173 research outputs found
LoRaWAN-enabled Smart Campus: The Dataset and a People Counter Use Case
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 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
Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach
In many massive IoT communication scenarios, the IoT devices require coverage
from dynamic units that can move close to the IoT devices and reduce the uplink
energy consumption. A robust solution is to deploy a large number of UAVs (UAV
swarm) to provide coverage and a better line of sight (LoS) for the IoT
network. However, the study of these massive IoT scenarios with a massive
number of serving units leads to high dimensional problems with high
complexity. In this paper, we apply multi-agent deep reinforcement learning to
address the high-dimensional problem that results from deploying a swarm of
UAVs to collect fresh information from IoT devices. The target is to minimize
the overall age of information in the IoT network. The results reveal that both
cooperative and partially cooperative multi-agent deep reinforcement learning
approaches are able to outperform the high-complexity centralized deep
reinforcement learning approach, which stands helpless in large-scale networks
Meta-Learning Based Few Pilots Demodulation and Interference Cancellation For NOMA Uplink
Non-Orthogonal Multiple Access (NOMA) is at the heart of a paradigm shift
towards non-orthogonal communication due to its potential to scale well in
massive deployments. Nevertheless, the overhead of channel estimation remains a
key challenge in such scenarios. This paper introduces a data-driven,
meta-learning-aided NOMA uplink model that minimizes the channel estimation
overhead and does not require perfect channel knowledge. Unlike conventional
deep learning successive interference cancellation (SICNet), Meta-Learning
aided SIC (meta-SICNet) is able to share experience across different devices,
facilitating learning for new incoming devices while reducing training
overhead. Our results confirm that meta-SICNet outperforms classical SIC and
conventional SICNet as it can achieve a lower symbol error rate with fewer
pilots
- …