TY - GEN
T1 - Design and implementation of a fire detection andcontrol system with enhanced security and safety for automobiles using neuro-fuzzy logic
AU - Sowah, Robert
AU - Ofoli, Abdul
AU - Koumadi, Koudjo
AU - Osae, George
AU - Nortey, Gilbert
AU - Bempong, Amewugah M.
AU - Agyarkwa, Benedict
AU - Apeadu, Kwaku O.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/24
Y1 - 2018/10/24
N2 - Automobiles provide comfort and mobility to owners.While they make life more meaningful they also pose challenges and risks intheir safety and security mechanisms. Some modern automobiles are equippedwith anti-theft systems and enhanced safety measures to safeguard itsdrivers.But at times, these mechanisms for safety and secured operation ofautomobiles are insufficientdueto various mechanisms used by intruders andcar thieves to defeat them. Drunk drivers cause accidents on our roads andthus the need to safeguard the driver when he is intoxicated and renderthecar to be incapable of being driven. These issues merit an integratedapproach to safety andsecurity of automobiles. In the light of thesechallenges, an integrated microcontroller-based hardware and software systemfor safety and security of automobiles to be fixed into existing vehiclearchitecture, was designed, developed and deployed. The system submodulesare: (1) Two-step ignition for automobiles, namely: (a) biometric ignitionand (b) alcohol detection with engine control, (2) Global Positioning System(GPS) based vehicle tracking and (3) Multisensor-based fire detection usingneuro-fuzzy logic. All submodules of the system were implemented using onemicrocontroller, the Arduino Mega 2560, as the central control unit. Themicrocontroller was programmed using C++11.The developed systemperformed quite well with the tests performed on it. Given the rightconditions, the alcohol detection subsystem operated with a 92%efficiency. The biometric ignitionsubsystem operated with about 80% efficiency. The fire detection subsystem operated with a 95% efficiencyin locations registered with the neuro-fuzzy system. The vehicle trackingsubsystem operated with an efficiency of 90%.
AB - Automobiles provide comfort and mobility to owners.While they make life more meaningful they also pose challenges and risks intheir safety and security mechanisms. Some modern automobiles are equippedwith anti-theft systems and enhanced safety measures to safeguard itsdrivers.But at times, these mechanisms for safety and secured operation ofautomobiles are insufficientdueto various mechanisms used by intruders andcar thieves to defeat them. Drunk drivers cause accidents on our roads andthus the need to safeguard the driver when he is intoxicated and renderthecar to be incapable of being driven. These issues merit an integratedapproach to safety andsecurity of automobiles. In the light of thesechallenges, an integrated microcontroller-based hardware and software systemfor safety and security of automobiles to be fixed into existing vehiclearchitecture, was designed, developed and deployed. The system submodulesare: (1) Two-step ignition for automobiles, namely: (a) biometric ignitionand (b) alcohol detection with engine control, (2) Global Positioning System(GPS) based vehicle tracking and (3) Multisensor-based fire detection usingneuro-fuzzy logic. All submodules of the system were implemented using onemicrocontroller, the Arduino Mega 2560, as the central control unit. Themicrocontroller was programmed using C++11.The developed systemperformed quite well with the tests performed on it. Given the rightconditions, the alcohol detection subsystem operated with a 92%efficiency. The biometric ignitionsubsystem operated with about 80% efficiency. The fire detection subsystem operated with a 95% efficiencyin locations registered with the neuro-fuzzy system. The vehicle trackingsubsystem operated with an efficiency of 90%.
KW - Alcoholdetection
KW - Biometric ignition
KW - Data fusion
KW - Fire detection
KW - Fuzzy logiccontrol
KW - Neural networks
KW - Vehicle tracking
UR - http://www.scopus.com/inward/record.url?scp=85056850183&partnerID=8YFLogxK
U2 - 10.1109/ICASTECH.2018.8507143
DO - 10.1109/ICASTECH.2018.8507143
M3 - Conference contribution
AN - SCOPUS:85056850183
T3 - IEEE International Conference on Adaptive Science and Technology, ICAST
BT - 2018 IEEE 7th International Conference on Adaptive Science and Technology, ICAST 2018
PB - IEEE Computer Society
T2 - 7th IEEE International Conference on Adaptive Science and Technology, ICAST 2018
Y2 - 22 August 2018 through 24 August 2018
ER -