The growth of the Internet of Things (IoT) has intensely enlarged the number of related devices creating and consuming data. To handle this ever-growing data flow, Next-Generation networks are developing near a hybrid architecture, weaving organized edge computing power (Fog) with the cloud’s vast resources. However, orchestrating and scheduling jobs across this dissimilar landscape presents a difficult task. Scheduling in Next-Generation IoT-Fog-Cloud Networks is a dangerous facet in attaching the full potential of the organized landscape of IoT, fog computing, and cloud infrastructure. By authorizing effectual scheduling, metaheuristic algorithms donate to improved survivability in Next-Generation systems. They guarantee on-time task implementation, diminish resource bottlenecks, and allocate computational loads efficiently, decreasing the effect of potential failures. With strong scheduling, these networks can adjust to unpredictable states, ensuring seamless data flow and constant service for both real-time and non-real-time uses. This manuscript offers the design of a Metaheuristic Mountain Gazelle Optimization Algorithm based task scheduling approach (MMGOA-TSA) in the Next-Generation IoT Fog-Cloud Networks. The foremost intention of the MMGOA-TSA technique is to optimally plan the IoT demands in the IoT fog-cloud network. The MMGOA-TSA technique follows the concept of MGOA, which is stimulated by the social life and wild mountain gazelles (MG) hierarchy. Meanwhile, the MMGOA-TSA technique determines the optimal candidate solutions from the fog or cloud nodes for offloading any IoT demands which can be executed in such a method that the effective trade-off among response time and energy utilization in the method can be accomplished. The experimental validation of the MMGOA-TSA technique is verified by employing a set of simulations. The comparative result analysis stated that the MMGOA-TSA technique gains better performance over other techniques in terms of distinct actions.

中文翻译：

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提高下一代物联网雾云网络的生存能力：利用元启发式山羚算法进行调度优化