Abstract
Spatial mobile crowdsourcing (SMCS) enables a task requester to commission workers to physically travel to specific locations to perform a set of spatial assignments (i.e., tasks are related to a specific geographical location besides time). To efficiently perform such tasks and guarantee the best possible quality of returned results, optimizing the worker recruitment and task assignment processes must be conducted. Because both workers and task requesters impose certain criteria, this procedure is not obvious. To tackle this issue, we propose a novel formulation of the SMCS recruitment where task matching and worker scheduling are jointly optimized. A Mixed Integer Linear Program (MILP) is first developed to optimally maximize the quality of matching measured as a weighted score function of different recruitment metrics while determining the trajectory of each selected worker executing tasks. To cope with NP-hardness, we propose a heuristic SMCS recruitment approach allowing the achievement of sub-optimal matching and recruitment solution by iteratively solving a weighted bipartite graph problem. Simulation results illustrate the performance of the SMCS framework for selected scenarios and show that our proposed SMCS recruitment algorithm outperforms an existing greedy recruitment approach. Moreover, compared to the optimal MILP solution, the proposed SMCS recruitment approach achieves close results with significant computational time saving.
Highlights
Over the last decade, mobile device technology has advanced at an amazing pace
This is an example of a mobile crowdsourcing (MCS) technique that collectively utilizes the power of people to satisfy the needs of public good
We investigate a generic spatial mobile crowdsourcing (SMCS) recruitment framework aiming at minimizing the total travel delay of workers and maximizing the number of completed tasks while taking into consideration several parameters such as the spatio-temporal nature of workers and tasks, the tasks’ budgets and their expiration times, and the workers’ speed
Summary
Mobile device technology has advanced at an amazing pace. As a matter of fact, the industry has witnessed a major technological shift and a perpetual development touching several fields such as the mainstream automotive industry (e.g., autonomous vehicles) and the robotic field (e.g., drones). The server could assign it to any available users (e.g., resident) to take measurement using their smart phones equipped with air quality monitoring sensors. This is an example of a mobile crowdsourcing (MCS) technique that collectively utilizes the power of people to satisfy the needs of public good. Many similar real-life services are location-based, meaning they can be tagged with time and location and workers are required to travel to a specific location to execute the task This type of services is known as spatial mobile crowdsourcing (SMCS) [4], where task requesters submit spatial tasks and the SMCS platform assigns them to suitable and willing workers, which may receive monetary reward in exchange
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