LoRaWAN-enabled Smart Campus: The Dataset and a People Counter Use Case
- URL: http://arxiv.org/abs/2304.13366v1
- Date: Wed, 26 Apr 2023 08:14:56 GMT
- Title: LoRaWAN-enabled Smart Campus: The Dataset and a People Counter Use Case
- Authors: Eslam Eldeeb and Hirley Alves
- Abstract summary: This paper presents a detailed description of the Smart Campus dataset based on LoRaWAN.
LoRaWAN is an emerging technology that enables serving hundreds of IoT devices.
- Score: 9.835561936689357
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: IoT has a significant role in the smart campus. This paper presents a
detailed description of the Smart Campus dataset based on LoRaWAN. LoRaWAN is
an emerging technology that enables serving hundreds of IoT devices. First, we
describe the LoRa network that connects the devices to the server. Afterward,
we analyze the missing transmissions and propose a k-nearest neighbor solution
to handle the missing values. Then, we predict future readings using a long
short-term memory (LSTM). Finally, as one example application, we build a deep
neural network to predict the number of people inside a room based on the
selected sensor's readings. Our results show that our model achieves an
accuracy of $95 \: \%$ in predicting the number of people. Moreover, the
dataset is openly available and described in detail, which is opportunity for
exploration of other features and applications.
Related papers
- TinyML for Speech Recognition [3.9134031118910264]
We train and deploy a quantized 1D convolutional neural network model to conduct speech recognition on an IoT edge device.
This can be useful in various Internet of Things (IoT) applications, such as smart homes and ambient assisted living for the elderly and people with disabilities.
arXiv Detail & Related papers (2025-04-22T19:00:40Z) - Effective Feature Selection for Predicting Spreading Factor with ML in Large LoRaWAN-based Mobile IoT Networks [0.5749787074942512]
This paper addresses the challenge of predicting the spreading factor (SF) in LoRaWAN networks using machine learning (ML) techniques.
We evaluated ML model performance across a large publicly available dataset to explore the best feature across key LoRaWAN features.
The combination of RSSI and SNR was identified as the best feature set.
arXiv Detail & Related papers (2025-03-12T08:58:28Z) - A Grid-based Sensor Floor Platform for Robot Localization using Machine
Learning [0.0]
We investigate machine learning methods using a new grid-based WSN platform called Sensor Floor.
Our goal is to localize all logistic entities, for this study we use a mobile robot.
The CNN model with regularization outperforms the Random Forest in terms of localization accuracy with aproximate 15 cm.
arXiv Detail & Related papers (2022-12-09T08:29:50Z) - Topics in Deep Learning and Optimization Algorithms for IoT Applications
in Smart Transportation [0.0]
This thesis investigates how different optimization algorithms and machine learning techniques can be leveraged to improve system performance.
In the first topic, we propose an optimal transmission frequency management scheme using decentralized ADMM-based method.
In the second topic, we leverage graph neural network (GNN) for demand prediction for shared bikes.
In the last topic, we consider a highway traffic network scenario where frequent lane changing behaviors may occur with probability.
arXiv Detail & Related papers (2022-10-13T11:45:30Z) - Learning to Learn with Generative Models of Neural Network Checkpoints [71.06722933442956]
We construct a dataset of neural network checkpoints and train a generative model on the parameters.
We find that our approach successfully generates parameters for a wide range of loss prompts.
We apply our method to different neural network architectures and tasks in supervised and reinforcement learning.
arXiv Detail & Related papers (2022-09-26T17:59:58Z) - 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) - Communication-Efficient Separable Neural Network for Distributed
Inference on Edge Devices [2.28438857884398]
We propose a novel method of exploiting model parallelism to separate a neural network for distributed inferences.
Under proper specifications of devices and configurations of models, our experiments show that the inference of large neural networks on edge clusters can be distributed and accelerated.
arXiv Detail & Related papers (2021-11-03T19:30:28Z) - Computational Intelligence and Deep Learning for Next-Generation
Edge-Enabled Industrial IoT [51.68933585002123]
We investigate how to deploy computational intelligence and deep learning (DL) in edge-enabled industrial IoT networks.
In this paper, we propose a novel multi-exit-based federated edge learning (ME-FEEL) framework.
In particular, the proposed ME-FEEL can achieve an accuracy gain up to 32.7% in the industrial IoT networks with the severely limited resources.
arXiv Detail & Related papers (2021-10-28T08:14:57Z) - Optimizing Resource-Efficiency for Federated Edge Intelligence in IoT
Networks [96.24723959137218]
We study an edge intelligence-based IoT network in which a set of edge servers learn a shared model using federated learning (FL)
We propose a novel framework, called federated edge intelligence (FEI), that allows edge servers to evaluate the required number of data samples according to the energy cost of the IoT network.
We prove that our proposed algorithm does not cause any data leakage nor disclose any topological information of the IoT network.
arXiv Detail & Related papers (2020-11-25T12:51:59Z) - LETI: Latency Estimation Tool and Investigation of Neural Networks
inference on Mobile GPU [0.0]
In this work, we consider latency approximation on mobile GPU as a data and hardware-specific problem.
We build open-source tools which provide a convenient way to conduct massive experiments on different target devices.
We experimentally demonstrate the applicability of such an approach on a subset of popular NAS-Benchmark 101 dataset.
arXiv Detail & Related papers (2020-10-06T16:51:35Z) - MS-RANAS: Multi-Scale Resource-Aware Neural Architecture Search [94.80212602202518]
We propose Multi-Scale Resource-Aware Neural Architecture Search (MS-RANAS)
We employ a one-shot architecture search approach in order to obtain a reduced search cost.
We achieve state-of-the-art results in terms of accuracy-speed trade-off.
arXiv Detail & Related papers (2020-09-29T11:56:01Z) - Proactive Tasks Management based on a Deep Learning Model [9.289846887298852]
We propose an intelligent, proactive tasks management model based on the demand.
We rely on a Deep Machine Learning (DML) model and more specifically on a Long Short Term Memory (LSTM) network.
We provide numerical results and reveal that the proposed scheme is capable of deciding on the fly while concluding the most efficient allocation.
arXiv Detail & Related papers (2020-07-25T05:28:14Z) - Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G
Networks [84.2155885234293]
We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC.
To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
arXiv Detail & Related papers (2020-02-22T14:38:11Z)
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.