Mitigating Procrastination in Spatial Crowdsourcing Via Efficient Scheduling Algorithm
CoRR(2024)
摘要
Several works related to spatial crowdsourcing have been proposed in the
direction where the task executers are to perform the tasks within the
stipulated deadlines. Though the deadlines are set, it may be a practical
scenario that majority of the task executers submit the tasks as late as
possible. This situation where the task executers may delay their task
submission is termed as procrastination in behavioural economics. In many
applications, these late submission of tasks may be problematic for task
providers. So here, the participating agents (both task providers and task
executers) are articulated with the procrastination issue. In literature, how
to prevent this procrastination within the deadline is not addressed in spatial
crowdsourcing scenario. However, in a bipartite graph setting one
procrastination aware scheduling is proposed but balanced job (task and job
will synonymously be used) distribution in different slots (also termed as
schedules) is not considered there. In this paper, a procrastination aware
scheduling of jobs is proliferated by proposing an (randomized) algorithm in
spatial crowdsourcing scenario. Our algorithm ensures that balancing of jobs in
different schedules are maintained. Our scheme is compared with the existing
algorithm through extensive simulation and in terms of balancing effect, our
proposed algorithm outperforms the existing one. Analytically it is shown that
our proposed algorithm maintains the balanced distribution.
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