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Autonomous Relocation Strategies for Cells on Wheels in Environments with Prohibited Areas

Published

Author(s)

Kamran Sayrafian, Ladan Rabieekenari, John Baras

Abstract

Public safety organizations increasingly rely on wireless technology for their mission critical communication during disaster response operations. In such situations, a communication network could face much higher traffic demands compared to its normal operation. Given the limited capacity of base stations in the network, such peak traffic scenarios could lead to high blocking probability or equivalently service interruptions during critical communications. At the same time, networking infrastructure can breakdown during a disaster. Proper deployment of mobile cells - Cells on Wheels - can help to enhance the network coverage or accommodate excess traffic in areas with high concentration of users. In addition, an intelligent relocation strategy can be used to efficiently adapt the cell locations to match variations in the spatial distribution of the traffic. In practical scenarios, these mobile base stations may not be able to relocate to all positions within the target field. Such prohibited areas introduce additional constraints on designing an intelligent relocation strategy. In this paper, we propose a decentralized relocation algorithm that enables mobile cells to adapt their positions in response to potentially changing traffic patterns in a field with prohibited areas. Extensive simulations show considerable improvement in supporting spatially variable traffic throughout the target field.
Conference Dates
May 21-25, 2017
Conference Location
Paris
Conference Title
IEEE International Conference on Communications ICC 2017

Citation

Sayrafian, K. , Rabieekenari, L. and Baras, J. (2017), Autonomous Relocation Strategies for Cells on Wheels in Environments with Prohibited Areas, IEEE International Conference on Communications ICC 2017, Paris, -1, [online], https://doi.org/10.1109/ICC.2017.7997091 (Accessed April 16, 2024)
Created July 31, 2017, Updated May 13, 2020