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

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

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

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

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

7. Limited Processing power and memory Low energy low data rate Real-time Requirements Unreliable Measurements Wireless Interference Constraints WSN Challenges

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

9. 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. Embedded Device Energy Model CPUNetwork interface 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 V dd – supply voltage f – CPU frequency CC – CPU cycles T – execution time

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

12. DMS (radio): Energy depends … 12 Signal Transmission Energy Circuit Consumption Energy Radio Energy Consumption Depends on distance, transmission time. Linearly dependent on transmission time

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

14. 14 Energy-aware combined scheduling of tasks & messages – the problem Wireless Network ( single hop ) T1 T3 T2 T4 m1m1 m2m2 m3m3 m4m4 Complex periodic tasks 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. Deadline = period = D

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

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

17. 17 System-level Energy vs. Delay Tradeoffs AB Message should reach B before a deadline, D. Compute Communicate TATA MAMA D ∆ t1t1 t2t2 0 ?? Comm. energy Transmission Delay t1 t2 (e1,t1) (e2,t2) t3 (e3,t3)

18. System-level energy-delay tradeoffs 18 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 ? Pick up the highest energy gain entity Reduce its performance mode by one level ? Reduce its performance mode by one level Remove e i from Q exit Yes No Yes No

20. Gain Based Algorithm: Example 109876543 M1 M2 Message Movement Table 20 400300200 T1T1 T1T1 Task Movement Table Can I move to the next col. ? Yes T1 0 f = 400 M1 b = 10 M2 b = 10 T1 0 f = 300 b = 7 M2 b = 7 M1 400  300 Complexity: (n t + n m )(n t k t +n m k m )

21. Dynamic Slack Utilization – Distributed Algo 21 Shared Wireless Medium M7 T1T2 T6 T3T4 M8M9 M10 P1 P2 Channel Goal: Utilize dynamic slack  performance scaling to further reduce energy consumption Conditions: (1) Correctness – deadlines & precedence constraints (2) Overhead – no additional messaging Dynamic slack T1T5 P2 P1

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

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

24. 24 Related Work Research FocusBasic IdeaReferences Processor Energy Management DVS based Task Scheduling. DPM policies. [Aydin et al., Pillai et al.] CPU + Memory + I/ODVS based Task scheduling [Shin et al.] Communication energy management Power Adaptation, DMS, sleep-wakeup [Schurgers et al.] CPU + Network interfaceNode Level, DVS + sleep/wakeup [Bren at al.] Computation sys. + communication sys. Network Level with (DMS + DVS) [Anil et. al. – TPDS 2008]

25. 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 26 Root/sink End-to-end deadline Problem Given: Aggregation tree for each node (i) – T i and M i Modulations: [b min,b max ] CPU Freq: [f min, f max ] Objective: Minimize total energy consumption Constraints: end-to-end deadline (D) precedence constraints Leaf Nodes (sense, compute, Communicate)

27. Solution Approach 27 Obtain a feasible schedule Assign message modulation levels and task frequencies MEME MCMC MDMD TDTD MBMB f = 400 b = 10 370 318 Assign Task Frequencies and Message Modulation Levels While guaranteeing Precedence, ready time and end-to-end deadline constraints

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

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) 29

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

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

32. 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. 33 TALK OUTLINE System-level Energy Management Problem End-to-End Energy Management Problem CPS Applications in Smart Grid Conclusions

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

35. 35 Smart Grid: A Cyber-Physical System Source: http://cnslab.snu.ac.kr/twiki/bin/view/Main/Research 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. Given: Evidence, E = (e 1,..., e k ), where e k is observed state of variable X i Find: Probability of variable X j being in a certain state x = P(X j = x | E) Bayesian Network Fault Diagnosis Cause I Cause II Effect Graph Theory Probability Theory Bayesian Networks

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

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

41. 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 ! 42

43. 43 Thank You ! Acknowlegements Sudha Anil Kumar Benazir Fateh