1. Using a History-Based Approach to Predict Topology Control Information in Mobile Ad Hoc Networks 7 th Int. Conf. on Internet and Distributed Computing Systems, IDCS 2014 September 24 th, 2014. Calabria, Italy Pere Millán 1, C. Molina 1, R. Meseguer 2, S. F. Ochoa 3, R. Santos 4 1 Universitat Rovira i Virgili, Tarragona, Spain 2 Universitat Politècnica de Catalunya, Barcelona, Spain 3 Universidad de Chile, Santiago, Chile 4 Universidad Nacional del Sur, Bahia Blanca, Argentina

2. Motivation Predicting Topology Control Information (TCI) Experimental Framework & Results Conclusions & Future Work OLSR Outline 2

3. Motivation 3

4. Several social computing participation strategies use mobile ad hoc or opportunistic networks 4

5. 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 … 5

6. … can overload the network!!! 6

7. … can we address the problem of delivering much control information through the network? 7

8. – Extends the previous work on TCI prediction [6-8]. – Uses a time window with historical node TCI info … … to predict next TCI. – Named “History-Based Prediction” (HBP). – HBP Performance: determined by simulations in several mobile scenarios. We present and evaluate a new strategy for predicting TCI in mobile ad hoc and opportunistic networks 8

9. Predicting TCI 9

10. 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 10

11. 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 11

12. Control Packets sequence: AAAABABAACBAABBAB Table contents (patterns with 2 control packets) : HBP table example PatternNextCountLast AA ABCABC 221221 # AB ABAB 2121 # AC B1# BA ABAB 2222# BB A1# CBA1# 12

13. Unbounded – More flexibility to identify movement patterns. One table per node. Movement pattern (stored in the table): – Sequence of 1+ TCI packets seen in the past. Attached to every pattern stored in the table: – A list of all packets appeared after each pattern. – Statistical information: last packet, most frequent. HBP tables with historic information 13

14. 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 14

15. Results 15

16. To help us understand predictability and prediction opportunity limits of our proposal. Maximum reachable prediction accuracy: – Count if a certain TCI packet has ever appeared in the past. – If it appeared once, we assume it could be predicted. We quantify TCI repetition over time 16

17. 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. 17

18. We also analyze the representativeness of the most-frequent packets, with respect to the whole set of packets received by a node over time. This will give us: – A first understanding about how difficult is to make right predictions. – And which is the amount of historical data that must be tracked to make these predictions. Packet representativeness 18

19. 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. 19

20. In case of wrong predictions (miss), classification: – It could be correctly predicted, if the right control packet was in the list of this pattern (missPred). – If not, it could not be predicted (missNoPred). This identifies the limits of HBP and how far an approach is from the best. Types of wrong predictions 20

21. History Depth (HD) metric: – Number of TCI packets in the movement patterns. HD range considered: – 0 to 5 TCI packets. High HD values (long sequences): – More accurate predictions, few opportunities to predict. Low HD values (short sequences): – Less accurate predictions, more opportunities to predict. History Depth 21

22. History-based prediction (varying HD) 22 HD HD=0: largest %hits But important %misses too Large HD: - Predictions + Accurate. Large HD: - Predictions + Accurate. noPred increases with number of nodes (predictability limits)

23. Last value  last packet seen after this pattern. Most-frequent  highest count packet. History-based Random  any past packet seen after this pattern. Previous example: AAAABABAACBAABBAB HBP flavors PatternNextCountLast AA ABCABC 221221 # AB ABAB 2121 # AC B1# BA ABAB 2222# BB A1# CB A1# Last value: B Most frequent: A History-based random: any of A/B Pure random: any of A/B/C 23 ?

24. History-based prediction (different policies) 24 Always predicts (wrongly) Much better results when using history (even random) History information provides more accurate predictions.

25. History-based prediction (different mobility) 25 Similar behavior (difference <10%) Mobility models do not present significant differences in prediction capability.

26. HBP flavors must succeed in the predictions but also not predict when success is not guaranteed. – Success reduces network traffic and saves energy. – Wrong predictions can skew the network topology map and decrease the reliability of the process. We include a confidence mechanism to determine the likelihood that a prediction is correct. – Aim: maximize right and minimize wrong predictions. Prediction confidence 26

27. Simple confidence mechanism for HBP: – Saturated counter for each pattern in history table. – 2-bit counter (values range: 0 to 3). – Counter incremented when the prediction is right. – Counter decremented when the prediction is wrong. – Prediction is confident when counter ≥ 2. – Counter initialized as 1 (no confidence). Implementing confidence 27

28. History-based prediction (2-bit confidence) 28 Our goal: Maximize noConf/miss Minimize noConf/hit Our goal: Maximize noConf/miss Minimize noConf/hit Using confidence: Less predictions (mainly hits, few misses) Using confidence: Less predictions (mainly hits, few misses) Using a confidence mechanism we can minimize prediction errors. Using a confidence mechanism we can minimize prediction errors.

29. Previous HBP flavors use fixed History Depth (HD). We analyze an additional HBP flavor where History Depth is dynamic (prediction tree): – Start the prediction with the largest HD. – If prediction is not possible (not confident or missing movement pattern), decrease HD value (shorter pattern), and repeat. – Repeat until prediction is possible or HD reaches 0. Dynamic history depth (tree) 29

30. Fixed History-Depth vs. Dynamic (tree) 30 Tree minimizes noPred But increases significantly total hits … decreases hits+misses Confidence mechanism + tree = Better results: total hits maximized, few misses.

31. 31 Conclusions Future Work

32. 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) OLSR History-based Prediction: Conclusions 32

33. OLSR with prediction [6] Assume the last TCI send by a node will probably be repeated during the next round of information delivery (Last value, HD=0) – Less amount of control packets transmitted – Saves computational processing and energy – Independent of the OLSR configuration – Self-adapts to network changes – Medium density scenario: 57% control packets reduction, 60% energy reduction, with same OLSR performance OLSRp 33

34. OLSR 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. 34

35. Questions? Thanks for Your Attention 7 th Int. Conf. on Internet and Distributed Computing Systems, IDCS 2014 September 24 th, 2014. Calabria, Italy

36. Questions? 7 th Int. Conf. on Internet and Distributed Computing Systems, IDCS 2014 September 24 th, 2014. Calabria, Italy

37. ANEXOS 37

38. 38

39. OLSR OLSR: Control Traffic and Energy Traffic and energy do NOT scale !!! OLSR is one of the most intensive energy-consumers OLSR is one of the most intensive energy-consumers 39

40. OLSR TC vs HELLO OLSR: Messages distribution Ratio of TC messages is significant for low density of nodes 40

41. 41 OLSRp

42. Prevent MPRs from transmitting duplicated TC throughout the network: OLSR OLSRp: Basis – Last-value predictor placed in every node of the network – MPRs predicts when they have a new TC to transmit – The other network nodes predict and reuse the same TC – 100% accuracy: If predicted TC ≠ new TC  MPR sends the new TC – HELLO messages for validation The topology have changed and the new TC must be sent The MPR is inactive and we must deactivate the predictor 42

43. Upper Levels Lower Levels OLSR Input OLSR Output Wifi Input Wifi Output TC Wifi  TC OLSR if MPR: TC OLSR  TC Wifi OLSR OLSRp: Layers Upper Levels Lower Levels OLSR Input OLSR Output OLSRp Input OLSRp Output Wifi Input Wifi Output TC Wifi  TC OLSR else: TC OLSRp  TC OLSR (TC[n]=TC[n-1]) if MPR  if(TC[n]=TC[n-1]): TC OLSRp else: TC OLSR  TC Wifi 43

44. OLSR OLSRp: Basis – Each node keeps a table whose dimensions depends on the number of nodes – Each entry records info about a specific node: The node’s @IP The list of @IP of the MPRs (O.A.) that announce the node in their TCs and the current state of the node (A or I). (HELLO messages received). A predictor state indicator for MPR nodes (On or Off): – On when at least one of the TC that contains information about the MPR is active – Off when the node is inactive in all the announcing TC messages (new TC message will be sent) 44

45. OLSRp: experimental framework 45

46. OLSR OLSRp: Benefits Reduction in energy consumption 46

47. OLSR OLSRp: Benefits Reduction in control traffic & CPU processing 47

48. OLSR OLSRp: Benefits Delivery percentage is not affected 48