  In this paper, we study a novel spatial crowdsourcing system where the
workers' time availabilities and their spatial locations are known a priori.
Consequently, the tasks assignment to workers is performed not only based on
the current location of the human workers and the tasks available in the
region, but also based on the availability of the workers during the specific
times that a given task should be accepted, processed, and completed. Having
the system determine the daily pattern of the workers (either by predefined
questionnaires when the workers register, or by archiving data from the
worker's mobile devices, or by on the road and real-time entered status data)
eliminates many unsuccessful task assignments and therefore significantly
increases the efficiency of the system. In the original Spatial Crowdsourcing
(SC) framework, the SC-server optimizes the task assignment locally at every
instance of time and whenever a new task, or a new worker, enters the system.
Our new framework (PSC), on the other hand, allows the users to enter their
daily routine, and temporal, spatial, and availability patterns a priori. This
makes the system much more stable and pattern-opportunistic. The PSC servers
can focus on receiving and archiving new entries (e.g., workers, tasks, and
their criteria) during busy times (e.g., when there are many new entries in the
system), and can focus on optimization and computations during quiet times
(e.g., when there are fewer new entries in the system). Having the task
optimization process happen during quiet times, and when there are few changes
to the system, makes the performance more stable and reliable. It also allows
the PSC system to have a global view of the system and and perform global
optimizations to improve the performance.
