Real-Time Detection of Intruders Using an Acoustic Sensor and Internet-of-Things Computing

Author:

Al-Khalli Najeeb12ORCID,Alateeq Saud3,Almansour Mohammed3ORCID,Alhassoun Yousef3ORCID,Ibrahim Ahmed B.1ORCID,Alshebeili Saleh A.13ORCID

Affiliation:

1. KACST-TIC in Radio Frequency and Photonics for the e-Society (RFTONICS), King Saud University, Riyadh 11421, Saudi Arabia

2. King Abdullah Institute for Nanotechnology (KAIN), King Saud University, Riyadh 11451, Saudi Arabia

3. Electrical Engineering Department, King Saud University, Riyadh 11421, Saudi Arabia

Abstract

Modern home automation systems include features that enhance security, such as cameras and radars. This paper proposes an innovative home security system that can detect burglars by analyzing acoustic signals and instantly notifying the authorized person(s). The system architecture incorporates the concept of the Internet of Things (IoT), resulting in a network and a user-friendly system. The proposed system uses an adaptive detection algorithm, namely the “short-time-average through long-time-average” algorithm. The proposed algorithm is implemented by an IoT device (Arduino Duo) to detect people’s acoustical activities for the purpose of home/office security. The performance of the proposed system is evaluated using 10 acoustic signals representing actual events and background noise. The acoustic signals were generated by the sounds of keys shaking, the falling of a small object, the shrinking of a plastic bag, speaking, footsteps, etc. The effects of different algorithms’ parameters on the performance of the proposed system have been thoroughly investigated.

Funder

Deputyship for Research and Innovation, Ministry of Education, Saudi Arabia

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Reference20 articles.

1. Edge of things: The big picture on the integration of edge, IoT and the cloud in a distributed computing environment;Sankar;IEEE Access,2017

2. Cicero, S., Cromwell, C., and Hunt, E. (2018). Cisco Predicts More IP Traffic in the Next Five Years Than in the History of the Internet, Cisco.

3. Hussain, M.Z., and Hanapi, Z.M. (2023). Efficient Secure Routing Mechanisms for the Low-Powered IoT Network: A Literature Review. Electronics, 12.

4. Abhinay, D., Chaitanya, K., and Ram, P.S. (2022). Advances in Signal Processing and Communication Engineering: Select Proceedings of ICASPACE 2021, Springer.

5. Vandana, G., Pardhasaradhi, B., and Srihari, P. (2022, January 8–10). Intruder Detection and Tracking using 77GHz FMCW Radar and Camera Data. Proceedings of the 2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3