1. Energy Neutral Systems
CSE 591 – Green Computing

2. Randomness in Available Energy
Intermittent energy supply
Depends on the environment
Solar cells produce energy only in the day time
Cloudy weather hampers energy production
Statistical variance in available power
The scavenging sources put a cap on the instantaneous power and not on energy
Principal difference from the batteries, which put a cap on the energy.

3. Shift in Design Consideration
Energy efficiency alone is not enough
Optimized power profile is more important
System design under an energy constraint is no longer valid
The energy constraint itself varies over time, given the randomness in the energy source
More relevant design objective –
Perpetual energy neutrality –
Maximize the performance of a system without depleting the battery ever

4. System Model Battery Scavenging Source Scavenging Source Computing
Scavenging source generates energy
The computing system uses the energy from the scavenging source to perform computational tasks
Scavenging source generates energy
The energy is used to charge a battery
The computing system uses the energy from the battery to perform computational tasks
Computing
System
Computing
System

5. Scavenging Source Metrics
Design considerations –
Conversion Efficiency – Percentage of energy available from the source that can be used as electrical energy for powering systems
Cost constraints
Form factor constraints
Technology constraints
E.g. Solar cells have a conversion efficiency of 5%
Reliability – Failure to supply power, uncontrolled power supply
E.g. scavenging from ambulation has low reliability

6. I-V characteristics Battery Scavenging Sources Voltage sources
Limit on the current drawn (Irating)
Current depends on the load across the battery
Current sources
Limit on the voltage supplied
The operating point, both voltage and current depends on the load
Highest Instantaneous power
Voltage (V)
Current (A)
Voltage (V)
Aim – Operate in the Maximal Power Point (MPP)
Harvesting Aware Power Management for Sensor Networks by Aman Kansal, Jason Hsu, Mani B Srivastava, and Vijay Raghunathan

7. Techniques for using harvested energy
Adjust the load on the scavenging source.
Use a current switch in the circuit
Essentially a voltage regulator or a transformer.
Allow the scavenging source to operate in the MPP
The voltage or current is then scaled according to the load using the switch
Inefficiencies –
The switching circuit itself draws power
Leakage current of the transformer will cause wastage of power.
Storage of scavenged energy
Use fuel cells or ultracapacitors
Round trip efficiency – Percentage of energy lost in storing in the capacitor or battery and then transferring for use.
Main cause is the leakage of energy
Depth of Discharge (DoD) – The residual battery capacity just before it is recharged
The lifetime of a battery increases exponentially with increasing depth of discharge
System level workload scheduling
Schedule workload to match the available power profile from the sources.

8. Harvesting theory
Energy Neutrality – A system is energy neutral, for a given time t, if the energy stored in the battery after the system operation for time t is greater than or equal to the previous.
Mathematically, if is the battery energy at time t = 0 and is the battery energy after time t.
If then the system is energy neutral
If then the system is perpetually energy neutral

9. Modeling of harvesting system
is the power obtained from a scavenging source
is the efficiency of the storage (fraction of usable electrical energy )
is the leakage power
is the power consumed by the computing unit
For a given time t, if , then the storage unit stores energy
Else the storage unit gets depleted of the energy
From the stored energy only a fraction can be used to power computing units

10. Energy Neutrality Criteria
Amount of energy
stored from scavenging
sources
Amount of energy depleted from storage units due to lack of scavenged energy
Amount of
energy
depleted
from storage
units due
to leakage
Previously
stored
energy
Harvesting Aware Power Management for Sensor Networks by Aman Kansal, Jason Hsu, Mani B Srivastava, and Vijay Raghunathan

11. Scavenging Source Definition of scavenging source
(ρ1,σ1,σ2) tuple defines a scavenging source
ρ1 is the constant power supply model
σ1 is the upper limit
σ2 is the lower limit
If Ps(t) is the power obtained from the scavenging souce at any time then we have,
Average case – σ1 and σ2 are zero.
Performance Aware Tasking for Environmentally Powered Sensor Networks by Aman Kansal, Dunny Potter and Mani B Srivastava

12. Power source consumer Definition of variable power source consumer
Consumer (ρ2 ,σ3 ) has a power consumption which satisfies the following equation
for any given time T

13. Theorem – Energy Neutral Operation
The sufficient conditions for a system to be energy neutral are –
Here Bmax is the storage capacity while B0 is the initially stored energy

14. System Performance Characterization
Utility – A function of duty cycling
Example - speed tracking sensors

15. Performance maximization objective
Maximize average utility
Constraints –
Energy consumption model
Energy neutrality constraint
Performance constraint

16. Prediction of available power
The optimization assumes that the available power is known before hand
However, scavenged power depends on several environmental factors along with diurnal variations
Exponentially weighted predictive function
where is a historically weighted function for the energy available from solar source and x(i) is the current amount of available energy

17. Typical BSN Workload
Ayushman [2] health monitoring application is considered as the workload
Ayushman has three phases of operation –
Sensing Phase – Sensing of physiological values (Plethysmogram signals) from the sensors and storing it in the local memory
Transmission Phase – Send the stored data to the base station in a single burst
Security Phase – Perform network wide key agreement for secure inter-sensor communication using Physiological value based Key Agreement Scheme (PKA) [3].
The Security phase occurs once in a day
The Sensing phase and Transmission phase alternate forming a sleep cycle
Ayushman Phase
Rate of Execution
Sensing
60 samples per second
Data Transmission
every 5 seconds
PKA execution
Once in 24 hrs
(the processor can sleep during sensing phase while it can be active during the transmission phase)
This slide is there to show how the strategies mentioned in the previous slide can be used in Ayushman application to reduce the energy consumption
The workload timeline provided in the figure can be used to explain the opprtunity to schedule sleeping cycles of the processor. Due to loose real time requirements on the PKA execution the frequency of the Atom processor can be throttled.
Sensor CPU Utilization
Time
Sensing Phase
Transmission Phase
Security Phase
Sleep Cycle
Ayushman Workload
Enables Sleep Scheduling
Frequency Throttling during security phase

18. Sustainability Analysis of Atom
Duty Cycling of Atom operation during Ayushman execution
Sleep mode (C6) during Sensing Phase
Power consumption = Psleep for time ts
Active mode during data transmission phase
Power consumption = Pactive + radio power Pradio for data transmission time ttx
Active mode during PKA execution
Power Consumption = Pactive + PKA execution power PPKA for time tPKA
PKA involves transmission of security related information (vault) between two sensors
The Atom processor must be in active state with the radio on. Power Consumption = Pactive + Pradio for time tvault
Total energy consumption for n BSN nodes
x is the number of sleep cycles required in a day
Total Energy
Sensing Energy
Transmission Energy
PKA Computation Energy (Pair wise PKA)
PKA communication Energy (Pair wise PKA)
Total Sleep Cycle Time
Pair wise PKA Execution Time

19. Sustainability Analysis Results
Four energy scavenging sources were considered
Body Heat, Ambulation, Respiration and Sun Light
Three energy efficient techniques used
processor level sleep scheduling and communication (radio sleep) scheduling (P-M),
no processor level sleep scheduling but with communication scheduling (NP-M), and
no processor level sleep scheduling or communication scheduling (NP-NM).
Here we consider the average case power consumption of the scavenging sources.
Variance in the power availability can be captured using the theory discussed earlier.
Scavenging Source
Available Power (W)
Scavenge Time (Hrs)
Body Heat
0.1 – 0.15 24
Ambulation
1.5 2
Respiration
0.42 6
Sun Light
0.1 3

20. Sustainability Results

21. Thank You