Abstract
Crowdsensing has emerged as an efficient and inexpensive way to perform specialized tasks by leveraging external crowds. In some crowdsensing systems, different tasks may have different requirements, and there may be precedence constraints among them, such as the Unmanned Aerial Vehicle (UAV) crowdsensing systems. Moreover, minimizing the total execution time is a regular target for finishing the crowdsensing tasks with precedence constraints. As far as we know, only a few existing studies consider the precedence constraints among crowdsensing tasks, and none of them can minimize the total execution time simultaneously. To tackle this challenge, an efficient allocation mechanism for tasks with precedence constraints is first proposed, which can minimize the total execution time. Then, a case study is given to show how to fit our mechanism in the UAV crowdsensing system. Finally, the simulation results show that the proposed mechanisms have good approximate optimal ratios under different parameter settings and are efficient for the UAV crowdsensing system as well.
Highlights
With the emergence of various wireless technologies (4/5G, DSRC, and etc.), ubiquitous terminal equipment, such as smartphones, vehicles and Unmanned Aerial Vehicle (UAV), can collect real-time data from the environment and transmit the data to the IoT central server effectively [1,2,3]
Task allocation mechanism is crucial for crowdsensing, which directly decides the performance of the crowdsensing system
A variety of task allocation mechanisms have been proposed for crowdsensing systems [19,20,21,22,23,24,25,26,27,28,29,30,31]
Summary
With the emergence of various wireless technologies (4/5G, DSRC, and etc.), ubiquitous terminal equipment, such as smartphones, vehicles and UAVs, can collect real-time data from the environment and transmit the data to the IoT central server effectively [1,2,3]. As an important application of IoT, crowdsensing can leverage the power of large crowds to complete the complicated sensing tasks by using their smartphones or other mobile devices [4,5]. Compared with the conventional data collection methods, crowdsensing provides a low-cost and time-efficient solution for large-scale sensing tasks. With the dramatic proliferation of mobile devices, a set of crowdsensing systems have been implemented in recent year [6,7,8,9,10,11,12,13,14,15,16,17]. A variety of task allocation mechanisms have been proposed for crowdsensing systems [19,20,21,22,23,24,25,26,27,28,29,30,31]. Reddy et al proposed to maximize the spatial coverage
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