Machine learning is a pervasive development at the intersection of statistics and computer science. While it can benefit many data-related applications, the technical nature of the research literature and the corresponding algorithms slows down its adoption. Scikit-learn is an open-source software project that aims at making machine learning accessible to all, whether it be in…
GetMobile Mobile Computing and Communications Template
Write in a clean editor, then format for GetMobile Mobile Computing and Communications in one click — DocuGuru applies the official ACM template with numbered references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
About the GetMobile Mobile Computing and Communications format
GetMobile Mobile Computing and Communications is a peer-reviewed journal published by ACM, covering Diverse Scientific and Economic Studies, Energy Harvesting in Wireless Networks, Mathematics, Computing, and Information Processing.
| Publisher | ACM |
|---|---|
| Reference style | Numbered (ACM) Numbered — [1], [2] in the text [1] Ada Smith, Ben Jones, and Cara Lee. 2023. A representative article title. GetMobile Mobile Computing and Communications 12, 3, Article 45 (2023).
Formats any DOI in GetMobile Mobile Computing and Communications style. No sign-up. |
| Publishes research in | Diverse Scientific and Economic Studies Energy Harvesting in Wireless Networks Mathematics, Computing, and Information Processing Interactive and Immersive Displays Context-Aware Activity Recognition Systems |
| ISSN | 2375-0529 |
| Citation impact (2-yr) | 3.77 |
| h-index | 26 |
| i10-index | 70 |
| Total citations | 3,315 |
| Top institutions publishing here | University of Washington |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Papers published in GetMobile Mobile Computing and Communications per year
Citation impact of GetMobile Mobile Computing and Communications by publication year
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Most-cited papers in GetMobile Mobile Computing and Communications
Large language models (LLMs) have transformed numerous AI applications. On-device LLM is becoming increasingly important: running LLMs locally on edge devices can reduce cloud computing costs and protect users' privacy. However, the astronomical model size and the limited hardware resources pose significant deployment challenges. To solve these issues, we propose Activation-aware Weight Quantization (AWQ) and…
With the growth of mobile data and devices, cellular networks have great potential to support various vehicular communication services for safety and non-safety applications. This paper presents an overview of the 3GPP cellular vehicle-to-everything (V2X) standards. We discuss various aspects of V2X standards, including types of V2X application support in 3GPP, requirements to support eV2X…
column Share on A Brief History of Cloud Offload: A Personal Journey from Odyssey Through Cyber Foraging to Cloudlets Author: Mahadev Satyanarayanan Carnegie Mellon University Carnegie Mellon UniversityView Profile Authors Info & Claims GetMobile: Mobile Computing and CommunicationsVolume 18Issue 4October 2014 pp 19–23https://doi.org/10.1145/2721914.2721921Published:13 January 2015Publication History 85citation1,382DownloadsMetricsTotal Citations85Total Downloads1,382Last 12 Months68Last 6 weeks2 Get Citation…
Wi-Fi has traditionally been considered a power-consuming communication system and has not been widely adopted in the sensor network and Internet of Things (IoT) space. We introduce Passive Wi-Fi that demonstrates that one can generate 802.11b transmissions using backscatter communication, while consuming 3-4 orders of magnitude lower power than existing Wi-Fi chipsets. Passive Wi-Fi transmissions…