Reducing Energy Consumption in Human-centric Wireless Sensor Networks

1. The 2012 IEEE International Conference on Systems, Man, and Cybernetics October 14-17, 2012, COEX, Seoul, Korea Reducing Energy Consumption in Human-centric Wireless Sensor Networks Roc Meseguer1, Carlos Molina2, Sergio F. Ochoa3, Rodrigo Santos4 1Universitat Politècnica de Catalunya, Barcelona, Spain 2Universitat Rovira i Virgili, Tarragona, Spain 3Universidad de Chile, Santiago, Chile 4Universidad Nacional del Sur, Bahia Blanca, Argentina

2. OLSR Outline • Motivation • Potentiality • OLSRp • Conclusions & Future Work

3. Motivation

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

5. Motivation • 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 … but when the number of nodes is high …

6. … can overload the network!!!

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

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

9. OLSR DQ principle • 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? PREDICTING MESSAGES !!!!

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

11. Potentiality

12. OLSR Experimental Setup • 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

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

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

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

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

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

18. OLSRp

19. OLSR OLSRp: Basis Prevent MPRs from transmitting duplicated TC throughout the network: • Last-value predictorplaced 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 OLSR OLSRp: Layers OLSR Input OLSR Output Upper Levels OLSR Input OLSR Output Lower Levels OLSRp Input OLSRp Output Wifi Input Wifi Output Lower Levels TCWifiTCOLSR if MPR: TCOLSRTCWifi Wifi Input Wifi Output if (TC[n]=TC[n-1]): TCOLSRp TCOLSR else: TCWifi TCOLSR if MPR if(TC[n]=TC[n-1]): TCOLSRp else: TCOLSR TCWifi

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 TCsand 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. OLSR Experimental Setup • 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

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

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

29. ANEXOS

30. E OLSR OLSRp: Example B B

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

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

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

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

35. OLSR OLSRp: Other Results

36. OLSR OLSRp: Other Results

37. OLSR OLSRp: Other Results