Bibliographic record
Abstract
Wireless Networks, supported by recent technological advances in low power wireless communications along with silicon integration of various functionalities such as sensing, communications, intelligence and actuations are emerging as a critically important disruptive computer class based on a new platform, networking structure and interface that enable novel, low cost, high volume applications such as nuclear, biological and chemical attack detection and protection, home automation, battlefield surveillance and environmental monitoring. Several of such applications have been difficult to realize because of problems involved with inputting data from sensors directly in to actor systems. To fulfill their large range of applications wireless networks will collaborate with other wired networks. The research community is working to develop high performance computing solutions to problems arising from the complexities of these wireless network systems. This special issue, highlights advances in various aspects of wireless networks and is organized mainly from the papers of the The 22nd IEEE International Conference on Advanced Information Networking and Applications AINA 2008, held in GinoWan, Okinawa, Japan, March 25–28, 2008. The conference received 469 submissions and every paper was reviewed carefully by 3 reviewers. Based on their quality and significance 146 papers were accepted in AINA 2008. We received 17 papers for this special issue. After two more rounds of review, we accepted 6 papers based on their quality and suitability to the special issue as well as the journal. The Medium Access Control (MAC) protocol is a major element that determines the efficiency in sharing the limited communication bandwidth in wireless networks. In the first paper, Mjidi et al. study the impact of dynamic RTS threshold adjustment for IEEE 802.11 MAC protocol. They show both analytically and with extensive simulations the advantages of the proposed scheme. In the second paper, Shigeyasu et al. propose a new MAC protocol, namely, IEEE802.11DCF with CCTS (Cancel CTS). The proposed protocol is analyzed by extensive simulations. Research results on wireless ad hoc and sensor networks shows that most of the node energy is spent in idle state. Mahfoudh and Minet propose SERENA, an algorithm to SchEdule RoutEr Nodes Activity.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.009 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".