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Smart city article details
| Title | A Secure Iot Applications Allocation Framework For Integrated Fog-Cloud Environment |
|---|---|
| ID_Doc | 4499 |
| Authors | Dubey K.; Sharma S.C.; Kumar M. |
| Year | 2022 |
| Published | Journal of Grid Computing, 20, 1 |
| DOI | http://dx.doi.org/10.1007/s10723-021-09591-x |
| Abstract | Applications of the Internet of Things (IoT) are used in several areas to create a smart environment such as healthcare, smart agriculture, smart cities, transportation, and water management, etc. Due to the high pace of IoT technology adoption, Big Data generation is increasing excessively, requiring an efficient platform like cloud computing to process a large amount of data. On the other hand, time/delay-sensitive and real-time applications cannot be processed in the cloud due to high latency and energy consumption. Hence, a new emerging computing model named fog has emerged to address the mentioned issues and provide a complementary solution. However, Fog nodes provide limited cloud services in minimum delay and energy at the local node, but they cannot process the highly computation-oriented IoT applications. Furthermore, an adaptive cloud-fog integrated framework is proposed to process entire IoT applications and significantly improve the latency, computation cost, load balancing, and energy consumption by accommodating the resources in the form of virtual machine instances. This article exploited the features of two metaheuristic-based techniques Cuckoo Search Optimization (CSO) and Partial Swarm Optimization (PSO). We have developed a secure framework to solve the allocation of the IoT services in the cloud-fog environment while minimizing the mentioned influential parameters. The performance of the proposed framework is rigorously evaluated at synthetic datasets and heterogeneity of resources in fog as well as cloud simulation environment. The simulation results proved that the proposed hybrid metaheuristic algorithm outperforms other baseline policies and improves the various influential parameters. © 2022, The Author(s), under exclusive licence to Springer Nature B.V. |
| Author Keywords | Cloud computing; Cuckoo search optimization; Fog computing; Internet of things; Particle swarm optimization |
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