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Register a new userPulse: Toward a Smart Campus by Communicating Real-time Wi-Fi Access Data
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Added by
Aoyu Wu
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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To enhance the mobility and convenience of the campus community, we designed and implemented the Pulse system, a visual interface for communicating the crowd information to the lay public including campus members and visitors. This is a challenging task which requires analyzing and reconciling the demands and interests for data as well as visual design among diverse target audiences. Through an iterative design progress, we study and address the diverse preferences of the lay audiences, whereby design rationales are distilled. The final prototype combines a set of techniques such as chart junk and redundancy encoding. Initial feedback from a wide audience confirms the benefits and attractiveness of the system.
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Real-Time Applications (RTA) are among the most important use cases for future Wi-Fi 7, defined by the IEEE 802.11be standard. This paper studies two backward-compatible channel access approaches to satisfy the strict quality of service (QoS) requirements of RTA on the transmission latency and packet loss rate that have been considered in the 802.11be Task Group. The first approach is based on limiting the transmission duration of non-RTA frames in the network. The second approach is based on preliminary channel access to ensure the timely delivery of RTA frames. With the developed mathematical model of these approaches, it is shown that both of them can satisfy the RTA QoS requirements. At the same time, the preliminary channel access provides up to 60% higher efficiency of the channel usage by the non-RTA traffic in scenarios with very strict RTA QoS requirements or with low intensity of the RTA traffic.
We show experimentally that workload-based AP-STA associations can improve system throughput significantly. We present a predictive model that guides optimal resource allocations in dense Wi-Fi networks and achieves 72-77% of the optimal throughput with varying training data set sizes using a 3-day trace of real cable modem traffic.
Wi-Fi is among the most successful wireless technologies ever invented. As Wi-Fi becomes more and more present in public and private spaces, it becomes natural to leverage its ubiquitousness to implement groundbreaking wireless sensing applications such as human presence detection, activity recognition, and object tracking, just to name a few. This paper reports ongoing efforts by the IEEE 802.11bf Task Group (TGbf), which is defining the appropriate modifications to existing Wi-Fi standards to enhance sensing capabilities through 802.11-compliant waveforms. We summarize objectives and timeline of TGbf, and discuss some of the most interesting proposed technical features discussed so far. We also introduce a roadmap of research challenges pertaining to Wi-Fi sensing and its integration with future Wi-Fi technologies and emerging spectrum bands, hoping to elicit further activities by both the research community and TGbf.
In 2019 IEEE 802 LAN/MAN Standards Committee started the development of the next major amendment of the Wi-Fi standard: the IEEE 802.11be, also known as Wi-Fi 7. This new amendment will introduce many new functions and will improve the existing ones that will make Wi-Fi more efficient in many new scenarios. One of the scenarios is the service of Real-Time Applications with strict requirements on latency and reliability of communications. Providing low latencies can be challenging in Wi-Fi because of the unlicensed spectrum and related interference from neighboring devices. In this paper, we consider the usage of OFDMA transmissions for Real-Time Applications and design resource allocation algorithms that can provide the required latency and reliability in the presence of interference.
Taking advantage of the rich information provided by Wi-Fi measurement setups, Wi-Fi-based human behavior sensing leveraging Channel State Information (CSI) measurements has received a lot of research attention in recent years. The CSI-based human sensing algorithms typically either rely on an explicit channel propagation model or, more recently, adopt machine learning so as to robustify feature extraction. In most related work, the considered CSI is extracted from a single dedicated Access Point (AP) communication setup. In this paper, we consider a more realistic setting where a legacy network of multiple APs is already deployed for communications purposes and leveraged for sensing benefits using machine learning. The use of legacy network presents challenges and opportunities as many Wi-Fi links can present with richer yet unequally useful data sets. In order to break the curse of dimensionality associated with training over a too large dimensional CSI, we propose a link selection mechanism based on Reinforcement Learning (RL) which allows for dimension reduction while preserving the data that is most relevant for human behavior sensing. The method is based on a sequential state decision-making process in which the CSI is modeled as a part of the state. From actual experiment results, our method is shown to perform better than state-of-the-art approaches in a scenario with multiple available Wi-Fi links.
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