Servilla: Service Provisioning in Wireless Sensor Networks. Chenyang Lu

Transcription

1 Servilla: Provisioning in Wireless Sensor Networks Chenyang Lu

2 Sensor Network Challenges Ø Device heterogeneity Ø Network dynamics q due to mobility and interference Ø Limited resources and energy Signal Strength (dbm) q e.g., memory Time Source: Hackmann, G., Chipara, O., and Lu, C., Robust Topology Control for Indoor Wireless Sensor Networks, SenSys 08. Device: Imote2 CPU: 32-bit 416MHz RAM: 32MB Power: 151mW Device: TelosB CPU: 16-bit 8MHz RAM: 10KB RAM Power: 0.33mW 2

3 Structural Health Monitoring Ø >26% of bridges were structurally deficient in 2009 [ASCE] Ø Locates damage by analyzing a structure s natural frequencies. Ø Evaluated on a 5.6m steel truss using 11 Imote2s q Imote2 necessary due to high computational complexity. q It worked! [RTSS 2008] Ø Problem: Imote2 is power-hungry Ø Solution: Use middleware to exploit device heterogeneity by integrating energy-efficient nodes 3

4 Pa7ent Monitoring Ø 4-17% of hospitalized patients suffer clinical deterioration. q Wireless clinical monitoring system collect patients vital signs. q Difficult due to patient mobility. Ø Dynamic Relay Association Protocol (DRAP) [SenSys 2010] q Automatically adapts to dynamic network topology q Real-time monitoring of mobile patients Ø Implemented and underwent clinical trials at Barnes-Jewish Hospital in St. Louis. Ø Problem: Application-specific Ø Solution: Provide adaptation via middleware 4

5 Servilla Ø An adaptive middleware for heterogeneous WSNs Ø Introduces -Oriented Computing into WSNs Ø Features q Multiple forms of service invocation q Energy-aware service binding q invocation sharing q Adaptive binding q Middleware asymmetry Network Dynamics Limited Resources Device Heterogeneity 5

6 Servilla s Programming Model Application Scripts (Platform-Independent & Interpreted) Device Heterogeneity script script script script Medical Patient Monitoring Building Automation Security Structural Health Monitoring Dynamic discovery and binding using SOC Patient Sensors Occupancy Sensor Door/Window Sensor s (Platform-Specific & Native) HVAC Actuators Energy Meter Vibration Sensor DSP Limited Resources Network Dynamics 6

7 - Oriented Compu7ng (SOC) User Application Invoke Specification User Application User Application Dynamic Discovery, Matching, and Binding 7

8 Evolu7on of SOC in WSNs Web Application Web Application User Application Registry Registry Atlas (King 06), TinySOA (Lopez 07), PhyNet (ArchRock 08), TinyWebs (Pryantha 08) Enables In-Network Processing Servilla (Fok 09) 8

9 Mul7ple Forms of Invoca7on On-Demand Periodic 1 4 These three types of service invocations are 2 commonly used in WSN applications. 3 Selecting a suitable type of invocation can significantly increase energy efficiency. Event-Based 9

10 Energy- aware Binding Ø Selectively bind to the most efficient service provider q Possible due to device heterogeneity Ø Requires additional information about energy efficiency q sends 8 attributes to consumer during discovery q calculates and selects the most efficient provider Ac7on / State TelosB Imote2 Idle (Sensor Off) Idle (Sensor On) Sensing Message Tx Message Rx Power (mw) Ac7on / State TelosB Imote2 Sensing Message Tx Message Rx Latency (ms) 10

11 Invoca7on Sharing Ø can optimize for energy efficiency by grouping multiple service invocations into one Ø Reflected in the provider s energy specification sent to the consumer q Process is opportunistic, automatic and hidden from consumer 11

12 Adap7ve Binding Ø Middleware performs adaptive binding transparently from the application Flush service registry and re-discover services after three consecutive failures Registry switch 12

13 Lightweight Specifica7ons Ø Traditional specifications impose too much overhead q WSDLInterpreter (Vaughan 07) consumes 48KB memory Ø ServillaSpec: Interface for Accuracy Attributes for Flexibility NAME = fft METHOD = fft-real INPUT = {int dir, int numsamples, float[] data} OUTPUT = float[] ATTRIBUTE Version = 5.0 Attribute MaxSamples = 5000 Attribute Power = 10 Implementation using TinyOS 2.x consumes only 1.4KB memory 13

14 Rapid Matching Ø 10s of milliseconds to establish service match q Proportional to specification size 14

15 Simple Interface Ø Applications access services using a single line of code q Hides the complexities of service discovery, binding, execution, and adaptation 1. uses AccelTrigger; // declare required service void main() { 4. int flag = 0; 5. while(flag == 0) { 6. flag = invoke(acceltrigger, check ); // invoke service 7. } 8. // send alert 9. } All complexity due to service discovery, network heterogeneity, network dynamics, and energy efficiency hidden 15

16 Middleware Asymmetry Ø Increases the types of devices supported Ø Possible due to the decoupling of consumers and providers in the SOC programming model Ø Hidden from the application developer Servilla Servilla Provisioning Framework Virtual Machine CPU: 32-bit 416MHz Memory: 32MB RAM Power: 151mW Powerful platform Provisioning Framework CPU: 16-bit 8MHz RAM: 10KB RAM Power: 0.33mW Weak platform 16

17 Servilla s Implementa7on Ø On top of TinyOS 2.x Ø Tested on Imote2 and TelosB devices Scripts execute on VM to ensure platform independence Provisioning Framework (SPF) modularized to enable middleware asymmetry Servilla Middleware Architecture 17

18 Memory Footprint on TelosB Ø Nearly all of the memory on a TelosB is consumed by just the virtual machine! Ø TelosB can still participate as a service provider 46KB 5KB 48KB Limit 29KB Plenty of room for services 18

19 Structural Health Monitoring Ø Do not continuously run DLAC due to high energy costs Ø During idle periods use AccelTrigger q An energy efficient service q Periodically sense vibration to determine likelihood of damage q Only perform DLAC if potential damage exists Ø Energy-aware service binding q Should AccelTrigger be bound locally or remotely? TelosB TelosB Imote2 Local invocation Imote2 Remote invocation 19

20 Significant Energy Savings Possible % Energy Savings Remote vs. Local Invoca7on Ø Energy-aware service binding is critical due to significant differences in energy efficiency among providers Ø In the structural health monitoring application, energy consumption can be reduced by up to 98.9% Periodic Invocation 1Hz Sensing Frequency 7Hz Sensing Frequency 14Hz Sensing Frequency Invoca7on Period (minutes) Event-based Invocation 1Hz Sensing Frequency 7Hz Sensing Frequency 14Hz Sensing Frequency Invoca7on Period (minutes) 20

21 Energy Footprint (mj) Valida7on of Energy- Awareness Ø The consumer can determine the most efficient provider using the eight provider attributes q 10% radio duty cycle, 1Hz invocation and sensing frequencies q Bind to remote service when number of service executions 4 Local InvocaUon Remote InvocaUon Number of Execu7ons Local Invoke (Predicted) Remote Invoke (Predicted) Local Invoke (Actual) Remote Invoke (Actual) 21

22 Medical Pa7ent Monitoring Ø Servilla hides the complexities of adapting to network topology changes due to patient mobility Ø Using Servilla, the application implementation is trivial 1. uses Sensor, DataRelay; void main() { 4. while(true) { 5. int data = invoke(sensor, get ); 6. invoke(datarelay, send, data); 7. sleep(15); // sleep 15 seconds 8. } 9. } 22

23 Experimental Setup Ø WSN test bed containing 73 TelosB nodes deployed across two buildings at Washington University in St. Louis Ø Patient travels along 359m path at two speeds q Slow: 0.7m/s average q Fast: 1.6m/s average Ø Patient s node invokes data relay service every 15s 23

24 Successful Adapta7on Ø Adaptive service binding enables the application to achieve 100% successful delivery of patient data Ø Adaptation process hidden from the application developer q Abstracted by a simple invoke interface Invocation Success Rate Servilla CTP Non-adaptive native implementation Fast Walk 100% ± 0% 31.6% ± 7.6% Slow Walk 100% ± 0% 40.47% ± 11.2% 24

25 Conclusions Ø Middleware can simplify sensor network applications while enhancing software flexibility and network efficiency Ø Servilla supports service-oriented computing over sensors q Multiple forms of service invocation q Energy-aware service binding q invocation sharing q Adaptive binding q Middleware asymmetry Ø Efficacy demonstrated via real-world applications q Medical patient monitoring reliability despite mobility q Structural health monitoring energy savings due to network heterogeneity 25

26 References Ø C.-L. Fok, G.-C. Roman and C. Lu, Servilla: a Flexible Provisioning Middleware for Heterogeneous Sensor Networks, Science of Computer Programming, Special Issue on Best Papers of Coordination'09, 77(6): , June Ø C.-L. Fok, G.-C. Roman and C. Lu, Adaptive Provisioning for Enhanced Energy Efficiency and Flexibility in Wireless Sensor Networks, Science of Computer Programming, Special Issue on Best Papers of Coordination'10, accepted. 26