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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


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


Balbi et al. [15]

Channel Allocation

Metric-based Algorithm

Coronado et al. [16]

Channel Allocation


Xu et al. [17]

Vehicles Location


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