Home Energy Security Prototype using Microcontroller Based on Fingerprint Sensor

Alrizal Akbar Nusantar Akbar Nusantar, Ilham Ari Elbaith Zaeni, Dyah Lestari

Abstract


The globalization era brings rapid development in technology.
The human need for speed and easiness pushed them to
innovate, such as in the security field. Initially, the security
system was conducted manually and impractical compared to
nowadays system. A security technology that is developed was
biometric application, particularly fingerprint. Fingerprintbased
security became a reliable enough system because of its
accuracy level, safe, secure, and comfortable to be used as
housing security system identification. This research aimed to
develop a security system based on fingerprint biometric taken
from previous researches by optimizing and upgrading the
previous weaknesses. This security system could be a solution
to a robbery that used Arduino UNO Atmega328P CH340 R3
Board Micro USB port. The inputs were fingerprint sensor, 4x5
keypad, and magnetic sensor, whereas the outputs were 12 V
solenoid, 16x2 LCD, GSM SIM800L module, LED, and
buzzer. The advantage of this security system was its ability to
give a danger sign in the form of noise when the system
detected the wrong fingerprint or when it detects a forced
opening. The system would call the homeowner then. Other
than that, this system notified the homeowner of all of the
activities through SMS so that it can be used as a long-distance
observation. This system was completed with a push button to
open the door from the inside. The maximum fingerprints that
could be stored were four users and one admin. The admin’s
job was to add/delete fingerprints, replace the home owner’s
phone number, and change the system’s PIN. The results
showed that the fingerprint sensor read the prints in a relatively
fast time of 1.136 seconds. The average duration that was
needed to send an SMS was 69 seconds while through call was
3.2 seconds.

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References


Y. Zhuo and S. Solak, “Optimal Policies for Information Sharing in Information System Security,”

Eur. J. Oper. Res., p. S0377221719310197, Dec. 2019, doi: 10.1016/j.ejor.2019.12.016.

F. De Rango, G. Potrino, M. Tropea, and P. Fazio, “Energy-aware dynamic Internet of Things security

system based on Elliptic Curve Cryptography and Message Queue Telemetry Transport protocol for

mitigating Replay attacks,” Pervasive Mob. Comput., vol. 61, p. 101105, Jan. 2020, doi:

1016/j.pmcj.2019.101105.

J. E. Hachem, V. Chiprianov, M. A. Babar, T. A. Khalil, and P. Aniorte, “Modeling, analyzing and

predicting security cascading attacks in smart buildings systems-of-systems,” J. Syst. Softw., vol. 162,

p. 110484, Apr. 2020, doi: 10.1016/j.jss.2019.110484.

M. Grimes and J. Marquardson, “Quality matters: Evoking subjective norms and coping appraisals by

system design to increase security intentions,” Decis. Support Syst., vol. 119, pp. 23–34, Apr. 2019,

doi: 10.1016/j.dss.2019.02.010.

H. Ai and X. Cheng, “Research on embedded access control security system and face recognition

system,” Measurement, vol. 123, pp. 309–322, Jul. 2018, doi: 10.1016/j.measurement.2018.04.005.

S. Yang et al., “Security situation assessment for massive MIMO systems for 5G communications,”

Future Gener. Comput. Syst., vol. 98, pp. 25–34, Sep. 2019, doi: 10.1016/j.future.2019.03.036.

R. M. Luque-Baena, D. Elizondo, E. López-Rubio, E. J. Palomo, and T. Watson, “Assessment of

geometric features for individual identification and verification in biometric hand systems,” Expert

Syst. Appl., vol. 40, no. 9, pp. 3580–3594, Jul. 2013, doi: 10.1016/j.eswa.2012.12.065.

M. Adán, A. Adán, A. S. Vázquez, and R. Torres, “Biometric verification/identification based on

hands natural layout,” Image Vis. Comput., vol. 26, no. 4, pp. 451–465, Apr. 2008, doi:

1016/j.imavis.2007.08.010.

S. Dargan and M. Kumar, “A comprehensive survey on the biometric recognition systems based on

physiological and behavioral modalities,” Expert Syst. Appl., vol. 143, p. 113114, Apr. 2020, doi:

1016/j.eswa.2019.113114.

M. Gomez-Barrero and J. Galbally, “Reversing the irreversible: A survey on inverse biometrics,”

Comput. Secur., vol. 90, p. 101700, Mar. 2020, doi: 10.1016/j.cose.2019.101700.

S. Bharadwaj, H. S. Bhatt, M. Vatsa, and R. Singh, “Periocular biometrics: When iris recognition

fails,” in 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and

Systems (BTAS), Washington, DC, USA, 2010, pp. 1–6, doi: 10.1109/BTAS.2010.5634498.

P. Porwik, R. Doroz, and K. Wrobel, “An ensemble learning approach to lip-based biometric

verification, with a dynamic selection of classifiers,” Expert Syst. Appl., vol. 115, pp. 673–683, Jan.

, doi: 10.1016/j.eswa.2018.08.037.

D. Peralta et al., “A survey on fingerprint minutiae-based local matching for verification and

identification: Taxonomy and experimental evaluation,” Inf. Sci., vol. 315, pp. 67–87, Sep. 2015, doi:

1016/j.ins.2015.04.013.

C. Mesec, “Fingerprint identification versus verification,” Biom. Technol. Today, vol. 15, no. 9, p. 7,

Sep. 2007, doi: 10.1016/S0969-4765(07)70157-1.

K. N. Win, K. Li, J. Chen, P. F. Viger, and K. Li, “Fingerprint classification and identification

algorithms for criminal investigation: A survey,” Future Gener. Comput. Syst., p.

S0167739X19315109, Nov. 2019, doi: 10.1016/j.future.2019.10.019.

M. Esteki, Z. Shahsavari, and J. Simal-Gandara, “Food identification by high performance liquid

chromatography fingerprinting and mathematical processing,” Food Res. Int., vol. 122, pp. 303–317,

Aug. 2019, doi: 10.1016/j.foodres.2019.04.025.

B. Topcu and H. Erdogan, “Fixed-length asymmetric binary hashing for fingerprint verification

through GMM-SVM based representations,” Pattern Recognit., vol. 88, pp. 409–420, Apr. 2019, doi:

1016/j.patcog.2018.11.029.

J. Song, C. Cho, and Y. Won, “Analysis of operating system identification via fingerprinting and

machine learning,” Comput. Electr. Eng., vol. 78, pp. 1–10, Sep. 2019, doi:

1016/j.compeleceng.2019.06.012.

R. P. Krish, J. Fierrez, D. Ramos, F. Alonso-Fernandez, and J. Bigun, “Improving automated latent

fingerprint identification using extended minutia types,” Inf. Fusion, vol. 50, pp. 9–19, Oct. 2019, doi:

1016/j.inffus.2018.10.001.

K. B. Raja, R. Raghavendra, and C. Busch, “Collaborative representation of deep sparse filtered

features for robust verification of smartphone periocular images,” in 2016 IEEE International

Conference on Image Processing (ICIP), Phoenix, AZ, USA, 2016, pp. 330–334, doi:

1109/ICIP.2016.7532373.

A. Selwal, S. K. Gupta, Surender, and Anubhuti, “Template security analysis of multimodal biometric

frameworks based on fingerprint and hand geometry,” Perspect. Sci., vol. 8, pp. 705–708, Sep. 2016,

doi: 10.1016/j.pisc.2016.06.065.

G. Panchal and D. Samanta, “A Novel Approach to Fingerprint Biometric-Based Cryptographic Key

Generation and its Applications to Storage Security,” Comput. Electr. Eng., vol. 69, pp. 461–478, Jul.

, doi: 10.1016/j.compeleceng.2018.01.028.

M. Su and W. Wen, “An analysis of chaos-based security solution for fingerprint data,” Optik, vol.

, no. 21, pp. 6530–6534, Nov. 2014, doi: 10.1016/j.ijleo.2014.08.026.




DOI: http://dx.doi.org/10.17977/um049v1i2p19-29

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