Provisioning Load Balancing in Time-Sensitive Task Allocation for Mobile Crowdsensing

Task allocation is the mechanism which enables the allotment of sensing tasks to participating users in a mobile crowdsensing (MCS) environment. Task allocation plays a vital role in the management of resources in crowdsensed networks which deploy mobile participants or devices. While conventional task allocation techniques focus on maximizing profit for either the platform or the user, our proposed task allocation scheme, called Load Balanced Task Allocation (LBTA) is geared towards user-oriented task allocation in order to mainly address altruistic MCS campaigns in which participants voluntarily contribute towards a common goal such as in citizen science-based projects. This paper deals with the problem of task allocation using a load balanced approach while trying to maximize the allocation of tasks at the same time. For this, we propose and formulate the LBTA algorithm, which is an extension of a greedy algorithm. The proposed LBTA algorithm has been compared with a known algorithm and their relative performances have been analysed. Simulation results demonstrate that the proposed algorithm performs better than the baseline algorithm for time-dependent MCS systems that operate without a budget constraint, and comparatively better up to a certain budget for those systems with budgeting limitations.