Allocation of Heterogeneous Resources of an IoT Device to Flexible Services
August 07, 2015 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
Vangelis Angelakis, Ioannis Avgouleas, Nikolaos Pappas, Emma Fitzgerald, Di Yuan
arXiv ID
1508.01742
Category
cs.NI: Networking & Internet
Citations
77
Venue
IEEE Internet of Things Journal
Last Checked
5 months ago
Abstract
Internet of Things (IoT) devices can be equipped with multiple heterogeneous network interfaces. An overwhelmingly large amount of services may demand some or all of these interfaces' available resources. Herein, we present a precise mathematical formulation of assigning services to interfaces with heterogeneous resources in one or more rounds. For reasonable instance sizes, the presented formulation produces optimal solutions for this computationally hard problem. We prove the NP-Completeness of the problem and develop two algorithms to approximate the optimal solution for big instance sizes. The first algorithm allocates the most demanding service requirements first, considering the average cost of interfaces resources. The second one calculates the demanding resource shares and allocates the most demanding of them first by choosing randomly among equally demanding shares. Finally, we provide simulation results giving insight into services splitting over different interfaces for both cases.
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