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Goal: energy-management /resource-allocation protocols for EnHants

Goal: energy-management /resource-allocation protocols for EnHants. C BAT large. C BAT small. C BAT large. C BAT small. ‘straight line’ solution. Stochastic sampling . ‘straight line’ solution. What we are brainstorming. What we are working on right now .

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Goal: energy-management /resource-allocation protocols for EnHants

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  1. Goal: energy-management /resource-allocation protocols for EnHants CBAT large CBAT small CBAT large CBAT small ‘straight line’ solution Stochastic sampling ‘straight line’ solution • What we are brainstorming • What we are working on right now

  2. Comments on battery size • Per day energy with: • 1% efficiency • 10cm^2 solar cell • Dim indoor environments: 0.1 - 0.3 J • Bright indoor environments: 1-4 J • Outdoor environments: 100- 300 J • Capacity ~60% of daily input • Allows for a `straight-line’ solution • Capacity ~2-4x daily input • Can function well with smart algorithms • Capacity ~10x daily input • Functions well with very simple algorithms

  3. Stochastic case: CBAT large • Capacity 2-4 times the daily energy supply • Exact battery state at a particular time of day not of a great importance • Solution developed for a constrained case an overkill for when the battery is not too high/ too low • Can assume individual days are i.i.d and have a dynamic programming solution • Days are not i.i.d. • Computationally complex • Extensions to more than one device become even more complex

  4. Stochastic case: CBAT small • Difference between the input and the prediction is not dramatic • Using simple adjustments, do not get a good solution

  5. Idea for the stochastic case: Sampling The problem In our case δ represents the vector of harvested energy Assume we have probability P on Δ The method Choosing δ(1),…,δ(N) according to P and solving the problem

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