An Efficient Task Allocation with Fuzzy Reptile Search Algorithm for Disaster Management in urban and rural area

This paper proposes an architecture for rescue operations in fire disasters by modeling the behavior of generated tasks scheduling to the fog or cloud devices. The problem is formulated with a dynamic optimization problem enriched with the support of task priority and task offloading functions. The goal of the algorithm is to schedule tasks by taking into account the location of each device and the available computing power of each processing device. As Fog computing has been proven to be an excellent choice for applications requiring lower delay requirements, the fundamental challenge of constraint-based problems can be effectively solved by utilizing the distributed environment of fog computing. The proposed algorithm, An Efficient Task Allocation using Fuzzy Reptile Search Algorithm for Disaster Management (ETARSA-DM), addresses the issue of balanced load and total energy consumption in an integrated fog/cloud platform. It consists of two phases; in first one, priority of tasks is categorized with a fuzzy system, and in the second phase, an improved Reptile Search Algorithm (RSA) is used with a novel validation function for real-time offloading of tasks in case of exceeding of a deadline at any device. This offloading considers the node’s proximity, generating the tasks for lesser delay and more throughput. The performance of the proposed algorithm is evaluated, and obtained results demonstrate its out-performance compared to the state-of-the-art.