Implications of solar energy harvesting on sensor networks design Ganesh Narayana Murthy Guided by: Prof. Purushottam Kulkarni

Motivation Sensor network issues Fault tolerance Localization Energy constraints, Power management Robustness Time Synchronization Still continues to be a dominant Problem

Lifetime vs Application Performance Smaller duty cycles lead to longer lifetime Lesser application performance Need to find the duty cycle that increases both lifetime and application performance. Source DestinationL L L Path latency Small duty cycle => Large path latency

Energy Harvesting Tapping energy from renewable sources of energy Ex: sunlight, wind, vibration Importance Can solve energy constraints in sensor networks Implications Lifetime of the sensor network can be improved without sacrificing application performance.

Unequal harvesting capabilities All nodes do not have equal battery levels and harvesting opportunities Effective energy is a parameter, that is representative of: Current battery level Future energy harvesting potential Its uses are: As cost metric in shortest path routing algorithms Taking decisions about nodes e.g. electing them as cluster heads

Power Management Managing the power consumption so as to increase both lifetime and application performance, in the presence of energy harvesting. Various levels Node level – lifetime of node Network level – lifetime of the network as a whole Ways of node level power management Duty cycling Dynamic voltage scaling ( Not studied in this seminar ) Ways of network level power management Finding effective energy

Duty Cycling Radio: Duty Cycle = Awake Time / Total Time Motive Power consumption in sleep mode is less than that in active mode. In sleep mode, all components like radio, processor are turned off. Receive Power(mW)38 Transmit Power at 0dbm (mW) 35 Microcontroller Active Power (mW) 3 Sleep Power (µW) 15 Wake up time (µs) 6 Fig: Telos mote specifications

Duty cycle estimation Can be estimated using energy neutrality principle Definition of energy neutrality: Total Energy Consumed <= Total Energy Harvested Two approaches that use the above principle: Kansal et. al. ( uses source modelling ) Vigorito et. al. ( uses battery status )

Duty cycle estimation using harvesting source modeling Kansal et. al.[1] approach

Source modeling To predict what is the amount of power that will be harvested at different points of time. Prediction interval ‘T’ Period of prediction Represents periodicity of source Ex: 1 day for solar energy Time slot ‘i’ The power harvested is determined for each time slot ‘i’ as a moving average of earlier values. n12 T ……… 3 Fig: Prediction interval Time slot

Predicting the values Values for 1 st prediction interval calculated from solar trace. Subsequent values are predicted through “exponential weighted moving average” Let x(i) denote the value of actual energy generated in slot i as observed at the end of that slot. Historical average maintained for each slot is given as:

Assumptions D min D max Utility Duty cycle 1. P c = Load power consumption (constant for all slots) 2. Power consumed = D(i) * P c More duty cycle, more performance

Sun and Dark slots Sun slot Ps(i) > Pc Environment directly provides the load power Dark slot Ps(i) < Pc Some power from environment and some from battery. Battery inefficiency leads to consuming more power than Pc

Basic idea 1. Assign duty cycle D min to all slots 2. Based on amount of energy that will be harvested Find the excess energy in the system. Decide which slots duty cycle should be increased 3. Predicted values deviate from actual generated energy. So change duty cycles of future slots. If more energy is harvested, decide which slots duty cycle to increase If less energy is harvested, decide which slots duty cycle to reduce.

Applying energy neutrality B(i) = Residual battery energy at beginning of slot ‘i’ Ps(i) = Power output from source in time slot ‘i’ Pc = Power consumption of load in active mode η = Efficiency of the battery Power drawn from battery when Ps(i) < Pc Power stored into battery during active mode when Ps(i) > Pc Power stored during sleep

Finding the duty cycles ……… Slots with lower coefficients (Sun slots ) Slots with higher coefficients (Dark slots) Excess Energy in system R = LHS - RHS Slots sorted based on coefficients Greater than Pc D(j) = D max if R is sufficient to support D max (or) D(j) = R last / P c + D min D(j) = Dmax if R is sufficient (or)

Adapting duty cycles

Duty cycle estimation using battery status Vigorito. et. al.[2] approach

Objectives 1. Avoid modeling of source. Source modeling approaches fail if the source is highly variable (Ex: Vibration energy Reason: More prediction errors, which lead to wastage of energy due to battery inefficiencies 2. Reduce the variance of duty cycles Nodes involved in event monitoring tasks must minimize their sleep time in order to detect fleeting and unpredictable events and report them with low latency (usually to a central location)

Energy neutrality Deviations of battery level Bt from the initial level B0 must be minimized. This ensures that Consumption does not exceed harvest Harvested energy is being consumed fully Parameters in the equation B t = battery level at time t u t = Duty cycle in slot t w t = Moving average of battery level increments produced by harvested energy where B t+1 = aB t + bu t + cw t + w t+1

Adaptive control theory Optimal tracking problem Apply external control to a dynamical system, to keep an output variable at a desired value Then, it can be shown that, u t that minimizes the cost function is: Can be equated to Bt Can be equated to B0 Can be equated to duty cycle in slot t Parameters a,b,c can be found by gradient descent method

Comparison Helio-CA dataset: 60 days of solar data collected from heliomote in Los Angeles US Climate Reference Network (USCRN), which maintains a database of environmental data collected from various monitoring stations across the US ρ min =0.01, ρ max =1, optimal battery level=65%, initial battery level=95% Low variance data set

Network level power management

Finding effective energy – EEHF[3] Predicted average energy for duration T Prediction confidence Finds period of source Estimates average energy harvested in interval T

Comparison with Kansal. et. al. EEHFKansal. Et. Al Mean energy for entire prediction interval ‘T’. Hence less accurate Energy predicted for each time slot ‘i’ of the interval ‘T’. Hence more accurate Use of spectral estimation like Fast Fourier Transforms to find ‘T’ of source. Hence, may be useful for non-solar sources No specific method. Since it is developed for solar, T=1 day is being used. So, doubtful of applications to non-solar sources. Energy neutrality not usedEnergy neutrality being used

Improvements – Extended EEHF[4] Problems in EEHF: ‘T’ represents period of the harvesting source. Hence, may not suit application requirements For solar energy, T= 1 day. So, the mean energy Em, is least indicative of energy harvested at different times. Solution: Dividing ‘T’ into ‘n’ rounds. Also, while predicting energy in a round, take into account: Same round in previous cycle Previous round in same cycle.

New Effective energy

Qualitative comparison EEHFE-EEHFKansal. et. al. Vigorito. et.al. Accuracy of prediction LeastBestBetterNo prediction Source Type Low variance Any

Cluster based routing – LEACH[5] Base Station Cluster head

Cluster head election Fig: Cluster formation

Conclusions Energy harvesting can remove energy constraints of sensor nodes. Energy neutrality principle helps in finding a way to adapt parameters so that application performance is also improved Source modeling and battery status tracking using adaptive control theory are two approaches to track harvested energy Source modeling can be used for less variable sources like solar energy. Effective energy can be used to improve lifetime of sensor networks as a whole

References 1. Aman Kansal, Jason Hsu, Sadaf Zahedi, and Mani B. Srivastava. Power management in energy harvesting sensor networks. ACM Trans. Embedded Comput. Syst, 6(4), Christopher M. Vigorito, Deepak Ganesan, and Andrew G. Barto. Adaptive control of duty cycling in energy-harvesting wireless sensor networks. In SECON, pages 21{30. IEEE, Aman Kansal and Mani B. Srivastava. An environmental energy harvesting framework for sensor networks. Pages K. Kinoshita, T. Okazaki, H. Tode, and K. Murakami. A data gathering scheme for environmental energy-based wireless sensor networks. pages , Jan W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan. An application-specic protocol architecture for wireless microsensor networks. Wireless Communications, IEEE Transactions on, 1(4): , 2002.

Thank you!

Applying energy neutrality (contd..) Needs to be solved using standard linear programming techniques (Computationally intensive)

Low Complexity Form S = { i | Ps(i) > Pc } D = { i | Ps(i) < Pc } Total battery energy used over the entire window. Power consumed in dark slots Power consumed in sun slots Summing LHS and RHS over entire window of slots: