TY - GEN
T1 - Base station allocation for users with overlapping coverage in wirelessly networked disaster areas
AU - Wang, Yu
AU - Meyer, Michael Conrad
AU - Wang, Junbo
N1 - Funding Information:
This research was supported by the Japan Science and Technology Agency (JST) Strategic International Collaborative Research Program (SICORP).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - After major disasters, temporary deployable cellular networks are often used to construct an emergency communication network. These networks do not have the same level of performance as a typical fog or cloud network, and in order to serve the users in a way that is sustainable, utilization of optimization algorithms must be considered. We proposed using a genetic algorithm (GA) to optimally allocate these users in the overlapping areas to a base station so that the system could provide an improved user experience. We tested our proposed algorithm against a greedy algorithm, a random algorithm, and allocating users to the closest MBS. The greedy algorithm outperformed the two other baseline algorithms, but the proposed algorithm was able to reduce the average and worst-case delay of the system by 80% compared to the greedy algorithm. The genetic algorithm had completely settled by 150 generations. This algorithm provided such an advantage that it warrants deeper study.
AB - After major disasters, temporary deployable cellular networks are often used to construct an emergency communication network. These networks do not have the same level of performance as a typical fog or cloud network, and in order to serve the users in a way that is sustainable, utilization of optimization algorithms must be considered. We proposed using a genetic algorithm (GA) to optimally allocate these users in the overlapping areas to a base station so that the system could provide an improved user experience. We tested our proposed algorithm against a greedy algorithm, a random algorithm, and allocating users to the closest MBS. The greedy algorithm outperformed the two other baseline algorithms, but the proposed algorithm was able to reduce the average and worst-case delay of the system by 80% compared to the greedy algorithm. The genetic algorithm had completely settled by 150 generations. This algorithm provided such an advantage that it warrants deeper study.
KW - Big data processing
KW - Fog computing
KW - Genetic algorithm
KW - Minimal delay
KW - Networks
KW - Optimization
KW - User allocation
UR - http://www.scopus.com/inward/record.url?scp=85075180665&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075180665&partnerID=8YFLogxK
U2 - 10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00175
DO - 10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00175
M3 - Conference contribution
AN - SCOPUS:85075180665
T3 - Proceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019
SP - 954
EP - 960
BT - Proceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019
Y2 - 5 August 2019 through 8 August 2019
ER -