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Mote Clusters Thanos Stathopoulos CENS Systems Lab Joint work with Ben Greenstein, Lewis Girod, Mohammad Rahimi, Tom Schoellhammer, Ning Xu, Richard Guy.

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Presentation on theme: "Mote Clusters Thanos Stathopoulos CENS Systems Lab Joint work with Ben Greenstein, Lewis Girod, Mohammad Rahimi, Tom Schoellhammer, Ning Xu, Richard Guy."— Presentation transcript:

1 Mote Clusters Thanos Stathopoulos CENS Systems Lab Joint work with Ben Greenstein, Lewis Girod, Mohammad Rahimi, Tom Schoellhammer, Ning Xu, Richard Guy and Deborah Estrin October 2004

2 Extensible Sensing System V2 A robust building block for distributed wireless environmental monitoring –James Reserve: Habitat Monitoring –Palmdale: Contaminant Transport Monitoring

3 Features of ESS Tiered architecture –Stargates acting as data sinks and mote controllers –Motes acting as sensor data generators Extensibile –Extend data collection function through querying API –Add more motes and stargates to the basic building block –Replicate the basic building block and interconnect the stargates Adaptive –Routing and transport adapts to radio connectivity –Self-configuring topology

4 System Components Control Flow Multihop Pub/Sub Data Service Engine sends a query to the stargate Stargate uses its attached mote to flood query to the mote network Motes receive the query and task their sampler accordingly Data Flow User receives query result Stargate receives the data packet via its attached mote Sampler receives data from sensor board Mote sends data towards the sink using multihop infrastructure

5 The Multihop Routing/Transport Protocol Tree-based routing algorithm –Nodes form multihop trees rooted at a sink –The network can have multiple sinks –Network routes data to the best sink Path-based routing metric based on: –Each individual link quality on the path to the sink Determined based on ingress and egress link statistics –Total number of hops Less is better Soft-state –Sinks periodically advertise their availability –Nodes periodically advertise their neighbor lists

6 Data Service Engine Query Engine: Manages and initializes query on each individual mote Data Map: Maps user-friendly measurement names to sensor specific ADC channels Transform Manager: Converts raw ADC values from sensors to meaningful query data types Query Table: Manages query storage and access in memory Configuration Manager: Handles sensor configuration changes by storing the new configuration request and updating the Data Map when appropriate

7 System Health Monitoring Motes use EmTOS status devices to report system information Data Extraction –Simulation: status devices –Emulation: HostMote serial debugging + logger –Deployment: Radio packets + Pub/Sub bus-EmProxy connector In all cases results appear in EmView Time Voltage

8 Extending the System Coordinate services such as triggered imaging –Registration protocol: bind motes to stargates as well as actuators Mote Herding MH Concatenate mote clusters –Microserver information sharing: present global state view to application –Coordinate coverage, backup, load sharing


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