Optimization Based Multi-Objective Framework in Mobile Social Networks for Crowd Sensing

Mobile crowd sensing is an appealing model, wherein mass users utilize smart devices to perform tasks in mobile social networks. Most conventional method selects subset of participants for maximizing the coverage. However, due to the budget constraints, the selection of most suitable participants becomes major issue. This paper presents a novel online task assignment framework using newly devised optimization algorithm, namely Crow-based Bacterial Foraging Algorithm (C-BFO), which is designed by combining Crow Search Algorithm and Bacterial Foraging Optimization. Here, scheduling in the cloud computing environments is simulated as an optimization problem, which is modeled using the proposed C-BFO, considering fitness function like communication duration, data sending rate, bandwidth, makespan, data receiving rate, time of sending task, time of receiving task, finish time, and ready time. The effectiveness of the proposed online task assignment model is revealed through the comparative analysis based on makespan by altering tasks, mobile user and requesters. The proposed C-BFO method shows enhanced performance with minimal makespan of 0.478 by varying number of tasks, and minimal makespan of 0.481 by varying number of users, and minimal makespan of 0.490 by varying requesters.

Automated summary

Mobile crowd sensing is an appealing model where users utilize smart devices to perform tasks in social networks. A novel online task assignment framework using the C-BFO optimization algorithm is proposed, leading to enhanced performance with minimal makespan.