Game theoretical approach for utility-based distributed balanced data routing in wireless sensor networks
Bibliographic record
Abstract
In this thesis, two distributed algorithms for the construction of load balanced routing trees in wireless sensor networks are proposed. In such networks load balanced data routing and aggregation can considerably decrease uneven energy consumption among sensor nodes and prolong network lifetime. The proposed algorithms achieve load balancing by adjusting the number of children among parents as much as possible. The solution is based on game theoretical approach, where child adjustment is considered as a game between parents and child nodes, in which parents arc cooperative and children are selfish players. The gained utility by each node is determined through utility functions defined per role. Utility functions determine the behavior of nodes in each role. At the game termination, each individual node gains the maximum benefit based on its utility function, and the network reaches the global goal of forming the balanced tree. The proposed methods are called Utility Driven Balanced Communication (UDBC) algorithm which is designed for homogenous environment, where all nodes are assumed to produce equal amount of information, and Heterogenous Balanced Data Routing (HBDR) algorithm which is proposed for heterogenous environment, where different applications use different aggregation functions, and nodes can be vary in terms of the amount of produced information, energy levels, data transmission rate and available of the amount of produced information, energy levels, data transmission rate and available bandwidth for transmission. The advantage of this work over similar work in the literature is the construction of more balanced trees which results in prolonging network lifetime, with the capability of adaption according to specific application needs for sensitivity to delay and reliability of data delivery.
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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 | 0.003 |
| 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 | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".