a qos adaptive mobility prediction scheme for wireless ... - CiteSeerX

A QOS ADAPTIVE MOBILITY PREDICTION SCHEME FOR WIRELESS NETWORKS J. Chan†, S. Zhou‡ and A. Seneviratne† †

Faculty of Engineering, University of Technology, Sydney PO Box 123, Broadway, NSW 2007, Australia ‡

CSIRO Telecommunication and Industrial Physics PO Box 76, Epping, NSW 2121, Australia

ABSTRACT Mobility prediction based on an individual’s movement history has been reported as an effective means to decrease call-dropping probability and to shorten handover latency. Applying various basic prediction schemes to a realistic office environment, it is shown that mobility prediction using an individual’s movement history has limitations, and the statistical randomness of user motion can prevent accurate prediction performance. In this paper, we propose a QoS Adaptive Mobility Prediction scheme to resolve these problems. A stricter QoS compliance can be achieved through a pro-active probability update mechanism, a supplementary correlative movement history and the concept of Prediction Confidence Ratio. 1. INTRODUCTION As a progressive evolution from today’s public cellular networks and local wireless LANs, future Personal Communication Systems (PCS) are likely to be an overlay of multiple broadband wireless networks of varying capacity. Applications with diverse QoS requirements will be delivered to a large population of users under various tariff schemes via satellites, outdoor micro-cells and indoor pico-cells. When a mobile terminal (MT) with on-going services is moving from one cell to another two main tasks have to be accomplished by networks in minimal time: request resources at the next base station (BS) for the radio access and update the network topology to reflect the MT’s new location. In recent years, many researchers have proposed mobility prediction schemes to reserve radio resources and to preconfigure networks, so that a smaller dropping probability and a shorter handover latency can be achieved. It is possible to predict a MT’s movement from its present coordinate and velocity, via different coverage zones within a cell [1], or the Global Positioning System (GPS) [2]. Another alternative is to employ the user’s mobility history and stochastic models for movement prediction. This approach requires neither the implementation of a complex cell structure for user tracking, nor the extra cost of GPS receivers. Besides the user’s mobility history, other valuable information such as connection pat-

terns and stay intervals can also be gathered for further analysis. To-date, much of the research has focused on improving prediction accuracy, or reserving appropriate resources at nearby cells. Unfortunately, these schemes do not use realistic data or similar simulation test sets. As a result, it is not possible to evaluate and compare these prediction schemes. In this paper, we verify several basic prediction algorithms using real-life movement traces. Then a novel approach called QoS Adaptive Mobility Prediction (QoSAMP) is introduced, which has three key features: •

a prediction scheme driven by QoS demand or tariff preference

•

emphasis on the resource reservation and the network pre-configuration at appropriate location(s) in the direction of travel

•

consistent performance despite the presence of random movement patterns and the change of a user’s behaviour

The rest of the paper is organised as follows. Section 2 provides an overview of related works on mobility prediction and their limitations. We present some realistic movement traces collected by the Active BadgeI system in section 3, and apply these traces to compare various basic prediction algorithms in section 4. Section 5 presents the concept of QoSAMP and its prediction performance. Finally, concluding remarks are given in section 6. 2. SOME APPLICATIONS OF MOBILITY PREDICTION In the early studies of handover schemes in wireless networks, the Virtual Connection Tree (VCT) scheme was introduced [3]. In VCT systems, connections have to be pre-established at surrounding cells to achieve low handover latency. However, advance configurations should not be too aggressive otherwise the network availability drops drastically. To overcome this, several mobility prediction methods have been proposed for resource reservation and network pre-configuration.

I

Active Badge is a registered trademark of Ing. C. Olivetti & C., S.p.A.

Based on individual movement patterns, Liu [4] proposes a mobile motion prediction scheme in which migrations of mobile-floating agents are used for the resource pre-arrangement and the data pre-fetching. Using handover history profiles of MTs and BSs in [5], a profile-based next-cell prediction scheme is proposed to reserve resources at the wireless interface. Using a similar approach, Chan [6] reported a two-tier prediction scheme to pre-configure network connections. These three schemes prepare resources and configurations in advance at the predicted location. Thus a welldefined user movement is required to guarantee good prediction performance.

5m

10m

their own sets of simulation data, and it is very difficult to compare the performance of their proposed schemes. In the next two sections, we attempt to explore these two issues by a comparison of several basic prediction algorithms using the Active Badge movement traces.

190 Floor 3

191 188

Floor 2 211

195

199

193

204

206 205

3. ADAPTATION OF THE ACTIVE BADGE MOVEMENT TRACES

183

In February 1994, a trial was conducted within ORL (Olivetti and Oracle Research Laboratory) which tracked the movement of all its 32 staff for a period of 15 days. Logged by the Active Badge Location System, the traces provide mobility information such as the sighted location, the badge ID and the time when the user was sighted [9].

Floor 1 166

168

178

184 185

Floor G 209

167

UG Car Park Exit

test duration (Thu 10/2/94 - Fri 25/2/94)

20:00

12:00

16:00

8:00

4:00

0:00

20:00

16:00

8:00

12:00

4:00

0:00

Fri

Thu

Tue

Wed

Sun

Mon

Fri

Sat

Thu

Wed

Tue

Mon

Sat

Sun

Fri

500 400 300 200 100 0 35 30 25 20 15 10 5 0

Thu

num of mobile users per hour

crossing generated per hour

It does not make sense to partially Figure 1. Adaptation of BS coverage complete or distribute the process of network configurations because other An Active Badge is a simple device that emits infrared users cannot share these arrangements. However, it is signalling every 15-second to a nearby sensor. Hence, the possible to freely reserve an appropriate amount of raw data collected cannot be immediately used as a repshared resources along the direction of travel for MTs. resentation of users roaming in a pico-cell environment. This has been proposed by Levine [7] using a shadow To closely resemble a futuristic wireless network layout, cluster in which probabilistic information about users’ we grouped those sensors of Active Badge to form a bigmovements is shared among at neighbouring cells. An ger cell size of 10-15m (see Figure 1). Moreover, we alternative referred to as shadow clique [8], employs user eliminated those rapid movements between adjacent cells mobility profiles to calculate the shadow area. These two and inserted some missing steps into movement paths schemes concentrate on resource reservation but pay because of the slow location update period. Our modifilittle attention to network pre-configurations. cations still preserve general movement paths of those For convenience of analysis, many mobility prediction Active Badge wearers. To verify this claim, we analysed schemes assume that a radio cell has a hexagonal shape, the number of mobile users and the number of cell keeping contact with its six neighbouring cells to form a crossing as shown in Figure 2. On average, each mobile two-dimensional space. However, this model does not user generated 10 cell crossings per hour during office reflect a realistic environment, in which the surroundings hours, but higher rates of up to 16 crossings per hour will impose certain level of constraints on the user’s were also recorded. The data exhibited behaviour patmovement. In addition, most of researchers provided terns such as working days of the week, and office hours

enlargement (Mon 14/2/94 - Tue 15/2/94)

Figure 2. The number of mobile user and cell crossing during trial period

in a working day. This analysis agrees with the mobility factor proposed in [4], which attempts to approximate real-world activity. 4. COMPARISON OF SOME BASIC PREDICTION ALGORITHMS The movement traces from ORL were used to verify several elementary prediction algorithms for the comparison of performance. First of all, we propose a simple prediction scheme named Random Selection to be our benchmark, which randomly picks a neighbouring cell as the next-cell prediction. We then introduce five prediction algorithms based on individual mobility patterns. They are Location Criterion, Direction Criterion, Segment Criterion, Bayes’ Rule and Time Criterion. They will be described in more details in following sections.

• Location Criterion departure history present location

prediction = B (55%) 25% 55% 20%

A B C

Figure 3. Location Criterion algorithm As a MT leaves its present location, it records the next BS and keeps track of the number of times it visits each of the BSs. This information from the mobility history forms a probability distribution of next moves, and we refer to the distribution of each departure as a departure history. The Location Criterion algorithm identifies the MT’s present location and uses the departure history of this location to predict the next move of the MT (see Figure 3). The BS that is most frequently visited will be predicted as the next BS.

• Direction Criterion

previous location

prediction = B (80%) 15% direction of travel 80% 5%

• Segment Criterion seg. A-F

A

B

C

D

E

A

B

C

I

A

B

C

I

A

B

C

Rather than simply using location, it is possible to improve the accuracy if the MT’s direction of travel is taken into account, i.e., if its present and previous locations are known (see Figure 4). The Direction Criterion algorithm thus incorporates direction information with the departure history. It identifies the MT’s present direction of travel and uses the departure history of this direction to predict the next move of MT. The move with the highest departure rate is predicted as the next BS.

G

H

J

K

L

M

J

O

P

Q

N

prediction = I (occurs twice)

Figure 5. Segment Criterion algorithm The Segment Criterion algorithm extends the Direction Criterion scheme further. All previous movements are partitioned into a number of segments and then stored. A segment starts with a stationary cell in which the MT stays for a sufficiently long time (e.g. 40 seconds in our pico-cell environment). As the MT begins to travel in the wireless network, all cells encountered are appended to the segment. The segment ends with the same or a different stationary cell, which will become the beginning of a new segment (see Figure 5). This algorithm keeps on matching the segment currently under construction with those segments already stored. A match is found if the present segment is identical to the initial portion of a stored segment. Then the cell immediately after the initial portion of the matched segment is predicted to be the next move. If multiple stored segments contain the same beginning portion like the present segment, a match segment is selected if it occurs more often in the previous movements (see Figure 5 for an illustration).

• Bayes’ Rule B1

50% Ai 50%

Figure 4. Direction Criterion algorithm

F

multiple segments contain the same beginning portion (A, B, C) as the present segment

present seg.

A i-1

A B C

seg. F-A

prediction = B 2 (highest prob.)

B2

60%

C1

40%

C2

90%

C2

10%

C3

the most likely step for visit two hops away

Figure 6. Bayes’ Rule algorithm Instead of considering the route already taken, it is possible to extend the Direction Criterion algorithm so that all departure histories along the future direction of travel are considered. Bayes’ Rule [10] is used to calculate the probability distribution of all possible next moves once a reference point further down the direction of travel is given. This reference point, for the ease of our implementation, is taken as the most likely step two hops away from the present location (see Figure 6). The Bayes’ Rule formula can be expressed as: P ( A i −1 A i B x | C m ) =

P (A i −1 A i B x )× P (C m | A i −1 A i B x )

∑ P (A n

j =1

i −1

A i B j )× P (C m | A i −1 A i B j )

• Time Criterion prediction = B (70%) direction of 1 5 % A travel + previous 70% B location time of 15% C cell crossing

Segment Direction Criterion Bayes' Criterion Rule

0.9

Prediction Accuracy Ratio

where Ai-1 and Ai are the previous and present locations, Bx is the xth possible next move, Cm is the most likely step for visit two hops away, and n is the total number of possible next moves. Once the conditional probabilities of all possible next moves are calculated, the one with the highest value is selected as the predicted movement.

0.8

0.6 Random Selection

0.5 0.4 0.3 0.2

Benchmark

There are two limits on those schemes relying on individual mobility patterns. The first is concerned with the change of user behaviour. Since the mobility history can be logged for a long time, a recent change of the user’s behaviour may not have a significant impact on the overall probability distribution. The second appears when a MT visits new places in which no past history is available for the probability calculation. These two issues can be resolved by following methods.

• Pro-active Probability Update Mechanism This mechanism dynamically updates the probability distribution “on-the-fly” (see Figure 8). A probability editor examines the accuracy of the last ten predictions when it is about to update a departure history. If six out of ten predictions were incorrect, the latest update would incur a heavier weight. This weight would gradually decrease to 1 if recent predictions came inside a preset criterion of success.

30%

60% 10%

25%

retrieve

OLD 80%

20%

75%

Prediction

75%

25%

BS 168

NEW

Recent local movement of all users

Figure 9. Performance of prediction algorithms

4.1. Limitations of the Individual Movement History and their Solutions

BS 185

Individual mobility pattern

Prediction Algorithm

To explore the dependency of user movements on certain time of the day, the Time Criterion algorithm imposes the time of cell crossing (eg. accurate to the nearest hour) into the Direction Criterion scheme (see Figure 7). Based on the assumption that people trend to have some regular daily activities, the collected probability should better describe the users’ movement.

Probability Editor

Correlation Criterion

Location Criterion

0.7

Figure 7. Time Criterion algorithm

Collections of Departure History

Time Criterion

• Correlation Criterion If no individual past history is available, a supplementary prediction can be performed by observing the movement patterns of other users, similarly to those described in [5] and [6]. Since different people may display different behaviour, a long aggregated history of their movements may only show equal probability for all neighbouring cells. Instead, we argue that a user’s next move tends to follow the movement pattern of other people nearby if they move in the same direction. For each location, the Correlation Criterion algorithm collects the statistics of all users in last 30 minutes and constructs a departure history for prediction purposes. 4.2. The Performance of Basic Prediction Algorithms We partitioned the movement traces from ORL into days and randomly selected the days, so that 60% of the data was used to initialise the system to a normal operating state (i.e. to form initial history files), while the remaining 40% was used to carry out experiments. The results of these experiments for proposed prediction algorithms are shown in Figure 9 using a Box PlotII notation. In general, we observe that the Direction Criterion has the best performance, with a median prediction accuracy ratio of 72% (i.e. 38% higher than that of our benchmark). However it appears that most prediction schemes have a prediction accuracy ratio in the range 50 to 70%. It is suggested that a high level of statistical randomness in users’ movements may cause this low prediction accuracy. As expected, the Location Criterion does not perform well as it does not take direction of travel into account. Also, the median of the prediction accuracy for the Time Criterion is roughly 12% lower than that of the Direction

Crossing

update recent accuracy

II

Figure 8. Pro-active probability update mechanism

For simplicity, only the median, interquartile range and outliers of prediction accuracy ratio for frequent Active Badge wearers are shown in this paper.

Prediction Accuracy Ratio

0.9

(a)

0.8 0.7 0.6 0.5 0.4 3.5

(b)

3.0 2.5 2.0 1.5 1.0 .30

QoS Adaptive Correlation Criterion

Average num of cells involved

Average num of cells involved

Prediction Accuracy Ratio

QoS Adaptive Direction Criterion 1.0

.35 .40

.45 .50 .55 .60

.65 .70

1.0 0.9

(c)

0.8 0.7 0.6 0.5 0.4 3.5

(d)

3.0 2.5 2.0 1.5 1.0 .30 .35 .40

.75 .80 .85 .90 .95 1.0

Prediction Confidence Ratio (PCR)

.45 .50 .55 .60 .65 .70

.75 .80 .85 .90 .95 1.0

Prediction Confidence Ratio (PCR)

Figure 10. Prediction Accuracy and the average number of cells involved Criterion. This is probably due to our early assumption about the users’ regular daily activities not being correct. Another interesting finding is that more complicated algorithms, such as Segment Criterion and Bayes’ Rule, do not guarantee an improvement on the prediction accuracy. In particular, the inferior performance of the Segment Criterion is caused by the lack of matching. Even though those mismatches are not counted (shown as the grey Box Plot in Figure 9), the accuracy of the Segment Criterion is similar to that of the Direction Criterion. Comparing the median and the outlier prediction accuracy values, it appears that the Direction Criterion has a superior performance when compared with the Correlation Criterion. Hence, we argue that it is worthwhile to use an individual’s movement history to predict the next move, but the correlative history can be used when a user arrives at new places. 5. QoS ADAPTIVE MOBILITY PREDICTION (QoSAMP) ALGORITHM For a pico-cell environment, it is proposed that a handover dropping probability cannot be greater than 10% [5]. In a populated network environment, it seems that advance reservations and configurations are an effective way to decrease call-dropping probability and to shorten handover latency. From the discussion of previous sections, issues such as • • •

the change of users’ behaviour the arrival of new locations the variability of user’s movement

may prevent a prediction scheme from achieving an acceptable accuracy. We have already proposed the use of a pro-active probability update mechanism and a supplementary Correlation Criterion to resolve the first two issues. Now we introduce a new term called Prediction Confidence Ratio (PCR) to minimise the effect of statistical

randomness in a user’s movement. Basically, we avoid predicting the random movements, and argue that there is a trade-off between the optimisation of resource usage and the guarantee of QoS measures. To achieve a stricter QoS compliance, we can transformIII the QoS requirement of applications or the tariff preference of mobile users into a particular value of PCR. This PCR value is then applied to a QoS adaptive mechanism as follows. A prediction is derived from a probability distribution of all possible next moves. If the first predicted cell does not contain a probability higher than the PCR, one or more extra cell(s) can be involved for additional service preparations until the sum of their probability exceeds the PCR. To verify this approach, our QoS Adaptive mechanism was implemented into the Direction Criterion and the Correlation Criterion algorithms. Using different values of PCR, we simulated the improvement in the prediction accuracy and the average number of cells involved per prediction. The simulation results for these two algorithms are presented in Figure 10, again using a Box Plot notation. Our simulation results confirm that the prediction accuracy can be improved by increasing the value of PCR (see Figure 10 (a) and (c)). However, the higher the PCR, the larger the number of cells which will be involved in service predictions (see Figure 10 (b) and (d)). It is also found that the QoS Adaptive Direction Criterion performs better than the QoS Adaptive Correlation Criterion. As an illustration, we can compare the performance III

The feasibility of this is currently under investigation. It appears that transformations based on tariff preference are more practical and easier for implementation. Details about this topic will be presented in a future paper.

of these two schemes at 75% PCR in Figure 10. For the first scheme, the median of prediction accuracy ratio is 87% and the average of cell involvement per crossing is 1.5. For the second scheme, however, the corresponding values are only 82% and 1.7. Since the QoS Adaptive Direction and the Correlation Criteria rely on individual and correlative mobility patterns respectively, we propose to implement the former scheme at the MT and the later scheme to supplement this at the BS. 6. CONCLUSION AND FURTHER WORKS Using the realistic movement traces from ORL, we have compared the performance of various basic prediction algorithms. The results show that these basic schemes provide improvement over arbitrarily selecting an adjacent cell, but the randomness of a user’s movement may prevent good prediction accuracy. Instead of trying to predict the random movements, we have proposed a QoS Adaptive Mobility Prediction scheme. It can provide a stricter QoS in wireless networks if this is required by some applications. The simulation results confirm that a high prediction accuracy can be achieved at the expense of an appropriate increase in network and resource preparations. Although the test scenario is an indoor office, QoSAMP should apply equally well in other environments. Recently, Stanford University has released a set of mobile activity traces [11], and we intend to use this data and some other GSM call traces to verify our findings in this paper. ACKNOWLEDGMENTS The authors would like to thank Olivetti and Oracle Research Laboratory for their supply of the movement traces, and Dr. W.T. Hung with Foundation for Australian Resources at UTS for his suggestions on statistical methods. The work of J. Chan is funded through a CSIRO Postgraduate Scholarship and an Australian Postgraduate Award. REFERENCES [1] H. Kim and C. Moon, “A Rerouting Strategy for Handoff on ATM-based Transport Network,” in Proc. IEEE 47th Vehicular Technology Conference, 1997, pp. 285-289. [2] S. Bush, “A Control and Management Network for Wireless ATM Systems,” in Proc. IEEE ICC’96, 1996, pp 459-463. [3] A. Acampora and M. Naghshineh, “An Architecture and Methodology for Mobile-Executed Handoff in Cellular ATM Networks,” IEEE JSAC, 12(8), Oct. 1994, pp. 1365-1374.

[4] G. Liu, “The Effectiveness of a Full-Mobility Architecture for Wireless Mobile Computing and Personal Communications,” Ph.D. Thesis, Royal Institute of Technology, Sweden, Mar. 1996. [5] V. Bharghavan and J. Mysore, “Profile Based Nextcell Prediction in Indoor Wireless LAN,” in Proc. IEEE SICON’97, Apr. 1997, available at http:// shiva.crhc.uiuc.edu/publications.html [6] J. Chan et. al., “A Hybrid Handoff Scheme with Prediction Enhancement for Wireless ATM Network,” in Proc. APCC’97, Dec. 1997, pp 494-498. [7] D. Levine et. al., “A Resource Estimation and Call Admission Algorithm for Wireless Multimedia Networks Using the Shadow Cluster Concept,” IEEE/ACM Trans. on Networking, 5(1), Feb. 1997, pp. 1-12. [8] Z. Haas and I. Akyildiz, “Mobility and Resource Management for Multimedia Mobile Computing,” project title description, available at http:// www.ee.cornell.edu/~haas/nsf.html. [9] R. Want et. al., “The Active Badge Location System,” ORL technical report, available at ftp:// ftp.orl.co.uk:/pub/docs/ORL/tr.94.2.ps.Z [10] W. Winston, “Operations Research, Applications and Algorithms,” third edition, Duxbury Press, pp. 619-621. [11] “Stanford University Mobile Activity TRAces,” available at http://www-db.stanford.edu/sumatra.