1. Impact of Radio Irregularity on Wireless Sensor Networks
Gang Zhou, Tian He, Sudha Krishnamurthy, John A. Stankovic
Computer Science Department,University of Virginia
June 2004

2. Outline Motivation, State of Art and Contributions
Analyze Radio Irregularity
Radio Irregularity Model (RIM)
Impact on Routing and MAC Layer
Solutions for Radio Irregularity
Conclusion and Future Work

3. Motivation
Evidence of radio irregularity of low power wireless devices in physical environment
Need for models to regenerate radio irregularity in simulations
Need for better protocols to address irregularity in running systems

4. State of Art Spherical radio range assumption in current research
Localization, Sensing Coverage, Topology Control
Experiments Related to Radio Irregularity
Deepak Ganesan, etc., “Complex Behavior at Scale: An Experimental Study of Low-Power Wireless Sensor Networks” , UCLA/CSD-TR , 2002
Alberto Cerpa, etc., “SCALE: A Tool for Simple Connectivity Assessment in Lossy Environments”, CENS-TR , 2003
Jerry Y. Zhao, etc., “Understanding Packet Delivery Performance in Dense Wireless Sensor Network”, ACM SenSys, 2003
Alec Woo, etc., “Taming the Underlying Challenges of Reliable Multihop Routing in Sensor Networks”, ACM SenSys, 2003
DOI Concept (our previous work)
Tian He, etc., “Range-Free Localization Schemes in Large Scale Sensor Networks”, MobiCom, 2003

5. Contributions
RIM: a new radio energy model that considers irregularity
Implemented in GlomoSim
Review the impact of radio irregularity on
MAC layer
Routing layer
Solutions to deal with radio irregularity
Symmetric Geographic Forwarding
Bounded Distance Forwarding
Bidirectional Flooding
Learning Function
RTS Broadcast
High Energy Asymmetry Detection

6. Motivation, State of Art and Contributions
Analyze Radio Irregularity
Radio Irregularity Model (RIM)
Impact on Routing Layer
Impact on Routing and MAC Layer
Solutions for Radio Irregularity
Conclusion and Future Work

7. Radio signal properties - 1
Non-isotropic Path Loss: The radio signal from a transmitter has different path losses in different directions.
Figure 1: Signal Strength over Time in Four Directions

8. Non-isotropic Path Loss
Figure 2: Signal Strength Values in Different Directions
Reasons:
Reflection, diffraction and scattering in environment
Hardware calibration differences (non-isotropic antenna gain)

9. Radio signal properties - 2
Continuous variation: The signal path loss varies continuously with incremental changes of the propagation direction from a transmitter.
Figure 2: Signal Strength Values in Different Directions

10. Radio signal property - 3
Heterogeneity: Different nodes have different signal sending powers
(a) One mote with different battery status
(b) Different motes with the same battery status
Reasons:
Different battery status
Different hardware calibration

11. Motivation, State of Art and Contributions
Analyze Radio Irregularity
Radio Irregularity Model (RIM)
Impact on Routing Layer
Impact on Routing and MAC Layer
Solutions for Radio Irregularity
Conclusion and Future Work

12. Figure 4: Degree of Irregularity
RIM - DOI
Degree of Irregularity (DOI):
Definition: the maximum received signal strength percentage variation per unit degree change in the direction of radio propagation.
Account for non-isotropic path loss
Figure 4: Degree of Irregularity

13. RIM - VSP Variance of Sending Power (VSP):
Definition: the maximum percentage variance of the signal sending power among different devices.
Account for heterogeneous sending power

14. RIM – propagation formula
Signal receiving power = signal sending power - path loss + fading
Signal receiving power = signal sending power – DOI adjusted path loss + fading
DOI adjusted path loss = path loss* KD
Signal receiving power = VSP adjusted signal sending power –
DOI adjusted path loss + fading
VSP adjusted signal sending power =

15. Motivation, State of Art and Contributions
Analyze Radio Irregularity
Radio Irregularity Model (RIM)
Impact on Routing and MAC Layer
Solutions for Radio Irregularity
Conclusion and Future Work

16. Analyze the Impact Impact on: Path-Reversal technique
Multi-Round technique
Used in AODV, DSR, LAR
Figure 6: Route Discovery Using Multi-Round Technique
Figure 5: Impact on Path-Reversal Technique

17. Figure 7: Impact on Neighbor Discovery Technique
Analyze the Impact
Impact on:
Neighbor-Discovery technique
Used in GF, GPSR, SPEED
Figure 7: Impact on Neighbor Discovery Technique

18. Simulation Configuration
Components
Setting
Simulator
GloMoSim
Terrain
(150m,150m)
Node Number
100
Node Placement
Uniform
Payload Size
32 Bytes
Application
Many-to-one CBR streams
Routing Protocol
AODV, DSR, GF
MAC Protocol
CSMA, (DCF)
Radio Model
RIM
Radio Bandwidth
200Kb/s
Runs
140
Confidence Intervals
The 95% confidence intervals are within 0~25% of the mean

19. E2E Loss Ratio GF has rapidly increasing E2E loss ratio
Increase DOI
Increase VSP
GF has rapidly increasing E2E loss ratio
AODV and DSR have low E2E loss ratio

20. Average E2E Delay GF has constant E2E delay
Increase DOI
Increase VSP
GF has constant E2E delay
AODV and DSR have increasing E2E delay

21. # of Control Packets GF has constant # of control packets
Increase DOI
Increase VSP
GF has constant # of control packets
AODV and DSR have increasing # of control packets

22. Energy Consumption GF has decreasing energy consumption
Increase DOI
Increase VSP
GF has decreasing energy consumption
AODV and DSR increasing energy consumption

23. Motivation, State of Art and Contributions
Analyze Radio Irregularity
Radio Irregularity Model (RIM)
Impact on Routing and MAC Layer
Solutions for Radio Irregularity
Conclusion and Future Work

24. Solutions Symmetric Geographic Forwarding
Bounded Distance Forwarding
Bidirectional Flooding
Learning Function
RTS Broadcast
High Energy Asymmetry Detection
Solutions
Symmetric Geographic Forwarding
Detect and block asymmetric channels
Only use symmetric channels for geographic forwarding
Implementation: Add all neighbors’ IDs in beacon messages
Optimization: estimate the channel quality statistically
Currently implemented in a tracking system [MobiSys 2004]

25. SGF --- E2E Loss Ratio SGF has constantly low E2E loss ratio
Increase DOI
Increase VSP
SGF has constantly low E2E loss ratio

26. SGF --- Average E2E Delay
Increase DOI
Increase VSP
SGF has almost constant E2E delay

27. SGF --- # of Control Packets
Increase DOI
Increase VSP
SGF has the same # of control packets as that of GF

28. SGF --- Energy Consumption
Increase DOI
Increase VSP
SGF has a little increasing energy consumption

29. Bounded Distance Forwarding
Bounded Distance Forwarding restricts the distance over which a node can forward a message in a single hop.
An add-on rule
Tested in a running system with 60 MICA2 motes
Figure 7: Percentage of Reporting Nodes

30. Motivation, State of Art and Contributions
Analyze Radio Irregularity
Radio Irregularity Model (RIM)
Impact on Routing and MAC Layer
Solutions for Radio Irregularity
Conclusion and Future Work

31. Conclusion - 1 The first effort to bridge the gap:
between isotropic radio energy models assumed by most simulators in WSN and the real non-isotropic radio properties

32. Conclusion - 2
Review the impact of radio irregularity on Routing and MAC layers
Radio irregularity has a greater impact on the routing layer than on the MAC layer.
Routing protocols, such as AODV and DSR, that use multi-round discovery technique, can deal with radio irregularity, but with high overhead.
Routing protocols, such as geographic forwarding, which are based on neighbor discovery technique, are severely affected by radio irregularity.
Solutions for radio irregularity
SGF has as low loss ratio as that of AODV and DSR, but much lower control overhead and energy consumption.

33. Future work To evaluate and further refine the RIM model
Experiments in more types of environments
Experiments with different types of devices and different types of antennas
Radio pattern variation with system aging and environment changes
Analyze the impact of radio irregularity on other protocols
Localization, Sensing Coverage, Topology Control
Analyze and evaluate the remaining four solutions
Bidirectional Flooding
Learning Function
RTS Broadcast
High Energy Asymmetry Detection

34. Thanks to the MobiSys Shepherd and anonymous reviewers for their valuable criticisms!
The End!