With the rapid advancement of mobile networks and the widespread use of mobile devices, spatial crowdsourcing, which involves assigning location-based tasks to mobile workers, has gained significant attention. However, most existing research focuses on task assignment at the current moment, overlooking the fluctuating demand and supply between tasks and workers over time. To address this issue, we introduce an adaptive task assignment problem, which aims to maximize the number of assigned tasks by dynamically adjusting task assignments in response to changing demand and supply. We develop a spatial crowdsourcing framework, namely demand-based adaptive task assignment with dynamic worker availability windows, which consists of two components including task demand prediction and task assignment. In the first component, we construct a graph adjacency matrix representing the demand dependency relationships in different regions and employ a multivariate time series learning approach to predict future task demands. In the task assignment component, we adjust tasks to workers based on these predictions, worker availability windows, and the current task assignments, where each worker has an availability window that indicates the time periods they are available for task assignments. To reduce the search space of task assignments and be efficient, we propose a worker dependency separation approach based on graph partition and a task value function with reinforcement learning. Experiments on real data demonstrate that our proposals are both effective and efficient.

With the rapid evolution of sensing techniques and the proliferation of mobile devices, spatial crowdsourcing (SC) has gained significant attention in both academia and industry. SC involves assigning location-based tasks to mobile workers, with task recommendation playing a key role in helping workers identify suitable and appealing tasks. However, most existing studies focus on task completion rate, worker satisfaction, or efficiency, without consideration of the environmental impact, e.g., pollutant emissions from the increased vehicle usage associated with SC applications like Uber, Lyft, and FoodPanda. In this study, we consider a novel problem of sustainable task recommendation in SC, which aims to minimize the environmental footprint (i.e., pollution) while maintaining acceptable levels of task completion, worker satisfaction, and overall task recommendation efficiency. We develop an innovative Sustainability-Oriented Task Recommendation framework encompassing two major components: speed-driven pollutant emission estimation and task recommendation. Specifically, the pollutant emission estimation component aims to estimate future pollutant emissions based on worker trajectories and speeds, using a context-enhanced spatio-temporal network for road speed prediction. In the task recommendation component, we provide a completion-sensitive recommendation algorithm to maximize the expected number of completed tasks. Further, we design an efficient emission-optimized KM ranking algorithm to minimize emissions. Experiments on real data offer insight into the effectiveness and efficiency of the proposals, providing valuable insights into its potential for sustainable spatial crowdsourcing.

With the fast-paced development of mobile networks and the widespread usage of mobile devices, Spatial Crowdsourcing (SC), which refers to assigning location-based tasks to moving workers, has drawn increasing attention in recent years. One of the critical issues in SC is task assignment that allocates tasks to appropriate workers. In this paper, we propose a novel SC problem, namely Coalition-based Task Assignment (CTA), where the spatial tasks (e.g., house removals, furniture installation) may require more than one workers (forming a coalition) to cooperate in order to maximize the overall rewards of workers. To tackle the CTA problem, we design both greedy method and equilibrium-based method. In particular, the greedy method aims to form a set of worker coalitions greedily to perform the tasks, in which we introduce an acceptance possibility to find the high-value task assignments. In the equilibrium-based algorithm, workers form coalitions in sequence and update their strategy (i.e., selecting a best-response task) at their turn, in order to maximize their own utility (i.e., reward of the coalition they stay in) until Nash equilibrium is reached. Since the equilibrium point obtained by the best-response approach is not unique and optimal in terms of total rewards, we further propose a simulated annealing scheme to find a better Nash equilibrium. The extensive experiments demonstrate the efficiency and effectiveness of the proposed methods on both real and synthetic datasets.

The widespread diffusion of smartphones offers a capable foundation for the deployment of Spatial Crowdsourcing (SC), where mobile users, called workers, perform location- dependent tasks assigned to them. A key issue in SC is how best to assign tasks, e.g., the delivery of food and packages, to appropriate workers. Specifically, we study the problem of Fairness-aware Task Assignment (FTA) in SC, where tasks are to be assigned in a manner that achieves some notion of fairness across workers. In particular, we aim to minimize the payoff difference among workers while maximizing the average worker payoff. To solve the problem, we first generate so-called Valid Delivery Point Sets (VDPSs) for each worker according to an approach that exploits dynamic programming and distance- constrained pruning. Next, we show that FTA is NP-hard and proceed to propose two heuristic algorithms, a Fairness-aware Game-Theoretic (FGT) algorithm and an Improved Evolutionary Game-Theoretic (IEGT) algorithm. More specifically, we formulate FTA as a multi-player game. In this setting, the FGT approach represents a best-response method with sequential and asynchronous updates of workers' strategies, given by the VDPSs, that achieves a satisfying task assignment when a pure Nash equilibrium is reached. Next, the IEGT approach considers a setting with a large population of workers that repeatedly engage in strategic interactions. The IEGT approach exploits replicator dynamics that cause the whole population to evolve and choose better resources, i.e., VDPSs. Using the property of evolutionary equilibrium, a satisfying task assignment is obtained that corresponds to a stable state with similar payoffs among workers and good average worker payoff. Extensive experiments offer insight into the effectiveness and efficiency of the proposed solutions.