1. Improving Prediction in the Routing Layer of Wireless Networks Through Social Behaviour 2 nd URV Doctoral Workshop in Computer Science and Mathematics, DCSM-2015 November 13 th, 2015. Tarragona Pere Millán 1 (PhD advisors: Carlos Molina 1, Roc Meseguer 2 ) 1 Universitat Rovira i Virgili, Tarragona, Spain 2 Universitat Politècnica de Catalunya, Barcelona, Spain

2. Motivation Link & End-to-End/Path Quality Prediction in Wireless Community Networks Predicting Topology Control Information (TCI) Improving Prediction Through Social Behaviour Conclusions & Future Work OLSR Outline 2

3. Emerging models for Future Internet across Europe: – Community networking (FunkFeuer Viena, Guifi.net, …) – Bottom-up-Broadband initiative Description: – Communities of citizens build, operate and own open IP-based networks – A key infrastructure for individual and collective digital participation Motivation 3

4. Community networks create measurable social impact providing the right and opportunity of communication 4

5. Community networks: – Large, heterogeneous, dynamic and decentralized structures Some challenges: – What is the effect of the asymmetrical features and unreliability of wireless communications on network performance and routing protocols? – Link quality tracking is a key method to apply when routing packets through an unreliable network. – Routing algorithms should avoid weak links whenever possible and as soon as possible. Motivation 5

6. Link quality (LQ) estimation/prediction approach increases the improvements in routing performance achieved through link quality tracking. – RT metrics do not provide enough information to detect degradation/activation of a link at the right moment. – Prediction techniques are needed to foresee link quality changes in advance and take the appropriate measures. Motivation 6

7. End-to-End Quality (EtEQ) or Path Quality (PQ) extends the Link Quality (LQ) concept to the full communication path (sender-receiver) and is computed based on the quality (ETX) of the individual links that conform the communication path. Motivation 7

8. Link & End-to-End/Path Quality Prediction in Wireless Community Networks Predicting Topology Control Information (TCI) Improving Prediction Through Social Behaviour Conclusions & Future Work OLSR Outline 8

9. FunkFeuer uses OLSR Routing Protocol: – Prediction of path changes can improve local node routing decisions, since it can provide the node with an estimation about the future local and remote events. – OLSR uses ETX to choose the next hop. ETX Metric: – Link: number of expected transmissions to send a packet over the link. – Path: sum of ETX values of links that form the path (≥ path Hops). Path ETX prediction: – Will allow more efficient routing decisions in an unstable environment. Background 9

10. Goals of Network Prediction: Routing Traffic Reduction: OLSRp, Kinetic Multipoint Relaying (KMPR). Energy Efficient Routing: LPR, MDR, E-DSR routing protocol. Link Quality prediction. Link Quality Prediction in WCN: LQ tracking: select higher quality links  max. delivery rate & min. traffic congest. LQ prediction: determine beforehand which links are more likely to change behavior. Idea: The routing layer can make better decisions at the appropriate moment. Link Quality Estimators (LQE) metrics: Measure quality of links between nodes based on physical or logical metrics. Physical metrics focus on the received signal quality: LQI, SNR, RSSI. Logical metrics focus on % of lost packets: RNP, ETX, PSR. To select the more suitable neighbor nodes when making routing decisions. Network Prediction 10

11. Funkfeuer WCN (Austria): – 2.000+ links, OLSR-NG routing protocol. Open data set (Confine Project): – OLSR info, 404 nodes, 7 days, av. degree: 3.5, diameter: 18. – 1.032 links with variations in LQ (higher prediction accuracy using all links). 4 Prediction Algorithms: – Support Vector Machines (SVM), k-Nearest Neighbors (kNN), Regression Trees (RT), Rule-Based Regression (RBR). Time Series Analysis & Forecasting: – Training and test sets validation approach. – Weka: machine learning/data mining approach to model time series, encodes time dependency via additional input fields (“lagged” variables). Metrics and Plots: Mean Absolute Error (MAE). MAE = sum(abs(predicted - actual)) / N Boxplots: classic representations of a statistical distribution of values. Experimental Methodology 11

12. A sample of variation of LQ values of a link over a day Variation of LQ values 12

13. Results 1: Link Quality 13

14. Comparison of learning algorithms Time series analysis and prediction can be used to predict the next link quality value? 4 classification algorithms : Support Vector Machines (SVM) k-Nearest Neighbors (KNN) Regression Trees (RT) Gaussian Processes for Regression (GPR) 4 classification algorithms : Support Vector Machines (SVM) k-Nearest Neighbors (KNN) Regression Trees (RT) Gaussian Processes for Regression (GPR) 14 Data sets: Training: 1728 instances (6 days) Test: 288 instances (1 day) Lag window: last 12 instances Data sets: Training: 1728 instances (6 days) Test: 288 instances (1 day) Lag window: last 12 instances BEST WORSE Very high success rate: >95%

15. Degradation of RT model over time What is the accuracy of the prediction models over time? 15 Average MAE of the overall network and its approximation to a linear function Linear function: slope = 0.0212 b = 0.0132 Linear function: slope = 0.0212 b = 0.0132 A linear function can be used to model the degradation of the RT over time ½ day 6 days Variability of errors increases linearly with the number of instances of the test data set It is important to train the model again after a period of time Linear function: we could easily determine a trade-off between error & frequency of model updates. 97%  84%

16. Results 2: Path Quality 16

17. Comparison of TS learning algorithms Time series analysis can be used to predict future End-to-End quality values? 4 classification algorithms : Support Vector Machines (SVM) k-Nearest Neighbors (KNN) Regression Trees (RT) Rule-Based Regression (RBR) 4 classification algorithms : Support Vector Machines (SVM) k-Nearest Neighbors (KNN) Regression Trees (RT) Rule-Based Regression (RBR) 17 Data sets: Training: 2016 instances (7 days) Test: 288 instances (1 day) Lag window: last 12 instances Data sets: Training: 2016 instances (7 days) Test: 288 instances (1 day) Lag window: last 12 instances BEST WORST High percentage of success

18. EtEQ Prediction with RBR algorithm How can we reach a satisfactory level of prediction? 18 Same Data sets: Training: 2016 instances (7 days) Test: 288 instances (1 day) Lag window: last 12 instances Same Data sets: Training: 2016 instances (7 days) Test: 288 instances (1 day) Lag window: last 12 instances Some hop paths have high dispersion … but we successfully predict a big percentage of fluctuations

19. EtEQ Prediction with RBR: accuracy Real and predicted values are very close 19 Deviation remains below 0.5 throughout the whole prediction Why ETX error has an increasing trend? (8:00 – 8:30 am)

20. EtEQ Prediction accuracy (day/night) Future work: 2 different predictors (day/night)? 20 Less deviations/errors Data sets: Training: 2016 instances (7 days) Test: 144 instances (1/2 day) Lag window: last 12 instances Data sets: Training: 2016 instances (7 days) Test: 144 instances (1/2 day) Lag window: last 12 instances Day (12 am – 12 pm) Night (12 pm – 12 am) More deviations/errors

21. Motivation Link & End-to-End/Path Quality Prediction in Wireless Community Networks Predicting Topology Control Information (TCI) Improving Prediction Through Social Behaviour Conclusions & Future Work OLSR Outline 21

22. Motivation Several social computing participation strategies use mobile ad hoc or opportunistic networks 22

23. Routing protocols in mobile collaboration scenarios – Must be simple, efficient, reliable and quickly adapt to changes in the network topology – Should minimize delivery of topology control information (TCI) to avoid consuming too much devices’ energy Link-state proactive-routing protocols: – Low latency (using an optimized and known data-path ) – Cost: periodically flooding the network with TCI Motivation … and when the number of nodes is high … 23

24. … can overload the network!!! 24

25. … can we address the problem of delivering much control information through the network? 25

26. Idea: – Use historical Topology Control Information (TCI) to make predictions of the next control packets (CP). Questions to answer: – What performance and limits has this approach? – In which mobile computing scenarios this proposal can provide a real benefit? Predicting TCI using Past Information 26

27. Each node keeps updated locally (in a table) the recent TCI history received from its neighbors. Prediction at each node: – Input: recent TCI history. – Output: a prediction of TCI for each neighbor (guess network topology without delivering control info). Prediction can be done when previous TCI received matches TCI previously stored. HBP predicts a state already appeared in the past. HBP Assumptions 27

28. NS-3 (4 hours) + BonnMotion. Mobility: Random Walk, Nomadic, SLAW. OLSR protocol (HELLO: 2 s / TC: 3 s). 300x300 m open area (beach, park). Free to move/interact. Node devices: All similar (capabilities ≈ iPhone 4). Wi-Fi (detect others & Exchange CI). Range: 80 m / BW ≥ 50 kbps. 10, 20, 30, 40 nodes, randomly deployed. 1 m/s (walking), 2 m/s (trotting), 4 m/s (running), and 6 m/s (bicycling). Experimental Framework 28

29. Results: Predictability Limits %TCI packets appeared in the past 3 mobility models (1 m/s) 10-40 nodes density %TCI packets appeared in the past 3 mobility models (1 m/s) 10-40 nodes density About 80% for 10 nodes High prediction potential About 80% for 10 nodes High prediction potential Prediction capability does not depend on mobility model Prediction limits decrease when node density increases. 29

30. Frequency of Observed Control Packets What control packets appear most frequently? 30% of control packets represent 70% total observed A small subset of packets represent the most delivered A small subset of packets represent the most delivered Does not depend on node density nor mobility models Many opportunities to predict with a small subset of packets. 30

31. History-based prediction (different policies) 31 Always predicts (wrongly) Much better results when using history (even random) History information provides more accurate predictions.

32. Fixed History-Depth vs. Dynamic (tree) 32 Tree minimizes noPred But increases significantly total hits … decreases hits+misses Confidence mechanism + tree = Better results: total hits maximized, few misses.

33. Motivation Link & End-to-End/Path Quality Prediction in Wireless Community Networks Predicting Topology Control Information (TCI) Improving Prediction Through Social Behaviour Conclusions & Future Work OLSR Outline 33

34. 34 Improving Prediction through Social Behavior In a real scenario, wireless nodes ≈ smartphones Current research topic: Incorporate social behavior in prediction process Does this new factor improve results? Is this “social behavior” real or just virtual? Current research topic: Incorporate social behavior in prediction process Does this new factor improve results? Is this “social behavior” real or just virtual? Focus of our analysis: People mobility (guided visit in a museum) Time factors (day/night, workdays/weekend) Focus of our analysis: People mobility (guided visit in a museum) Time factors (day/night, workdays/weekend)

35. Motivation Link & End-to-End/Path Quality Prediction in Wireless Community Networks Predicting Topology Control Information (TCI) Improving Prediction Through Social Behaviour Conclusions & Future Work OLSR Outline 35

36. Time series analysis is a promising approach to accurately predict LQs in Community Networks All algorithms achieved high percentages of success with average MAE per link between 2.4% and 5% when predicting the next value of EtEQ, being the Rule-Based Regression the best one. The error variability is similar for 3 of the algorithms: RT, RBR, SVM. – kNN performs worse due to outliers with larger errors. OLSR Link/Path Q Prediction: Conclusions 36

37. OLSR Future Work To extend this analysis to other community networks to evaluate if the observed behavior can be generalized. To identify which paths contribute most to the errors in the PQ prediction and to understand what factors make it more difficult to predict them. To study the impact of errors in routing decisions. To study a solution with 2 different predictors: Day/Night. To improve the prediction process by discarding those paths whose relation between PQ and prediction accuracy is above a certain threshold. 37

38. Reduces network traffic and saves energy 50%-80% of packets appeared in the past, HBP upper limits are high for many scenarios Few packets contribute to total packets (high opportunity to predict TCI) At least 30% of correct predictions in a worst-case scenario (many nodes) History-based CI Prediction: Conclusions 38

39. Future Work 1)Analyze in detail all combinations of work scenarios - Considering node density, speed, and mobility patterns 2)Develop more complex confidence mechanisms -and combine prediction approaches -Their benefits can be accumulated? 3)Analyze prediction performance in opp networks involving heterogeneous environments - To address IoT-based solutions. 39

40. Thanks for Your Attention… Questions? 2nd URV Doctoral Workshop in Computer Science and Mathematics, DCSM-2015. November 13th, 2015. Tarragona