Abstract
Task allocation is a key problem in Mobile Crowd Sensing (MCS). Prior works have mainly assumed that participants can complete tasks once they arrive at the location of tasks. However, this assumption may lead to poor reliability in sensing data because the heterogeneity among participants is disregarded. In this study, we investigate a multitask allocation problem that considers the heterogeneity of participants (i.e., different participants carry various devices and accomplish different tasks). A greedy discrete particle swarm optimization with genetic algorithm operation is proposed in this study to address the abovementioned problem. This study is aimed at maximizing the number of completed tasks while satisfying certain constraints. Simulations over a real-life mobile dataset verify that the proposed algorithm outperforms baseline methods under different settings.
Highlights
An era of “Internet of Things” has been reached given the development of wireless communication, sensor technology, smartphones, and wearable devices
To address the multitask allocation to heterogeneous participants (MTHP) problem, we propose the GDPSOGA
We focus on the problem of MTHP in Mobile Crowd Sensing (MCS) to maximize the completed tasks while satisfying certain constraints
Summary
An era of “Internet of Things” has been reached given the development of wireless communication, sensor technology, smartphones, and wearable devices. A new sensing paradigm called Mobile Crowd Sensing (MCS), where mobile devices play an important role in large-scale sensing and information sharing, has become research issue in academia and industry. In contrast to traditional wireless sensor networks (WSN) [5], MCS is a human-centered sensing model, in which MCS applications must recruit participants to complete the sensing tasks. A straightforward way for obtaining a highly reliable sensing data is to recruit as many participants as possible to complete tasks. This strategy results in a high sensing cost. A key issue is allocating the tasks to proper participants, while accounting for the various initial locations of different participants, sensing data reliability, and sensing cost. Task allocation is an important issue in linebreak MCS
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