Efficient Collecting, Clustering, Scheduling, and Indexing Schemes for Advertisement of Services

over Wireless Billboard Channels

Zhanlin Ji, Ivan Ganchev, Máirtín O'Droma Telecommunications Research Centre, University of Limerick, Limerick, Ireland

{Zhanlin.Ji;Ivan.Ganchev;Mairtin.O’Droma}@ul.ie

Abstract— Efficient mechanisms for broadcasting of service advertisements to Mobile Terminals (MTs) over Wireless Billboard Channels (WBC) are of significant importance for the emerging Ubiquitous Consumer Wireless World (UCWW) 1 . Reducing the MT access time and tuning time are the main goals of these broadcasting mechanisms. The challenge is to define reasonable WBC data management algorithm. Diverse from previous studies, our approach takes into consideration four important data organization schemes, namely the collecting, clustering, scheduling, and indexing of service description. The results show that the performance of the resultant WBC algorithm is close to optimal.

Keywords- Ubiquitous Consumer Wireless World (UCWW); Consumer-centric Business Model (CBM); Wireless Billboard Channels (WBCs); Advertisement, Discovery and Association (ADA); Service Description (SD); Data Collecting, Clustering, Scheduling, Indexing.

I. INTRODUCTION Wireless Billboard Channels (WBCs) are fundamental to the

Consumer-centric Business Model (CBM) [4, 3], which is integral to the new emerging Ubiquitous Consumer Wireless World (UCWW) [1, 5]. In CBM-UCWW, the Service Providers (xSPs2) advertise their services over WBCs, and Mobile Users/Mobile Terminals (MUs/MTs) discover and associate with “best” services under the Always Best Connected and Served (ABC&S) paradigm [2]. The service advertisement, discovery and association (ADA) mechanisms have been considered in more detail in [6-8].

The WBCs are simplex and broadcast channels. Comparing with the traditional client-server unicast approach, the WBC broadcasting is independent of the client population, and the streaming of service advertisements is available simultaneously to a large number of clients. With the push-based WBC data delivery system, the wireless bandwidth is efficiently used, and on the other hand, the mobile terminals only passively listen to the channel thus consuming less battery power. These properties make WBCs suitable to broadcast an explosive incremental number of service advertisements in UCWW.

1 This publication has emanated from research conducted with the financial

support of Science Foundation Ireland under the Basic Research Grant Ref. No. 04/BR/E0082.

2 xSP is an encompassing acronym for both Access Network Providers (ANPs) and Teleservice Providers (TSPs).

Two main parameters are used for the evaluation of the data access efficiency in WBCs: • Access time: The total amount of time from the moment

when a MT first tunes in the WBC channel, until that MT receives the needed data;

• Tuning time: The time that a MT keeps active mode for listening to the channel and receiving the needed data. (If data indexing is not used, then both times are equal.)

Some algorithms have been proposed recently for minimizing the access time and tuning time. We adopted some of the features of the most efficient algorithms [9-11] and elaborated them further for use in WBCs. The result is an overall algorithm that minimizes the MT access time and tuning time by taking into account all different types of data organization schemes used in WBCs: • Collecting scheme – used to collect service descriptions (SDs)

from service providers and to generate SD broadcasting frequencies based on intelligent collecting rules;

• Clustering scheme – used for clustering of SDs into categories and generating fixed-size segments, both facilitating the MT access.

• Scheduling scheme – with different versions depending on the WBC carrier technology used (e.g. DRM, DAB, DVB-H, 3GPP etc). It applies an intelligent algorithm to arrange data in an optimal broadcasting sequence in order to reduce the access time. An extended and modified broadcast disks algorithm [9] was adopted for this scheme.

• Indexing scheme assists MTs to predict the arrival time of the SDs needed by the user, thus reducing the tuning time and saving the MT power consumption. An (1, M ) indexing algorithm was adopted from [10] and elaborated further.

The rest of the paper is structured as follows. Section II explains the data organization on WBCs. In section III the SD collecting, clustering, scheduling, and indexing schemes are derived. Section IV evaluates the performance of the overall WBC algorithm. Finally section V concludes the paper.

II. DATA ORGANIZATION ON WBCS The smallest logical unit in WBCs is the service description

(SD). All SDs are encoded with the packed encoding rules (PER) of the abstract syntax notation (ASN.1) [12] to minimize their size.

The SD lengths are in range of 60 to 270 bytes, with an

average value of 180 bytes. Due to the fact that all SDs within a category use the same service template, the SD lengths within a category are almost equal (Figure 1).

1 6 11 16 21 26 31 36 41 4650

100

150

200

250

300

SD sequence number in each category

SD

Len

gth

VideoCallUMTSVoiceCallMusicCDNewsStockTickerSMS

Figure 1. The SD lengths in different categories.

The SDs are organized into segments for broadcasting over WBCs. To make it easier for mobile terminals to find needed segments in the broadcasting sequence, all segments are of equal length. The time needed to broadcast one segment is called a time slot. To reduce the access time in the non-uniform WBC broadcasting scheme, the broadcasting frequencies of SDs/segments should follow closely the client access pattern. In other words, if some ‘hot’ SDs/segments are more frequently requested by MUs/MTs comparing to other ‘cold’ SDs/segments, then the hot ones should be broadcast more frequently.

The following notations are used in the rest of the paper:

l if – broadcasting frequency of i-th service description, i.e. the number of instances of iSD in a broadcasting cycle;

l il – length of iSD (measured in bytes);

l jl – (average) SD length in j-th category (recall that all SDs

within the same category have almost equal length); l κ – number of distinct SD categories in a broadcasting

cycle; l jS – number of distinct SDs of category j (1 j k≤ ≤ ) in a

broadcasting cycle; l m – total number of distinct SDs in a broadcasting cycle,

1

kjj

m S=

= ∑ ;

l n – total number of SDs in a broadcasting cycle ,

1

mii

n f=

= ∑ ;

l L – segment length (measured in bytes). Because each segment contains service descriptions of only one category, the segment itself belongs to this category;

l jD – number of SDs for category j in a segment;

jj lLD /= ;

l jM – number of distinct segments of category j in a

broadcasting cycle, /j j jM S D = ;

l M – total number of distinct segments in a broadcasting

cycle,1 1

/ /k k

j j j jj jM S D S l L

= = = = ∑ ∑ ；

l nF – broadcasting frequency of n-th segment ( Mn ≤≤1 ), i.e. the number of instances of segment n in a broadcasting cycle;

l np – current access probability for segment n, Mn ≤≤1 ; l N – total number of data segments in a broadcasting cycle ,

∑ ==

M

n nFN1

;

l λ – scattering factor, /N Mλ = ( N≤≤ λ1 ); l ,p nI – inter-arrival gaps for n-th segment, 1 1np F≤ ≤ − .

This is the gap (measured in number of segments) between two consecutive instances of segment n in a broadcasting cycle;

l nT – mean access time for a particular (n-th) segment (measured in time slots). This is the average time that a mobile terminal waits for the n-th segment. It can be shown that nT is minimal when the gaps between all consecutive instances of segment n in a broadcasting cycle are equal, i.e. if for each p:

, 1, /p n p n n nI I I N F+= = = (*),

then: ( ) / 2n nMin T N F= (1) ; l T – mean segment access time (measured in time slots). This

is the average time that a MT waits for any segment,

1

Mn nn

T p T=

= ∑ . If for each given n, , 1,p n p nI I += for each p,

then: 1

*( / 2 )M

n nnT p N F

== ∑ (2) ;

l ,q jC – inter-arrival gaps for j-th category. This is the gap

(measured in number of segments) between two consecutive segments of same category j in a broadcasting cycle;

l η – number of minor cycles. Within a minor cycle, each category is broadcast at least once. So η is equal to the

minimum value, ( )Min η , of 1jM

j nnFη

== ∑ ( 1 j k≤ ≤ ),

where jη is the total number of segments of jcategory in a

broadcasting cycle, i.e. including all instances of all segments of jcategory ;

l jρ – current access probability for category j,

1 j κ≤ ≤ ；

l jϕ – mean access time for a particular (j-th) category

(measured in time slots). If for each q: , 1,q j q j jC C C+= = (**),

then jϕ reaches its minimum:

1

( ) /(2 ) / 2jMj n jn

Min N F Nϕ η=

= = ∑ (3);

l ϕ – mean category access time (measured in time slots):

1 j jj

κϕ ρ ϕ=

= ∑ (4),

with minimum value reached when equality (**) holds; l S – final segment broadcasting sequence; l nsτ – mean tuning time for a particular (n-th) segment.

If no data indexing is used, n ns Tτ = (5); l ncτ – mean tuning time for a particular (j-th) category.

If no data indexing is used, ncτ = jϕ (6).

In practice, due to the properties of SDs, it is difficult to achieve the equalities (*) and (**). In the next section, reasonable clustering and scheduling schemes are proposed to approach the optimal access time. When the access time is optimal, it is easy to develop an indexing scheme for reducing the tuning time.

III. PROPOSED SCHEMES FOR COLLECTING, CLUSTERING, SCHEDULING, AND INDEXING OF SDS OVER WBCS

A. Collecting Scheme This scheme is used to collect SDs from service providers

and generate SD broadcasting frequencies. This paper only focuses on the frequency generating scheme.

In a broadcasting cycle, the SD broadcasting frequency if is obtained as:

( )i j i if click donateψ α β γ = × × + × − ,

where Af i ≤≤0 and 0 , , , 1jψα β γ< < . If (calculated if ) > A,

then the value of if is set to A. If (calculated if ) < 0, then the value of if is set to 0.

The parameter iclick indicates the current popularity of iSD among the users (the number of user requests/clicks made for a particular service). Its value is supplied by an independent third-party monitoring infrastructure, which gathers statistics from users about their use of different services. The parameter

idonate reflects the cost of iSD advertisement paid by its service provider to the WBC service provider (WBC-SP). Its value is supplied by an online price-competition system run by the WBC-SP. The coefficient γ is used to delete cold SDs from the broadcasting cycle (e.g. if iSD has been broadcast in 1/ γ consecutive cycles, and there have been no ‘clicks’ or ‘donations’, then if is set to 0, and this SD is deleted from the next broadcasting cycle). The coefficient jψ is a specific

coefficient for category j . Parameter A is defined by the WBC-SP as the maximum number of appearances of a particular SD in a broadcasting cycle. The initial value of if for a newly published SDi is set to B ; usually B =2.

Example 1: The broadcasting frequencies for four SDs (of same category) in five consecutive broadcasting cycles are shown in Table I. Let 0.05α β= = , 0.25γ = ,

1ψ = , 3A = , 2B = , 7 / 4λ = . Assuming that BSD is released

in cycle 66, 4.25af = >A in cycle 70, hence af is set to 3.

On the other hand, 0bf = in cycle 70, because there were neither ‘clicks’ nor ‘donations’ for bSD in 1/ γ = 4 cycles.

TABLE I. SD BROADCATING FREQUENCY GENERATING SCHEME: EXAMPLE.

66 67 68 69 70 Cycle No.

SD click / donate / if

a 20/30/2 20/30/2 30/30/2 40/40/3 40/50/3

b 0/0/1 0/0/1 0/0/1 0/0/1 0/0/0

c 100/0/3 70/0/3 60/0/3 40/0/2 40/50/3

d 0/30/1 0/30/1 10/20/1 10/10/1 20/20/1

B. Clustering Scheme The clustering scheme is used to install the SDs into

segments in an optimal way as to reduce the access time and tuning time. Only SDs of same category are inserted into each segment, and in addition the SDs are ordered from ‘hottest’ to ‘coldest’ ones based on their broadcasting frequency f . To generate reasonable SD sequence, a set of intelligent decision rules [13] apply, such as the Composite Capabilities/Preferences Profile (CC/PP) [14] rule, Quality of Service (QoS) [15] rule, scopeList rule, and arbitrary rule. Using these rules, a cluster agent asserts the SDs into the working memory and uses the “salience” conflict resolution strategy to decide which rule should be “fired” [16]. The following is an example of the arbitrary rule:

rule arbitrary salience 10 when SD( f ==$ f && CCPP ==$CCPP && QoS

== $QoS && scopeList == $ scopeList ) then SD → sequence. end

The cluster agent executes the following algorithm:

for j=1 to κ Cluster SDs in category j by using an expert system. Insert SDs into segments.

C. Scheduling Scheme After generating the segments, the next step is to apply a

reasonable scheduling scheme for reducing the access time. From the user’s viewpoint, the access time could be divided into: (i) particular-segment access time nT , and (ii) particular-

category access time jϕ . In this section, hot segments scheduling and cold segments scheduling are proposed for reducing these two access times.

Recall that in order to minimize the segment access time, the hot segments are broadcast more frequently in the WBC non-

uniform broadcasting scheme as to reflect the client access pattern. If the segment inter-arrival gaps are equal, the segment access time is minimal. The segment broadcasting frequencies

)1( MnFn ≤≤ and the number of segments M and N are supplied by the clustering scheme. In this paper we propose a modification of the broadcast disks algorithm [9], where the hot segments )1( >nF are inserted into S (by a scheduling agent) in a novel way:

for n=1 to M when 1nF >

then for j = 0 to nF 1/ 2*(N mod )nw F←

S nSegment← with scheduling rules w w+(N - N mod ) /n nF F←

1, j j 1n nF F← + ← + end

n=n+1 end end

Using this algorithm, the hot segments are inserted into S uniformly. Scheduling rules are used for solving the collision problems. The difference from the broadcast disks algorithm is the relative spin speed, i.e. in [9] it does not make sense to have two broadcast disks with relative speed 11:5. On the contrary, our algorithm does not depend on the relative speed. Moreover the intelligent scheduling agent inserts automatically all hot segments into S in a manner corresponding to the user access pattern, and the segments inter-arrival gaps approach equality.

Example 2: Figure 2 depicts a broadcasting example for three service categories: 1Category (segments A, D, E, F),

2Category (segment B), and 3Category (segments C, G, H, I, J, K). AF =4, BF =3, CF =2, othersF =1, 11, 17.M N= = With hot segments scheduling algorithm, the hot segments A, B, C are inserted into the scheduling cache with hot segments rules ensuring equality for segments inter-arrival gaps.

Figure 2. The Hot Segments Scheduling performed in Example 2.

The next step is to insert the cold segments ( 1nF = ). Differently from [9-11], we propose a new criterion – the mean category access time ( ϕ ). Reducing ϕ is also an important goal in WBC. As shown in (3), to minimize the value ofϕ , the

inter-arrival gaps ,q jC should be (almost) equal within each

jCategory . To achieve this we propose an intelligent cold segments scheduling algorithm (based on the broadcast disks) executed by a scheduling agent as follows:

Step 0: Determine the number of minor cycles ( ( )jMinη η= ) and the length of a minor cycle

/L N kη = ; generate κ disks, and

j nDisk Segment← , where 1nF = ,

n jSegment category∈ .

Step 1: Set the spin speed jSS of jDisk as

jSS ( )jLength Disk= .

Step 2: Write the available segment places ( i ) within

each minor cycle iµ to the blackboard (the blackboard pattern acts as a knowledge container in the WBC multi-agent system [7]).

Step 3: All disks spin at their own speed. for i=0 to η -1 Extract the first i segments appearing on the

top of each disk (if not enough free space, give priority to faster disks).

Insert (in order) those i segments into the

minor cycle iµ . Step 4: Insert the unscheduled segments into the tail of the

scheduling cache.

In example 2, 1 2 37, 3, 7η η η= = = ⇒ ( ) 3;jMinη η= =

/ 17 / 3 5;L N kη = = = 1 3SS = , 2 0SS = , 3 5SS = ;

1 2= , 2 3= , 3 1= . After executing the cold segments scheduling algorithm the segment order in the scheduling cache will be (G,D),(H,E,I),(J),(K,F) as shown in Figure 3.

1µ 2µ 3µ

i

Figure 3. The Cold Segments Scheduling performed in Example 2.

To avoid the Bus Stop Paradox [9], the scheduling agent slightly changes the segment order to (D,G),(E,H,I),(J),(K,F). The final sequence S is shown in Figure 4.

S Figure 4. The final segment broadcasting sequence for Example 2.

D. Indexing Scheme In this section we describe the WBC indexing scheme which

allows mobile terminals to save battery power by staying in a sleeping mode and only turning on when the data (the user is interested in) is broadcast on the billboard channel. The

indexing algorithms developed in the past [10] only consider flat broadcasting; some also distinguish index segments from data segments completely. Our indexing scheme includes a segment inner-index and segment inter-index.

D.1 Inner-Index. The segment inner-index is a full index for the inter-index

and the segment in whose header it is located. It has the following ASN.1 structure:

ASegmentIndex ::= SEQUENCE { service-Type Service-Type, seg_totalSDNumber INTEGER, seg_totalSDSize INTEGER, segmentValueTag OCTET STRING(SIZE(1)), ccpp OCTET STRING OPTIONAL, indexSD SEQUENCE of IndexSD }

D.2 Inter-Index. The inter-index is an index tree which indexes all data

segments and saves index data in separate fixed-size segments. The inter-indexing is used for reducing the segment access time and category access time. Taking into account the fact that the size of interleaved data of WBC indexing tree is small, we adopt the (1, M ) indexing algorithm [10] as the most efficient for the inter-indexing. This means that the inter-index segment is broadcast M times during a broadcasting cycle (Figure 5). The ASN.1 structure of the inter-index follows:

AIndexTree ::= SEQUENCE { totalSegmentNumber INTEGER, totalIndexNumber INTEGER, currentInexNumber INTEGER, serviceType SEQUENCE of ServiceType }

E. Final Segment Broadcasting Sequence ( S ) After adding the inner-index to each segment and adding

the inter-index to S , the final S is generated as shown in Figure 5.

Figure 5. The generic format of the WBC broadcasting cycle.

F. Access Protocol. The protocol used by mobile terminals to access a particular

n-th segment follows:

1) Tune in WBC and wait for the next inner-index segment.

2) Read the position of the next inter-index. 3) Tune out and wait for the next inter-index. 4) Turn in, read the inter-index data and determine the

position of the needed n-th data segment. 4) Tune out and wait for the n-th data segment. 5) Tune in again and read the n-th data segment. 6) Tune out. The mean access time and mean tuning time are estimated

as follows: Mean access time for the n-th segment ( nT ) = = Inner-index waiting time + Inner-index reading time +

Inter-index waiting time+ Inter-index reading time + n-th segment waiting time + n-th segment reading time ≈

≈ 0.5 + ( ≈ 0) + 2NM

+ 1 + 2 n

N MF+

+ 1 ≈

≈ 2.5 +2NM

+2 n

N MF+

time slots (7).

Mean tuning time for the n-th segment ( nsτ ) = = Inner-index waiting time + Inner-index reading time +

Inter-index reading time + n-th segment reading time ≈ ≈ 0.5 ( 0) 1 1 2.5+ ≈ + + = time slots (8). When comparing (7) to (1) and (8) to (5) respectively, it is

obvious that with the WBC indexing scheme, the access time is slightly increased but the tuning time is reduced significantly.

IV. PERFORMANCE EVALUATION In this section, we compare our overall WBC data

organization and management algorithm (presented in the previous sections) to the broadcast disks algorithm in terms of the mean segment access time T achieved. The software implementation of our WBC algorithm is described in [7].

In our simulations we use a 32 Kbps pure ideal channel for broadcasting. The segment length L is 8 Kbytes, A=10, and the final segment broadcasting sequence S follows a geometric distribution with P=0.4945:

1(1 )nnR P P−= − , where 1 n M≤ ≤ .

So, the scattering factor λ = /N M = 2. Similarly to the segments, the categories also follow a geometric distribution.

Previous studies [9, 10] show that the client access pattern usually follows a skewed distribution. In this paper we assume that the current client access pattern follows a zip distribution:

1 11/ /( 1/ ) 1

M M

jp j and pθ θ

σ σσσ

= == =∑ ∑ .

The simulation results in Figure 6 show that the mean segment access timeT decreases when the client access pattern becomes more skewed, i.e. when θ increases. These results prove that the WBC algorithm is more efficient than the broadcast disks algorithm due to the intelligent WBC collision solutions used. For example, if several segments meet the

conditions for inserting into iS , the broadcast disks algorithm selects one of them in arbitrary way, whereas the WBC algorithm uses a number of intelligent rules to decide which segment should be inserted into iS .

0.1 0.175 0.25 0.325 0.4 0.475 0.55 0.625 0.7 0.775500

1000

1500

2000

2500

3000

3500

Current Client Access Pattern - θ

Mea

n S

egm

ent A

cces

s Ti

me

(tim

e sl

ots)

N=5000, P=0.4945

Broadcast DisksWBC Algorithm With IndexWBC AlgorithmOptimal (No Collision)

Figure 6. Mean segment access time (T) for different client access patterns ( θ ).

The simulation results in Figure 7 show that the mean segment access time T increases when the number (M) of distinct segments in the broadcasting cycle increases. Similarly to Figure 6, these results show that the WBC algorithm outperforms the broadcast disks algorithm in terms of the mean segment access time T achieved, which is closed to optimal (i.e. with no collision).

0.5 0.75 1 1.25 1.5 1.75 2 2.25 2.5 2.75

x 104

1500

2000

2500

3000

3500

4000

4500

5000

5500

6000

6500

Number of distinct segments in a broadcasting cycle (M)

Mea

n S

egm

ent A

cces

s Ti

me

(tim

e sl

ots)

P=0.4945 and θ=0.4999

Broadcast DisksWBC Algorithm With IndexWBC AlgorithmOptimal (No Collision)

Figure 7. Mean segment access time (T) for different number of distinct segments in the broadcasting cycle (M).

V. CONCLUSION This paper has described a novel overall algorithm

facilitating service advertisements over Wireless Billboard Channels (WBCs) in the emerging Ubiquitous Consumer Wireless World (UCWW). This algorithm is used for collecting, clustering, scheduling, and indexing of service descriptions (SDs). The main goal is to minimize the access time and tuning time. A novel SDs collecting and clustering schemes along

with hot- and cold segments scheduling algorithms have been proposed for minimizing the access time. Efficient inner-index and inter-index schemes have been adopted for reducing the tuning time. The performance evaluation of the proposed overall WBC data organization and management algorithm shows that the mean segment access time achieved is pretty close to optimal.

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