Improvement of Task Offloading for Latency Sensitive Tasks in Fog Environment Parmeet Kaur and Shikha Mehta Abstract Fog computing is gaining rapid acceptance as a distributed computing paradigm that brings cloud-like services near the end devices. It enhances the compu- tation capabilities of mobile nodes and IoT (Internet of Things) devices by providing compute and storage capabilities similar to the cloud but at a lower latency and using lesser bandwidth. Additional advantages of fog computing include its support for node mobility, context awareness, reliability and scalability. Due to its multiple benefits, fog computing is used for offloading tasks from applications executing on end devices. This allows faster execution of applications using the capabilities of fog nodes. However, the task offloading problem in the fog environment is chal- lenging due to the dynamic nature of fog environment and multiple QoS (Quality of Service) parameters dependent on the application being executed. Therefore, the chapter proposes a QoS-aware task offloading strategy using a novel nature-inspired optimization algorithm, known as the Smart Flower Optimization Algorithm (SFOA). The proposed strategy takes into account the QoS parameters such as the task dead- lines and budget constraints in selection of appropriate fog nodes where computation tasks can be offloaded. The proposed strategy has been simulated and the results have verified the efficacy of the strategy. Keywords Task offloading · Fog computing · IoT · Smart Flower Optimization Algorithm · Particle Swarm Optimization · Shuffled Frog Leaping Algorithm P. Kaur (B ) · S. Mehta Department of Computer Science & Enginnering and Information Technology, Jaypee Institute of Information Technology, Noida, India e-mail: parmeet.kaur@jiit.ac.in S. Mehta e-mail: shikha.mehta@jiit.ac.in © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 R. Tiwari et al. (eds.), Energy Conservation Solutions for Fog-Edge Computing Paradigms, Lecture Notes on Data Engineering and Communications Technologies 74, https://doi.org/10.1007/978-981-16-3448-2_3 49