Prediction-based Handoff in Heterogeneous Networks Bill Phillips 09 March 2007

March 9, 2007Prediction-based Handoff in Heterogeneous Networks 2 Outline General motivation Dartmouth large-scale Wi-Fi study Predictors –Markov –LZ –PPM –SPM Discussion of Dartmouth results References

March 9, 2007Prediction-based Handoff in Heterogeneous Networks 3 Motivation Accurate mobile user location prediction facilitates: –Smoother session handoff –Improved network resource allocation –Better mobility management –Enhanced assignment of cells to location areas –More efficient paging –Decreased infrastructure costs No location predication: resources potentially reserved in each cell Location predication: resources reserved in only cell B AA B

March 9, 2007Prediction-based Handoff in Heterogeneous Networks 4 Dartmouth Wi-Fi Study Data collected from April 2001 – May 2003 from Dartmouth Wi-Fi campus network Over 6,000 WLAN users –Average of 3,000 users active each day 543 access points cover entire campus (interior and exterior) Recorded registered access point every 5-seconds by MAC address –Not physical location of user –Not physical movement of user Analyzed 4 domain-independent predictors –Markov –Lempel-Ziv Algorithm (LZ) –Prediction by Partial Matching (PPM) –Sampled Pattern Matching (SPM)

March 9, 2007Prediction-based Handoff in Heterogeneous Networks 5 Dartmouth Campus 200 acres 161 buildings –82 residential –32 academic –6 library –19 recreational –22 administrative b WLAN 543 APs 81 Subnets 400 feet

March 9, 2007Prediction-based Handoff in Heterogeneous Networks 6 Test Environment Wireless network included all offices, classrooms, dorm rooms, and athletic/recreational facilities All students required to have a computer, 70% laptop. Indoor AP range feet Dartmouth population: 5,500 students & 1,215 faculty Residential – dominated all traffic A few APs never received any traffic due to remoteness, malfunction, or misconfiguration Data collection: syslog events (3.5 million records), SNMP polling (193 million records), and tcpdump sniffers (22 APs, all packet headers)

March 9, 2007Prediction-based Handoff in Heterogeneous Networks 7 Format of data collection Domain-Independent measurements: –Only changes in location are recorded –No time data –No geographic data, no association between access points, mobile users, and geographic coordinates Sample cell map & Sample location history: L = gbdcbgcefbdbde(off)ecd g b c d e f

March 9, 2007Prediction-based Handoff in Heterogeneous Networks 8 Distribution of data collected 50% of users had a trace length smaller than 500 points 50% of users had a trace length greater than 500 points 50% of users visited less than 25 APs 50% of users visited more than 25 APs CDF: # users vs trace length CDF: # users vs # APs visited X X Half the WLAN users remained in a fairly small subset of the overall network

March 9, 2007Prediction-based Handoff in Heterogeneous Networks 9 Markov Predictors Order-k Markov predictors, O(k), assume that the next location can be predicted using the last k locations. Example: L = abacbcbcabab… –O(1): P(a|b) = 1/2, P(b|b) = 0, P(c|b) = 1/2 O(1) Markov predictor would not know whether to choose a or c as the next location –O(2): P(a|ab) = 1, P(b|ab) = 0, P(c|ab) = 0 O(2) Markov predictor would choose a as the next location Probabilities are represented by a transition probability matrix, M. Using the O(1) example: After each prediction, history is incremented, and M is recalculated Moving from this state into this state Rows always sum to 1

March 9, 2007Prediction-based Handoff in Heterogeneous Networks 10 Lempel-Ziv (LZ) Predictors Often used in text compression (UNIX compress, WinZip) Consider the text compression implementation first Initialize dictionary with length 1 blocks Encode entry with index Add 1 to the length of the previously encoded entry, and enter into dictionary a b b a a b b a a b a b b a a a a b a a b b a Dictionary IndexEntryIndexEntry 0 a 7 baa 1 b 8 aba 2 ab 9abba 3 bb 10 aaa 4 ba 11 aab 5 aa 12baab 6 abb 13 bba repeat

March 9, 2007Prediction-based Handoff in Heterogeneous Networks 11 Lempel-Ziv (LZ) Predictors Parses string into unique substrings from left to right Each new substring differs in length by only 1 from a previously listed substring Example: L = abacbcbcaba –Substrings:  a, b, ac, bc, bca, ba Substrings are commonly represented by a LZ tree that includes measurement frequency. Predication occurs by using the relative frequency of substrings Empty String  a:2 b:4 c:1 c:2 a:1  is an empty string

March 9, 2007Prediction-based Handoff in Heterogeneous Networks 12 Lempel-Ziv (LZ) Predictors Prediction probability is dependent on the prefix frequency Example: L = abacbcbcaba … –Substrings:  a, b, ac, bc, bca, ba Prefixes: –a (2 times) –b (4 times) –bc (2 times) P(a|L) = 1/4, P(b|L) = 1/2, P(bc|L) = 1/4 Empty String  a:2 b:4 c:1 c:2 a:1

March 9, 2007Prediction-based Handoff in Heterogeneous Networks 13 Prediction by Partial Matching (PPM) Used in text compression Similar to Markov predictors in that a variable subset, k, of past measurements is used to predict the next location Differs from Markov predictors in that only the last immediate k measurements are used for the prediction PPM constructs multiple O(k) Markov models for this subset of k measurements, and blends them into a singel prediction Example: L = … abdcada O(6) PPM uses the last 6 measurements, and O(1 to 6) Markov predictors to determine that the next most like value is a

March 9, 2007Prediction-based Handoff in Heterogeneous Networks 14 Sampled Pattern Matching (SPM) Sampled Pattern Matching Similar to Markov predictors, but with a variable order that depends on the longest common substring Example: acabcdcabcbabc –Longest substring = abc (length n) –The variable order O(k=  /n) depends on  where 0 <  <1 –  = 1/3, yields a O(1) Markov predictor –  = 2/3, yields a O(2) Markov predictor This process is repeated for each user after each predication = Computationally intensive  is a predetermined, fixed constant

March 9, 2007Prediction-based Handoff in Heterogeneous Networks 15 Tie Breaking At times the prediction algorithm has equal probability of choosing two different outcomes. The following are 3 tie breaking techniques: –First added: use the location that appears first in the location history –Most recently added: (opposite of first added) use the location that does not appear first in the location string –Most recent: use the location that was most recently visited by the mobile user The Dartmouth study showed no difference in prediction between any of the 3 methods, so the first added technique was used L = acabcbab... a if using first added c if using most recently added a if using most recent Predicted value Assuming equal probability of predicting a or c

March 9, 2007Prediction-based Handoff in Heterogeneous Networks 16 Markov Results This figure shows that the accuracy of Markov prediction improved with larger samples (rightmost curve) O(2) was the best predictor (rightmost curve, above) O(2) out-performed O(3) and O(4) Among 543 access points, pattern lengths of 3 or 4 were not as common as 2 Most accurate for location histories greater than 1000 (35% of users)

March 9, 2007Prediction-based Handoff in Heterogeneous Networks 17 Markov fine-tuning Investigated potential improvements to the O(2) Markov predictor in the case that the algorithm could make no prediction (i.e. encountered a new location pair) –Fallback to O(1) –Fallback to most frequently used location –Time aided prediction (classrooms during the day/dining halls, recreational facilities at night) O(2) with fallback to O(1) was the best performer Time aided prediction topic for future study

March 9, 2007Prediction-based Handoff in Heterogeneous Networks 18 Other Predictors LZ performed well, but not as good as Markov O(2) LZ PPM SPM PPM performed equally well at O(2), O(3), O(4), and O(5), so number of previously considered locations did not seem to matter once greater than 2 SPM performed marginally better at  = 0.5, but not as well as Markov O(2) with O(1) fallback  = 0.5 is a good balance between too much and too little information

March 9, 2007Prediction-based Handoff in Heterogeneous Networks 19 Conclusion More computationally complex predictors yielded results that were at best equivalent to the simpler Markov model, O(2) with O(1) fallback All predictors (Markov, LZ, PPM, SPM) faired poorly with movement histories under 100 (not enough samples) There was negligible improvement for trace lengths greater than 1000 locations Best predictor, Markov O(2) with O(1) fallback –Median prediction accuracy of 63% overall –Median prediction accuracy of 72% for location histories exceeding 1000 –Not known whether this is the optimal predictor for longer traces, or once time dependent measurements are considered

March 9, 2007Prediction-based Handoff in Heterogeneous Networks 20 References David Kotz, and Kobby Essien, “Analysis of a Campus- wide Wireless Network,” MOBICOM ’02, Sep 23-26, Libo Song, David Kotz, Ravi Jain, and Xiaoning He, “Evaluating Next-Cell Predictors with Extensive Wi-Fi Mobility Data,” IEEE Transactions on Mobile Computing, vol. 5, no.12, pp , Dec Leon-Garcia, Alberto. Probability and Random Processes for Electrical Engineering, Addison Wesley,