An efficient task allocation framework for scheduled data in edge based Internet of Things using hybrid optimization algorithm approach

Abstract

Allocation of tasks in IoT is an integral and critical approach to finding a perfect match between scheduled tasks of a particular application and Edge-based processing devices for instant response and efficient utilization of resources to make them renewable. We need a protocol to help optimize the problem of allocating processing devices to the tasks, as task allocation is considered an NP-hard problem to prevent problems with energy consumption and response time problems. For this, a hybrid bio-inspired Swarm-based approach will improve the solution to optimize the matching of a task to a particular device. This paper proposed a Meta-heuristic algorithm to optimize Energy and Time-delay for allocating tasks to the edge-based Processing device in IoT. The proposed algorithm called the Hybrid Artificial Bee Colony whales Optimization algorithm (HAWO) is formulated by integrating Artificial Bee Colony with the Whales Optimization algorithm to overcome the search process of an Artificial Bee Colony, which converges too soon due to the local search of Employee Bee phase and Onlooker Bee phase causing the problem of looping. From the simulation results conducted in Matlab, it is observed that the integrated HAWO method shows promising results in terms of Energy and Time Delay when compared with Artificial Bee Colony and Whales Optimization algorithms separately. Also, proposed method when compared with the benchmark work shows significant improvements of 50%, 25% and 60% in terms of Energy, Time Delay and Best cost, respectively.