A Low-cost Intelligent Car Break-in Alert System - Using Smartphone Accelerometers for Detecting Vehicle Break-ins

Behringer, Reinhold; Ramachandran, Muthu; Chang, Victor

doi:10.5220/0005914001790184

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…In addition, the combination is widely used wherein Raspberry Pi is an IoT gateway [255][256][257] and Arduino boards serve as edge monitoring devices [262][263][264]. Similarly, smartphones exhibit their versatility being used as gateway [267][268][269], user interface [270,271], and sensing [265,266] IoT devices. Meanwhile, FPGAs have exhibited a sigmoid exponential phase growth in the last two years, with applications such as data encryption [273][274][275], routing algorithms optimization [276], and parallel networks simulation [277].…”

Section: Discussionmentioning

confidence: 99%

“…Sixty-six documents were found related to smartphones in IoT, be they used as sensors [265,266], gateway [267][268][269], or user interface [270,271]. The Field-Programmable Gate Array (FPGA) is a hardware reconfigurable component that contains an array of computational (logic) elements, with a functionality specified by a hardware description language [272].…”

Section: Device Operating Systems (Os) and Hardwarementioning

confidence: 99%

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Internet of Things: A Scientometric Review

et al. 2017

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Abstract:Internet of Things (IoT) is connecting billions of devices to the Internet. These IoT devices chain sensing, computation, and communication techniques, which facilitates remote data collection and analysis. wireless sensor networks (WSN) connect sensing devices together on a local network, thereby eliminating wires, which generate a large number of samples, creating a big data challenge. This IoT paradigm has gained traction in recent years, yielding extensive research from an increasing variety of perspectives, including scientific reviews. These reviews cover surveys related to IoT vision, enabling technologies, applications, key features, co-word and cluster analysis, and future directions. Nevertheless, we lack an IoT scientometrics review that uses scientific databases to perform a quantitative analysis. This paper develops a scientometric review about IoT over a data set of 19,035 documents published over a period of 15 years (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016) in two main scientific databases (Clarivate Web of Science and Scopus). A Python script called ScientoPy was developed to perform quantitative analysis of this data set. This provides insight into research trends by investigating a lead author's country affiliation, most published authors, top research applications, communication protocols, software processing, hardware, operating systems, and trending topics. Furthermore, we evaluate the top trending IoT topics and the popular hardware and software platforms that are used to research these trends.

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Section: Discussionmentioning

confidence: 99%

Section: Device Operating Systems (Os) and Hardwarementioning

confidence: 99%

Internet of Things: A Scientometric Review

et al. 2017

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“…Advances in network technology (see Behringer et al, 2016;Rostirolla et al, 2016;D. Xu et al, 2016) could be beneficial for image forgery detection.…”

Section: Potential Of Big Datamentioning

confidence: 98%

Copy-move forgery detection: Survey, challenges and future directions

Warif

Wahab

Idris

et al. 2016

Journal of Network and Computer Applications

176

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“…A key observation is that magnetic signals change patterns especially when a smartphone passes through a door. Behringer et al [8] proposed a car security system that used GPS and an accelerometer of a smartphone. It could detect a variety of carrelated events such as door opening/closing, engine start, and movement.…”

Section: Related Workmentioning

confidence: 99%

A Home Intrusion Detection System using Recycled Edge Devices and Machine Learning Algorithm

Kwon¹,

Song²,

Choi³

et al. 2020

IJACSA

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This paper proposes a home intrusion detection system that makes the best use of a retired smartphone and an existing Wi-Fi access point. On-board sensors in the smartphone mounted on an entrance door records signals upon unwanted door opening. The access point is reconfigured to serve as a home server and thus it can process sensor data to detect unauthorized access to home by an intruder. Recycling devices enables a home owner to build own security system with no cost as well as helps our society deal with millions of retired devices and waste of computing resources in already-deployed IT devices. In order to improve detection accuracy, this paper proposes a detection method that employs a machine learning algorithm and an analysis technique of time series data. To minimize energy consumption on a battery-powered smartphone, the proposed system utilizes as few sensors as possible and offloads all the computation to the home edge server. We develop a prototype and run experiments to evaluate accuracy performance of the proposed system. Results show that it can detect intrusion with probability of 95% to 100%.

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A Low-cost Intelligent Car Break-in Alert System - Using Smartphone Accelerometers for Detecting Vehicle Break-ins

Cited by 6 publications

References 5 publications

Internet of Things: A Scientometric Review

Internet of Things: A Scientometric Review

Copy-move forgery detection: Survey, challenges and future directions

A Home Intrusion Detection System using Recycled Edge Devices and Machine Learning Algorithm

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