Neeraj Jaggi - Witchita State University - Wichita State University

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• Difficulty Imperfect State Information 14 ◦ State partially observable when sensor is inactive – (L , E ) • Partially Observable Markov Decision Process ◦ Is converted to a completely observable MDP With a transformed state space of the form (L, E, t) ◦ Information available while decision making L – Current Energy Level E – State of event process last observed (while in active state) t – Number of time slots spent in inactive state ◦ Optimal policy Can now be computed numerically using value iteration Optimal actions do not seem to have a closed form expression Neeraj Jaggi Dept of EECS Wichita State University
Structure of Optimal Policy • Optimal actions computed using value iteration algorithm ◦ Threshold energy wakeup functions f 0 – (L, 0, t), f 1 – (L, 1, t) 15 ◦ Properties Last observed state ON Aggressive Wakeup Last observed state OFF Reluctant Wakeup ◦ Sensitive to system parameters ◦ Convergence of f 0 and f 1 Effects of temporal correlations diminish over time Neeraj Jaggi Dept of EECS Wichita State University
Page 1 and 2: Rechargeable Sensor Activation unde
Page 3 and 4: • Sensor Nodes ◦ Tiny, low cost
Page 5 and 6: Dynamic Sleep Scheduling 5 • How
Page 7 and 8: Sensor Energy Consumption Model 7
Page 9 and 10: Approach/Methodology 9 • Perfect
Page 11 and 12: Perfect State Information (contd.)
Page 13: Optimal Activation Policy • Optim
Page 17 and 18: EB-CW Policy • Energy balancing C
Page 19 and 20: Summary and Contributions • We co
Page 21: Q & A 21 THANK YOU !! Neeraj Jaggi

Neeraj Jaggi - Witchita State University - Wichita State University

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