Modeling Classroom Occupancy using Data of WiFi Infrastructure in a
University Campus
- URL: http://arxiv.org/abs/2104.10667v1
- Date: Mon, 19 Apr 2021 06:15:45 GMT
- Title: Modeling Classroom Occupancy using Data of WiFi Infrastructure in a
University Campus
- Authors: Iresha Pasquel Mohottige and Hassan Habibi Gharakheili and Vijay
Sivaraman and Tim Moors
- Abstract summary: In this paper, we develop machine learning based models to infer classroom occupancy from WiFi sensing infrastructure.
We analyze metadata from a dense and dynamic wireless network comprising of thousands of access points (APs)
We achieve 84.6% accuracy in mapping APs to classrooms while the accuracy of our estimation for room occupancy is comparable to beam counter sensors with a symmetric Mean Absolute Percentage Error (sMAPE) of 13.10%.
- Score: 1.8352113484137622
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Universities worldwide are experiencing a surge in enrollments, therefore
campus estate managers are seeking continuous data on attendance patterns to
optimize the usage of classroom space. As a result, there is an increasing
trend to measure classrooms attendance by employing various sensing
technologies, among which pervasive WiFi infrastructure is seen as a low cost
method. In a dense campus environment, the number of connected WiFi users does
not well estimate room occupancy since connection counts are polluted by
adjoining rooms, outdoor walkways, and network load balancing.
In this paper, we develop machine learning based models to infer classroom
occupancy from WiFi sensing infrastructure. Our contributions are three-fold:
(1) We analyze metadata from a dense and dynamic wireless network comprising of
thousands of access points (APs) to draw insights into coverage of APs,
behavior of WiFi connected users, and challenges of estimating room occupancy;
(2) We propose a method to automatically map APs to classrooms using
unsupervised clustering algorithms; and (3) We model classroom occupancy using
a combination of classification and regression methods of varying algorithms.
We achieve 84.6% accuracy in mapping APs to classrooms while the accuracy of
our estimation for room occupancy is comparable to beam counter sensors with a
symmetric Mean Absolute Percentage Error (sMAPE) of 13.10%.
Related papers
- Collaborative Learning with a Drone Orchestrator [79.75113006257872]
A swarm of intelligent wireless devices train a shared neural network model with the help of a drone.
The proposed framework achieves a significant speedup in training, leading to an average 24% and 87% saving in the drone hovering time.
arXiv Detail & Related papers (2023-03-03T23:46:25Z) - Federated Zero-Shot Learning for Visual Recognition [55.65879596326147]
We propose a novel Federated Zero-Shot Learning FedZSL framework.
FedZSL learns a central model from the decentralized data residing on edge devices.
The effectiveness and robustness of FedZSL are demonstrated by extensive experiments conducted on three zero-shot benchmark datasets.
arXiv Detail & Related papers (2022-09-05T14:49:34Z) - Online Data Selection for Federated Learning with Limited Storage [53.46789303416799]
Federated Learning (FL) has been proposed to achieve distributed machine learning among networked devices.
The impact of on-device storage on the performance of FL is still not explored.
In this work, we take the first step to consider the online data selection for FL with limited on-device storage.
arXiv Detail & Related papers (2022-09-01T03:27:33Z) - Domain Adversarial Graph Convolutional Network Based on RSSI and
Crowdsensing for Indoor Localization [8.406788215294483]
We present a novel WiDAGCN model that can be trained using a small number of labeled site survey data and large amounts of unlabeled crowdsensed WiFi fingerprints.
Our system is evaluated using a public indoor localization dataset that includes multiple buildings.
arXiv Detail & Related papers (2022-04-06T08:06:27Z) - Parallel Successive Learning for Dynamic Distributed Model Training over
Heterogeneous Wireless Networks [50.68446003616802]
Federated learning (FedL) has emerged as a popular technique for distributing model training over a set of wireless devices.
We develop parallel successive learning (PSL), which expands the FedL architecture along three dimensions.
Our analysis sheds light on the notion of cold vs. warmed up models, and model inertia in distributed machine learning.
arXiv Detail & Related papers (2022-02-07T05:11:01Z) - Location Leakage in Federated Signal Maps [7.093808731951124]
We consider the problem of predicting cellular network performance (signal maps) from measurements collected by several mobile devices.
We formulate the problem within the online federated learning framework: (i) federated learning enables users to collaboratively train a model, while keeping their training data on their devices.
We consider an honest-but-curious server, who observes the updates from target users participating in FL and infers their location using a deep leakage from gradients (DLG) type of attack.
We build on this observation to protect location privacy, in our setting, by revisiting and designing mechanisms within the federated learning framework including: tuning the FL
arXiv Detail & Related papers (2021-12-07T02:28:12Z) - Mobility-Aware Cluster Federated Learning in Hierarchical Wireless
Networks [81.83990083088345]
We develop a theoretical model to characterize the hierarchical federated learning (HFL) algorithm in wireless networks.
Our analysis proves that the learning performance of HFL deteriorates drastically with highly-mobile users.
To circumvent these issues, we propose a mobility-aware cluster federated learning (MACFL) algorithm.
arXiv Detail & Related papers (2021-08-20T10:46:58Z) - Estimating indoor occupancy through low-cost BLE devices [2.462983746099006]
This article presents a low-cost system for occupancy detection.
It builds upon detecting variations in Bluetooth Low Energy (BLE) signals related to the presence of humans.
On average, in different environments, we can correctly classify the occupancy with an accuracy of 97.97%.
arXiv Detail & Related papers (2021-01-30T09:54:31Z) - WAFFLe: Weight Anonymized Factorization for Federated Learning [88.44939168851721]
In domains where data are sensitive or private, there is great value in methods that can learn in a distributed manner without the data ever leaving the local devices.
We propose Weight Anonymized Factorization for Federated Learning (WAFFLe), an approach that combines the Indian Buffet Process with a shared dictionary of weight factors for neural networks.
arXiv Detail & Related papers (2020-08-13T04:26:31Z) - Analyzing the Impact of Covid-19 Control Policies on Campus Occupancy
and Mobility via Passive WiFi Sensing [0.0]
This paper conjectures that analyzing user occupancy and mobility via deployed WiFi infrastructure can help institutions monitor and maintain safety compliance.
Using smartphones as a proxy for user location, our analysis demonstrates how coarse-grained WiFi data can sufficiently reflect indoor occupancy spectrum.
arXiv Detail & Related papers (2020-05-25T11:47:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.