Extending Coverage of Stationary Sensing Systems with Mobile Sensing Systems for Human Mobility Modeling

2020 ◽  
Vol 4 (3) ◽  
pp. 1-21
Author(s):  
Yu Yang ◽  
Zhihan Fang ◽  
Xiaoyang Xie ◽  
Fan Zhang ◽  
Yunhuai Liu ◽  
...  
Keyword(s):  
Human Mobility ◽  
Mobile Sensing ◽  
Mobility Modeling ◽  
Sensing Systems

scholarly journals Privacy Leakage in Mobile Sensing: Your Unlock Passwords Can Be Leaked through Wireless Hotspot Functionality

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Jie Zhang ◽  
Xiaolong Zheng ◽  
Zhanyong Tang ◽  
Tianzhang Xing ◽  
Xiaojiang Chen ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Mobile Sensing ◽  
Experimental Results ◽  
Smart Devices ◽  
Detection Accuracy ◽  
Phase Information ◽  
Current Sensing ◽  
Privacy Leakage ◽  
Sensing Systems ◽  
Additional Hardware

Mobile sensing has become a new style of applications and most of the smart devices are equipped with varieties of sensors or functionalities to enhance sensing capabilities. Current sensing systems concentrate on how to enhance sensing capabilities; however, the sensors or functionalities may lead to the leakage of users’ privacy. In this paper, we present WiPass, a way to leverage the wireless hotspot functionality on the smart devices to snoop the unlock passwords/patterns without the support of additional hardware. The attacker can “see” your unlock passwords/patterns even one meter away. WiPass leverages the impacts of finger motions on the wireless signals during the unlocking period to analyze the passwords/patterns. To practically implement WiPass, we are facing the difficult feature extraction and complex unlock passwords matching, making the analysis of the finger motions challenging. To conquer the challenges, we use DCASW to extract feature and hierarchical DTW to do unlock passwords matching. Besides, the combination of amplitude and phase information is used to accurately recognize the passwords/patterns. We implement a prototype of WiPass and evaluate its performance under various environments. The experimental results show that WiPass achieves the detection accuracy of 85.6% and 74.7% for passwords/patterns detection in LOS and in NLOS scenarios, respectively.

A multi-agent architecture for mobile sensing systems

2019 ◽  
Vol 11 (11) ◽  
pp. 4439-4451 ◽  
Author(s):  
Francisco Laport ◽  
Emilio Serrano ◽  
Javier Bajo
Keyword(s):  
Mobile Sensing ◽  
Agent Architecture ◽  
Sensing Systems ◽  
Multi Agent

scholarly journals Characterizing Pairwise Social Relationships Quantitatively: Interest-Oriented Mobility Modeling for Human Contacts in Delay Tolerant Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Jiaxu Chen ◽  
Yazhe Tang ◽  
Chengchen Hu ◽  
Guijuan Wang
Keyword(s):  
Social Relationships ◽  
Mobile Networks ◽  
Human Mobility ◽  
Synthetic Data ◽  
Delay Tolerant Networks ◽  
Mobility Model ◽  
Interaction Matrix ◽  
Mobility Modeling ◽  
The Social ◽  
Delay Tolerant

Human mobility modeling has increasingly drawn the attention of researchers working on wireless mobile networks such as delay tolerant networks (DTNs) in the last few years. So far, a number of human mobility models have been proposed to reproduce people’s social relationships, which strongly affect people’s daily life movement behaviors. However, most of them are based on the granularity of community. This paper presents interest-oriented human contacts (IHC) mobility model, which can reproduce social relationships on a pairwise granularity. As well, IHC provides two methods to generate input parameters (interest vectors) based on the social interaction matrix of target scenarios. By comparing synthetic data generated by IHC with three different real traces, we validate our model as a good approximation for human mobility. Exhaustive experiments are also conducted to show that IHC can predict well the performance of routing protocols.

scholarly journals Urban-Scale Human Mobility Modeling With Multi-Source Urban Network Data

2018 ◽  
Vol 26 (2) ◽  
pp. 671-684 ◽  
Author(s):  
Desheng Zhang ◽  
Tian He ◽  
Fan Zhang ◽  
Chengzhong Xu
Keyword(s):  
Human Mobility ◽  
Network Data ◽  
Urban Network ◽  
Mobility Modeling

scholarly journals Mobile Sensing for Data-Driven Mobility Modeling

2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Kashif Zia ◽  
Arshad Muhammad ◽  
Katayoun Farrahi ◽  
Dinesh Kumar
Keyword(s):  
Mobile Sensing ◽  
Data Driven ◽  
Mobility Modeling

CellSense

2021 ◽  
Vol 5 (3) ◽  
pp. 1-22
Author(s):  
Zhihan Fang ◽  
Yu Yang ◽  
Guang Yang ◽  
Yikuan Xian ◽  
Fan Zhang ◽  
...  
Keyword(s):  
Cellular Network ◽  
Large Scale ◽  
Human Mobility ◽  
Ground Truth ◽  
Mobility Patterns ◽  
Billing Data ◽  
Ground Truth Data ◽  
Mobility Modeling ◽  
High Penetration ◽  
Recovery System

Data from the cellular network have been proved as one of the most promising way to understand large-scale human mobility for various ubiquitous computing applications due to the high penetration of cellphones and low collection cost. Existing mobility models driven by cellular network data suffer from sparse spatial-temporal observations because user locations are recorded with cellphone activities, e.g., calls, text, or internet access. In this paper, we design a human mobility recovery system called CellSense to take the sparse cellular billing data (CBR) as input and outputs dense continuous records to recover the sensing gap when using cellular networks as sensing systems to sense the human mobility. There is limited work on this kind of recovery systems at large scale because even though it is straightforward to design a recovery system based on regression models, it is very challenging to evaluate these models at large scale due to the lack of the ground truth data. In this paper, we explore a new opportunity based on the upgrade of cellular infrastructures to obtain cellular network signaling data as the ground truth data, which log the interaction between cellphones and cellular towers at signal levels (e.g., attaching, detaching, paging) even without billable activities. Based on the signaling data, we design a system CellSense for human mobility recovery by integrating collective mobility patterns with individual mobility modeling, which achieves the 35.3% improvement over the state-of-the-art models. The key application of our recovery model is to take regular sparse CBR data that a researcher already has, and to recover the missing data due to sensing gaps of CBR data to produce a dense cellular data for them to train a machine learning model for their use cases, e.g., next location prediction.

scholarly journals Detecting On-Street Parking Spaces in Smart Cities: Performance Evaluation of Fixed and Mobile Sensing Systems

2018 ◽  
Vol 19 (7) ◽  
pp. 2234-2245 ◽  
Author(s):  
Cristian Roman ◽  
Ruizhi Liao ◽  
Peter Ball ◽  
Shumao Ou ◽  
Martin de Heaver
Keyword(s):  
Performance Evaluation ◽  
Smart Cities ◽  
Mobile Sensing ◽  
Sensing Systems

A review of mobile sensing systems, applications, and opportunities

2019 ◽  
Vol 62 (1) ◽  
pp. 145-174 ◽  
Author(s):  
Francisco Laport-López ◽  
Emilio Serrano ◽  
Javier Bajo ◽  
Andrew T. Campbell
Keyword(s):  
Mobile Sensing ◽  
Sensing Systems
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