Current Issue : January-March Volume : 2022 Issue Number : 1 Articles : 5 Articles
Mobile crowdsensing (MCS) is a popular way of data collection, which forms the large-scale sensing system by smart mobile terminal users and provides multimodal sensor data. In the sensing scenario, there are various sense resource requirements of tasks released by the platform. One of the most urgent issues in MCS is how to choose corresponding users with appropriate sense resources to accomplish assigned tasks. In this article, cooperating among a host of users to perform sense tasks is considered. Firstly, the cooperation among users to accomplish the sense tasks is described as an overlapping coalition formation game (OCF game). In addition, an initial coalition method of using social networks (SN) is proposed to accelerate the formation of coalition. Finally, the cooperation degree is used to describe the cooperative relationships among users, and virtual terminal coalition formation (VTCF) algorithm is proposed to simplify the process of coalition formation. .e simulated results show that the proposed approach effectively improves the system’s utility under the constraints of task cost and sense quality....
With the development of building information technology, Building Information Modeling (BIM) has become an important way to effectively solve the cross-organization information collaboration of Public-Private Partnership (PPP) projects, and how to promote the adoption of BIM in PPP projects has become a realistic problem to be solved urgently. +is study discusses the adoption of BIM among stakeholders in PPP projects based on prospect theory and evolutionary game theory. A tripartite evolutionary game model including governments, social capitals, and contractors is established. +e behavioral evolution mechanism of each stakeholder on BIM adoption is explored by analyzing the evolutionary equilibrium, and the key influencing factors of equilibrium strategy are analyzed by using numerical simulation. +eresults demonstrate that first, the degree of the cost to all stakeholders involved in the adoption of BIM, as well as the punishment for governments’ passive promotion of BIM, the punishment for social capitals’ passive adoption of BIM and the reward for contractors’ active application of BIM are the key factors affecting evolutionary stability. Second, according to prospect theory, the main stakeholders usually make decisions through subjective judgment and perceived value which ultimately lead to deviation in their behaviors. +e deviations will hinder the establishment of ESS point (1, 1, 1) and make the system difficult to converge to the optimal state. Finally, from the perspective of governments, social capitals, and contractors, countermeasures and management implications are put forward to effectively promote the adoption of BIM in PPP projects....
Based on evolutionary game theory, this paper proposes a new information acquisition mechanism for intelligent mine construction, which solves the problem of incomplete information acquisition in the construction of new intelligent mining area and reduces the difficulty of information acquisition, which solves the problem of the imperfect mine information acquisition in the construction of a new smart mine regions and decreases the difficulty of a mine information acquisition. Based on the evolutionary game model, the perceptual incentive model based on group is established. The reliability of information collection is ensured by sharing and modifying the information collector. Through the analysis of the simulation results, it is found that the regional coverage model based on the cooperation in game theory and evolutionary game theory has a good effect on solving the bottleneck problem of the current intelligent mining area. This paper has an enlightening effect on the optimization of the mine information acquisition system. Through the improvement of the mine information acquisition system, the working efficiency of the information acquisition terminal can be effectively increased by 6%....
Lane changing is an important scenario in traffic environments, and accurate prediction of lane-changing behavior is essential to ensure traffic and driver safety. To achieve this goal, a vehicle lane-changing prediction model based on game theory and deep learning is developed. In the game theory component, the interaction between vehicles during lane changing is analyzed according to the running state of the vehicle, with the probability of lane changing as its output. For the deep-learning component, long short-term memory and a convolutional neural network are used to extract and learn data features during the lane-changing process as well as combine the output of the game theory component to obtain the prediction result of whether the vehicle will change lanes. By using an open-source traffic dataset to train and verify the proposed model, the verification results show that the prediction accuracy can reach 94.56% within 0.4 s of lane-changing operation and that the model can achieve timely and accurate prediction of the lane-changing behavior of vehicles....
In a real-world network confrontation process, attack and defense actions change rapidly and continuously. +e network environment is complex and dynamically random. +erefore, attack and defense strategies are inevitably subject to random disturbances during their execution, and the transition of the network security state is affected accordingly. In this paper, we construct a network security state transition model by referring to the epidemic evolution process, use Gaussian noise to describe random effects during the strategy execution, and introduce a random disturbance intensity factor to describe the degree of random effects. On this basis, we establish an attack-defense stochastic differential game model, propose a saddle point equilibrium solution method, and provide an algorithm to select the optimal defense strategy. Our method achieves real-time defense decision-making in network attack-defense scenarios with random disturbances and has better real-time performance and practicality than current methods. Results of a simulation experiment show that our model and algorithm are effective and feasible....
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