Dynamic Offloading Technique for Latency-Sensitive Internet of Things Applications using Fog Computing

Abstract

Internet of Things (IoT) has evolved as a novel paradigm that provides com-putation power to different entities connected to it. IoT offers services to multiple sectors such as home automation, industrial automation, traffic management, healthcare sector, agriculture industry etc. IoT generally relies on cloud data centers for extended analytics, processing and storage support. The cloud offers highly scalable and robust platform for IoT applications. But latency sensitive IoT applications suffer delay issues as the cloud lies in remote location. Edge/fog computing was introduced to overcome the issues faced by delay-sensitive IoT applications. These platforms lie close to the IoT network, reducing the delay and response time. The fog nodes are usually distributed in nature. The data has to be properly offloaded to available fog nodes using efficient strategies to gain benefit from the integration. Differ-ent offloading schemes are available in the literature to overcome this prob-lem This paper proposes a novel offloading approach by combining two effi-cient metaheuristic algorithms, Honey Badger Algorithm (HBA) and Fla-mingo Search Algorithm (FSA) termed as HB-FS algorithm. The HB-FS is executed in an iterative manner optimizing the objective function in each it-eration. The performance evaluation of the proposed approach is done with different existing metaheuristic algorithms and the evaluations show that the proposed work outperforms the existing algorithms in terms of latency, response time and execution time. The methodology also offers better degree of imbalance with proper load balancing under different conditions.