Research Article
Optimizing the Operational Time of IoT Devices in Cloud-Fog Systems
@INPROCEEDINGS{10.1007/978-3-030-67101-3_12, author={Nguyen Thanh Tung}, title={Optimizing the Operational Time of IoT Devices in Cloud-Fog Systems}, proceedings={Context-Aware Systems and Applications, and Nature of Computation and Communication. 9th EAI International Conference, ICCASA 2020, and 6th EAI International Conference, ICTCC 2020, Thai Nguyen, Vietnam, November 26--27, 2020, Proceedings}, proceedings_a={ICCASA \& ICTCC}, year={2021}, month={1}, keywords={IoT Cloud-Fog system Battery constraint Operation time Linear Programming}, doi={10.1007/978-3-030-67101-3_12} }- Nguyen Thanh Tung
Year: 2021
Optimizing the Operational Time of IoT Devices in Cloud-Fog Systems
ICCASA & ICTCC
Springer
DOI: 10.1007/978-3-030-67101-3_12
Abstract
With the increasing number of connected devices, sensors, data generated need to be analyzed. The current cloud computing model, which concentrate on computing and storage resources in a few large data centers, will inevitably lead to excessive network load, end-to-end service latency, and overall power consumption. This leads to the creation of new network architectures that extend computing and storage capabilities to the edge of the network, close to end-users. The emerging problem is how to efficiently deploy the services to the system that satisfies service resource requirements and QoS constraints while maximizing resource utilization.
In this paper, we investigate the problem of IoT services deployment in Cloud Fog system to provide IoT services with minimal energy consumption. We formulate the problem using a Linear Programming (LP) model to maximize the operational time of Cloud-Fog system as well as the IoT services specific requirements [1]. We propose a new heuristic algorithm to simplify the problem. We compare the lifetime of the proposed algorithm with the optimal solution solved by Linear Programming. The experimental results show that our proposed solution is very close to optimum solutions in terms of energy efficiency.
