1. Reducing Energy Consumption in Human- centric Wireless Sensor Networks The 2012 IEEE International Conference on Systems, Man, and Cybernetics October 14-17, 2012, COEX, Seoul, Korea Roc Meseguer 1, Carlos Molina 2, Sergio F. Ochoa 3, Rodrigo Santos 4 1 Universitat Politècnica de Catalunya, Barcelona, Spain 2 Universitat Rovira i Virgili, Tarragona, Spain 3 Universidad de Chile, Santiago, Chile 4 Universidad Nacional del Sur, Bahia Blanca, Argentina

2. Motivation Potentiality OLSRp Conclusions & Future Work OLSR Outline

3. Motivation

4. Human-Centric Wireless Sensor Networks (HWSN) oppnet that uses mobile devices to build a mesh Human-Centric Wireless Sensor Networks (HWSN) oppnet that uses mobile devices to build a mesh

5. Human-centric Sensor Wireless Networks: – Need for maintaining network topology – Control messages consume network resources Proactive link state routing protocols: – Each node has a topology map – Periodically broadcast routing information to neighbors Motivation … but when the number of nodes is high …

6. … can overload the network!!!

7. 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

8. … can we increase scalability of routing protocols for Human-centric Wireless Sensor Networks? …

9. Data per query × Queries per second →constant – For routing protocols: D = Size of packets Q = Number of packets per second sent to the network We focus on Q: – Reducing transmitted packets – Without adding complexity to network management HOW? OLSR DQ principle PREDICTING MESSAGES !!!!

10. – Called OLSRp – Predicts duplicated topology-update messages – Reduce messages transmitted through the network – Saves computational processing and energy – Independent of the OLSR configuration – Self-adapts to network changes. We propose a mechanism for increasing scalability of HWSN based on link state proactive routing protocols

11. Potentiality

12. NS-2 & NS-3 Grid topology, D = 100, 200, … 500 m 802.11b Wi-Fi cards, Tx rate 1Mbps Node mobility: Static, 0.1, 1, 5, 10 m/s Friis Prop. Model ICMP traffic OLSR control messages: – HELLO=2s – TC=5s OLSR Experimental Setup

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

14. OLSR Control Information Repetition Number of nodes does not affect repetition

15. Density of nodes slightly affects repetition OLSR Control Information Repetition

16. Repetition is mainly affected by mobility OLSR Control Information Repetition

17. OLSR Control Information Repetition Repetition still being significant for high node speeds

18. OLSRp

19. 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

20. 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 if (TC[n]=TC[n-1]): TC OLSRp  TC OLSR else: TC Wifi  TC OLSR if MPR  if(TC[n]=TC[n-1]): TC OLSRp else: TC OLSR  TC Wifi

21. 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)

22. NS-2 Physical area of 200m X 200m 25 stationary nodes & 275 mobile nodes Nodes are randomly deployed (11 simulations) All nodes assume IPhone 4 features Mobile nodes assume: random mobility and walking speed (0.7m/s) Wifi Channel assumes Friis Propagation loss model OLSR control messages: HELLO=2s & TC=5s Data traffic assumes UDP packets transmitted every second OLSR Experimental Setup

23. OLSR OLSRp: Benefits Reduction in energy consumption

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

25. Conclusions & Future Work

26. OLSR Conclusions & Future Work Conclusions: – OLSRp has similar performance than standard OLSR – Can dynamically self-adapt to topology changes – Reduces network congestion – Saves computer processing and energy consumption Future Work: – Further evaluation of OLSRp performance – Assessment in real-world testbeds – Application in other routing protocols

27. Questions? Thanks for Your Attention The 2012 IEEE International Conference on Systems, Man, and Cybernetics October 14-17, 2012, COEX, Seoul, Korea

28. Questions? The 2012 IEEE International Conference on Systems, Man, and Cybernetics October 14-17, 2012, COEX, Seoul, Korea

29. ANEXOS

30. OLSR OLSRp: Example B B B B E E

31. OLSR OLSRp: Example B B B B E E NODE D TABLE

32. OLSR OLSRp: Example B B B B E E NODE D TABLE X X X X X X X X

33. OLSR OLSRp: Example B B B B E E NODE D TABLE X X X X X X X X

34. OLSR OLSRp: Example B B B B E E NODE D TABLE X X X X X X X X

35. OLSR OLSRp: Other Results

36. OLSR OLSRp: Other Results

37. OLSR OLSRp: Other Results