A Cross-Layer Optimization Approach for Energy Efficient Wireless Sensor Networks: Coalition-Aided Data Aggregation, Cooperative Communication, and Energy Balancing
Bibliographic record
Abstract
We take a cross-layer optimization approach to study energy efficient data transport in coalition-based wireless sensor networks, where neighboring nodes are organized into groups to form coalitions and sensor nodes within one coalition carry out cooperative communications. In particular, we investigate two network models: (1) many-to-one sensor networks where data from one coalition are transmitted to the sink directly, and (2) multihop sensor networks where data are transported by intermediate nodes to reach the sink. For the many-to-one network model, we propose three schemes for data transmission from a coalition to the sink. In scheme 1, one node in the coalition is selected randomly to transmit the data; in scheme 2, the node with the best channel condition in the coalition transmits the data; and in scheme 3, all the nodes in the coalition transmit in a cooperative manner. Next, we investigate energy balancing with cooperative data transport in multihop sensor networks. Built on the above coalition-aided data transmission schemes, the optimal coalition planning is then carried out in multihop networks, in the sense that unequal coalition sizes are applied to minimize the difference of energy consumption among sensor nodes. Numerical analysis reveals that energy efficiency can be improved significantly by the coalition-aided transmission schemes, and that energy balancing across the sensor nodes can be achieved with the proposed coalition structures.
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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 | 0.002 |
| 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 | 0.002 |
| 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".