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The development of modern communication technologies and smart mobile devices has driven the evolution of mobile crowdsensing (MCS). Optimizing the task assignment process under constrained resources to maximize utility is a key challenge in MCS. However, most existing studies presuppose a sufficient pool of available workers during the task assignment process, overlooking the impact of temporal fluctuations in worker numbers under online scenarios. Additionally, existing studies commonly publish sensing tasks to the MCS platform for immediate assignment upon their arrival. However, the uncertainty in the number of available workers in online scenarios may fail to meet task demands. To address these challenges, this paper proposes a two-stage online task assignment scheme. The first stage introduces an adaptive task pre-assignment strategy based on worker quantity prediction, which determines task acceptance and assigns tasks to suitable subareas. The second stage employs a dynamic online recruitment method to select workers for the assigned tasks, aiming to maximize platform utility. Finally, the simulation experiments conducted on two real-world datasets demonstrate that the proposed methods effectively solve the challenges of online task assignment in MCS.

Mobile crowdsensing (MCS) has been an effective sensing paradigm by exploiting the pervasive sensor-rich mobile devices for sensor data collection. Online task assignment is an important issue for mobile crowdsensing since tasks typically arrive dynamically and need to be handled in an online manner. In this paper, we study online task assignment for maximizing the total profit of the MCS platform while satisfying the time window requirement of each task. We first describe the crowdsensing model and then study the online task assignment in the following two different scenarios: (1) user-offline-arriving scenario, where all users are fully available throughout the whole sensing period and their movements are fully planned by the platform; (2) user-online-arriving scenario, where users arrive and depart dynamically and each user has a specific participatory time window for task executions. For the former scenario, we propose a benchmark algorithm and also an online heuristic algorithm. The benchmark algorithm tries to provide a best-case performance by assuming all future task arrival information is known in advance. The online algorithm adopts bipartite-matching-based strategy for task assignment and further performs minimal detour based data offloading for reducing the data upload cost, whenever possible. For the latter scenario, we propose an effective online algorithm, which adopts a maximum-profit-first strategy for task assignment and also minimal detour based data offloading for reduction of data upload cost whenever applicable. For all the proposed algorithms, we present their detailed design and deduce their time complexities. Extensive simulations are conducted and the results demonstrate that our proposed algorithms can largely increase the total profit of the platform as compared with existing work.

Mobile crowd sensing is an appealing model, wherein mass users utilize smart devices to perform tasks in mobile social networks. Most conventional method selects subset of participants for maximizing the coverage. However, due to the budget constraints, the selection of most suitable participants becomes major issue. This paper presents a novel online task assignment framework using newly devised optimization algorithm, namely Crow-based Bacterial Foraging Algorithm (C-BFO), which is designed by combining Crow Search Algorithm and Bacterial Foraging Optimization. Here, scheduling in the cloud computing environments is simulated as an optimization problem, which is modeled using the proposed C-BFO, considering fitness function like communication duration, data sending rate, bandwidth, makespan, data receiving rate, time of sending task, time of receiving task, finish time, and ready time. The effectiveness of the proposed online task assignment model is revealed through the comparative analysis based on makespan by altering tasks, mobile user and requesters. The proposed C-BFO method shows enhanced performance with minimal makespan of 0.478 by varying number of tasks, and minimal makespan of 0.481 by varying number of users, and minimal makespan of 0.490 by varying requesters.

The prevalence of mobile internet techniques stimulates the emergence of various spatial crowdsourcing applications. Certain of the applications serve for the requesters, budget providers, who submit a batch of tasks and a fixed budget to platform with the desire to search suitable workers to complete the tasks in maximum quantity. Platform lays stress on optimizing assignment strategies on seeking less budget-consumed worker-task pairs to meet the requesters’ demands. Existing research on the task assignment with budget constraints mostly focuses on static offline scenarios, where the spatiotemporal information of all workers and tasks is known in advance. However, workers usually appear dynamically on real spatial crowdsourcing platforms, where existing solutions can hardly handle it. In this paper, we formally define a novel problem called B udget-aware O nline task A ssignment(BOA) in spatial crowdsourcing applications. BOA aims to maximize the number of assigned worker-task pairs under budget constraints where workers appear dynamically on platforms. To address the BOA problem, we first propose an efficient threshold-based greedy algorithm called Greedy-RT which utilizes a random generated threshold to prune the pairs with large travel cost. Greedy-RT performs well in the adversarial model when compared with simple greedy algorithm, but it is unstable in the random model for its random generated threshold may produce poor quality in matching size. We then propose a revised algorithm called Greedy-OT which could learn near optimal threshold from historical data, and consequently improves matching size significantly in both models. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real and synthetic datasets.

Task allocation is a critical issue of spatial crowdsourcing. Although the batching strategy performs better than the real-time matching mode, it still has the following two drawbacks: (1) Because the granularity of the batch size set obtained by batching is too coarse, it will result in poor matching accuracy. However, roughly designing the batch size for all possible delays will result in a large computational overhead. (2) Ignoring non-stationary factors will lead to a change in optimal batch size that cannot be found as soon as possible. Therefore, this paper proposes a fine-grained, batching-based task allocation algorithm (FGBTA), considering non-stationary setting. In the batch method, the algorithm first uses variable step size to allow for fine-grained exploration within the predicted value given by the multi-armed bandit (MAB) algorithm and uses the results of pseudo-matching to calculate the batch utility. Then, the batch size with higher utility is selected, and the exact maximum weight matching algorithm is used to obtain the allocation result within the batch. In order to cope with the non-stationary changes, we use the sliding window (SW) method to retain the latest batch utility and discard the historical information that is too far away, so as to finally achieve refined batching and adapt to temporal changes. In addition, we also take into account the benefits of requesters, workers, and the platform. Experiments on real data and synthetic data show that this method can accomplish the task assignment of spatial crowdsourcing effectively and can adapt to the non-stationary setting as soon as possible. This paper mainly focuses on the spatial crowdsourcing task of ride-hailing.

Online task assignment is one of the core research issues of spatio-temporal crowdsourcing technology. The current researches on minimizing travel cost all focus on the scenario of two objectives (task requesters and workers). This paper proposes a two-stage framework (GH) based on Greedy algorithm and Hungarian algorithm for three-objective online task assignment to minimize travel cost. In order to further optimize the efficiency and average travel cost, this paper proposes GH-AT (Adaptive Threshold) algorithm based on GH algorithm, and redesigns the Hungarian algorithm into the sHungarian algorithm. sHungarian algorithm has lower time complexity than Hungarian algorithm. sHungarian algorithm is not only suitable for the problem studied in this paper, but also for all task assignment problems with constraints. Compared with Greedy algorithm, GH-AT algorithm has lower travel cost and higher total utility. In terms of the number of matches, GH-AT is slightly lower than Greedy algorithm. In terms of time cost, GH-AT algorithm is higher than Greedy algorithm, but much lower than GH algorithm.

The rise in online and multichannel retailing has pushed retailers to give increased attention to their order-fulfillment operations. We study “chaotic storage” fulfillment systems in which dissimilar items are stored together in a single location. This necessitates a searching task as part of the picking process, which has not been previously studied. We show that pick times increase by as much as 16% as the searching task becomes more difficult. However, the deleterious effect of searching decreases with pick-worker experience. Using simulation, we show that pick times can be improved by incorporating distance, bin density, and picker experience into pick assignments and pick routing. Through properly combining the details of the task and the workers, order-fulfillment productivity can be increased by approximately 5%. This paper was accepted by Vishal Gaur, operations management.

Crowdsourcing and crowd computing are a trend that is likely to be increasingly popular, and there remain a number of research and operational challenges that need to be addressed. The human-centric computational abstraction called situation may be used to cope with these difficulties. In this paper, we focus on one such challenge, which is how to assign crowd assessment tasks about security and privacy in online social networks to the most appropriate users efficiently, effectively and accurately. Specifically, here we propose a novel task assignment method to facilitate crowd assessment, which improves the security and trustworthiness of social networking platforms, as well as a task assignment algorithm based on SocialSitu, which is a social-domain-focused situational analytics. Findings from our crowd assessment experiments on a real world social network Shareteches show that the precision and recall of the proposed method and algorithm are 0.491 and 0.538 higher than those of a random algorithm’s, as well as 0.336 and 0.366 higher than users’ theme-aware algorithm’s, respectively. Moreover, these results further suggest that our experimental evaluation enhance the security and privacy of online social networks.

In recent years, spatial crowdsourcing has emerged as an important new framework, in which each spatial task requires a set of right crowd-workers in the near vicinity to the target locations. Previous studies have focused on spatial task assignment in the static crowdsourcing environment. These algorithms may achieve local optimality by neglecting the uncertain features inherent in real-world crowdsourcing environments, where workers may join or leave during run time. Moreover, spatial task assignment is more complicated when large-scale crowd-workers exist in crowdsourcing environments. The large-scale nature of task assignments poses a significant challenge to uncertain spatial crowdsourcing. In this paper, we propose a novel algorithm combining spatial optimization and multi-agent temporal difference learning (SMATDL). The combination of grid-based optimization and multi-agent learning can achieve higher adaptability and maintain greater efficiency than traditional learning algorithms in the face of large-scale crowdsourcing problems. The SMATDL algorithm decomposes the uncertain crowdsourcing problem into numerous sub-problems by means of a grid-based optimization approach. In order to adapt to the change in the large-scale environment, each agent utilizes temporal difference learning to handle its own spatial region optimization in online crowdsourcing. As a result, multiple agents in SMATDL collaboratively learn to optimize their efforts in accomplishing the global assignment problems efficiently. Through extensive experiments, we illustrate the effectiveness and efficiency of our proposed algorithms on the experimental data sets.