Distributed and localized strategies for energy restoration in wireless sensor networks
Bibliographic record
Abstract
Energy management is one of the main hurdles in the quest for autonomous and reliable Wireless Sensor Networks (WSN). This thesis examines several scenarios where mobility is added to the network components in order to carry out energy management tasks. The main goal is to increase network availability by recharging or redeploying depleted sensors with the help of mobile entities. For static sensors networks we provide a cluster-based solution where (1) workload is balanced, (2) the movement of the maintenance entities is minimized and (3) there is a small number of sensor communications. This problem is a variant of the so-called Facility Location Problem (FLP). Although finding the optimal network partition and placement of the facilities is a NP-hard problem, we show a simple and efficient solution that provides partitions of remarkable quality. The experimental analysis shows that sensor communication cost remains low as the size of the network increases, there is a rapid progression towards convergence and the quality of the final partition is similar to centralized clustering benchmarks. We also study a particular instance of the Frugal Feeding Problem (FFP), where mobility becomes a sensor's attribute and service stations are static. In this scenario, we examine the mobility strategies, underlying topologies and network parameters that guarantee an autonomous sensor recharge. The experimental results show that taking a proactive approach to energy redistribution and network fatigue outperforms passive strategies. The greedy closest-first swapping-based mobility strategy offered the best overall performance among all the proactive approaches studied and the proposed Compass Directed Graph provides a good underlying topology to achieve energy equilibrium.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".