Description:
The development of smart vehicles brings drivers and passengers a comfortable and safe environment. Various emerging applications are promising to enrich users' traveling experiences and daily life. However, how to execute computing-intensive applications on resource-constrained vehicles still faces huge challenges. In this article, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as a joint optimization problem to maximize users' Quality of Experience (QoE). Due to its complexity, the original problem is further divided into two sub-optimization problems. A two-sided matching scheme and a deep reinforcement learning approach are developed to schedule offloading requests and allocate network resources, respectively. Performance evaluations illustrate the effectiveness and superiority of our constructed system.
Description:
The development of smart vehicles brings drivers and passengers a comfortable and safe environment. Various emerging applications are promising to enrich users' traveling experiences and daily life. However, how to execute computing-intensive applications on resource-constrained vehicles still faces huge challenges. In this article, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as a joint optimization problem to maximize users' Quality of Experience (QoE). Due to its complexity, the original problem is further divided into two sub-optimization problems. A two-sided matching scheme and a deep reinforcement learning approach are developed to schedule offloading requests and allocate network resources, respectively. Performance evaluations illustrate the effectiveness and superiority of our constructed system.
Description:
Complex network analysis has gained research interests in a wide range of fields. Network motif, which is one of the most popular network properties, is a statistically significant network subgraph. In this paper, we propose a fast methodology, called Four-node Motif Detection Algorithm (FMDA), to extract four-node motifs in complex networks. Specifically, we employ a two-way spectral clustering method to cut big networks into small sub-graphs, and then identify motifs by recognition algorithm to reduce the computational complexity. After that, we use three isomorphic four-node motifs to analyze network structure by American Physical Society (APS) data set.
Description:
The scale of scholarly data has been expanded due to the fact that scientific productions are increasing rapidly and new scholars affiliate academia incessantly. Scholars are shifting their research patterns from individual research to academic teamwork due to the complexity of scientific issues. In order to achieve higher reputations and better performance, academic teams with leaders are constructed to speed up knowledge sharing and problem solving. It is significant to explore team-based issues with the increasing interests of information exploration in big scholarly data. However, existing academic team definitions are commonly not quantitative, which makes it difficult to identify real academic teams. In this work, we propose a collaboration relationship evaluation index called Collaboration Intensity Index (CII), which is a two-way and quantitative index to evaluate collaboration intensity between two scholars in the network. Then, we construct a new type of co-author network with edges weighted by CII, which differs from the original co-author networks. This network reflects the newly scientific research patterns inside or outside academic teams. Furthermore, we propose TRAC (Team Recognition Algorithm based on CII) to identify academic teams from large co-author networks. Finally, we use DBLP data set, which contains 1,250,440 scholars and 1,575,949 published papers, to identify teams by TRAC. Comparing with fast unfolding algorithm and real team data, the effectiveness of our method can be demonstrated.