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Record W2896800240 · doi:10.1109/jiot.2018.2876375

Toward a Perpetual IoT System: Wireless Power Management Policy With Threshold Structure

2018· article· en· W2896800240 on OpenAlexaff
Yang Zhang, Zehui Xiong, Dusit Niyato, Ping Wang, Dong In Kim

Bibliographic record

VenueIEEE Internet of Things Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsYork University
Fundersnot available
KeywordsComputer scienceNode (physics)WirelessComputer networkMarkov decision processWireless networkWireless power transferMarkov processEnergy (signal processing)TelecommunicationsEngineering

Abstract

fetched live from OpenAlex

With the advancement of wireless energy harvesting and transfer techniques, an Internet of Things (IoT) node equipped with a wireless charging facility can request and receive energy from wireless chargers deployed at different locations. This provides more opportunity for the mobile IoT node to replenish its battery and be able to operate without interruption due to shortage of energy supply. In this paper, we develop an optimal energy charging scheme for the mobile IoT node, considering the states of location, traffic generation, and energy storage. We formulate the problem of energy charging as a Markov decision process (MDP) to obtain the mobile IoT node’s optimal policy. The objective is to maximize the expected utility. Furthermore, we prove that the optimal policy of the proposed MDP has a threshold structure. The numerical results show the performances of the mobile IoT node under various scenarios and parameter setting. Furthermore, the proposed MDP-based wireless energy charging scheme outperforms conventional baseline schemes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.208
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2018
Admission routes1
Has abstractyes

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