Energy-Aware Synchronization in Wireless Sensor Networks

1. Energy-Aware Synchronization in Wireless Sensor Networks Yanos Saravanos Major Advisor: Dr. Robert Akl Department of Computer Science and Engineering

2. Outline • Background on wireless sensor networks • Flooding to create network topology • Existing synchronization algorithms • Reference Broadcast Synchronization (RBS) • Timing-sync Protocol for Sensor Networks (TPSN) • Hybrid algorithms • Flooding • Synchronization • Root node re-election • Results • Conclusions

3. Wireless Sensors • Physically small sensing unit • Battery • Processor • Slow • Drift • Radio/antenna • Sensor modules • Covert • Short battery life

4. Temperature Fire detection Brake usage Humidity Flood detection Pressure Object tracking Animal movement and migrations Vehicle tracking Noise levels Search and rescue efforts Locating a sniper’s position Contamination levels Monitoring pollution levels Chemical/biological agent detection Mechanical stress on supporting structures Applications

5. Wireless Sensor Network (WSN) • Network using many wireless sensors • Dropped from a plane to monitor area • Random placement • Sensors build hierarchical network once deployed

6. Wireless Communication • Signal strength decays over distance • PT: initial power of transmission • d: distance from transmitter • c: path loss coefficient

7. Network Flooding • Broadcast packet from root node • If packet received for the first time • Set Parent on Tree = Source of message • Change Source field to MyId • Increment HopCount field • Rebroadcast packet

8. Network Flooding

9. Motivation for Time Synchronization • Most applications require some synchronization accuracy • Fire and flood tracking • Animal movement • Vehicle movement • Gunshot detection

10. Existing Synchronization Solutions • Global Positioning System (GPS) • Power-hungry • Network Time Protocol (NTP) • Computationally infeasible for wireless sensors • Reference Broadcast Synchronization (RBS) • Receiver-receiver synchronization • Timing-sync Protocol for Sensor Networks (TPSN) • Transmitter-receiver synchronization

11. Reference Broadcast Synchronization • Receiver-to-receiver synchronization • Two stages • Transmitter broadcasts clock time • Receivers exchange observations

12. RBS Synchronization

13. RBS Energy Usage • Given n receivers: • Transmissions grow as O(n) • Receptions grow as O(n2)

14. Timing-sync Protocol for Sensor Networks • Traditional handshake approach • Timestamp at the MAC layer • Two stages • Level Discovery Phase (Flooding) • Synchronization Phase

15. TPSN Model – Level Discovery Phase • Assign root (level 0) node • Broadcast level_discovery packet • Nodes 1 hop away assigned to level 1 • Ignore all subsequent level_discovery packets • Broadcast level_discovery packet …

16. TPSN Model – Synchronization Phase • Each node (A) broadcasts synchronization_pulse • Timestamped at T1 • Node B receives pulse at T2, broadcasts ack at T3 • Node A receives ack at T4 • Δ is clock drift • d is propagation delay

17. TPSN Synchronization

18. TPSN Energy Usage • Given n receivers: • Transmissions and receptions grow as O(n) • Large energy savings over RBS for large n • Less efficient for small n

19. Sources of Packet Delay • Send time: time to create the packet • Access time: delay until channel is accessible • Transmission time: time each bit takes to get onto physical medium • Reception time: time to receive bits off physical medium • Receive time: time to reconstruct packet

20. Uncertainties • Sender uncertainty • RBS removes it completely • Minimized in TPSN by timestamping at MAC layer • Propagation/receiver uncertainties, and relative local clock drifts • TPSN outperforms RBS by factor of 2

21. Accuracy Comparison

22. Hybrid Summary • Complete system for WSN operation • Three stages • Build hierarchical tree with flooding • Transmitters know how many receivers are connected • Periodically synchronize sensors • Re-elect new root when current one dies

23. Hybrid Flooding Algorithm • Broadcast flood_packet from root node • If current_node receives flood_packet • Set parent of current_node to source of broadcast • Set current_node_level to parent’s node level + 1 • Rebroadcast flood with current_node_ID and current_node_level • Broadcast ack_packet with current_node_ID • Ignore subsequent flood_packets • Else If current_node receives ack_packet • Increment num_receivers

24. Hybrid Synchronization • RBS best for small n, TPSN best for large n • Calculate optimal cutoff value to choose RBS or TPSN algorithm (receiver_threshold) • Transmissions and receptions draw different current • where α is reception-to-transmission current ratio

25. Hybrid Synchronization • Equate energies of both RBS and TPSN • Solve equation to find receiver_threshold

26. Mica2DOT architecture TX: 25 mA RX: 8 mA α=0.32 n=4.4 MicaZ architecture TX: 14.0 mA RX: 19.7 mA α=1.41 n=3.4 Reception-to-Transmission Ratio

27. Hybrid Synchronization Algorithm • If num_receivers < receiver_threshold • Transmitter broadcasts sync_request • For each receiver • Record local time of reception for sync_request • Broadcast observation_packet • Receive observation_packet from other receivers • Else • Transmitter broadcasts sync_request • For each receiver • Record local time of reception for sync_request • Broadcast ack_packet to transmitter with local time

28. Hybrid Synchronization

29. Hybrid Root Election Algorithm • If root node’s power allows 1 more TX • Broadcast elect_packet with cur_node_ID • If cur_node_level == 2 and receives elect_packet from root • Broadcast elect_packet with cur_node_ID, cur_node_power • If cur_node receives elect_packet and elect_packet_power >= cur_node_power • Set elect_packet_ID to root node

30. Simulation Results • Two sets of simulations • Change the sensor architecture • Change the number of sensors in network • 1000m x 1000m • Path loss coefficient = 3.5 • 20 networks per simulation • Assume perfect directional antennas • Minimum number of receptions

31. Sensor Synchronization Simulations • Verify the hybrid synchronization algorithm works with several sensor architectures • Run RBS, TPSN, hybrid using optimal receiver_threshold • Run hybrid using non-optimal receiver_threshold values • Change sensor architecture • Used 500 sensors per network

32. Sensor Synchronization Simulations • Mica2DOT • TX: 25 mA • RX: 8 mA • α=0.32 • n=4.4

33. Sensor Synchronization Simulations • MicaZ • TX: 17.4 mA • RX: 19.7 mA • α=1.41 • n=3.4

34. Sensor Synchronization Simulations • Hypothetical • TX: 25 mA • RX: 2.7 mA • α=0.11 • n=6.1

35. Sensor Synchronization Simulations • Hypothetical • TX: 25 mA • RX: 0.7 mA • α=0.03 • n=10.3

36. Synchronization Simulations forVariable Network Size • Verify the hybrid synchronization algorithm works with various network sizes • Run RBS, TPSN, hybrid using optimal receiver_threshold • Run hybrid using non-optimal receiver_threshold values • Change number of sensors deployed in network • Used Mica2DOT architecture

37. Synchronization Simulations for 250 Sensors

38. Synchronization Simulations for 500 Sensors

39. Synchronization Simulations for 750 Sensors

40. Synchronization Simulations for 1000 Sensors

41. Synchronization Simulations for 1250 Sensors

42. Synchronization Simulations for 1500 Sensors

43. Network Size Simulations • Hybrid saves up to 50% over RBS, up to 20% over TPSN • Hybrid is still more efficient in networks favoring either RBS or TPSN

44. Conclusions • Synchronization is necessary for most sensor networks to operate effectively • Both TPSN and RBS synchronize sensor clocks locate origin of gunshot blast • Neither TPSN nor RBS are designed for low energy usage • Hybrid algorithm adapts to any size network and saves energy over other algorithms

45. Future Work • Physical implementation • Localized re-flooding • Non-uniform path loss coefficient • Dropped packet analysis

46. Questions?