Deep Information Fusion-Driven POI Scheduling for Mobile Social Networks
With the growing importance of green wireless communications, point-of-interest (POI) scheduling in the mobile social network (MSN) environment has become important in addressing the high demand for innovative scheduling solutions. To enhance feature expressions for the complicated structures in MSNs, this article explores a deep information, fusion-based POI scheduling system of the MSN environment via the implementation of an edge-cloud deep hybrid sensing (PS-MSN) framework. Cloud sensing modules utilize the explicit contextual real-time information for each user, while edge sensing modules detect the real-time implicit linkages among users. Based on these two types of modules, a deep representation scheme is embedded into the hybrid sensing framework to improve its feature expression abilities. As a result, this type of framework is able to integrate multisource information so that more fine-grained feature spaces are built. In this work, two groups of experiments are conducted on a real-world dataset to evaluate the efficiency, as well as stability, of the designed PS-MSN. Using three benchmark methods to make comparisons, the excellent overall performance of PS-MSN is properly verified. IEEE
Computational modeling, Feature extraction, Semantics, Sensors, Social networking (online), Training, Wireless sensor networks, Benchmarking, E-learning, Online systems, Scheduling, Semantic Web, Social networking (online), User profile, Wireless sensor networks
Z. Guo, K. Yu, A. K. Bashir, D. Zhang, Y. D. Al-Otaibi and M. Guizani, "Deep Information Fusion-Driven POI Scheduling for Mobile Social Networks," in IEEE Network, doi: 10.1109/MNET.102.2100394.