An empirical performance study of Intra-vehicular Wireless Sensor Networks under WiFi and Bluetooth interference
February 22, 2015 Β· Declared Dead Β· π Global Communications Conference
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Jiun-Ren Lin, Timothy Talty, Ozan K. Tonguz
arXiv ID
1502.06188
Category
cs.NI: Networking & Internet
Citations
44
Venue
Global Communications Conference
Last Checked
6 months ago
Abstract
Intra-Vehicular Wireless Sensor Network (IVWSN) is a new automotive architecture that applies wireless technologies to the communications between Electrical Control Units (ECUs) and sensors. It can potentially help achieve better fuel economy, reduce wiring complexity, and support additional new applications. In the existing works, most of the popular wireless technologies applied on IVWSNs occupy the same 2.4 GHz ISM frequency bands as WiFi and Bluetooth do. It is therefore essential to evaluate the performance of IVWSNs under interference from WiFi and Bluetooth devices, especially when these devices are inside the vehicle. In this paper, we report the results of a comprehensive experimental study of IVWSNs based on ZigBee and Bluetooth Low Energy under WiFi and Bluetooth interference. The impact of the interference from Bluetooth and WiFi devices can be clearly observed from the experiments. The results of the experiments conducted suggest that Bluetooth Low Energy technology outperforms ZigBee technology in the context of IVWSNs when WiFi interference exists in the car.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Networking & Internet
R.I.P.
π»
Ghosted
π
π
The Cartographer
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
π
π
The Cartographer
A Survey of Indoor Localization Systems and Technologies
R.I.P.
π»
Ghosted
Survey of Important Issues in UAV Communication Networks
π
π
The Cartographer
Network Function Virtualization: State-of-the-art and Research Challenges
π
π
The Cartographer
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted