ACO-based Media Content Adaptation for E-learning Environments

M. Shamim Hossain1, Mehedi Masud2, Abdulhameed A. Alelaiwi1, and Abdullah Alghamdhi1 1 Software Engineering Dept., CCIS, King Saud University, Riyadh 11451, KSA

2 Computer Science Dept. Taif University, KSA Email: {shamim, aalelaiwi, ghamdi}@ksu.edu.sa

Abstract—The advances of ubiquitous communication infrastructures, the rapid adoption of mobile devices and pervasive computing technologies has allowed e-learning users access to multimedia learning contents in e-learning environments. However, because of the diversity and heterogeneity of the mobile users, their preferences, and the rich multimedia learning content, it is a major challenge for the access of learning content by the desired devices in the e- learning environment to user’s satisfaction in terms of QoS demands. In order to alleviate the challenge of learning content mismatch, content adaptation is essential. To this end, we propose an ACO-based multimedia content adaptation approach, which inherits the adoption of ACO-based path selection behavior in the path computation for appropriate learning content customization. We compare our proposed approach with other two competitive algorithms, measure the performance and find that our proposed algorithms outperforms the basic AntNet and Genetic in terms of success rate, latency, runtime comparison and convergence. The performance evaluations are conducted using NetLogo simulation environment.

Keywords- ACO-based approach, e-learning, user context, media content adapation, distributed algorithm, simulation.

I. INTRODUCTION Today most of the learning contents, which are designed for

desktop computers are not suitable for accessing through handheld devices because of the resource constraints, user preferences and learner’s needs. Therefore, in order to mitigate the problem of learning content mismatch, content adaptation or customization for the learning content is essential. Customized representation of learning media content, e.g. text, image, audio, video etc., based on device capability plays a significant role for an end user in accessing a learning object from an e-learning environment.

Multimedia content adaptation or customization for diverse e-learning [1] clients is challenging due to their heterogeneity in terms of limited capability and resources, diversity, user preferences and strict demand for QoS. The resource-limited e-learning clients may range from notebook computers to handheld devices (e.g. cell phones, PDAs), and have very limited varied capabilities and resources in terms of computation power, display (e.g. resolution and colors) and memory. These devices are diverse in terms of the different operating systems that they are running on, the limited set of formats (e.g. H.263, MPEG-4 and H.264) they support and finally the incompatibility of supported codec’s (data formats) that two different users communicate.

To support learning content customization or (adaptation) terms of computational complexity, diversity, heterogeneity and deployment, a wide range of content adaptation services is required. Because, a single software solution or a single adaptation service [11] is not enough to accommodate all customization needs of the clients. A multimedia learning content may require multiple conversions to customize the content for the target user, which can be performed by using multiple adaption services. In this case, one or more simple repurposing services are selected for composition to customize the user’s desired content.

To address the above issues, an ant-based [7] selection algorithm to solve the media content adaptation service selection problem in a scenario, where the number of adaptation services are distributed in the networks. This algorithm is based on the biological “foraging” concept [8] and provides an efficient utilization of network resources. The idea behind the algorithm is that after receiving the request from the sender, the server generates a number of ants at fixed intervals. Those ants then move through intermediary service nodes, for the desired adaptation services. On returning to the source from the destination, ant follows an adaptation path traversed by forward ants, and updates QoS parameters as well as pheromone. After arriving at the sender, the best adaptation path or service is selected.

Previously researchers [2]-[5], [12-15] have used, Ant Colony Optimization (ACO)–based approach for different aspects of e-learning. Santos et al. [2] uses ACO- heuristic for sequencing learning activities in a learning environment. Allach [3] uses ACO for modelling educational elements. Naji and Ramdani [5] to establish the best learning path achieve a learning objective. In [12] Canfora et al. use the Genetic Algorithm for service selection, prior to service composition. They also compare their approach with the integer programming approach. However, genetic algorithm [13], [14] for service selection is also unsuitable for multimedia adaptation service selection. However, to the best of our knowledge, none so far have presented an all-inclusive algorithm that fits for media content adaptation well in all performance aspects and at the same time scalable.

In this paper, we discuss an ACO-based multimedia content adaptation approach, which inherits the adoption of ACO-based path selection behavior in the path computation for appropriate learning content customization. We use ant-inspired [7, 15] selection algorithm because it is observed that Ant agents have intelligence to find the best shortest path from an ant’s nest and the food source during their food search.

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Moreover, Ant-based approach is resistant to failures, adaptive to dynamic context, service uncertainty and service disruptions [11]. To justify our approach, we compare our proposed approach with other two competitive algorithms, measure the performances and find that our proposed algorithms outperforms the basic AntNet and Genetic in terms of success rate, latency, convergence time and runtime comparison. In addition, we also measure response time with varying service nodes.

The remainder of this paper is organized as follows: in section 2, we present a motivating scenario, followed by proposed approach in section 3. In section 4, we provide some experimental results and discussions; we conclude our work in section 5.

II. MOTIVATING SCENARIO Figure 1 shows a scenario, where mobile e-learning users

participate in a live video conferencing session. Each of the communicating parties has his own surroundings that may differ in terms of devices, networks and user preferences. Live video stream is captured from one e-learning user environment and is sent to another e-learning user environment. However, because of the heterogeneity of the e-learning usage environment, the captured video stream cannot be rendered to the satisfaction of the receiving user. In doing so, the captured video stream is adapted through some intermediary steps prior to delivering the content to the receiving mobile devices. The solution to such an anomaly lies in considering the individual’s context (e.g. mobile e-learning usage environment) for delivering the content. According to Chen and Kotz [17], context includes a) Computing context such as a network profile (e.g. network connectivity, bandwidth, communication cost and nearby resources), b) User context such as the user’s profile, user’s location and nearby users and people, c) Physical context such as illumination characteristics and temperature, d) Temporal context such as the time of day, week or year that refers to when the context information of the user is captured. This user context is crucial for determining the context of learning users for adapting learning content to have seamless access according to their preferences.

Figure. 1: A scenario of multimedia content adaptation in a mobile e-learning

(video conferencing) environment

III. ACO-BASED ADAPTATION APPROACH The objective of the proposed adaptation approach is to find a suitable service path from a source node (streaming service) to a receiver node through one or more intermediate adaptation

service nodes that maximize the user’s satisfaction in terms of QoS for the viewing service to be used by the e-learning users. To find an optimal path between the source and receiver (e.g., used by e-learning users), we consider a directed acyclic media application service graph [14], as a directed acyclic graph (DAG), G V, L , where V v , v v is a set of media service nodes and L l , l , l is a set of links between each pair of nodes v , v V i j . Between each pair of nodes i.e. each link l=( , , l includes number of optimization metrics such as Latency L p , and available frame rate F p , and convergence time t p . The objective is to find a suitable service composition path from source node to sink node that satisfies the following conditions: L p l l F p F

The cost (QoS) associated with each link ( on each routing path of the network is expressed as a positive weight, whose value is between 0 and 1. The cost is the function of response time, latency, frame rate, and convergence time.

The proposed ACO-based approach use link probability distribution at each media service node in the network, where a routing table and a local statistics traffic table are maintained. Those tables are used to select the next best desired neighbour node. Each node has a routing table that stores information about the outgoing links and their amount of pheromones (φ ). The routing table consists of a row for each destination of the network and a column for each neighbour node, for storing the pheromone values. At regular intervals, each node s launches a forward ant to a randomly selected destination d stochastically. After that, a forward ant applies the transition rule (1), [7], [15] to choose the next node. This transition rule (1) is based on the traffic load or link cost ( and the amount of pheromones. While moving, each forward ant collects information about the state of the network, which is later used by backward ants to update the routing tables along the routing path followed. In order to avoid the cycle, the forward ant is forced to return to have already been visited node. Upon successfully reaching the destination d, it generates a backward ant, transfers all the information to the backward ant, and then dies. The backward ant returns to the source node s using the same path that was used by forward ant. The backward ant updates the corresponding routing table and the traffic model of each visited node.

, 1 1 , 0.2 0.5 (1)

is updated as the QoS value of the selected service node

divided by the sum of all the QoSes ( ) of the neighbouring

transcoding service nodes times the number shown in (2). ∑ 1

Next we describe the data structure ofupdating at each node during the path comFrom Fig. 4 (b), a forward ant (F ) finsource node s (s=1) to a destination nodservices nodes 1, 2, 3 and 5. From Fig. 4 (c(B ) updates the selected path along servand 1. At node 3 for destination node 5, updates the QoS data model and routing tablthe value of neighbor node 5 and decrementhe other neighbors, such as nodes 1 and 2.

Figure 2. An example of updating data structures wibackward ant along the selected service

At node 2 for destination node 5, the back

the QoS data model and positively reinforcewhile negatively reinforcing nodes 1 and 4node 2 for destination node 3, which is on node 2, the backward ant updates the QoSrouting table. The sub-path is only updateassociated with the sub-path of the forward good. In this case, the backward ant posneighbor node 3 and negatively reinforcesSimilarly, the routing table and QoS modnode 1 for destination 5, while other nodessub-paths are also considered. The high-lproposed ACO-based service selection algorFig. 3.

I. RESULTS AND DISCUSIn order to justify the feasibility of our p

we used NetLogo simulator environmeperformance of the algorithms. Through Nparameters were varied in order to study t

of neighbours -1 as

(2)

f Ant intelligence mputation process. nds a path from a de d (d=5) along c), a backward ant vice nodes 5, 3, 2,

the backward ant le by incrementing nting the values of

ith forward ant and e path

kward ant updates es neighbor node 3 4. Additionally, at the sub-path after

S data model and ed when the QoS ant is statistically

sitively reinforces s nodes 1 and 4.

del are updated at s (2 and 3) on the level steps of the rithm are shown in

SIONS proposed approach, ent to test the NetLogo, network their effect on the

overall performance of eacfacilitates to deploy the numbconnectivity based on [14]. Thdimensional node grid with a nand r number of node connectiwe considered the total numbenode grid) with a node conevaluation, we used success rtime as performance metric.

Figure 3. ACO-base

A. Latency The latency is defined as th

from the source node to tsimulation, the latency is expreach route as opposed to the acroute. The final latency of the t4 and Table I.

Figure 4. Latenc

Standard deviations of the are 2.79, 2.51, and 1.07 respecshows that the readings are clu

0

1

2

3

4

5

6

7

0 10 20 30

Late

ncy

Simulation

ch algorithm. The simulator ber of service nodes and their he simulation was run on an n-number of nodes equal to n x n ivity. In the simulation test bed, er of nodes as 25 (n x n=5 x 5 nnectivity of 3 (r=3). In this rates, latency, and convergence

ed selection algorithm

he time it takes for an ant to go the destination node. In this ressed as the number of hops in ctual time it takes to traverse the three algorithms is shown in Fig.

cy for n = 5 / r = 3

final latency for each algorithm ctively. Genetic’s low deviation ustered closely around the mean

40 50 60

n Time

AntNet-based

Proposed-ACO-based

Genetic

which gives indication of a higher reliability of the algorithms’ performance for this metric. As for the AntNet and Proposed ACO-based, the relatively higher standard deviation value indicates that forward ants require more time for searching the destination node.

TABLE I. FINAL LATENCY FOR N = 5 / R = 3

AntNet-based 12

Proposed ACO-based 10.1

Genetic 9.4

B. Success Rate The success rate of individual algorithms is expressed in

terms of the ratio of the total number of ants that arrive at the destination node versus the total number of ants dispatched from the source node.

Fig. 5 shows that the Proposed ACO-based has a higher success rate while maintaining comparable latency value as that of the AntNet and Genetic. The Proposed ACO-based shows positive increase in the success rate. Genetic achieves also higher success rate than AntNet. Because forward ants mark the paths that end up in loops with a negative pheromone while preventing other forward ants from venturing into paths that lead away from the destination node and into unwanted loops.

C. Path Discovery Time The path discovery time is a metric which is referred to as

convergence time. It is important to know how much time is required for each algorithm before the source node is aware of a route to the destination node and is able to begin transmitting data. As more paths are discovered, the source node can always adjust the route of its data path if a shorter route is discovered.

The path discovery time is expressed in ticks or number of cycle units. This unit is a NetLogo counter that represents execution time. Table II illustrates the time to path discovery values observed in the evaluation. The unit of the result is shown as a number of ticks:

Figure 5. Success Rate for n = 5 / r = 3

The standard deviations observed for the time to path discovery values for the three algorithms are 9.07, 6.67 and 9.78 respectively. These values indicate to what degree each data value is dispersed around the mean. They can also be an indication of accuracy.

TABLE II. TIME TO PATH DISCOVERY FOR n = 5 / r = 3

AntNet-based 39.3

Proposed ACO-based 37

Genetic 36.5

The AntNet and Genetic have slightly more disperse time values than Proposed ACO-based. This is an indication of how accurate the Proposed ACO-based could be in this regard. Looking at the measurements results, we can draw the following conclusions regarding the improvements introduced in Proposed ACO-based and Genetic algorithms: Proposed ACO-based has the highest success rate and convergence time while the Genetic has the least latency.

To evaluate our proposed algorithm in terms of session request rate and success rate, we used a similar simulation environment to [15] to generate the network topology and measure performance. Similar to [15], during each 60-s interval, a certain number of user requests are generated. Each user session lasts between 15 and 30 min.

For the comparisons, we used the proposed algorithm, the AntNet algorithm and Genetic algorithm in the simulator. We ran the test for 100 time units, which were considered as ticks. For every performance metric 10 samples were taken and the means were calculated. In order to better compare the proposed algorithms, we showed the performance of each metric in standard deviation. The performance metrics and their comparisons for each algorithm are described below:

We also have performed the runtime comparison of our proposed algorithm with a AntNet based algorithm and Genetic under different media request rates. The runtime is calculated as the total session set up time for all requests. As shown in the Table III, the proposed algorithm is faster than that of the compared algorithm in most cases.

TABLE III. RUN TIME COMPARISON (SESSION REQUEST RATE)

Session request rate

Proposed algorithm

Genetic AntNet

15 889 1067 932 30 1490 2750 1234 45 2255 3487 2450 60 3376 3850 3290 75 4210 4683 4322 90 5695 5756 5822

The experimental results demonstrate that using proposed approach’s capability to scale under: a) an increased number of services in the system; and b) an increased number of service requests by e-learning users. Our system provides linear scalability to a certain extent. We used no of request and number of varying service nodes as work load, server as

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60

Succ

ess

Rat

e

Simulation Time

AntNet-Based

Proposed ACO-Based

Genetic

resources and finally response time as performance metric. Response time for varying Service nodes

TABLE IV. RESPONSE TIME COMPARISON FOR VARYING SERVICE NODES

Service Request n= 25 n= 49 n= 64

10 1000 1164.599 1222.349

1107.179 1218.927 1338.449

1207.392 1392.378 1512.478

1313.214 1512.051 1623.198

1414.797 1482.606 1596.835

1529.484 1706.197 1816.772

150 1629.507 1637.252 1750.018

1735.226 1916.044 2038.819

1838.597 1860.152 1982.672

1949.278 2090.283 2219.605

2057.878 2061.489 2183.429

2171.193 2266.318 2387.895

300 2274.063 2276.684 2387.448

2375.841 2408.974 2536.748

2480.523 2634.233 2755.219

2588.959 2632.73 2757.943

2696.539 2757.218 2874.563

2803.251 2933.276 3061.906

450 2907.701 2914.363 3035.006

3014.725 3067.652 3177.664

3115.93 3280.43 3409.168

3223.477 3235.02 3358.696

3333.408 3386.618 3513.263

3434.034 3594.358 3717.476

Table IV shows the response time with varying nodes to handle more service requests. We have observed that as the load is distributed to next available proxy server, response time increases linearly with the increasing load (e.g. the number of service requests. It is found that by increasing the number of service nodes, the response time did not dramatically increase. As shown in Table IV, at 450 requests, the average response time slightly increases with 25 service nodes (around 2907.701 ms), compared to time with 49 services (around 2914.363 ms). While running 64 services, the response time has increased to 3035.0061 ms. We conclude from the results that the system is linearly scalable for this test phase.

The goal of this experiment is to present how the proposed e-learning framework facilitates service selection to select suitable service in order satisfy user QoS requirements in delivering learning content (media service) for video conferencing session. As for example, an e-learning client (e.g., Laptop or Galaxy note) is accessing a multimedia learning content (e.g. audio, video, text). Suddenly, he decides to access the same session through another e-learning client, such as a PDA. He is unable to get service according to his

QoS demands, due to the incompatibility and the heterogeneity of the learning content, the PDA’s small display, as well as the low bandwidth, frame rate, etc. Now, the e-learning client has to search for and select the appropriate service for rendering the desired media content that satisfies his QoS requirements. Our service selection framework fulfils the above demands part of which can be seen in the following Table V.

As shown in the Table V, for H.264 CIF stream is adapted into H.264/MPEG-4 CIF and QCIF. The repurposed stream of CIF slightly outperforms the QCIF in terms of PSNR; however, it highly outperforms it in terms of bit rate.

TABLE V

Y-PSNR PERFORMANCE COMPARISON FOR H.264

Resolution Average PSNR

Average Bitrate

CIF 34.77dB 195 (kbs) QCIF 34.22dB 75 (kbs)

II. CONCLUSION With the popularity of ubiquitous learning access, there is

an increasing demand for appropriate media content adaptation service selection that best satisfies the QoS constraints of the e-learning clients. To this end, this paper presents a selection algorithm based on Ant Colony Optimization (ACO) to solve the media content adaptation service selection problem, when the numbers of adaptation services are distributed in the network. The proposed approach uses ant intelligence for searching suitable services in an E-learning environment according to user’s context. We compared the proposed approach with other two competitive algorithms in order to see their performances. The conducted experiment shows the effectiveness and suitability of the proposed approach, which reduces the media processing between service nodes and ensures seamless access of the desired media streams on the handheld devices of e-learning users.

ACKNOWLEDGEMENT This work was supported by the Research Center of

College of Computer and Information Sciences (CCIS), King Saud University through the research project no RC120908. The authors are grateful for this support

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