Device to Remotely Track and Locate the Position of a Child for Safety
- URL: http://arxiv.org/abs/2008.00211v1
- Date: Sat, 1 Aug 2020 08:11:21 GMT
- Title: Device to Remotely Track and Locate the Position of a Child for Safety
- Authors: S.M.K.C.S.B. Egodawela, H.M.D.M.B. Herath, R.D. Ranaweera, J.V.
Wijayakulasooriya
- Abstract summary: This paper proposes a solution which enables parents to call, locate and track their children using a child-friendly mobile device.
The device can be calibrated to keep track of a typical route of travel.
A probability matrix based nov-el algorithm is introduced to detect route deviation.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Parents are always worried about the wellbeing of their children. As per the
Statistics Report 2017 by Missing Children Europe Organization, a child is
reported missing every 2 minutes. Due to the imminent threat, parents are prone
to buy their children mobile phones to keep in touch with them. However, giving
a Mobile phone to a child can cause issues including cyber bullying, improper
use of social networks, access to mature age and illicit content on the
internet and possibly, phone theft. As an effort to tackle some of those
issues, this paper proposes a solution which enables parents to call, locate
and track their children using a child-friendly mobile device. The common
scenario the device would come to play is in enhancing the safety of a child
who would travel alone on a typical route; for instance a child who walks from
home to school and back. The device can be calibrated to keep track of a
typical route of travel. Then, if the device de-tects some deviation from the
usual route, it would trigger a notification to parents. A probability matrix
based nov-el algorithm is introduced to detect route deviation. De-sign details
of the mobile device, along with the details of the route deviation detection
algorithm are presented in this paper.
Related papers
- Your Car Tells Me Where You Drove: A Novel Path Inference Attack via CAN Bus and OBD-II Data [57.22545280370174]
On Path Diagnostic - Intrusion & Inference (OPD-II) is a novel path inference attack leveraging a physical car model and a map matching algorithm.
We implement our attack on a set of four different cars and a total number of 41 tracks in different road and traffic scenarios.
arXiv Detail & Related papers (2024-06-30T04:21:46Z) - "Don't forget to put the milk back!" Dataset for Enabling Embodied Agents to Detect Anomalous Situations [49.66220439673356]
We have created a new dataset, which we call SafetyDetect.
The SafetyDetect dataset consists of 1000 anomalous home scenes.
Our approach utilizes large language models (LLMs) alongside both a graph representation of the scene and the relationships between the objects in the scene.
arXiv Detail & Related papers (2024-04-12T21:56:21Z) - Detection of Children Abuse by Voice and Audio Classification by
Short-Time Fourier Transform Machine Learning implemented on Nvidia Edge GPU
device [0.0]
This experiment uses machine learning to classify and recognize a child's voice.
If a child is found to be crying or screaming, an alert is immediately sent to the relevant personnel.
arXiv Detail & Related papers (2023-07-27T16:48:19Z) - A Remote Baby Surveillance System with RFID and GPS Tracking [0.0]
In Malaysia, thousands of child abuse cases have been reported from babysitting centres every year.
This paper proposes to construct a remote baby surveillance system with radio-frequency identification (RFID) and global positioning system (GPS) tracking.
arXiv Detail & Related papers (2022-11-26T12:42:27Z) - CAN-LOC: Spoofing Detection and Physical Intrusion Localization on an
In-Vehicle CAN Bus Based on Deep Features of Voltage Signals [48.813942331065206]
We propose a security hardening system for in-vehicle networks.
The proposed system includes two mechanisms that process deep features extracted from voltage signals measured on the CAN bus.
arXiv Detail & Related papers (2021-06-15T06:12:33Z) - Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object
Detection [55.12894776039135]
State-of-the-art 3D object detectors, based on deep learning, have shown promising accuracy but are prone to over-fit to domain idiosyncrasies.
We propose a novel learning approach that drastically reduces this gap by fine-tuning the detector on pseudo-labels in the target domain.
We show, on five autonomous driving datasets, that fine-tuning the detector on these pseudo-labels substantially reduces the domain gap to new driving environments.
arXiv Detail & Related papers (2021-03-26T01:18:11Z) - Betrayed by the Guardian: Security and Privacy Risks of Parental Control
Solutions [0.0]
We present an experimental framework for systematically evaluating security and privacy issues in parental control software and hardware solutions.
Our analysis uncovers pervasive security and privacy issues that can lead to leakage of private information, and/or allow an adversary to fully control the parental control solution.
arXiv Detail & Related papers (2020-12-11T17:06:00Z) - Ridesharing Services and Car-Seats: Technological Perceptions and Usage
Patterns [0.0]
Child safety seats (CSSs) can decrease the severity of crash outcomes for children.
The usage of CSSs has significantly improved in the U.S. over the last 40 years.
It is anticipated that the usage of CSSs in popular ridesharing services, such as Uber and Lyft, is not widespread.
arXiv Detail & Related papers (2020-11-02T06:52:33Z) - Urban Sensing based on Mobile Phone Data: Approaches, Applications and
Challenges [67.71975391801257]
Much concern in mobile data analysis is related to human beings and their behaviours.
This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.
arXiv Detail & Related papers (2020-08-29T15:14:03Z) - Mind the GAP: Security & Privacy Risks of Contact Tracing Apps [75.7995398006171]
Google and Apple have jointly provided an API for exposure notification in order to implement decentralized contract tracing apps using Bluetooth Low Energy.
We demonstrate that in real-world scenarios the GAP design is vulnerable to (i) profiling and possibly de-anonymizing persons, and (ii) relay-based wormhole attacks that basically can generate fake contacts.
arXiv Detail & Related papers (2020-06-10T16:05:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.