A Practical User Mobility Prediction Algorithm for ... - CiteSeerX

A Practical User Mobility Prediction Algorithm for Supporting Adaptive QoS in Wireless Networks Jonathan Chan and Aruna Seneviratne School of Electrical Engineering & Telecommunications The University of New South Wales Sydney 2052, Australia [jchan, aruna]@ee.unsw.edu.au

Abstract A number of user mobility prediction algorithms have been reported in the literature. These may be used for resource reservation and service pre-configuration/ adaptation in future wireless networks to provide QoS guarantees. However, our analysis of some of these techniques using measured cellular performance shows that these models do not accurately represent the mobility patterns of users. As a result, resource reservation schemes need to reserve excessive resources, and the preconfiguration/adaptation does not work well. To over come this, we propose an adaptive user mobility prediction algorithm that limits the reservation and configuration procedure to a subset of cells around the user. The viability and effectiveness of the proposed scheme is then demonstrated through a simulation based on measured data.

1. Introduction Future wireless systems will be required to support the increasingly nomadic lifestyle of people. This support will be provided through the use of multiple overlaid networks which have very different characteristics [1]. Moreover, these networks will be required to support the seamless delivery of today’s popular desktop services, such as web browsing, interactive multimedia and video conferencing to the mobile devices. Thus one of the major challenges in the design of these mobile systems will be the provision of the quality of service (QoS) guarantees that the applications demand under this diverse networking infrastructure. We believe that it is necessary to use resource reservation and adaptation techniques to deliver these

QoS guarantee to applications. However, reservation and pre-configuration in the entire service region [2] is overly aggressive, and results in schemes that are extremely inefficient and unreliable. To overcome this, the mobility pattern1 of a user can be exploited. If the movement of a user is known, the reservation and configuration procedure can be limited to the regions of the network a user is likely to visit. Recently, a number of schemes that apply user movement prediction to various aspects of mobility management have been reported in the literature. It has been shown through modelling and simulation, that the use of movement prediction is effective to enhance the performance of resource reservation [3, 4, 5, 6, 7], handover management [8, 9, 10], and location management [8, 11, 12] schemes. Furthermore, it has been shown that movement prediction can be used for adaptive resource management in wireless systems [10, 13]. Although the use of movement prediction seems to be a promising approach for improving the efficiency, reliability and adaptivity of wireless networks, the actual user mobility patterns are not yet well understood. The above performance studies and others reported in the literature, have used simplified movement models that do not accurately characterise user mobility and consequently lead to unrealistic conclusions. Recently we analysed mobility traces obtained through the use of infrared sensors within a building [14]. This showed that, despite being in a well-defined and stable indoor environment, there was a significant amount of variation, and user movement could not be accurately predicted. To overcome the inaccuracy of movement prediction, an adaptive prediction algorithm was used [9]. 1 Mobility pattern is also known as movement pattern in the literature. These terms are used interchangeably in this paper.

This paper extends this work and provides an evaluation of this adaptive prediction algorithm in a wide area setting. Using measurement of a cellular mobile user, it is shown that the adaptive prediction scheme can equally well be used in the wide area networks for the resource reservation and adaptation necessary to provide QoS guarantees. The rest of the paper is organised as follows. In Section 2, we present a brief description of related work which uses user movement prediction for mobility management, along with their assumptions about user mobility. In Section 3, we describe the call traces and illustrate the discrepancies between actual observations and movement modelling schemes that have been used.

Proposed Scheme

Section 4 presents the results of applying the proposed adaptive user mobility prediction scheme to these traces. In Section 5, we explore the potential use of this algorithm for providing adaptive QoS in a wireless network. Finally, concluding remarks are given in Section 6.

2. Related Work The use of user movement prediction to improve mobility management makes one primary assumption; namely that user movements follow a pattern and display some regularity, despite actual user mobility patterns not

Prediction approach and user mobility model

Type of improvement and prediction accuracy

• Prediction is based on the user’s movement history. • Movements consist of regular and random components, which can either be matched with circle/track patterns or simulated by the Markov chain model.

• Migration of mobile-floating agents for service pre-connection, resource preassignment and data pre-fetching. • Prediction is highly accurate with regular movements but decreases linearly with increasing random component.

• Prediction is based on the user’s movement history and instantaneous RSSI measurements of surrounding cells. • User’s movement can be mapped into previous mobility patterns, with matching operations such as insertion, deletion and changing. • Intercell movements can be also estimated by current location, velocity and cell geometry.

• Setting up and reserving resources along a mobile’s path, and planning quick handovers between the base stations. • Minimising the occurrence of location registration and update procedures. • Prediction remains reasonably accurate (75%) despite the influence of random movements.

• Prediction is based on the user’s movement history and the classification of locations. • Mobility can be purely random, mostly random, purely deterministic, and mostly deterministic. • The type of location can be office, corridor and common room.

• Providing advance reservation and adaptation in resource management. • Prediction is highly accurate with fully predictable movements, 80% accurate for typically observed movements and 70% for fully random movements.

Shadow Cluster Concept [3]

• Prediction is based on the user’s movement history. • Movements simulate highway traffic with various constant speeds travelling in forward and backward directions.

• Estimation of resource requirement and decision of call admission. • The percentage of dropped call reduces from 14% to 1%, with a minimal deduction of bandwidth utilisation from 30% to 25%.

Per-user Profile Replication Scheme [11]

• Prediction is based on the user’s movement history. • Mobility model is derived from statistical analysis of actual call traffic traces, vehicle and aeroplane traffic data, and government transportation surveys. • Simulated movements can be random walks and repetitive roundtrips.

• Improving the efficiency of location management. • Compared with a basic hierarchical model, this scheme can serve more than 90% of calls by local database lookups, with the penalty of a slight increase in bandwidth requirement and twice the memory requirements.

Mobile Motion Prediction Algorithm [10] Hierarchic PositionPrediction Algorithm [8]

Profile Based Next-cell Prediction Algorithm [4]

Table 1. A brief description of some movement-based mobility management proposals.

30 20 10 0

A 68

B 90

C 83

D 109

E 94

F 48

G 138

H 100

60 (b) Outbound handover patterns

50 40 30 20 10 0

6 h/o

A 103

B 108

C 134

D 146

E 124

F 60

G 152

H 74

num of handover at a regional scale

variance of each handover pattern

40

6 h/o num of occurrence for each handover pattern

(a) Inbound handover patterns

50

6 var variance of each handover pattern

num of occurrence for each handover pattern

60

1.1 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

6 var

(c) Variance of inbound handover patterns

MAX

A 1.42

B C 1.73 0.87

D 2.65

E 1.71

F 1.61

G 2.64

H 1.94

(d) Variance of outbound handover patterns

MAX

A 2.37

B 1.62

C 0.99

D 2.42

E 2.31

F 1.13

G 2.19

H 1.32

handover variance at a regional scale

Figure 1. An overview of inbound and outbound handover patterns with their variances. being well understood. To increase the prediction accuracy rate, some researchers make a secondary assumption that the next cell crossing is a logical function of the user’s current position, velocity, and cell geometry. A sample of these proposals that exploit user mobility patterns, which have been recently reported in the literature, is shown in Table 1. This indicates the diversity of user movement models that have been used and summarises the improvements in system performance that have been reported. It should be noted that the majority of these proposals use simulated user movement patterns to evaluate the system performance. The per-user profile replication scheme (shown in Table 1) is the only model that uses real data derived from statistical analysis of call traffic traces, data from motor vehicles and aeroplanes, and government transportation surveys. However, these traces are essentially for large-scale movements between different zones of a location management database, and is unsuitable for use in the simulation of resource reservation and service pre-configuration among base stations.

3. User Mobility in Wireless Networks The frequently used assumptions described in section 2 focus on the physical movements of users. These do not really pay attention to user mobility from the perspective of a wireless network. What is required in a wireless

system is for the mobility prediction scheme to predict the network access point through which the mobile user will connect to the network, i.e. the cell /base station to which the user will next connect. In an ideal environment the handover would be to the closest base station. However base station overloading and anomalous propagation effects frequently result in handovers to base stations other than those adjacent to the current cell.

3.1. Call Traces in a Cellular system To determine a user’s movement patterns in wireless mobile networks, we logged the base station ID a mobile phone was connected to as a user drove between the central business district of a city to one of its outer suburbs. All inbound and outbound trips followed the same highway and were repeated during office hours for five days. An overview of these traces is given in Figure 1. It presents separate views on inbound and outbound trips, and provides a quantitative measurement of each handover pattern2 in the traces, including the number of occurrence and the variance. Based on the geographical location of those base stations, we have grouped all handover patterns (shown as small columns in Figure 1) into 8 regions (A to H) and calculated their occurrence 2 In this paper, each handover pattern is a unique pattern identifying the currently cell and the cell which the mobile user will cross over to.

(b) Outbound Handover Events Ratio of handover events beyond the ±25% tolerance

Ratio of handover events beyond the ±25% tolerance

(a) Inbound Handover Events 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 A

B

C

D

E

F

G

H

Regional Name

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 A

B

C

D

E

F

G

H

Regional Name

Figure 2. Inbound and outbound handover events which are out of pre-set tolerance. and variance on a regional scale, which are indicated underneath the corresponding region name in Figure 1.

3.2. Differences between Actual Call Traces and Conventional Mobility Models In the field trial described in the previous section, a mobile user was driving forward and backward along a fixed route. Most mobility modelling schemes in the literature would consider this to be a one-dimensional handover issue. That is, the movement traces of this user should indicate either a track or circle pattern with high tendency of reoccurrence during each trip. The remaining of this section will compare this assumption to the actual measurements. In an ideal forward-backward model, the occurrences of each handover pattern should be equal to the number of trips and the variance of their occurrences should be zero. As a practical model, however, one may allow some BS4

User Behavioural Patterns = 1, 2, 3

BS2 BS3 BS1

User Mobility Patterns = 1, 2, 1, 2, 4, 3

4

2 1

variation (say r25% tolerance) on the ideal values, which can be roughly translated into a maximum variance of 0.2 (shown in Figure 1 as dashed lines). According to these conditions and ignoring the sequence of events, it is possible to estimate the portion of handover events that is beyond our pre-set tolerance (i.e. outside r25% of the ideal values). As before, those handover events with excessive variance are grouped into regions and summarised in Figure 2. Figure 2 shows that a large proportion (30% - 95%) of handover events have excessive variance compared to the ideal forward-backward model. By comparing the inbound and outbound movement traces separately, it was found that most “random components” of the traces came from the Ping-Pong effect between adjacent base stations or some temporary handovers to other base stations relatively far apart (i.e. not neighbouring base stations). We believe that the above effects are caused as a result of a combination of signal fluctuations, constraints of the surroundings, congested cells, and moving obstacles. A simple example of the differences between the user mobility patterns and the user behavioural patterns3 are illustrated in Figure 3. Another common assumption of user mobility is that the forward and backward movement traces are very similar except their order is reversed. In spite of the presence of some key base stations along the path, our analysis does not strongly support this claim of reverse mapping between forward and backward movements (shown easily by comparing the differences in appearance for inbound and outbound cases in Figure 1 and Figure 2). We believe that this variance is a consequence of the use of hysteresis in handover algorithm that intends to reduce the Ping-Pong effect. Unfortunately, the incorporation of hysteresis makes the handover behaviour much more

3

Figure 3. Differences between user behaviour patterns and user mobility patterns.

3 In this paper, user behavioural pattern adopts a different meaning from user mobility pattern or user movement pattern. It represents a person’s physical movement pattern rather than his connection pattern through the wireless access points of a mobile network.

100

fail to uphold the desired QoS, but also degrade the efficiency of the overall system.

(%)

Prediction Accuracy Rate

90 80

4.1. Performance of Some Basic Movement Prediction Algorithms

70 60 50 40 30 20 10 0 Location Criterion

Direction Criterion

Segment Criterion

Bayes' Rule

Time Criterion

Prediction Algorithms

Figure 4. Performance of basic prediction algorithms. difficult to predict, because the decision at any instant depends explicitly on previous decisions. Therefore it is clear that there is a large discrepancy between those proposed user mobility models and actual system operation. This has at least two causes: the lack of consideration of wireless characteristics in user mobility models and the imperfect decision making mechanisms of handover in cellular systems.

4. Accurate Prediction of User’s Movements For the provision of QoS guarantees to mobile customers, it is possible to exploit user mobility prediction to reserve resources and pre-configure services. Good prediction accuracy is vital to this approach because misplaced reservation of network resources will not only

We applied the call traces described in Section 3 to a set of basic mobility prediction algorithms. These schemes are taken from our previous study [14], and are referred to as the Location Criterion (based on current location), the Direction Criterion (based on current direction), the Segment Criterion (based on pattern matching), Bayes’ Rule (based on conditional probability of future direction), and the Time Criterion (based on Direction Criterion with additional time constraint). Roughly 60% of the data was used to establish a history database and 40% of the data was used to carry out movement estimations. Figure 4 presents the results of these predictions using a Box Plot notation, in which the median, interquartile range and outliers of each algorithm are shown. The performance of these prediction schemes resembles our previous findings for the indoor office environment [14]. For example, the Direction Criterion has the best prediction performance (with a median value of 64%), followed by Bayes’ Rule and the Location Criterion. Both the Segment and Time Criteria have poor accuracy due to the lack of matching in this set of movement traces. In addition, these results support our earlier claim [14] that prediction schemes with user mobility modelling do not necessarily guarantee a better performance in the real environment. This argument may not be surprising if we

Prediction Accuracy Rate

(%) 100 90 80 70 60 50 (%)

Average num of BS involved in advance preparation

40 5

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

5

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

3.5 3.0 2.5 2.0 1.5 1.0

(%)

Prediction Confidence Ratio (PCR)

Figure 5. Relationship among PCR, prediction accuracy rate and the number of cells involved.

Applying various PCR values to the prediction of cellular call traces, we have obtained the prediction accuracy rate and the number of cells that need to be involved. This is again shown in Figure 5, using a Box Plot notation. For example, Figure 5 shows that a 75% PCR corresponds to a prediction accuracy rate of 80% and an average involvement of 1.65 nearby cells.

recall the large discrepancy between user movement models and observed call traces in the previous section.

4.2. Performance of the Adaptive User Mobility Prediction Algorithm

Prediction Accuracy Rate

The prediction of user behavioural patterns is itself a non-trivial problem without the complications of the wireless link and handover process. To achieve accurate prediction, the user mobility model needs to incorporate user behavioural patterns, wireless link characteristics and the handover decision-making mechanism. Unfortunately, these are complicated problems, and comprehensive solutions may not be available in the near future. Nevertheless, we believe that the user’s movement history still contains valuable information about his/her behavioural patterns and useful hints about the stability of wireless link. As a practical solution to this complex problem, we have proposed an adaptive user mobility prediction algorithm that can achieve the desired prediction accuracy by carefully choosing a subset of nearby cells as the potential candidates [14, 9]. It was shown that the prediction accuracy and the number of cells depended on a common factor called Prediction Confidence Ratio (PCR). Our adaptive user mobility prediction algorithm is defined as follow [14]: 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 cells will be added to the group of cells in which resources will be reserved in advance and services will be pre-configured. This process will continue until the sum of their probabilities exceeds the PCR. (%)

y1

5. Implementation of an Adaptive QoS Service We now present an example to illustrate how our adaptive user mobility prediction algorithm can be used to provide adaptive QoS in wireless systems. Consider a simple set of service classes (C1 – C4). Class C1 is the best effort service, which provides a level of service

#1:desired level of accuracy

p2

p1

prediction to the most probable cell 10

Average num of BS involved in advance preparation

We have analysed movement traces for both wide area and indoor office environment. In spite of the differences in the physical surroundings and available history, a similar relationship is maintained between the PCR, prediction accuracy and the number of cells involved. We can generalise their relationship with a logistic curve (Figure 6(a)) and an exponential curve (Figure 6(b)). Given a prediction accuracy rate (y2), we are able to calculate the corresponding value of PCR (x1) and then estimate the number of cells involved (y4). Of course, the prediction accuracy rate cannot be set to any desirable values but is bounded by the performance of “predicting the most probable cell” (y1) and “predicting all known cells” (y3) scenarios.

prediction to all known nearby cells

y3 y2

4.3. Generalisation of Prediction Confidence Ratio

20

30

40

50

(%)

x1 60

70

80

90

100

#2:corresponding value of PCR

y4

p4

#3:estimated cost of resources p3

(%)

1.0 10

20

30 40 50 60 70 80 Prediction Confidence Ratio (PCR)

90

100

Figure 6. Generalisation of relationships among PCR, prediction accuracy and the scope of cell involvement.

backbone

backbone

B0

B1

Class C2

0

B3

3 1

B2 (75%)

B0

B1

Class C3

2 0

(a)

similar to that can be provided with today’s wireless networks. Applications may experience a relatively high probability of call droppings and service interruption during handover. Hence, recovery or suspension mechanisms should be implemented at the upper layers. Typical applications for this class are E-mail, paging, file transfer and retrieval service. Class C2 is intended to serve applications requiring sustainable service from the wireless network but able to tolerate losses. The prediction accuracy rate should be sufficient to ensure that the resource reservation at nearby cells is likely to be used in the next handover. However, this service must also be price-competitive, and hence resource reservation should not be too aggressive. As a compromise, points p1 and p3 shown in Figure 6 seems to be a good operating point with relatively high prediction accuracy (y2) and low cost (y4). An example application would be voice calls or videophone calls. Class C3 is used for stricter service requirements with higher tariffs. The prediction accuracy would be very close to the optimum value but the amount of wireless resources reserved can also be high (e.g. points p2 and p4 in Figure 6). Possible applications are video-conferencing and interactive multimedia services. Class C4 is the premium class and used for applications which require minimum call dropping probability and little service interruption. Besides resource reservation at nearby cells, it can be used to establish optimum route establishment in an ATM backbone [8], or to invoke mobile RSVP (MRSVP) proxy agent [15] for path reservations in an IP backbone. Moreover, it allows other time consuming service configuration tasks in advance, such as migration of mobile agents [10] and pre-binding of CORBA objects [16]. Figure 7 is a pictorial presentation of these service classes, in which PCR = 75% for C2 and PCR = 90% for both C3 and C4. By applying our adaptive user mobility prediction algorithm, the amount of system preparation for a connection depends on not only a user’s service requirements, but also his/her mobility patterns. Moreover, we can easily change the requested resources anytime in a session to fit our budget by adjusting the PCR value.

RSVP IP or ATM backbone

B2

B3 (20%)

3 1

(75%)

B0

B1

Class C4

2 0

(b)

B3

B2

(20%)

3 1

(75%)

2

(c)

6. Conclusion The sole purpose of resource reservation and service pre-configuration is to provide a sustained QoS for mobile customers. We have shown that there is a large discrepancy between the user mobility models that have been reported in the literature and actual system measurements. This is caused by not considering the characteristics of the wireless channel when developing user mobility models, and the imperfect decision making mechanisms when performing handover in cellular systems. Since it is difficult to achieve accurate mobility prediction, we proposed the use of an adaptive user mobility prediction algorithm which can be used for advanced resource reservation and service preconfiguration at minimum cost. The viability of the proposed scheme was then demonstrated using real mobile user traces. The results are encouraging, and therefore we believe this method can be used effectively to support adaptive QoS in emerging heterogeneous wireless networks.

Acknowledgements The authors would like to thank G. Daniels and C. Wilson at CSIRO TIP for their expert comments on this work, 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.

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