Applying Data Mining to Wireless Networks

Transcription

1 Applying Data Mining to Wireless Networks CHENG-MING HUANG 1, TZUNG-PEI HONG 2 and SHI-JINN HORNG 3,4 1 Department of Electrical Engineering National Taiwan University of Science and Technology, Taipei, 106, TAIWAN 2 Department of Computer Science and Information Engineering National University of Kaohsiung, Kaohsiung, 811, TAIWAN 3 Department of Computer Science and Information Engineering National Taiwan University of Science and Technology, Taipei, 106, TAIWAN 4 Department of Electronic Engineering National United University, Miao-li, 360, TAIWAN Abstract: - Wireless networks and mobile applications have grown very rapidly and have made a significant impact on computer systems. Especially, the usage of mobile phones and PDA is increased very rapidly. Added functions and values with these devices are thus greatly developed. If some regularity can be known from the user mobility behavior, then these functions and values can be further expanded and used intelligently. This paper thus attempts to mine appropriate mobility patterns for being used by mobile-system managers in future strategy planning. The location areas in which mobile users visit can be found from the log data stored in the home-location-register module. The preprocessing task for preparing appropriate data from HLR is then done. The mobility sequences from mobile users are formed. The algorithm then calculates the count of each term in all mobility sequences. The mining process based on counts is then performed to find mobility patterns. Key-Words: - data mining, mobility pattern, location area, home location register. 1 Introduction Wireless networks and mobile applications have recently become very popular and have made a significant impact on our daily life [3]. Especially, the usage of mobile phones and PDA is increased very rapidly. Added functions and values with these devices are thus greatly developed. In a wireless system of mobile phones, a module called home location register (HLR) keeps related user mobility data. When a mobile user moves from a location area to its next one, his/her corresponding data in HLR are updated. HLR can thus help the system successfully and efficiently find users current locations and distribute desired messages to them. The log data stored in HLR can also be used for analyzing users mobility behavior. The regular patterns found from the mobility data may provide some appropriate suggestions to mobile-system managers. The added functions and values with the system may also be further expanded. Data-mining has become a process of considerable interest in recent years as the amounts of data in many databases have grown tremendously large. It has been developed to turn data into useful task-oriented knowledge. It is used to identify effective, coherent, potentially useful, and previously unknown patterns in large databases [4]. Some data-mining approaches for mobile systems were proposed in the past [9, 10]. In this paper, we emphasize on the automatic discovery of user mobility patterns in wireless networks. The preprocessing task for preparing appropriate data from HLR is first done. The preprocessing task for preparing appropriate data from HLR is then done. The mobility sequences from mobile users are formed. The algorithm then calculates the count of each term in all mobility sequences. The mining process based on counts is then performed to find mobility patterns. The concept of the algorithm is similar to that in the AprioriAll algorithm [1] for finding sequential patterns, but much simpler since a user can visit only one location each time. The remaining parts of this paper are organized as follows. The architecture of wireless networks for mobile users is reviewed in Section 2. Some related mining approaches are introduced in Section 3. An algorithm for mining mobility patterns from log data in HLR of a wireless network is proposed in Section 4. An example to illustrate the proposed algorithm is given in Section 5. Experimental results are described in Section 6. Conclusions and future work are stated in Section 7.

2 2 Review of wireless networks for mobile users The architecture of a typical wireless communication system for mobile users is shown in Figure 1 [2, 11]. Location Area VLR BSC MSC BSC MSC HLR VLR Figure 1: A typical wireless communication system All the communication space of a wireless system is divided into many units called cells. Each cell has a base station, in charge of broadcasting the contents to mobile users located within the cell. Several neighboring cells are grouped into a location area, which are managed by a base-station controller (BSC) and a mobile switch service center (MSC). BSC is used to control all the base stations in a location area for performing their jobs; MSC maintains a visitor location register (VLR) which stores the location area identifier and some related information to the current visitors in the location area. All the visitors with their relevant VLR data are sent to the home location register (HLR). When calls come, HLR determines users current location areas according to the identification number of mobile users called, and invokes corresponding BSCs to broadcast messages from base stations to them. The action is called paging. Besides, when mobile users move from a location area to another one, they must register and update both the VLR data in MSC and the HLR data. This action is called location update. Many researches were thus proposed in the past for making a good trade-off between the costs of these two actions [5, 6, 7, 8, 11]. The location areas in which mobile users visit can be easily found from the mobility data stored in VLR and HLR. This paper thus attempts to mine appropriate mobility patterns from these data for being used by mobile-system managers in future strategy planning. 3 Review of Related Mining Approaches Agrawal and Srikant proposed a mining algorithm to discover sequential patterns from a set of transactions [1]. Five phases are included in their approach. In the first phase, the transactions are sorted first by customer ID as the major key and then by transaction time as the minor key. This phase thus converts the original transactions into customer sequences. In the second phase, the set of all large itemsets are found from the customer sequences by comparing their counts with a predefined support parameter. This phase is similar to the process of mining association rules. Note that when an itemset occurs more than one time in a customer sequence, it is counted once for this customer sequence. In the third phase, each large itemset is mapped to a contiguous integer and the original customer sequences are transformed into the mapped integer sequences. In the fourth phase, the set of transformed integer sequences are used to find large sequences among them. In the fifth phase, the maximally large sequences are then derived and output to users. 4 Mining Mobility Patterns In this paper, the log data stored in HLR in a wireless network are used to analyze the mobility patterns on that system. The four attributes mobile-id, date, time, and location-area are used in the mining process. The log data to be analyzed are sorted first in the order of mobile-id and then in the order of date and time. The mobility sequences from mobile users are then formed. The algorithm then calculates the count of each term on all mobility sequences. The mining process based on counts is then performed to find mobility patterns. The details of the proposed mobility-pattern mining algorithm are described as follows. The mobility-pattern mining algorithm: INPUT: A set of log data in HLR, a predefined minimum support value α. OUTPUT: A set of mobility patterns. STEP 1: Extract the log data with only the four attributes mobile-id, date, time, and location-area; denote the resulting log data as D. STEP 2: Transform the mobile-ids into contiguous integers (called encoded mobile ID) for convenience, according to their first turn-on time. Note that the same mobile-id with

3 two turn-off connections is given two integers. STEP 3: Sort the resulting log data first by encoded mobile ID and then by date and time. STEP 4: Form a mobility sequence D j for each mobile user c j by sequentially listing his/her n j location areas visited, where n j is the number of location areas visited by c j. STEP 5: Calculate the number (count j ) of each location area I j appearing in the mobility sequences for 1 j n; if there are more than one appearance of I j in a mobility sequence, count it only once. STEP 6: Set the support (support j ) of each I j as count j / n, where n is the number of mobility sequences. STEP 7: Check whether the support of each location area I j is larger than or equal to the predefined minimum support valueα. If I j satisfies the condition, put it in the set of large 1-itemsets (L 1 ). That is, L 1 = {I j support j α, 1 j n }. STEP 8: If L 1 is null, then exit the algorithm; otherwise, do the next step. STEP 9: Set r = 1, where r is used to represent the length of mobility patterns currently being processed. STEP 10: Generate the candidate set C r+1 from L r in a way similar to that in the aprioriall algorithm [1]. Restated, the algorithm first joins L r and L r, under the condition that r-1 items in the two itemsets are the same and with the same orders. Different permutations represent different candidates. The algorithm then keeps in C r+1 the sequences which have all their sub-sequences of length r existing in L r. STEP 11: Calculate the number (count s ) of each candidate (r+1)-sequence s in C r+1 appearing in the mobility sequences; if there are more than one appearance of s in a mobility sequence, count it only once. STEP 12: Set the support (support s ) of each s as count s / n. STEP 13: Check whether the support of candidate (r+1)-sequences s in C r+1 is larger than or equal to the predefined minimum support valueα. If s satisfies the condition, put it in the set of large (r+1)-sequences s in L r+1. That is, L r+1 = {s support s α, s C r+1 }. STEP 14: IF L r+1 is null, then do the next step; otherwise, set r = r + 1 and repeat STEPs 10 to 14. STEP 15: Output the maximally large q-sequences, q 2, to mobile-system managers as the mobility patterns. After STEP 15, the mobility patterns found out can serve as meta-knowledge concerning the given log data in HLR of a wireless network. 5 An Example In this section, a simple example is given to show how the proposed algorithm can be used to generate mobility patterns for mobile users' moving behavior according to the log data in HLR. Assume the location areas in a wireless network are shown in Figure 2. There are five location areas A, B, C, D and E in this example. A D Figure 2: The location areas used in this example Each transaction in the log data of HLR includes the four fields: mobile-id, date, time, and location-area, among others. Only the data in the above four fields are extracted for mining. Assume the extracted log data in HLR are shown in Table 1. Table 1: The extracted log data in HLR Mobile-id Date Time Location-area :56 E :38 B :52 D :08 D :30 A :54 B :25 D :46 C :02 D :46 B :06 C :07 Turn-off :17 Turn-off :31 Turn-off :47 E :46 D :45 B :53 B :56 C :19 C :38 Turn-off :53 D :33 Turn-off :13 C B C E

4 :14 E :16 B :33 Turn-off In this example, six mobile users logged in HLR. Only one location area is contained in each transaction. The values in the field mobile-id are then transformed into contiguous integers according to the order of each mobile user s first visiting time. The extracted log data in Table 1 are sorted first by encoded client ID and then by date and time. After that, all the location areas visited by each mobile user are listed as a mobility sequence. The resulting mobility sequences are shown in Table 2. Table 2: The mobility sequences formed from Table 1. Encoded Mobile-id Mobility Sequence 1 E, B, D, C 2 D, B, D 3 A, D 4 B, C, E, B, C 5 D, B,C 6 D, C, E, B The count of each location area is then checked against the predefined minimum support value α. Assume α is set at 3 (or 50%) in this example. The four location area B, C, D, and E are thus put in L 1. The candidate set C 2 is then generated from L 1. The large 2-sequences found in this example are (B, C), (D, B), (D, C), and (E, B). There are no large 3-sequences in this example. These four mobility patterns are then output as meta-knowledge concerning the given log data in HLR. 6 Experiments The section reports on experiments made to show the effects of the parameters on the proposed algorithm for mobility patterns. They were implemented in JAVA on a Pentium-IV 2.6GHz personal computer with 1 GB memory. There were 50 location areas. Data sets with different numbers of mobile users were run by the proposed algorithm. In each data set, the numbers of visited location area in mobility sequences were first randomly generated. Experiments were first performed to find the relationships between numbers of mobility patterns and minimum supports when the mobile user number was set at 800, the location areas was 50 and the average number of location areas visited by a mobile user was 12. The results for mobility patterns are shown in Figure 3 Number of mobility patterns Minimum support Figure 3. The relationship between numbers of mobility patterns and minimum support values It can be observed from Figure 3 that the number of mobility patterns decreased along with the increase of the minimum support value. It was consistent with the property of data mining. Note that the mobility patterns in Figure 3 do not include L 1. Also in the experimental environments, a reasonable minimum support value is 0.6, below which the number of mobility patterns increased sharply. The execution time with the minimum support value set at 0.5 along with different numbers of mobile users is shown in Figure 4. It can be observed from Figure 4 that the execution time increased along with the increase of mobile uesrs. The line is nearly linear. Time (Sec.) Mobile user numbers Figure 4. The relationship between execution times and mobile user numbers 7 Conclusions and Future Work In this paper, we have attempted to discover mobility patterns from the extracted log data in HLR. A mining process has then been performed to find mobility patterns. The concept of the mining process is similar to that in the AprioriAll algorithm for

5 finding sequential patterns, but is much simpler since a user can visit only one location each time. The mobility patterns may also be used by mobile-system managers in future strategy planning, such as for location size re-planning. For example, if a mobility pattern (A -> B) is mined, the location area B may be considered to be merged into the location A. Also, the message distribution strategy may also be decided based on these patterns. In the future, we will attempt to design other mining models for solving various mobile problems. References: [1] R. Agrawal, R. Srikant: Mining Sequential Patterns, The Eleventh International Conference on Data Engineering, 1995, pp [2] I. F. Akyildiz, J. McNair, J. Ho, H. Uzunalioglu and W. Wang, "Mobility management in current and future communications networks", IEEE Network, Vol. 12, No. 4, 1998, pp [3] D. Barbara, Mobile computing and databases - a survey, IEEE Transactions on Knowledge and Data Engineering, Vol. 11, No. 1, 1999, pp [4] M. S. Chen, J. Han and P. S. Yu, Data mining: an overview from a database perspective, IEEE Transactions on Knowledge and Data Engineering, Vol. 8, No. 6, 1996, pp [5] R. V. J. Chintalapati, V. Kumar and A. Datta, "An adaptive location management algorithm for mobile computing", The 22th Annual IEEE Conference on Local Computer Networks, 1997, pp [6] I. Han and D. H. Cho,"Group location management for mobile subscribers on transportation systems in mobile communication networks", IEEE Transactions on Vehicular Technology, Vol. 53, No. 1, 2004, pp [7] N. E. Kruijt, D. Sparreboom, F. C. Schoute and R. Prasad, "Location management strategies for cellular mobile networks", IEEE Electronics & Communication Engineering Journal, Vol. 10, No.2, 1998, pp [8] W. Ma and Y. Fang, A new location management strategy based on user mobility pattern for wireless networks, The 27th Annual IEEE Conference on Local Computer Networks, [9] W. C. Peng and M. S. Chen, Developing data allocation schemes by incremental mining of user moving patterns in a mobile computing system, IEEE Transactions on Knowledge and Data Engineering, Vol. 15, No. 1, 2003, pp [10] Y. Saygin and O. Ulusoy, Exploiting data mining techniques for broadcasting data in mobile computing environments, IEEE Transactions on Knowledge and Data Engineering, Vol. 14, No. 6, 2002, pp [11] K.Wang, J. M. Liao and J. M Chen, Intelligent location tracking strategy in PCS, The IEE Proceedings on Communications, 2000, Vol. 147, No. 1, pp