With the continuous development of mobile edge computing and the improvement of unmanned vehicle technology, unmanned vehicle could handle ever-increasing demands. As a significant application of unmanned vehicle, spatial crowdsourcing will provide an important application scenario, which is about to organize a lot of unmanned vehicle to conduct the spatial tasks by physically moving to its locations, called task assignment. Previous works usually focus on assigning a spatial task to one single vehicle or a group of vehicles. Few of them consider that vehicle team diversity is essential to collaborative work. Collaborative work is benefits from organizing teams with various backgrounds vehicles. In this paper, we consider a spatial crowdsourcing scenario. Each vehicle has a set of skills and a property. The property denotes vehicle’s special attribute, (e.g., size, speed or weight). We introduce a concept of entropy to measure vehicle team diversity. Each spatial task (e.g., delivering the take-out, and carrying freight) is under the time and budget constraint, and required a set of skills. We need to assure that the assigned vehicle team is diverse. To address this issue, we first propose a practical problem, called team diversity spatial crowdsourcing (TD-SC) problem which finds an optimal team-and-task assignment strategy. Moreover, We design a framework which includes a greedy with diversity (GD) algorithm and a divide-and-conquer (D&C) algorithm to get team-and-task assignments. Finally, we demonstrate efficiency and effectiveness of the proposed methods through extensive experiments.

Software maintenance and evolution play an important role in the software engineering field, especially when current software becomes more and more complex and powerful. As an entity to implement business processes and gain revenue, valuable software is composed of business logic and corresponding organization role interaction interfaces. With the enterprise development, the organization architecture also evolves, like expanding, cross department cooperation, and so on. However, existing software process adaptive approaches mainly focus on handling the change of the business (program) logic instead of organization structure. Therefore, we propose an adaptive software business process approach that supports organization architecture evolution and automatically migrates the run‐time process instances to the latest version. First, a business process adaptation model is designed, which includes the organization layer, business process layer and event layer that connects the two. Based on the model, the organization changing impact and business process model modification are formalized. Besides, the business process adaptation approach is designed. According to the dependence between the organization architecture and the business process activities, the affected domain detection algorithms for three basic business process structures and the business process instance migration algorithm are developed. Finally, the feasibility and stability of the proposed system are comprehensively evaluated with the synthetic data sets.

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