Robust Systems

Faults at James Reserve

Faults on a volcano in Ecuador [WLJ + 06]

Data faults in Bangladesh

Calibration

Motivation Summary Sensing Channel Transducer Network Analog Processing ADC Digital H/W + Software Phenomenon User Noise Interference Obstructions Adversaries Noise Calibration Faults Noise Calibration Faults Quantization Error Faults Bugs Adversaries Packet Loss Adversaries Smart Sensors Stable gain; Auto zero-offset correction; Transducer interference compensation; Compensation for temperature, package strain etc.; Integrated trimming for end-of- line calibration; Outstanding calibration stability Well Studied Image courtesy Mani Srivastava Hard Problems

Summary People pay for robustness in other systems –Higher quality hardware –Technicians to monitor the data –Wired infrastructure In sensor networks when we pay, we pay for scale The burden on software has increased Robustness in sensor networks requires research and engineering

CentRoute Designed for robustness –Minimizes routing inconsistencies, including loops –Minimizes memory (state) requirements on motes –Increases routing stability –Can scale to dense networks Routing table, neighbor table, local decisions Distributed decision making on very limited RAM hardware Distributed Mote RoutingCentralized Mote Routing All routing decisions & state at microserver Bypasses mote hardware limitations through global view at microserver Additional functionality –Bidirectional unicast routing (to and from the sink) –Global view of the entire mote network at each sink Work by Thanos Stathopoulos

Memory Protection LIGHTHOUSE Develop simple and intuitive memory model – Each block of memory is under the control of exactly one program at any time – Controlling program is responsible for either tracking, freeing, or transferring ownership of the data Found significant memory management errors in both kernel and user SOS code using new analysis tool Accomplishes analysis via basic data-flow analysis on source code SANDBOX Create multiple protection domains within single address space CPUs – Restrict write accesses of a domain to memory it owns – Restrict control flow in and out of a domain Designed for small memory CPUs – No static partitioning of address space – Compact memory map tracks ownership and layout Enforced by inline run-time checks – All write accesses are checked – All control flow operations are checked Checks introduced through binary re-write – Binary verified at every node – Verifier independent of re-writer – Correctness of scheme depends only upon correctness of verifier Work by Ram Kumar and Roy Shea

Tenet Show counter on LEDs Sense and send data back to the sink... with time-stamp and sequence number Get memory status for node 10 If temperature is above 50, send temperature, node ID and next routing hop WaitCountLightsSendSampleSendCountStampTimeSendSample MemStatsSendAddressNEQ(10)DeleteIf SampleLT(50)DeleteATaskIfAddressNexthopSend Paek, Greenstein et al.

Environment Sensors Mote Batteries Radio Network Final Destination Sensorboard Sympathy & Confidence Data Generation PathData Delivery Path BothUser Actions Remediate Action-Refinement Probes + Database Refine & Adapt BothHardware Rules identify locations data could be corrupted Data Flow Rules identify locations data could be lost Diagnose BothTrack end-to-end data quality Track end-to-end data quantity Detect ConfidenceData IntegritySympathy Nithya Ramanathan

Fault Detection Contextual or multiscale information Another modality on the same node Nodes of Same Altitude or Depth Proximate Nodes Measurements at same time previous day Recent Measurements Exploit sensor data by finding correlations between different variables Recognize a fault when sensor data breaks its strongest correlations Variable space is too high dimensional Signal processing techniques may provide an efficient correlation model