Capturing Personal and Crowd Behavior with Wi-Fi Analytics

Acer, Utku Günay; Vanderhulst, Geert; Masshadi, Afra; Boran, Aidan; Forlivesi, Claudio; Scholl, Philipp M.; Kawsar, Fahim

doi:10.1145/2935651.2935656

Cited by 12 publications

(8 citation statements)

References 15 publications

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“…In the context of MBD, some researches adopt Wi-Fi analytics to locate objects or users. The idea is to use management and control packets, such as probes and beacons from IEEE 802.11 to determine the location or offset of objects or users [12,13]. It is possible to locate a user, with low accuracy, assuming he is in the vicinity of the access point to which he is associated.…”

Section: Radio Environment Network Topology and Characterizationmentioning

confidence: 99%

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

Medeiros

Neto

Lopez

et al. 2020

J Internet Serv Appl

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In this paper we focus on knowledge extraction from large-scale wireless networks through stream processing. We present the primary methods for sampling, data collection, and monitoring of wireless networks and we characterize knowledge extraction as a machine learning problem on big data stream processing. We show the main trends in big data stream processing frameworks. Additionally, we explore the data preprocessing, feature engineering, and the machine learning algorithms applied to the scenario of wireless network analytics. We address challenges and present research projects in wireless network monitoring and stream processing. Finally, future perspectives, such as deep learning and reinforcement learning in stream processing, are anticipated.

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Section: Radio Environment Network Topology and Characterizationmentioning

confidence: 99%

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

Medeiros

Neto

Lopez

et al. 2020

J Internet Serv Appl

View full text Add to dashboard Cite

show abstract

“…In the context of MBD, some researches adopt Wi-Fi analytics to locate objects or users. The idea is to use management and control packets, such as probes and beacons from IEEE 802.11 to determine the location or oset of objects or users [12,13]. It is possible to locate a user, with low accuracy, assuming he is in the vicinity of the access point to which he is associated.…”

Section: Radio Environment Network Topology and Characterizationmentioning

confidence: 99%

“…In this context, machine learning is one of the most promising articial intelligence paradigm, designed to support intelligent radio terminals [40]. [12] Connected Objects Fingerprint Platform to analyze WiFi trace [13] Crowd Behavior Data Analysis [14] User Association WiFi SDN [15] Channel Allocation Metric Based Algorithm [16] Channel Allocation Wi-Fi SDN [17] Vehicles Location Survey [18] Trac Jam Prediction Combine data from dierent wireless sources [21] Optimal Channel Allocation [22] Heuristic Channel Allocation Dynamic channel selection [23] Channel Allocation Interference observed between APs [20] Channel Allocation Interference observed between clients and APs [24] Decrease Latency during hand-o…”

Section: Next-generation Networkmentioning

confidence: 99%

“…[11] IDEA products, available on https://idea.caseware.com/. [12] DataSunrise Data Audit for MongoDB, available on https://www.datasunrise.com/audit/mongodb/ [13] Oracle Warehouse Builder 10g Release 2, available on https://www.oracle.com/technicalresources/articles/rittman-owb.html [14] IBM SPSS, available on https://www.ibm.com/analytics/spss-statistics-software the extraction, the data required for the analysis is selected, then it is modied in the transformation stage by applying a set of rules, converting the data into a standard format. Finally, in the loading stage, the transformed data is imported into the storage infrastructure and usually comes from a single source [55].…”

Section: Data Preprocessing Techniquesmentioning

confidence: 99%

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A Survey on Data Analysis on Large-Scale Wireless Networks: Online Stream Processing, Trends, and Challenges

Medeiros

Neto

Lopez

et al. 2020

Preprint

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“…The possibility of detecting and tracking the movements of users through their Wi-Fi enabled devices [41] has lead to the emergence of the field of physical analytics [2,49] that collect information on the human activity in the physical world. The main objective of this type of system is to be able to analyze human mobility at a large scale and to use the findings of this analysis for task such as urban planning or transportation optimization.…”

Section: Introductionmentioning

confidence: 99%

Privacy-preserving Wi-Fi Analytics

Alaggan

Cunche

Gambs

2018

Proceedings on Privacy Enhancing Technologies

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As communications-enabled devices are becoming more ubiquitous, it becomes easier to track the movements of individuals through the radio signals broadcasted by their devices. Thus, while there is a strong interest for physical analytics platforms to leverage this information for many purposes, this tracking also threatens the privacy of individuals. To solve this issue, we propose a privacy-preserving solution for collecting aggregate mobility patterns while satisfying the strong guarantee of ε-differential privacy. More precisely, we introduce a sanitization mechanism for efficient, privacy-preserving and non-interactive approximate distinct counting for physical analytics based on perturbed Bloom filters called Pan-Private BLIP. We also extend and generalize previous approaches for estimating distinct count of events and joint events (i.e., intersection and more generally t-out-of-n cardinalities). Finally, we evaluate expirementally our approach and compare it to previous ones on real datasets.

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Capturing Personal and Crowd Behavior with Wi-Fi Analytics

Cited by 12 publications

References 15 publications

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

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

A Survey on Data Analysis on Large-Scale Wireless Networks: Online Stream Processing, Trends, and Challenges

Privacy-preserving Wi-Fi Analytics

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