Database Management Difficulties in the Internet of Things

Lourens, Melanie; Tamizhselvi, A.; Goswami, Brijesh; Alanya-Beltrán, Joel; Aarif, Mohd; Gangodkar, Durgaprasad

doi:10.1109/ic3i56241.2022.10072614

Cited by 4 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the number of IoT devices grows, so does the volume of data created by these devices, necessitating storage and processing systems that are both efficient and scalable. Cloud computing has evolved as a solution to these problems, enabling the analysis and administration of massive quantities of data created by IoT devices [7][8][9][10]. By utilizing cloud computing, IoT applications may make use of flexible and scalable processing and storage capabilities, as well as a pay-as-you-go pricing model that can help reduce expenses [11].…”

Section: Introductionmentioning

confidence: 99%

“…By utilizing cloud computing, IoT applications may make use of flexible and scalable processing and storage capabilities, as well as a pay-as-you-go pricing model that can help reduce expenses [11]. The integration of the cloud and IoT is a vision of the future internet that enables the development of a new, and more comprehensive set of intelligent services [8,9]. Despite the numerous benefits of cloud computing, the utilization of a centralized cloud infrastructure for IoT applications entails the transmission of all data to the cloud for additional processing, thereby increasing network traffic and service time [12,13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Two-tier Multi-objective Service Placement in Container-based Fog-Cloud Computing Platforms

Dogani

Yazdanpanah

Zare

et al. 2023

Preprint

View full text Add to dashboard Cite

Using cloud computing for Internet of Things (IoT) applications necessitates the transmission of all data to the centralized structure of the cloud, thereby leading to an increase in network traffic and service time. Consequently, cloud computing proves impractical for latency-sensitive IoT applications. Fog computing, acting as an intermediate layer between the cloud and IoT, ensures low latency for such applications. The service placement problem, an NP-hard problem that determines which node should host each service, represents one of the major challenges in the fog computing paradigm. While lightweight containers have emerged as a highly efficient virtualization approach, prior research predominantly employed traditional VM-based architecture for service placement in fog computing. Therefore, this study introduces a multi-objective optimization approach for dynamic service placement in container-based fog computing, accounting for cost, latency, and energy consumption. Specifically, we propose a two-tier framework for resource management in fog computing based on Kubernetes. The multi-objective non-dominated sorting genetic algorithm II (NSGA-II) algorithm balances conflicting performance objectives, such as latency, cost, and energy consumption. Empirical results demonstrate that the proposed method outperforms existing state-of-the-art methods.

show abstract