Restless Bandits for Sensor Scheduling in Energy Constrained Networks
Bibliographic record
Abstract
We consider the problem of sensor scheduling in energy constrained network. It is modeled using restless multi-armed bandits with dynamic availability of arms. An arm represents the sensor and due to the energy constrained its availability is dynamic. The data transmission rate depends on the channel quality. Sensor scheduling problem is a sequential decision problem which needs to account both for the evolution of the channel quality and fluctuation in energy levels of sensor nodes. When sensor with available energy is scheduled, it yields data rate based on channel quality, this is referred to as immediate reward. The channel quality is modeled using two state Markov model. The higher channel state corresponds to higher quality, and hence higher immediate reward. When sensors are not scheduled, it yields no reward. Sensors with non-availability of energy are not scheduled. Further, channel quality of sensors is not observable to the decision maker but signals after data transmissions are observable. It is called as partially observable restless bandits. The objective of decision maker is to maximize infinite horizon discounted cumulative reward by sequentially scheduling sensors. We study Whittle's index policy, and describe algorithm to compute index formula. We also study online rollout policy and analyze the computation complexity. The simulation examples compare the performances of different policies-index policy, rollout policy, and myopic policy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".