Multicast Scaling of Capacity and Energy Efficiency in Heterogeneous Wireless Sensor Networks
Bibliographic record
Abstract
Motivated by the requirement of heterogeneity in the Internet of Things, we initiate the joint study of capacity and energy efficiency scaling laws in heterogeneous wireless sensor networks, and so on. The whole network is composed of n nodes scattered in a square region with side length L = n α , and there are m = n ν home points { c j } j=1 m , where a generic home point c j generates q j nodes independently according to a stationary and rotationally invariant kernel k ( c j , ⋅). Among the n nodes, we schedule n s independent multicast sessions each consisting of k − 1 destination nodes and one source node. According to the heterogeneity of nodes’ distribution, we classify the network into two regimes: a cluster-dense regime and a cluster-sparse regime. For the cluster-dense regime, we construct single layer highway system using percolation theory and then build the multicast spanning tree for each multicast session. This scheme yields the Ω( n ½+(α − ½)γ / n s √ k ) per-session multicast capacity. For the cluster-sparse regime, we partition the whole network plane into several layers and construct nested highway systems. The similar multicast spanning tree yields the Ω( n ½−(1− ν)γ/2 / n s √ k ) per-session multicast capacity, where γ is the power attenuation factor. Interestingly, we find that the bottleneck of multicast capacity attributes to the network region with largest node density, which provides a guideline for the deployment of sensor nodes in large-scale sensor networks. We further analyze the upper bound of multicast capacity and the per-session multicast energy efficiency. Using both synthetic networks and real-world networks (i.e., Greenorbs), we evaluate the asymptotic capacity and energy efficiency and find that the theoretical scaling laws are gracefully supported by the simulation results. To our best knowledge, this is the first work verifying the scaling laws using real-world large-scale sensor network data.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".