Mobile Device Detection Through WiFi Probe Request Analysis

Oliveira, Luiz; Schneider, Daniel; Souza, Jairo; Shen, Weiming

doi:10.1109/access.2019.2925406

Cited by 41 publications

(20 citation statements)

References 41 publications

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“…Location is used to describe the spatial context (physical context) of users/devices within a local or global network [ 24 , 113 ]. It represents the geophysical position of a sensor or actuator in absolute values, identifying the coordinates (latitude and longitude) or relative specifications through location tags [ 45 ], which is obtained manually or automatically [ 120 , 123 ]. It represents the definition of an area covered by a particular object [ 79 ].…”

Section: Proposed Ioe Taxonomymentioning

confidence: 99%

Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy

Costa

Oliveira

Souza

2021

Sensors

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The paradigm of the Internet of everything (IoE) is advancing toward enriching people’s lives by adding value to the Internet of things (IoT), with connections among people, processes, data, and things. This paper provides a survey of the literature on IoE research, highlighting concerns in terms of intelligence services and knowledge creation. The significant contributions of this study are as follows: (1) a systematic literature review of IoE taxonomies (including IoT); (2) development of a taxonomy to guide the identification of critical knowledge in IoE applications, an in-depth classification of IoE enablers (sensors and actuators); (3) validation of the defined taxonomy with 50 IoE applications; and (4) identification of issues and challenges in existing IoE applications (using the defined taxonomy) with regard to insights about knowledge processes. To the best of our knowledge, and taking into consideration the 76 other taxonomies compared, this present work represents the most comprehensive taxonomy that provides the orchestration of intelligence in network connections concerning knowledge processes, type of IoE enablers, observation characteristics, and technological capabilities in IoE applications.

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Section: Proposed Ioe Taxonomymentioning

confidence: 99%

Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy

Costa

Oliveira

Souza

2021

Sensors

Self Cite

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“…To calculate the desired paths for evacuees, our algorithm requires the estimated number of evacuees nearby and the severity level of the hazard as input parameters. To count the number of evacuees in the monitored area, various sensors such as WI-FI probe [43] can be used to sniff the presence of mobile devices carried by evacuees via capturing the features of the mobile devices such as MAC address and strength of the signal. To detect the severity level of the hazard, visual sensors such as camera can be used as a general way to detect the hazard type and severity level.…”

Section: A Problem Descriptions and Assumptionsmentioning

confidence: 99%

Cooperative and Energy-Efficient Strategies in Emergency Navigation Using Edge Computing

Shang

Chen

2020

IEEE Access

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Nowadays, public transportation junctions (PTJs) such as metro stations and railway stations can involve up to half of urban traffic and account for a large portion of energy usage in urban areas. The tremendous traffic and energy flows in PTJs have induced severe problems in safety and efficiency. Thus, in this paper, we present a comprehensive edge computing enabled navigation framework to aid the emergent evacuation processes within PTJs with respect to energy and safety restrictions. From the energy-saving aspect, due to the resource restraints of the on-site Internet of Things (IoT) based environmental monitoring system, a queueing network model is utilised to balance the energy utilisation and reduce the congestion of the sensing and navigation process during emergency. From the safety aspect, three edge computing aided cooperative strategies are proposed to dynamically assign evacuees into several groups to adapt their course of action with regard to their physical conditions and immediate environments. Simulation results show that the use of the queueing network model can reduce and balance the energy utilisation of the on-site IoT network. Experiments also show that the use of cooperative strategies to adjust the evacuees' category and the associated routing algorithm can achieve higher survival rates.

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“…It is certain that increasing sniffer density would obtain more data [19]. Typically in the 802.11b/g/n (2.4GHz) environment, using a three channel sniffer is suggested [20].…”

Section: Introductionmentioning

confidence: 99%

A Case Study of WiFi Sniffing Performance Evaluation

Barthélemy

Sun³

et al. 2020

IEEE Access

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Mobile devices regularly broadcast WiFi probe requests in order to discover available proximal WiFi access points for connection. A probe request, sent automatically in the active scanning mode, consisting of the MAC address of the device expresses an advertisement of its presence. A realtime wireless sniffing system is able to sense WiFi packets and analyse wireless traffic. This provides an opportunity to obtain insights into the interaction between the humans carrying the mobile devices and the environment. Susceptibility to loss of the wireless data transmission is an important limitation on this idea, and this is complicated by the lack of a standard specification for real deployment of WiFi sniffers. In this paper, we present an experimental analysis of sniffing performance under different wireless environments using off-the-shelf products. Our objective is to identify the possible factors including channel settings and access point configurations that affect sniffing behaviours and performances, thereby enabling the design of a protocol for a WiFi sniffing system under the optimal monitoring strategy in a real deployment. Our preliminary results show that four main factors affect the sniffing performance: the number of access points and their corresponding operating channels, the signal strength of the access point and the number of devices in the vicinity. In terms of a real field deployment, we propose assignment of one sniffing device to each specific sub-region based on the local access point signal strength and coverage area and fixing the monitoring channel belongs to the local strongest access point.

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Mobile Device Detection Through WiFi Probe Request Analysis

Cited by 41 publications

References 41 publications

Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy

Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy

Cooperative and Energy-Efficient Strategies in Emergency Navigation Using Edge Computing

A Case Study of WiFi Sniffing Performance Evaluation

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