Towards Bat Bio-inspired Decision-making for Task Allocation in Vehicular Fogs

Technological evolutions in intelligent transportation have enabled smart and connected vehicles to support novel safety and infotainment services. The provision of such services is guaranteed with effective sharing and allocation of resources for task offloading and processing. The use of vehicular fogs also helps this process by lowering the latency in communications and the resource share among the fog members. However, allocation in Fogs introduces challenges related to the intermittency of Fog vehicle nodes, clustering, topology changes, and resource allocation problems. The use of metaheuristic algorithms has been explored in several works to solve these optimization problems, such as resource allocation, clustering, task allocation, and network communications, especially regarding efficiency. We thus propose a bat bio-inspired decision-making algorithm for task allocation in vehicular fogs called AEGIS. AEGIS uses the cluster members and task parameters to do the decision-making process in the task allocation process that helps to choose the best vehicle of the fog to allocate a determined task. The AEGIS was compared to a GWO approach (meta-heuristic), Greedy, and Random (traditional) approaches. We considered allocated, denied, and lost tasks for the simulation criteria. AEGIS lost fewer tasks than the other algorithms and allocated more tasks than the traditional algorithms.