78,114 research outputs found
An Overview on IEEE 802.11bf: WLAN Sensing
With recent advancements, the wireless local area network (WLAN) or wireless
fidelity (Wi-Fi) technology has been successfully utilized to realize sensing
functionalities such as detection, localization, and recognition. However, the
WLANs standards are developed mainly for the purpose of communication, and thus
may not be able to meet the stringent requirements for emerging sensing
applications. To resolve this issue, a new Task Group (TG), namely IEEE
802.11bf, has been established by the IEEE 802.11 working group, with the
objective of creating a new amendment to the WLAN standard to meet advanced
sensing requirements while minimizing the effect on communications. This paper
provides a comprehensive overview on the up-to-date efforts in the IEEE
802.11bf TG. First, we introduce the definition of the 802.11bf amendment and
its formation and standardization timeline. Next, we discuss the WLAN sensing
use cases with the corresponding key performance indicator (KPI) requirements.
After reviewing previous WLAN sensing research based on communication-oriented
WLAN standards, we identify their limitations and underscore the practical need
for the new sensing-oriented amendment in 802.11bf. Furthermore, we discuss the
WLAN sensing framework and procedure used for measurement acquisition, by
considering both sensing at sub-7GHz and directional multi-gigabit (DMG)
sensing at 60 GHz, respectively, and address their shared features,
similarities, and differences. In addition, we present various candidate
technical features for IEEE 802.11bf, including waveform/sequence design,
feedback types, as well as quantization and compression techniques. We also
describe the methodologies and the channel modeling used by the IEEE 802.11bf
TG for evaluation. Finally, we discuss the challenges and future research
directions to motivate more research endeavors towards this field in details.Comment: 31 pages, 25 figures, this is a significant updated version of
arXiv:2207.0485
Reputation Agent: Prompting Fair Reviews in Gig Markets
Our study presents a new tool, Reputation Agent, to promote fairer reviews
from requesters (employers or customers) on gig markets. Unfair reviews,
created when requesters consider factors outside of a worker's control, are
known to plague gig workers and can result in lost job opportunities and even
termination from the marketplace. Our tool leverages machine learning to
implement an intelligent interface that: (1) uses deep learning to
automatically detect when an individual has included unfair factors into her
review (factors outside the worker's control per the policies of the market);
and (2) prompts the individual to reconsider her review if she has incorporated
unfair factors. To study the effectiveness of Reputation Agent, we conducted a
controlled experiment over different gig markets. Our experiment illustrates
that across markets, Reputation Agent, in contrast with traditional approaches,
motivates requesters to review gig workers' performance more fairly. We discuss
how tools that bring more transparency to employers about the policies of a gig
market can help build empathy thus resulting in reasoned discussions around
potential injustices towards workers generated by these interfaces. Our vision
is that with tools that promote truth and transparency we can bring fairer
treatment to gig workers.Comment: 12 pages, 5 figures, The Web Conference 2020, ACM WWW 202
Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review
The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features
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