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Table 1 Summary of Knowledge-driven Applications on Wireless Networks

From: A survey on data analysis on large-Scale wireless networks: online stream processing, trends, and challenges

Reference Research opportunity Method used
Acer et al. [12] Connected Objects Fingerprint Platform to analyze Wi-Fi trace
Acer et al. [13] Crowd Behavior Data Analysis
Gómez et al. [14] User Association Wi-Fi SDN
Balbi et al. [15] Channel Allocation Metric-based Algorithm
Coronado et al. [16] Channel Allocation Wi-Fi SDN
Xu et al. [17] Vehicles Location Survey
Chen et al. [18] Traffic Jam Prediction Combine data from different wireless sources
Leung and Kim [21] Optimal Channel Allocation
Maturi et al. [22] Heuristic Channel Allocation Dynamic channel selection
Lin et al. [23] Channel Allocation Interference observed between APs
Luiz et al. [20] Channel Allocation Interference observed between clients and APs
Shin et al. [24] Decrease Latency during hand-off Stores the set of channels each neighbor is operating and the set of
   neighboring APs on each channel
Zeljković et al. [25] Evaluates Handover Algorithms for QoS SDN + Machine Learning
Huang et al. [26] Monitor wireless AP MBD platforms, data analysis, and
   distributed acquisition tools
Wang et al. [28] Optimize use of the wireless spectrum Cooperation between access points to
   perform beamforming
Ghouti [29], Noulas et al. [30], Kulkarni et al. [31], Stynes et al. [32], Zhang and Dai [33], Bozkurt et al. [34] Positioning Analysis Machine Learning
Gonzalez et al. [35] 100.000 Traces of cellphones Dataset
Song et al. [36] People’s Movement Dataset
Toch et al. [37] Classify user mobility applications Clusters Similar Profiles and their future trajectory
Jiang et al. [41] Characterization and spectrum analysis in next-generation  
  wireless networks Survey