Dissecting Energy Consumption of NB-IoT Devices Empirically
April 15, 2020 Β· Declared Dead Β· π IEEE Internet of Things Journal
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Foivos Michelinakis, Anas Saeed Al-selwi, Martina Capuzzo, Andrea Zanella, Kashif Mahmood, Ahmed Elmokashfi
arXiv ID
2004.07127
Category
cs.NI: Networking & Internet
Citations
53
Venue
IEEE Internet of Things Journal
Last Checked
5 months ago
Abstract
3GPP has recently introduced NB-IoT, a new mobile communication standard offering a robust and energy efficient connectivity option to the rapidly expanding market of Internet of Things (IoT) devices. To unleash its full potential, end-devices are expected to work in a plug and play fashion, with zero or minimal parameters configuration, still exhibiting excellent energy efficiency. We perform the most comprehensive set of empirical measurements with commercial IoT devices and different operators to date, quantifying the impact of several parameters to energy consumption. Our campaign proves that parameters setting does impact energy consumption, so proper configuration is necessary. We shed light on this aspect by first illustrating how the nominal standard operational modes map into real current consumption patterns of NB-IoT devices. Further, we investigate which device reported metadata metrics better reflect performance and implement an algorithm to automatically identify device state in current time series logs. Then, we provide a measurement-driven analysis of the energy consumption and network performance of two popular NB-IoT boards under different parameter configurations and with two major western European operators. We observed that energy consumption is mostly affected by the paging interval in Connected state, set by the base station. However, not all operators correctly implement such settings. Furthermore, under the default configuration, energy consumption in not strongly affected by packet size nor by signal quality, unless it is extremely bad. Our observations indicate that simple modifications to the default parameters settings can yield great energy savings.
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