MWM: Map-based World Model for Wireless Sensor Networks

1. MWM: Map-based World Model for Wireless Sensor Networks Abdelmajid Khelil, Faisal Karim, Brahim Ayari, Neeraj Suri AUTONOMICS’ 08, Turin, Italy

2. World model @ Sink How to convert raw data into information? World model @ Network Wireless Sensor Networks (WSN): Bridge to Physical World Alarm Users, Admins.. User info. Event Query Sink: If report(s) received  fire  notify user Else: no fire Sink App. info. Update model Query model Sensor nodes: If (avg)temp > threshold report fire Else: no report Example: Detect forest fires Sensor network Create model Change world Deploy wireless battery-powered nodes with temperature sensors Raw data Raw data Raw data Physical world .. Independent from raw data, application and users!

3. Three Main System-level Design Paradigms • WSN as Network • Inherent node redundancy  Convergecast, filtering • Limited resources • Cross-layer • WSN as Database • Query dissemination • In-network aggregation • E.g. tinyDB • WSN as Event Service • Nodes provide/consume services • E.g. pub/sub WSN Query Result Abstraction level  These paradigms still address single sensor nodes and ignore spatial correlation of sensor readings  less accepted

4. Problem Statement and Objectives • Widely Accepted Abstraction Level is Needed • How to convert sensor data into information which is: Understandable, contextual, interactive and actionable. • Abstraction Should Consider • Inherent spatial correlation of sensor readings (Inherent node redundancy in WSN) • Requirements • Generalized • Unified incorporation of • Physical world and • Network world • Frugal and lightweight (creation, management etc.)  Our Approach: Map-based World Model (MWM)

5. Outline • System Model • Map-based World Model • Design Methodology • Two Case Studies • Detecting and predicting fires • Predicting network partitioning • Related Work • Conclusions

6. System Model • Nodes • Large number of static resource-limited sensor nodes (SNs): Motes.. • A few static powerful sinks • A few mobile resource-moderate assist nodes (ANs): PDAs, robots.. • Nodes Know their Own Geographic Position • Clocks are Synchronized • Nodes Functionality • SNs create the model • ANs manage the model • Sinks represent operator(s) SN AN

7. The MWM Approach • Appropriately Group Spatially-Correlated Readings into Regions and Maps • Maps • Natural way to represent the physical world (spatio-temporal data) • Efficient techniques exist • MWM: A Set of Relevant Maps • User maps (uMAP), e.g., temperature map • Network maps (nMAP), e.g., map of residual energy Region border nodes

8. Existing Map Construction Algorithms • The eScan Approach [1] • Map-construction along the aggregation-tree • Map is partial at SNs & complete at sink • Data with low time validity (chemicals etc.) • The Isoline Approach [2] • Local flood to label border nodes • Map is partial at SNs & complete at sink • Data with low time validity • The gMAP Approach [3] • AN collects data and construct map • Map at AN • Data with high time validity (energy etc.) [1] Y. Zhao et al. Residual Energy Scan for Monitoring Sensor Networks. In IEEE WCNC, 2002. [2] I. Solis and K. Obraczka. Isolines: Energy-efficient Mapping in Sensor Networks. In ISCC, 2005. [3] A. Khelil et al. gMAP: An Efficient Construction of Global Maps for Mobility-Assisted WSN, TR, 2007.

9. The MWM Architecture • Main Idea: Address Regions Instead of Nodes • Architecture Retains Existing Abstractions • Substitute node by a region • TinyDB (database) • Pub/sub (service) • Cross layer (network) • Architecture Simplifies • Design of application, • Design of network • Etc.

10. event attr1 > th1 attr2 > th2 event Queries and Events in MWM • Queries • SQL-like language, query regions instead of sensor nodes • Example: SELECT region, temp FROM tempMAP WHERE temp > threshold • Trade-offs: • Centralized vs. decentralized MWM • Pro-active vs. reactive regioning • Query dissemination [1] • Events • Event: Predicate P(attr1, .. attrk), attri of mapk, e.g., attr1 > th1 • Event composition ≡ geometric operation, e.g., attr1 > th1 & attr2 > th2 [1] R. Sarkar et al. Iso-Contour Queries and Gradient Routing with Guaranteed Delivery in Sensor Networks. infocom’08.

11. MWM-based WSN Design Methodology (Geometric) abstraction level acceptable by users, application designers and network developers  Simplifies requirement engineering, debugging, standardization etc. Step 1: Identify situations and events of interest (Geometric) Step 2: Identify the required maps (MWM) and define events and their operations in MWM (Geometric) Step 3: Sketch a solution assuming global MWM (Geometric) Step 4: Distribute the required MWM knowledge on nodes (Geometric) Step 5: Select requisite communication primitives

12. fire fire Case Study 1: Detecting and Predicting Fires Step 1: Fire and pre-fire regions Step 2: Temperature map. Step 3: Fire-temp threshold, pre-fire-temp threshold, regions report to sink Step 4: Border nodes report position and temp value Step 5: Local flood for isoline construction. Each border node unicasts to sink (Not all sensor nodes are illustrated) Existing techniques [1][2] do not • Provide for prediction • Deliver fire perimeter [1] M. Hefeeda et al. Wireless Sensor Networks for Early Detection of Forest Fires. In MASS, 2007. [2] D.M. Doolin et al. Wireless Sensors for Wildfire Monitoring. In SPIE, 2005.

13. Case Study 2: Predicting Network Partitioning Step 1: Predict coverage drops and isolated regions Step 2: Starting with connected network we require the residual energy map Step 3: Regions of weak energy should report to sink; Sink predicts partitioning Step 4: Border nodes report position and energy value Step 5: Local flood for isoline construction; Each border node unicasts to sink (Not all sensor nodes are illustrated) Existing techniques [1][2] do not • Provide for prediction • Provide important details (partition shape etc.) • Support all shapes/types of partitions [1] N. Shrivastava et al. Detecting Cuts in Sensor Networks. In IPSN, 2005. [2] K.P. Shih et al. PALM: A Partition Avoidance Lazy Movement Protocol for Mobile Sensor Networks. In WCNC, 2007.

14. Predictive Monitoring and Pro-active Reconfiguration • Predictive Monitoring of both Physical and Network Worlds • Combine (map) data from spatial and temporal domains • Event prediction • Pro-active Network Reconfiguration • Examples: Node displacement • To provide self-healing and graceful degradation • - E.g., by delaying network partition • MWM simplifies • Spatial intervention • Event-triggered autonomous reconfiguration  Predictability and pro-activeness enhance system autonomicity

15. Related Work • Modeling Technique in WSN • Network models, simulation models etc.: Complex and domain-specific • Geographic Information Systems (GIS) and spatial temporal databases • Modeling languages: SensorML, REACTIVEML and LUSSENSOR  MWM specification • Existing Real World Models • Context-awareness models: Complex, rely on powerful infrastructure, and involve user. • Sentient computing: Focus on indoor scenarios • Augmented and virtual reality models • Real-world models in autonomic computing • All models are „embedded“ in the infrastructure ; We argue for a model distribution • All models dynamically involve the user

16. Conclusions • The Ongoing Evolution of the Web Map • Interoperability/standardization between WSNs: SensorWeb, SensorGrid etc. • Enhances autonomicity of sensing and reacting • Implementation in OMNET++ simulator • Maps Provide a Widely Accepted Abstraction • We Developed Map-based System Architecture for WSNs • Unified Model for Both Physical and Network Worlds • Powerful Tool for Both Design and Deployment • A novel design methodology • Two case studies WSN 1 WSN 2 WSN 3 WSN 4 Geographic map Queries events

17. Thanks for your attention! Abdelmajid Khelil, Faisal Karim Shaikh, Brahim Ayari, Neeraj Suri Department of Computer Science TU Darmstadt, Germany {khelil, fkarim, brahim, suri}@informatik.tu-darmstadt.de