Push and pull participant recruitment system for vehicular crowdsensing

Crowdsensing, the use of everyday devices to collect and share data is paving the way for cost-efficient real-time data collection. Every day, information can be quickly sensed and shared publicly using smartphones. Beyond smartphones, modern vehicles have also shown great promise for crowdsensing. In contrast to mobile crowdsensing, vehicles are ideal platforms to collect, store, compute, and share large amounts of sensor data. Vehicles have greater mobility and cover wider sensing area. The mobility patterns of vehicles are predictable due to the prevalence of navigation systems. Most importantly, the abundance of on-board resources and lack of power constraints makes it possible to support complex and long-lasting sensing tasks. The idea behind vehicular crowdsensing is to leverage vehicles as mobile sensors and computing resources. Vehicles are recruited as sensing participants for large-scale crowdsensing tasks such as urban sensing or traffic condition monitoring. However, existing works often assume that sensing tasks are common tasks where sensing results are shared with the public. Instead of public information, we believe the benefit of the crowdsensing paradigm should be available for personal use. In this, we focus on small-scale sensing tasks, tasks that dynamically change over time and are unlikely to be shared. Thus, sensing information is collected on demand rather than continuously and at all times. We refer to this as personalized vehicular crowdsensing. Furthermore, most works assume the routing of vehicular participants cannot be changed. This further reduces a system's ability to fulfill dynamic sensing needs. Thus, in this thesis, we further explore the possibilities of improving crowdsensing performance by vehicle route planning. To achieve either public vehicular crowdsensing or personalized vehicular crowdsensing, we must resolve the problem of vehicular participant selection. We first propose two solutions based on push and pull for vehicular crowdsensing. We aim to maximize sensing coverage, improve load balance, error tolerance, and minimize costs. Next, we improve the sensing performance by allowing vehicles to reroute. Instead of greedily generating routes to maximize overall sensing coverage, we leverage a two-step global and local planning algorithm. Our global algorithm attempts to plan a vehicle's route based on the difference between the distribution of vehicular location and the distribution of task location. Our goal is to send vehicles to areas with a high number of tasks but having few vehicles to service them. The global algorithm does not determine which tasks it should service; this operation is handled by the local algorithm which decides how to optimally leverage a vehicle's sensing ability for a small area. Through the use of SUMO simulation and TAPAS Cologne Large Scale Mobility Dataset, we show that our proposed approaches deliver significant performance improvements compared to traditional approaches

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