Bibliographic record
Abstract
Wireless mesh networks (WMNs) have been envisioned to enhance flexibility, increase reliability, and improve performance of wireless networks. Although WMNs are not widely considered in wireless metropolitan area network (WMAN) deployments, IEEE 802.11 wireless local area network (WLAN) is an interesting technology to realize low-cost WMNs. In this chapter, we provide an overview of the salient features and most important specifications of WiFi-based WMNs and describe the major challenging issues of routing and medium access control (MAC) protocols. Moreover, we briefly describe the optional mesh mode of initial standard IEEE 802.16d for fixed WiMAX, which has been removed from the IEEE standard 802.16e for mobile WiMAX. Characteristics Typically, there are two main approaches in the design of wireless networks (Schiller [2003]): 1. Infrastructure networks: Wireless mobile stations (STAs) rely on an underlying infrastructure for communication. They communicate with each other via a central control point, e.g., an access point (AP). WMANs, such as the global system for mobile communications (GSM) and universal mobile telecommunications system (UMTS), are typical examples for infrastructure wireless networks. 2. Infrastructure-less networks: STAs communicate directly with each other. In infrastructure-less wireless networks, also known as mobile ad-hoc networks (MANETs), STAs are able to act as routers. Emergency search-and-rescue operations, corporate meetings, and military communications in hostile terrains are example applications of MANETs.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.020 |
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".