1. Network Dynamics Deepak Ganesan, Alec Woo, Bhaskar Krishnamachari

2. Motivation Why do sensor network algorithms not behave as expected? Distinguish the effect of density, scale and wireless connectivity Example Algorithms – Flooding based algorithms are intuitively appealing because of their robustness Beacon-based Routing : Flood the network and construct a tree using the reverse flooded path. Use this path to draw data from sensor nodes to base station Diffusion Routing : Flooding is the basic approach to setting up source-sink paths. Good reverse shortest paths are reinforced and data is sent along them – Localization Algorithms based on Radio Signal Strength make assumptions about the basic shape of connectivity cell (usually circular) The algorithm then computes target location from the intersection of multiple cells.

3. Deviations from Expected There are two distinct effects that influence algorithms for wireless sensor networks – Vagaries in connectivity (local effects) – Mechanics of Flooding (global effects) We dig deeper, by a set of carefully instrumented, large scale experiments, representative of the key characteristics of sensor networks – Experiment 1: Connectivity Matrix – Experiment 2: Broadcast Flooding

4. Understanding Connectivity Motivation: – Construct a complete 2D map of packet loss between all pairs of nodes for a wide range of transmit power settings. This map can be used to drive dense sensor network simulations – Look for deviations from normal behavior, that could impact protocol design. Experimental Setup – 13x13 grid of nodes – separation 2ft – flat open surface – Identical length antennas, pointing vertically upwards. – Fresh batteries on all nodes – Identical orientation of all nodes – The region was clean of external noise sources. Experiment Details – Each node transmits at a range of 16 small increments of potentiometer settings (translating to different transmit power). – For each transmit power setting, each node transmits 20 packets. – Receivers log the packets that they have successfully received. – Nodes transmitted one after the other in a token-ring fashion, thereby preventing collisions.

5. Connectivity Radius The connectivity radius of node n is measured as the radius of the circle that encompasses 75% of the nodes that have a “good” link from node n. Defining “Good Link”: A link that has more than threshold T of successful packet reception.

6. Link Symmetry in Wireless Networks The existence of asymmetric wireless links is known. Link asymmetry could be caused by many factors: – Presence of obstacles that do not symmetrically attenuate signals – Asymmetric multipath effects – Antenna directionality and orientation No comprehensive large scale studies of the extent of link asymmetries. We empirically evaluate extent of asymmetry and look at its impact on protocol design

7. Defining thresholds for asymmetric and symmetric links One possible definition is Asymmetric Link: Greater than 65% successful reception in one direction and less than 25% successful reception in the other direction Symmetric Link: Greater than 65% successful reception in both directions A -> B B -> A Frequency

8. Importance of Asymmetric Links Between 10%-25% asymmetric nature of links observed depending on transmit signal strength of nodes. Many asymmetric links are long links, in fact, the number of asymmetric long links is comparable to the number of symmetric long links Why are long links useful? – Beacon-based Routing: Long links can be used to build low-depth routing trees – Diffusion: short routing paths

9. Impact on Protocol Design For topology construction protocols (beacon-based routing), the assumption of symmetric links may be dangerous. – In a tree, an asymmetric link disconnects a sub-tree from the root. Option 1: Discard asymmetric links and use only symmetric ones – As shown earlier, long links that could be put to good use may be the ones discarded. Option 2: Use asymmetric links – This requires rethinking and redesigning many existing algorithms Either way, algorithms have to be designed to be robust to high unidirectionality.

10. Broadcast Flooding

11. Experiment 2: Broadcast Flooding Motivation – Expected Behavior: Tree progresses outwards from the base station, acquiring more nodes along the way – Observed Behavior: Some nodes seem to be attached at a completely different level to the tree from their peers Some nodes have parents that are significantly farther off from the base station than themselves – The deviation from expected behavior is only partially explained by local behaviors (asymmetry, packet loss). The huge redundancy should mostly mitigate the local effects. Experimental Setup – Same as Connectivity Experiment

12. Some observations from Broadcast Flooding Experiments The parameters affecting the flooding were evaluated using four metrics – Extent of clustering – Impact of Collisions Incidence of backward links Extent of Symmetry Correlation between distance from base-station and level of tree

13. Extent of Clustering A large percentage of the nodes have very few children Most clusters are small but a small number of large clusters exist. Most of the links belong to the large clusters These observations are consistent with visualizations where a few nodes have very large number of children.

14. Impact of Cluster Distribution Command Broadcast Trees – High degree of clustering results in “bushy” trees. – Since most nodes are leaves, very few nodes retransmit command Data Gathering Trees – Cluster-heads with many children use more energy listening and processing their children’s data.

15. Collisions have a significant effect Propagating flood leaves stragglers due to hidden terminal effects. As the tree propagates out to the edge of the network, it rebounds from the edge, picking up these stragglers. This effect was seen in many experiments

16. Backward Links Definition: A parent-child link is backward if the parent is farther away from the base- station than its child by at least 2ft. Approximately 10% of links exhibit this behavior corresponding to 10% stragglers.

17. Symmetry Definition: For the discovery experiment, a link between nodes a-b is symmetric if both a hears from b and b hears from b during tree formation. Very high asymmetry observed (70%) as compared to the lower levels of asymmetry observed for the connectivity experiment Reason: When two children that hear a packet (simultaneously) from a parent retransmit, the chances of hidden terminals happening are large. As a result, the parent node may miss many retransmissions.

18. Correlation between distance from base station and tree level The cumulative effect of collisions on the constructed tree can be seen from the graph Around 5% of far away nodes have a lower level than their peers (effect of long links) Around 5-10% of close nodes have much higher level than their peers (stragglers) Effect of long links stragglers

19. Conclusion Understanding local and global network effects, and the effect of their interaction Investigating large-scale sensor network effects – Defining the experiments – data collection mechanisms – metrics