Supporting Topographic Queries in a class of Networked Sensor Systems

1. Supporting Topographic Queries in a class of Networked Sensor Systems M. Singh and V. K. Prasanna Department of Computer Science University of Southern California {mitalisi, prasanna} @usc.edu

2. Outline • Topographic Querying • System Model • Various approaches • Simulations • Discussion

3. Topographic Querying • Process of extracting data from a sensor network for understanding the graphic delineation of features of interest in the terrain • For example, • Boundary of regions with temperature 26 >T ≥ 28 • Number of regions with T > 28 • Average temperature value in the largest region with T >29

4. Agriculture – Vineyards Groundwater Quality Control Topographic analysis of factors determining quality and composition of wine • Soil moisture, temperature • Nutrition status of vine –NO2 • State of maturity at harvest • Endogenous disease status (virus and phytoplasmas) • Nitrate monitoring • Measuring soil moisture • Conductivity analysis (at various depths) Target Detection Target signal amplitude over a field – peaks represent targets Habitat Monitoring Resource Monitoring Energy map Node failures Plant species distribution Utilization by wildlife Breeding patterns of birds Application Scenarios

5. Definitions (1) • Feature • A property of sensor data that is of interest to the end user • Example: a feature could correspond to the property “temperature >30” • A node can locally determine whether it satisfies a given feature • Number of features is constant • Feature node • A sensor node that satisfies a property (feature) of interest to the end user • Feature value • Actual value of the sensor data used to determine whether it is a feature node or not T=10 T=10 T=20 T=15 T=10 T=8 T=10 T=40 T=50 Feature Nodes Feature: Temperature >30 Feature value: 50, 40

6. ∆ Region 1 Region 2 Region 3 Definitions (2) • G-connected • Geographically contiguous • Partition network into grid cells of size ∆×∆ • Nodes in same or (NSWE) neighboring cells • ∆ is user-specified (map resolution) • Feature region • Geographically contiguous area with same feature • Connected components in graph G • G = (V, E) • Vertices represent sensor nodes • Edge( u, v ) = 1 iff • Node u and v store same feature • They are G-connected Features Temp >30 Temp <10

7. Topographic Queries (1) • Global aggregate queries • Gather global information about feature regions • Count the number of feature regions in the system corresponding to a given feature • Feature: signal strength above a threshold • Goal: Counting targets in a field • Identify the feature region for a given feature that contains the largest number of sensor nodes • Feature: battery power above a threshold • Goal: Identifying resource-surplus regions

8. Topographic Queries (2) • Region specific queries • Assumes individual regions can be addressed • Example Queries • Compute the boundary of a specific region in the system • Feature : nitrate content above threshold • Goal: contamination detection and prevention • What is the average feature value in the largest feature region in the system • Feature: soil moisture content • Goal: irrigation control

9. Challenges • Extract information about feature regions not specific locations • location-specific route optimization are not useful • state-less protocols result in large communication • Nontrivial computation is involved in resolving these queries • e.g., identification and labeling of feature regions • simple data aggregation techniques cannot be used to solve these problems

10. System Model (1) • n homogenous, uniquely identifiable sensor nodes uniformly distributed in a two-dimensional terrain • Each sensor node has a processor, memory, radio and local clock • Connectivity graph • Vertices represent sensor nodes • Edge(u, v) = 1 iff the two vertices are within radio range r (neighbors) • Symmetric and connected graph • Cv denotes degree of node v • (# neighbors) Dense, uniform node distribution r n sensor nodes fixed transmission range r

11. Unicast r r Broadcast Transmitter Receiver System Model (2) • Communication using asynchronous message passing between sensor nodes • No global clock • Two types of messages • Broadcast • Transmission to be received by all neighbors • Unicast • Transmission to be received by a single neighbor

12. Unicast r r Broadcast Transmitter Receiver Collision System Model (3) • Shared wireless channel • Concurrent transmission from two or more neighbors results in collision at the receiver • Channel access mechanism • Communication in conflict free rounds • Slot reservation, localized TDMA • Reliable delivery of messages

13. Energy and Time Costs For time analysis, we assume a global (logical) clock GlobalClock 1 clock cycle = 1 time step Communication Costs (per packet) Energy: E units at transmitter and at each receiver Time:T time steps Uniform cost for transmission and reception

14. Query Resolution Techniques • Localized approach • No global state maintained • State-full approaches • Assume prior identification and labeling of feature regions • Forest of trees (IPSN 2003) • Topographic Map (ASWAN 2004) Example query: Count the number of feature regions in the network for a user specified feature

15. 1/2 n 1/2 n Localized Approach • Does not assume prior labeling or identification of feature regions • Query Resolution • Query node broadcasts the query • On receiving a new query • a sensor node rebroadcasts the query • If its data satisfies the queried feature it sends a response to the query node • Data aggregation cannot be performed • A sensor node cannot determine locally whether two responses belong to the same region • All processing is performed at the query node Time: Ω (n1/2/r +f) Energy: Ω (n + f. n1/2/r) Memory: Ω (f) f=O(n) f = number of feature nodes, r = radio range, n = number of nodes

16. II. Forest of Trees • Assumes prior identification and labeling of feature region • A tree is constructed and maintained for each feature region • Min id node in region is selected as root (and region label) • Region specific aggregates (sum, max, min) maintained by the root • Query Resolution • Query is flooded in network (as before) • Only root nodes satisfying the feature respond Time: Ω (n1/2/r +F) Energy: Ω (n + F. n1/2/r) Memory: Ω (1) F=O(n) F = number of feature regions, r = radio range, n = number of nodes

17. III. Topographic Maps (1) • Assumes prior identification and labeling of feature regions in the network • Hierarchical infrastructure • Network is partitioned into single hop clusters • Each node has a unique clusterhead • Clusterheads organize into blocks • Blocks consist of 2×2 clusters • One of the 4 clusterheads is the block leader • Recursively block formation • Smaller blocks organize into larger blocks • A unique blockleader elected for each block • At kth iteration block consists of 2k×2k clusters • Top most block leader is called the root

18. III. Topographic Maps (2) • Each feature region is labeled • by the min id node in the region • Multi-resolution information storage • Every sensor node • Label of the region it belongs to • If it’s the min id node in the region • Feature aggregates for the entire region • Block leaders and clusterheads • For each feature in the overseen block • Max/min/avg of the feature value • Number of feature nodes and regions • Root • System-wide aggregates about each feature • e.g., number of regions in the network

19. III. Topographic Maps (3) • Query resolution • Query is routed to the root node • Root locally determines the number of feature regions for the queried feature • Root routes a response to the query node State maintenance overheads are amortized over subsequent queries

20. Network level simulations to validate our high level analysis • High level analysis • Simplifying assumptions • Conflict-free channel resolution • Reliable delivery of messages • Simplified energy and time cost functions • Worst-case scenario analysis • Asymptotic analysis – ignoring constants • Network-level simulations • Instantiation of networking protocols • Collisions and packet loss • Latency, buffer flow due to congestion • Real world application scenario • Realistic time and energy evaluation • How many queries amortize the state maintenance overheads

21. Simulations (1) • Application scenario • 1024 sensor nodes • 320 x 320 m terrain • uniform distribution • Feature • Hydraulic conductivity • Node instantiation • MICA mote • Radio range 10.876 m Groundwater Quality Control CENS, 2004

22. Simulations (2) • Simulations were performed using GloMoSim • Two ray path loss model • 802.11MAC protocol • Reliable unicasts and unreliable broadcasts • GPSR routing • Time • No global time synchronization –randomly offset clocks • Integrating computation delay using sleep functions • Energy • Communication: number of packets transmitted/received • Computation : scaling computation time to estimate energy • Random node failures • Soft state to detect node failures

23. Simulation Results

24. Discussion • Contributions of our work • Compared various techniques for resolving topographic queries • Demonstrated the advantages of adopting a “state-ful” approach towards designing algorithms for sensor systems • Discussed an efficient algorithm for constructing a topographic map in sensor systems in [ASWAN 2004] • Future work • Robust maintenance of topographic maps in presence of node failures • Implement the paging channel • Support for low cost updates of the maps as the if feature values change without requiring reconstruction from scratch