In this paper we analyze the performance of QUIC as a transport alternative for Internet of Things (IoT) services based on the Message Queuing Telemetry Protocol (MQTT). QUIC is a novel protocol promoted by Google, and was originally conceived to tackle the limitations of the traditional Transmission Control Protocol (TCP), specifically aiming at the reduction of the latency caused by connection establishment. QUIC use in IoT environments is not widespread, and it is therefore interesting to characterize its performance when in over such scenarios. We used an emulation-based platform, where we integrated QUIC and MQTT (using GO-based implementations) and compared their combined performance with the that exhibited by the traditional TCP/TLS approach. We used Linux containers as end devices, and the ns-3 simulator to emulate different network technologies, such as WiFi, cellular, and satellite, and varying conditions. The results evince that QUIC is indeed an appropriate protocol to guarantee robust, secure, and low latency communications over IoT scenarios.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8434189PMC
http://dx.doi.org/10.3390/s21175737DOI Listing

Publication Analysis

Top Keywords

iot scenarios
8
quic
6
lower latency
4
latency iiot
4
iiot evaluation
4
evaluation quic
4
quic industrial
4
iot
4
industrial iot
4
scenarios paper
4

Similar Publications

As the Internet of Things (IoT) expands globally, the challenge of signal transmission in remote regions without traditional communication infrastructure becomes prominent. An effective solution involves integrating aerial, terrestrial, and space components to form a Space-Air-Ground Integrated Network (SAGIN). This paper discusses an uplink signal scenario in which various types of data collection sensors as IoT devices use Unmanned Aerial Vehicles (UAVs) as relays to forward signals to low-Earth-orbit satellites.

View Article and Find Full Text PDF

Ensuring Reliable Network Communication and Data Processing in Internet of Things Systems with Prediction-Based Resource Allocation.

Sensors (Basel)

January 2025

Department of Computer Science and Systems Engineering, Faculty of Information and Communication Technology, Wrocław University of Science and Technology, 50-370 Wrocław, Poland.

The distributed nature of IoT systems and new trends focusing on fog computing enforce the need for reliable communication that ensures the required quality of service for various scenarios. Due to the direct interaction with the real world, failure to deliver the required QoS level can introduce system failures and lead to further negative consequences for users. This paper introduces a prediction-based resource allocation method for Multi-Access Edge Computing-capable networks, aimed at assurance of the required QoS and optimization of resource utilization for various types of IoT use cases featuring adaptability to changes in users' requests.

View Article and Find Full Text PDF

The proliferation of the Internet of Things (IoT) has worsened the challenge of maintaining data and user privacy. IoT end devices, often deployed in unsupervised environments and connected to open networks, are susceptible to physical tampering and various other security attacks. Thus, robust, efficient authentication and key agreement (AKA) protocols are essential to protect data privacy during exchanges between end devices and servers.

View Article and Find Full Text PDF

Human Occupancy Monitoring and Positioning with Speed-Responsive Adaptive Sliding Window Using an Infrared Thermal Array Sensor.

Sensors (Basel)

December 2024

Department of Computer and Information Systems, The University of Aizu, Aizuwakamatsu 965-8580, Fukushima, Japan.

In the current era of advanced IoT technology, human occupancy monitoring and positioning technology is widely used in various scenarios. For example, it can optimize passenger flow in public transportation systems, enhance safety in large shopping malls, and adjust smart home devices based on the location and number of occupants for energy savings. Additionally, in homes requiring special care, it can provide timely assistance.

View Article and Find Full Text PDF
Article Synopsis
  • The rise of Industry 4.0 has increased the need for effective fault diagnosis in servo motors, highlighting the limitations of traditional methods that rely on expert knowledge and handcrafted features.
  • A new approach combines multi-scale convolutional neural networks (MSCNNs), long short-term memory networks (LSTM), and attention mechanisms, making it more efficient for complex industrial settings.
  • This method is optimized for deployment on edge devices through techniques like knowledge distillation and model quantization, resulting in lower computational demands while maintaining high accuracy in diagnosing faults in servo motors.
View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!