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Real-Time Sensor Networks with Applications in Cyber-Physical Systems

Real-Time Sensor Networks with Applications in Cyber-Physical Systems. Manimaran Govindarasu Dept. of Electrical and Computer Engineering Iowa State University Ames, IA 50011, USA gmani@iastate.edu http://ecpe.ece.iastate.edu/gmani. TALK OUTLINE. System-level Energy Management

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Real-Time Sensor Networks with Applications in Cyber-Physical Systems

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  1. Real-Time Sensor Networks with Applications in Cyber-Physical Systems Manimaran Govindarasu Dept. of Electrical and Computer Engineering Iowa State University Ames, IA 50011, USA gmani@iastate.edu http://ecpe.ece.iastate.edu/gmani

  2. TALK OUTLINE • System-level Energy Management • End-to-End Energy Management • Cyber-Physical System applications in Smart Grid • Conclusions

  3. Battery Embedded System • Embedded Device • Processor - computation • Network Interface -communication • Others: • Memory • I/O Energy is the most important resource It needs to be managed efficiently

  4. Border Security Wireless Industrial Networks Traffic monitoring system Sensor Net Applications • Sense • Encrypt • Decrypt • Aggregate • Communicate

  5. Wireless Sensor Network (WSN) – Data Aggregation Tree Model Root/sink (compute(Ti), Communicate(Mi)) End-to-end deadline i (sense, compute, Communicate) Leaf Nodes

  6. WSN – Mesh network model Energy Management at the Computing Subsystem (considering all the tasks) C D Energy Management at the Communication Subsystem (considering all the messages) Wireless Network Computation + Communication A B Energy Management at the System-level (both messages & tasks) E F G

  7. WSN Challenges

  8. State-of-the-Art in Energy Management Energy Management Schemes Data Driven MAC with duty cycling Energy efficient Data Acquisition Joint Scheduling Tasks & Msgs Duty Cycling Sleep-Wakeup Data Reduction Online Adaptation

  9. System-level Energy Management Energy Management in Networked Real-Time Embedded Systems Computing subsystem Communication subsystem System-level (Comp. + Comm.) Cross-Layer Energy-aware Task Scheduling (DVS, DPM) Energy-aware Message Scheduling (DMS; Power Adaptation) • Energy-aware • System-level Scheduling • (DVS + DMS) • Single-hop • Multi-hop

  10. CPU Network interface Embedded Device Energy Model CPU Energy consumption Transmission energy consumption b – modulation level d – source-destination distance L – message length W – channel b/w in Hertz T – transmission time • Vdd – supply voltage • f – CPU frequency • CC – CPU cycles • T – execution time

  11. Vdd V ’dd = ½ Vdd Energy-Time Tradeoff (CPU) E Energy Time T Energy E/4 Time 2T

  12. DMS (radio): Energy depends … Depends on distance, transmission time. Linearly dependent on transmission time

  13. Energy Model: energy vs. delay tradeoff Computation energy management Communication energy management Dynamic Voltage Scaling (DVS) Dynamic Modulation Scaling (DMS) Varying processor voltage (v) & frequency (f) Varying message modulation (b) (b1) t’ (V1,F1) Comm. energy CPU energy t lower (b2) low trans. rate 2t’ Lower (V2,F2); processor slows down 2t Transmission Delay of message with length L Computation time of a task with CC CPU cycles

  14. Wireless Network ( single hop ) T1 m1 m2 T3 T2 m4 m3 T4 Energy-aware combined scheduling of tasks & messages – the problem Complex periodic tasks Deadline = period = D Problem Statement Given: ‘n` such complex Periodic tasks Goal: (1) Schedule Tasks & Messages (2) Assign task frequencies & msg mod. levels Objective: Minimize total system energy consumption. Constraints: Meet all the deadline, precedence & ready time constraints.

  15. Energy-aware combined scheduling of tasks & messages – the solution Feasible Schedule T1 T4 T7 1. Task Mapping P1 M1 M2 Ch 2. Schedule local tasks on the nodes T2 T5 P1 T3 T6 3. Schedule msgs on the network P2 Feasible schedule (Energy unaware) • Use the slack to assign • modulation levels to messages & • frequency levels to tasks • While guaranteeing: • Deadline • Precedence • Ready-time constraints • This is an NP-Hard Problem 4. Assign modulation levels to messages & frequency levels to tasks. Final Energy Aware Schedule

  16. Scheduling – Static & Dynamic Online Phase Offline Phase P0 Task and message parameters Shared wireless network Offline energy-aware Static Scheduling algorithm P1 Statically created schedule P3 P4 System-level energy-time tradeoff Analysis Energy-Aware StaticSched. Algo Energy-Aware DynamicSched. Algo Other scheduling problems

  17. ?? A B MA ∆ TA t1 t2 D 0 System-level Energy vs. Delay Tradeoffs Communicate Message should reach B before a deadline, D. Compute (e1,t1) t1 Comm. energy (e2,t2) t2 (e3,t3) t3 Transmission Delay

  18. System-level energy-delay tradeoffs 1. Subsequent gains decrease 2. All slack should not go to messages

  19. Gain based Static Scheduling (GSS) Insert all messages and tasks into set Q Is Q empty ? Remove ei from Q Yes exit No Pick up the highest energy gain entity Reduce its performance mode by one level ? No Yes Reduce its performance mode by one level

  20. Gain Based Algorithm: Example 400 300 T1 Can I move to the next col. ? Complexity: (nt + nm)(ntkt+nmkm) Message Movement Table Yes 10 9 8 7 6 5 4 3 Task Movement Table 400 300 200 M1 f = 400 b = 10 b = 10 T1 M1 M2 M2 T1 0 f = 300 T1 b = 7 b = 7 M1 M2 0

  21. Dynamic Slack Utilization – Distributed Algo Shared Wireless Medium P2 P1 • Goal: • Utilize dynamic slack  performance scaling • to further reduce energy consumption • Conditions: • (1) Correctness – deadlines & precedence constraints • (2) Overhead – • no additional messaging T3 T4 P1 M7 M8 M9 M10 Channel T1 T1 T2 T6 T5 P2 Dynamic slack

  22. Dynamic Slack Utilization Online Phase P0 Rules • Rules • Use dynamic slack locally. • Do not change the Finish times of any task/msg. Shared wireless network Rules P1 Rules P3 P4 Rules

  23. Effect of Channel Bandwidth 2. At Low b/w, Comp-only consumes lesser energy 3. At high b/w, Comm-only consumes lesser energy 1. As b/w increases, All schemes consume lesser energy 4. Throughout, GSS performs better than comp-only and comm-only

  24. Related Work

  25. TALK OUTLINE • System-level Energy Management Problem • End-to-End Energy Management Problem [1] • Cyber-Physical System applications in Smart Grid • Conclusions [1] G. Sudha Anil Kumar, G. Manimaran, and Z. Wang, "End-to-end energy management in networked real-time embedded systems," IEEE Trans. on Parallel and Distributed Systems, Dec. 2008.

  26. Data Aggregation Tree – End-to-end guarantees • Problem • Given: • Aggregation tree • for each node (i) – Ti and Mi • Modulations: [bmin,bmax] • CPU Freq: [fmin, fmax] • Objective: • Minimize total energy consumption • Constraints: • end-to-end deadline (D) • precedence constraints Root/sink End-to-end deadline (sense, compute, Communicate) Leaf Nodes

  27. Solution Approach f = 400 TD Obtain a feasible schedule 318 b = 10 MB Assign message modulation levels and task frequencies b = 10 MC MD b = 10 Assign Task Frequencies and Message Modulation Levels While guaranteeing Precedence, ready time and end-to-end deadline constraints ME 370

  28. Solutions space End-to-End Energy Management Problem Continuous Model (not realizable in practice) Discrete Model (realized in practice) Optimal Solution Optimal: MILP formulation (worst-case: non-polynomial) NP Hard Heuristics Scheduling Algorithms (GSA & EGSA)

  29. Performance Evaluation • Algorithms/schemes compared • Optimal: MILP solved using ILOG CPLEX 10.100 • Proposed: Gain based Algorithm (GSA) • Proposed: Extended gain based Algorithm (EGSA) • Baseline: comp-only (only tasks are scaled) • Baseline: comm.-only (only messages are scaled) • Simulation Parameters • Bandwidth • Radius factor (source – destination distance) • Computational Load (cycles per task)

  30. Effect of Communication Radius 2. Energy consumptions increase as we increase radius 1. At low distance, Comp-only consumes lesser energy 2. Throughout, GSA & EGSA are close to MILP

  31. Effect of Computation Load 2. Energy consumptions increase as we increase comp. load 1. At low Comp. Load, Comm-only consumes less energy 2. Throughout, GSA & EGSA are close to MILP

  32. Summary of Results • Communication energy consumption is NOT always the dominant factor • Computation energy ~ communication energy consumption • At low message modulation levels • Low bandwidth channels • Short-distance communication • High computation load • In some cases, computation energy consumption > communication energy consumption • System-level energy savings >> component-level savings • 20-50% improvement for evaluated conditions

  33. TALK OUTLINE • System-level Energy Management Problem • End-to-End Energy Management Problem • CPS Applications in Smart Grid • Conclusions

  34. Cyber Physical Systems (CPSs) • Applications: • Critical infrastructure monitoring • Automated traffic control • Home Area Networks • Ubiquitous healthcare monitoring

  35. Smart Grid: A Cyber-Physical System Source: http://cnslab.snu.ac.kr/twiki/bin/view/Main/Research

  36. Wireless Network Design and Fault Diagnosis Design a network for real-time data delivery in presence of latency and bandwidth constraints and an associated fault diagnosis

  37. Wireless Network Design for Transmission line monitoring Given a directed graph G = (V, E) and a set of N flows, Find a feasible path for each flow such that the sum of the cost of all the paths is minimized while respecting the delay and bandwidth constraints of each flow.

  38. Bayesian Network Fault Diagnosis • Given: • Evidence, E = (e1, ..., ek ), • where ek is observed state of variable Xi • Find: • Probability of variable Xj • being in a certain state x = P(Xj = x | E) Cause I Cause II Effect

  39. Sample BN modeling a tower Fault Diagnosis Temperature Sensor Tension Sensor Tilt Sensor Channel Tension Channel Calibration Channel Tilt Battery Battery Battery Calibration Calibration Temp. Tension Measurement Temperature Measurement Tilt Measurement

  40. TALK OUTLINE • System-level Energy Management Problem • End-to-End Energy Management Problem • Cyber-Physical System applications in Smart Grid • Conclusions

  41. Conclusions • System Level Energy Management offers significant savings • CPU time, Communication, Memory, I/0 • Commn (radio) energy is not always the dominant factor • Depends on: modulation level, Sender-Receiver distance, Bandwidth • End-to-end Energy management while meeting deadlines • Dynamic slack generation and utilization are key to energy savings • Cyber-Physical System poses constraints for network design • End-to-end Latency, Bandwidth constraints, legacy comm links • Fault diagnosis distinguishes true faults from false positives

  42. Future Work • Communication of Energy Management • Leveraging physical layer techniques (Dynamic Code Size Scaling) • Network coding + Energy-aware scheduling • System-level Energy Management • Exploit sensing redundancy (temporal and spatial)  more savings • Holistic Scheme: CPU + Commn + Memory + I/O • Distributed algorithms • Embedded sensor network design and operation (CPS) • Self-healing, Security, Fault diagnosis, Decision Algorithms • Applications of wireless sensor networks are endless !

  43. Thank You !AcknowlegementsSudha Anil Kumar Benazir Fateh

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