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Global index is replicated in channels 1 and 2 because key 35 belongs to index channel 1,while keys 40 and 44 belong to index channel 2. Also, notice that some non-leaf nodes arereplicated, whereas others are not. For example, non-leaf node 22 is not replicated and islocated only in index channel 1, whereas non-leaf node 25 is replicated to index channels 1and 2. It is also clear that the root node is fully replicated.

The data structure for the Global index can be described as follows: If a child node existslocally, the node pointer points to this local node only, even when this child node is alsoreplicated in other index channels. For example, from node 44 at channel 1, there is only

Figure 10 Post order traversal algorithm for Global Index.

Channel 1 Channel 2 Channel 3

30 Hilton 25% off inJanuary 2010

Hil564 25 Nikko 9% off in August 2010

Nik12346 Concorde

21% off in 15-25 June 2010

Con321

72 Marriott 10% off in February 2010

Mar455 28 West Inn 10% off in Sept 2010

WI90250 Regency 10.5% off in Nov

2010 Reg612

44 Four Season 7% off in March 2010

FS234 17 3.5% off from 5-20 May 2010

Bul46254

10% off from 21-30 July 2010

PP076

67 Mandarin 2.5% off from 1-10 April 2010

Man123 81 Ritz Carlton 5.5% off in April 2010

RC89257 YMCA

35% off in Aug. 2010 YMC641

53 Ibis 5% off from 3-11 May 2010

Ibi012 85 Sheraton 30% off in March 20 10

She66576 Rendezvous 5% off in July 2010 Ren123

99 Shangrila 12 % off from 5-15 June 2010

Sha919 22 Novotel 7% off in Oct 2010

Nov32182 Carlton

18% off from 1-10 Dec 2010

Car873

55 Crown 5.5 % off from 10-20 July 2010

Cro009 23 Oasis 25% off in July 2010

Oas75715 Bayview

20% off in April 2010 Bay521

78 Phoenix 25% off from 1-20 May 2010

Pho876 27 Royal 10% off in May 2010

Roy73656 Swissotel

17% off from 15-20 Aug 2010

Swi312

63 Excelsior 3.5 % off in June 2010

Exc823 31 River View 8% off in 1-20 July 2010

RV76440 Furama 40% off in March

2010 Fur233

66 Holiday Inn 7% off in July 2010

HI132 35 8.5% off in 15-20 July 2010

Sup63245 Hyatt 15% off in Feb

2010 Hya717

(d) Table (ID, Hotel Name, Special Package Ads, Promotion No.)

(a) Channel 1 (b) Channel 2 (c) Channel 3

o

o

44o 31

27 28

22

25

28 31

15 o 17 o 22

o 23 o 25

o

o 30

o 35 o 40

o

44

o

o 53 54 o 55

o50 56 o 57 o 63

o 66 o 67

50 63

44

55 67

o 45 o 46

25

28 31

44o 35 o 40 o

o 72 o 76 o 78

o 81 o 82

85 o99

78 82

o o

44

55 67

o

Bulgary

Supreme

Pan Pacific

Figure 11 Global indexing scheme.

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one node pointer to the local node 25. Child node 25 at channel 2 will not receive anincoming node pointer from root node 44 at channel 1; instead it will receive one nodepointer from the local root node 44 only.

If a child node does not exist locally, the node pointer will choose one node pointerpointing to the nearest child node (in case multiple child nodes exist somewhere else). Forexample, from root node 44 at channel 1, there is only one outgoing right node pointer tothe child node (55, 67) at channel 2. In this case, it is assumed that channel 2 is the nearestneighbour of channel 3. Child node (55, 67), which also exists at channel 3, will not receivea node pointer from root node 44 at channel 1.Using this single node pointer model, it isalways possible to trace a node from any parent node. For example, it is possible to tracenode (78, 82) from root node 44 at index channel 1, although there is no direct link fromroot node 44 at channel 1 to its direct child node (55, 67) at channel 3. Tracing to node (78,82) can still be done through node (55,67) at channel 2.

A more formal definition for the single node pointer model is as follows. First, given aparent node is replicated when its child nodes are scattered at multiple locations, there isalways a direct link from whichever copy of this parent node to any of its child nodes.Second, using the same methodology as the first statement above, given a replicatedgrandparent node, there is always a direct link from whichever copy of this grandparentnode to any of the parent nodes. Considering the first and the second statements above, itcan be concluded that there is always a direct link from whichever copy of the grandparentnode to any of its child nodes. As such, the Global indexing scheme retains the behaviourof the single index structure.

4.3 Data broadcast structure

The presence of the index in the broadcast cycle helps to minimize the query access time asthe client may switch to doze mode while waiting for the data to arrive. However,regardless of how efficient the index is, this will still greatly affect the query access time. Toaddress this, we present a new data structure that merges both data and index items.Figure 12 illustrates the scenario of a client accessing data items with our new datastructure.

Each leaf node is a pointer to a data packet that will be broadcast over the wirelesschannel. We adopt a ‘depth-first’ swizzle approach [10] in cascading the multi-level indexover a flat wireless space. Our Global index and its corresponding broadcast structure aredepicted in Figure 13.

As shown in Figure 13(a), there are 30 data items to broadcast. Starting from the root ofthe index tree, each index node is broadcast on a left-to-right basis. When being broadcast,each physical pointer to the next indexed item is replaced by a time value which indicatesthe channel and time to which the indexed item will be broadcast. For each index node,there is an nbranch pointer, which indicates the next possible time that all index items withindexed values greater than all indexed values in the current sub-tree will be broadcast. Inother words, the nbranch pointer is a sibling pointer, connecting sibling nodes under thesame parent. Figure 13(b) illustrates how the index structure is being broadcast in thechannel.

The index and data nodes are transmitted in a packet as the basic transmission unit inwireless broadcast channels. Each packet is the same size. Figure 14(a) illustrates themessage structure for a typical data packet. In this packet, the first field is the header fieldof the packet, which indicates the beginning of a new packet. The second field indicates thetype of node (either index or data) that the packet contains. The third field contains the

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attribute value, which corresponds to the unique key to index. The fourth field holds thedata item/s, and the last field is the Cyclic Redundancy Check (CRC) for recoveringpossible bit errors in the packet.

The message structure for typical index records is depicted in Figure 14(b). The firstthree fields are the same as the data packet’s fields. In the fourth field, the index structurespecifies the channel number where the index node or the data item may reside. The fifthfield indicates the branching factor, which explains the number of branches for each indexnode. The sixth field is the index payload. The seventh field contains the left-side pointer ofthe index node as illustrated in Figure 14. This pointer specifies the time value to childnodes. The eighth field of the structure defines the right-side pointer, which translates to thetime value of the subsequent index nodes of the same level. In the case of the leaf-node, thisfield contains the information to get the arrival time of the relevant data packet.

The message structure of our proposed scheme is shown in Figure 14(c). In thisstructure, similar to the other two, the first field contains the header field of the packet,which indicates the beginning of a new packet. The second field contains the attribute valueand the associated parent nodes of the index key. The third field is the channel number. Thefourth field contains the left-side pointer or nbranch pointer of the index node as illustrated

(b) Broadcast order in the wireless channel

(a) B+- tree index

To subsequent index node

To subsequent index node

Packet 3

25

22

15

17

22

28

23

25

27

28

31

30

Packet 1

Packet 2

Bro

adca

st O

rder

31

50

35

40

44

45

Packet 4

Packet 5

Packet 6

Packet 7

Packet 8

Packet 9

Packet 10

To subsequent index node

Packet 11

Packet 12

Packet 13

o

o 44 o 53 54 o 55

o 50 o 56 o 57 o 63 72 o 76 o 78o 31

27 28 o 66 o 67 o o 82

85 o99

22 50 63

44

25

28 31 78 82

55 67

15 o 17

o 23 o 25

o o

o 30

o 35 o 40

o 45 o 46

o

o o

o 22 o 81

Figure 13 Global index and its corresponding broadcast structure.

Client startsprobing

Data Item of Interest = Data item 5

Data &Index 1

Data &Index 2

Data &Index 3

Data &Index 4

Data &Index 5

Figure 12 Accessing broadcastdata with the new messagestructure.

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in Figure 15. This pointer specifies the time value to the subsequent node at the same indexlevel. The fifth field contains data payload. The sixth field of the structure defines the right-side pointer, which translates to the time value of the first branch of the non-leaf node toarrive in the channel. In the case of the leaf-node, this field contains the information thatdetermines the timing of the relevant data packet. The last field is the CRC.

The fourth field may hold more than one left-side pointer depending on the number oflevels in the entire index structure. Thus, this field contains different time values with themost right-side as the leaf-node pointer, followed by the non-leaf node pointers in a bottom-up manner. In brief, this field is composed of the (number of index level – 1) time values.The fifth field of the proposed structure contains the payload, whose value is the combineddata and index value. The sixth field is similar to the fifth field but this field contains thetime values of the right-side pointer. Likewise, this field may also store multiple right-sidepointers depending on the number of index levels. Being the most left-hand side indicatesthe time value of the leaf-node, followed by non-leaf node. This field contains the (numberof index levels) time values, which is eventually used as a reference to determine thenumber of index levels in the entire index structure.

The broadcast server allocates the information based on the given structure. Concerning thesecond field of the structure, the broadcast server needs to do a bit of processing prior totransmission. Let’s say that the attribute value of a packet is 27, our approach is to combine allthe index keys of its parent nodes into the field, such that it looks like the following in Figure 15.

As shown in Figure, the attribute value of the data is 27, and so the payload in the fifthfield contains information related to data key 27. This also constitutes the leaf-node pointerof index key #27. The second value (28) in the field denotes the immediate parent node ofthe index. It is followed by the third value (25), which indicates the second parent-nodegiven the index tree in Figure 13(a). Each value is separated by ‘#’ sign. The associatedpointers or the time values of these index nodes can be found in the fourth and sixth fieldsof the packet structure. With this, the client will be able to traverse the index from the leaf-node to its parent nodes based on the index key as reflected in the tree structure within asingle packet.

(b) Typical Index Structure

(a) Typical Data Structure

p_header p_type Attr_val Data Payload CRC

(c) Proposed Structure

p_type Attr_val b_factor left_ptr right_ptrIndexPayload

CRCp_header

p_header left_ptr (n_level,…,n_level=1)

Right_ptr(n_level,…n_level=1)

Chl_noAttr_val Payload CRC

Chl_no

Figure 14 Message structure.

Unique identifier(leaf-node index key)

Unique identifier(first parent node)

27#28#25#Root

Unique identifier(second parent node)

Attr val

Figure 15 Attr_val field in themessage structure.

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5 Simulation test-bed

This section studies the performance of the proposed approach in respect to the query access timeand client’s tuning time when retrieving data via broadcast channels. This includes a comparisonwith arbitrary and existing methods [25]. The simulation is carried out using Planimate, adiscrete event simulation tool written in C++ [27]. The simulation environment is set to applyexponential distribution for the data packet transmission rate. We run the simulation for twenty-five iterations, and derive the average result accordingly. The parameters of concern are given inTable 1. The channel bandwidth is set to 200Mbps which follows the approximate 4 G networkstandard [11]. This experiment considers single and multiple data item retrieval.

5.1 Performance analysis of the proposed scheme

In this first case, we study the performance of the proposed scheme when incorporating itselement from one to all the three elements with respect to the average query access time andclient’s tuning time. We set the broadcast items to 30. As can be seen from Figure 16, thereare three categories in the proposed scheme.

The first column in Figure 16 shows the performance of the proposed scheme with onlyone element in the broadcast ordering method. Since there is no index involved, the queryaccess time of the client is equal to the client’s tuning time. In this case, the client has tolisten all the time to the channel until the desired data items are received.

The middle column in Figure 16 shows the performance of the proposed scheme whentwo elements are incorporated, namely broadcast ordering with the Global index scheme.This aims to reduce both the client’s tuning time and power consumption when retrievingbroadcast data. However, the existence of the Global index increased the access time of theclient quite substantially.

The third column in Figure 16 shows the performance of our proposed scheme when allthree elements presented in this paper are incorporated. It can be seen that the access time iscomparable with the proposed scheme without the index but the tuning time is greatlyreduced and is better than the other two performances.

5.2 Proposed vs. Arbitrary scheme (access time)

In this case, we compare the performance of the proposed scheme with the arbitrary methodwhere the data is broadcast without a specific ordering scheme. For the arbitrary method,

Table 1 Parameters of concern.

Parameters Value

Size of each data packet 160 KB

Bandwidth 200 Mbps

Query Patterns/Profiles 10

Number of Dependent Items in Query 1–4

number of Antenna/Broadcast Channel 3

Number of Broadcast Data Items 30–300

Client’s processing time per index node 0.0019 sec

Node Pointer Size 24–32 bytes

Indexed Attribute Size 24 bytes

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we employ a conventional B+ -tree indexing where the entire index tree is broadcast in thechannel following (1,1) broadcast structure [10]. In addition, for better insight, we alsoemploy the arbitrary method with our Global indexing.

As can be seen from Figure 17(a), our proposed broadcast scheme substantiallyoutperformed the arbitrary method on both occasions. Our proposed method is, in order ofmagnitude, lower than the arbitrary-(1,1) index method. When the Global index isdeployed, the gap is smaller. This shows that the Global index is able to help minimise thequery access time of the client. Figure 17(c) shows the performance of the three methodswhen the number of antennae/broadcast channels is increased, when sending 300 broadcastitems. All three methods show an improvement of the access time due to the shorterbroadcast cycle and the simultaneous listening to channels.

Figure 17(e) depicts a case where the number of requests is increasing while the numberof broadcast items remains the same (30 and 45 items). We can see clearly that the accesstime of the proposed method is only slightly affected when the number of items increases.The increase is hardly notable. In contrast, the performance of the other methods hasdecreased largely due to the longer broadcast cycle. The arbitrary-Global index method hasalso been affected and it appears the arbitrary-(1,1) index method with 30 broadcast itemsoffers slightly better performance than the arbitrary-Global index with 45 data items.

5.3 Proposed vs. Existing scheme (access time)

This case investigates the performance of the proposed method compared to the existingordering method discussed in [25]. This method is selected for comparison due to thecloseness of the characteristics of this method with ours. It is also one of the first broadcastordering schemes, which is typically chosen for benchmarking purposes. Similarly, weemploy a (1,1) index method and the Global index to perform with this existing scheme.Figure 17(b) shows that the proposed method fares better in terms of access time, beingabout 2–3 times lower than the existing-Global index scheme. It can be seen once again thatthe existing method using a Global index results in a lower access time than the one using aconventional (1,1) index scheme.

We can also see in Figure 17(d) how the access time performance is reduced whendeploying more antennae/broadcast channels. Figure 17(f) depicts the case when increasingthe number of requests on 30 and 45 broadcast items. We can see from Figure 17(f) that theaccess time of the existing-Global index scheme is not as much affected by the increase of

Figure 16 Performance analysisof the proposed scheme.

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the data item as with the arbitrary-Global index shown in Figure 17(e). This is due to theordering scheme that has taken part in the process.

5.4 Proposed vs. No-index vs. (1,1) index scheme (tuning time/power consumption)

In this case, we investigate the tuning time performance of the proposed approach againstNo-index and (1,1) indexing method. The tuning time is mostly affected by the indextraversal.

Figure 18(a) suggests the tuning time performance of our scheme is smaller than theother two methods especially with the No-index scheme where the client has to listen to

(a) Varying broadcast items: proposed vs.arbitrary scheme

(b) Varying broadcast items: proposed vs. existing scheme

(c) Varying antennae/broadcast channel: proposed vs. arbitrary scheme

(d) Varying antennae/broadcast channel:proposed vs. existing scheme

(e) Varying requests: proposed vs. arbitrary scheme

(f) Varying requests: proposed vs. existing scheme

Figure 17 Proposed vs. arbitrary vs. existing scheme: access time comparison.

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each data item that arrives in the channel. The (1,1) indexing results in slightly larger tuningtime than the proposed method. This is possible as the proposed method has retained partialindex nodes every time it reads a data packet. Therefore, the tuning time is a little smallerthan the (1,1) index where the client still has to go through the typical traversal method.This tuning time represents the majority of energy or power consumption of the client whenretrieving data on-air.

We can translate this to power consumption following Imielinski et al.’s [10] equation inwhich a device with a Hobbit chip (AT&T) requires about 250 mW power consumptionduring active mode and 50μW during power-saving mode. For simplicity, in thisevaluation, other activities and components that require power are disregarded, and it isassumed that 250 mW relates to the total transmission power consumption. We can seefrom Figure 18(b) how our proposed method performs as compared to the (1,1) indexscheme and the associated power consumption as a result of the tuning activity.

6 Conclusion and future work

Data broadcast is considered the most effective method for wireless content delivery, thanksto its scalability feature. However, the advantage of data broadcast may be significantlydiminished when the size of broadcast data increases. The Fourth-Generation WirelessCommunication System (4 G) has partially addressed this issue by applying multiple-inputmultiple-output (MIMO) technology, which allows different data streams to be receivedsimultaneously from a limited number of antennaes. With this, client is able to accessmultiple channels at the same time, which reduces the query processing time significantly.In this paper, we have presented a comprehensive data broadcast service for 4 G networkswith MIMO antennae, which consists of three important elements, namely: (i) Globalindex; (ii) broadcast ordering scheme; and (iii) merging data broadcast structure. All theseelements constitute a holistic data broadcast approach that aims to minimize query accesstime and the client’s tuning time for accessing data on-air.

Given the results from the experiments, our proposed approach offers the smallest queryaccess time compared to the arbitrary and existing broadcast ordering scheme with (1,1)and the Global index scheme. The results of our experiments as shown in Figures 17(e) and17(f) also indicate that the growing number of mobile clients/requests does not have muchimplication on the average query access time of the clients. However, the results suggestthat the performance of the broadcast channel is mostly affected by the length of the

(a) Proposed vs. No-Index vs. (1,1)index scheme: tuning time

(b) Proposed vs. (1,1) index scheme:tuning time/power consumption

Figure 18 Proposed vs. arbitrary vs. existing scheme: tuning time/power consumption comparison.

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broadcast cycle. In brief, the broadcast system is scalable with respect to the number ofclients/requests, but it is sensitive to the length of the broadcast cycle. We also studied theperformance of the proposed approach in respect to the client’s tuning time. The tuningtime performance is compared with no-index and (1,1) indexing scheme, the result beingour proposed scheme outperforms the other two, with less power consumption for themobile client. To conclude, our proposed scheme will be an ideal data broadcast approachto support mobile information services based on 4 G wireless networks with MIMOantennae.

As part of the future work, we will investigate the potential of the proposed approach toincorporate mobility parameters, and this will be extended to serve moving objects/queriessuch as location-dependent queries [12, 35], continuous queries [46, 47], and mobilenavigation queries [48, 52]. Other relevant issues like interactive display and dynamicobject’s resolution [26, 30], context-awareness [3] and mobile caching [51] are also worthinvestigation.

Acknowledgment The authors are grateful to the three anonymous reviewers for their constructivefeedback, which considerably helped improve the paper. This research has been partially funded by theAustralian Research Council (ARC) Discovery Project (Project No: DP0987687).

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