A Multi Task Allocation Based Time Optimization Framework Using Social Networks in Mobile Crowd Sensing

Abstract

The quality of data and its sensing cost is the important concern for task allocation in crowd sensing. The sensing capabilities of a device to send the collection of sensor data to a cloud requires crowd sensing in order to receive reliable data. Crowd sensing is used in many areas such as traffic monitoring, smart cities, health care, transportations, environmental monitoring and many more. Most of existing works are only based on assumptions in task scheduling about the number of candidate users and are mainly performed optimization of single task allocation. If the candidate users are few, then the completion of task with in the schedule can be difficult for many sensor applications. In this work, we proposed a social network-based task allocation scheme for the optimization of multi task allocation. The main idea of the proposed work is to maximize the task completion within the allocated schedule. It is evident that the task scheduling algorithms are NP-hard and we introduced a decreasing threshold task allocation (DTT) and fast greedy selections (FGS) algorithms along with Crow COOT Foraging Optimization (CCFO) algorithm to allocate the tasks parallelly with maximum efficiency. The proposed algorithms such as C-DTT (CCFO-DTT) and C-FGS (CCFO-FGS_ are used for the efficient allocation of tasks. The combination of these algorithms can be helpful in selecting the candidate users who will perform the completion of maximum tasks. Due to the selection of proper users in each round, the time consumption of the tasks to be completed is greatly reduced. The experimental results also indicates that the proposed work performs well in the optimization of multi task allocation than the other state of the art models.