Most existing incentive mechanisms in mobile crowdsensing are platform-centered design or user-centered design without multidimensional consideration of sensing task requirements. Therefore, it is impossible to make user selection effectively based on sensing tasks and meet the maximization and diversification of the task requirements. To solve these problems, a Task Requirement-oriented user selection Incentive Mechanism (TRIM) was proposed, which is a task-centered design method. Firstly, sensing tasks were published by the sensing platform according to task requirements. Based on multiple dimensions such as task type, spatio-temperal characteristic and sensing reward, the task vectors were constructed to optimally meet the task requirements. To implement the personalized sensing participation, the user vectors were constructed based on the user preferences, individual contribution value, and expected reward by the sensing users. Then, by introducing Privacy-preserving Cosine Similarity Computation protocol (PCSC), the similarities between the sensing tasks and the sensing users were calculated. In order to obtain the target user set, the user selection based on the similarity comparison results was performed by the sensing platform. Therefore, the sensing task requirements were better met and the user privacy was protected. Finally, the simulation experiment indicates that TRIM shortens the computational time overhead of exponential increments and improves the computational efficiency compared with incentive mechanism using Paillier encryption protocol in the matching process between sensing tasks and sensing users; compared with the incentive mechanism using direct PCSC, the proposed TRIM guarantees the privacy of the sensing users and achieves 98% matching accuracy.

As a "killer" application in cloud computing, cloud gaming is leading the revolution of way of gaming. However, the high latency between the cloud and end devices hurts user experience. Aiming at the problem, a low latency cloud gaming system deployed on edge nodes, called Gaming@Edge, was proposed based on edge computing concept. To reduce the overhead of edge nodes for improving the concurrency, a cloud gaming running mechanism based on compressed graphics streaming, named GSGOD (Graphics Stream based Game-on-Demand), was implemented in Gaming@Edge system. The logic computing and rendering in the game running were separated and a computing fusion of edge nodes and end devices was built by GSGOD. Moreover, the network data transmission and latency were optimized through the mechanisms such as data caching, instruction pipeline processing and lazy object updating in GSGOD. The experimental results show that Gaming@Edge can reduce average network latency by 74% and increase concurrency of game instances by 4.3 times compared to traditional cloud gaming system.