A Low Complexity Relaxation for Minimizing Bandwidth Use in IoT Storage Without Newcomers

TY - GEN

T1 - A Low Complexity Relaxation for Minimizing Bandwidth Use in IoT Storage Without Newcomers

AU - Zhao, Xiaobo

AU - Lucani Rötter, Daniel Enrique

AU - Shen, Xiaohong

AU - Wang, Haiyan

PY - 2019

Y1 - 2019

N2 - This paper proposes a low-complexity solution for the data protection problem without newcomer nodes in Internet of Things (IoT) scenarios, i.e., when device losses cannot be replaced by new devices. Application scenarios include environmental monitoring, data collection, and industrial automation. Although the optimal solution and optimization framework have been studied in previous work to minimize the network costs and storage capacity requirements, this paper shows that the optimal solution has a high complexity as the number of devices increases. Given the massive number of IoT devices, we propose a relaxation to the cut capacity constraints that (a) guarantees data recoverability, (b) achieves the minimum network use, and (c) reduces the problem's complexity dramatically. Our numerical results show that the proposed relaxation allows us to change the computational scaling of the problem. More specifically, we show that the time taken to compute the optimal transmission policy with the relaxation for a system with 800 devices is the same as the time it takes the optimal solution to solve the case of 15 devices.

AB - This paper proposes a low-complexity solution for the data protection problem without newcomer nodes in Internet of Things (IoT) scenarios, i.e., when device losses cannot be replaced by new devices. Application scenarios include environmental monitoring, data collection, and industrial automation. Although the optimal solution and optimization framework have been studied in previous work to minimize the network costs and storage capacity requirements, this paper shows that the optimal solution has a high complexity as the number of devices increases. Given the massive number of IoT devices, we propose a relaxation to the cut capacity constraints that (a) guarantees data recoverability, (b) achieves the minimum network use, and (c) reduces the problem's complexity dramatically. Our numerical results show that the proposed relaxation allows us to change the computational scaling of the problem. More specifically, we show that the time taken to compute the optimal transmission policy with the relaxation for a system with 800 devices is the same as the time it takes the optimal solution to solve the case of 15 devices.

UR - http://www.scopus.com/inward/record.url?scp=85063522616&partnerID=8YFLogxK

U2 - 10.1109/CCNC.2019.8651819

DO - 10.1109/CCNC.2019.8651819

M3 - Article in proceedings

BT - 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC)

PB - IEEE

T2 - 16th IEEE Annual Consumer Communications and Networking Conference, CCNC 2019

Y2 - 11 January 2019 through 14 January 2019

ER -