TY - JOUR
T1 - A deep learning framework for target localization in error-prone environment
AU - Mohammed, Shahmir Khan
AU - Singh, Shakti
AU - Mizouni, Rabeb
AU - Otrok, Hadi
N1 - Funding Information:
This research is supported by ASPIRE, the technology program management pillar of Abu Dhabi’s Advanced Technology Research Council (ATRC), via the ASPIRE Awards for Research Excellence, United Arab Emirates [ AARE19-255 ].
Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2023/7
Y1 - 2023/7
N2 - The use of Internet of Things (IoT) in environment monitoring has led to the development of Smart Environmental Monitoring (SEM) paradigm. Target or source localization, that determines the underlying cause of environmental occurrences, is an important aspect of SEM. To prevent an environmental event in becoming a potential disaster, swift and early localization of its source is crucial. Target localization is performed with the help of IoT sensors, which are typically deployed in hazardous environments and are not easily serviceable, making energy efficiency a requirement for SEM systems. Furthermore, IoT sensors may provide anomalous readings due to low battery, low sensor sensitivity, faults, or malicious attacks. This alters the decision-making process and leads to inaccurate localization of the source. Hence, detecting and dealing with anomalies, while utilizing a limited number of sensor nodes, are key factors in addressing reliability and energy efficiency issues. The current works are iterative in nature, employs redundant nodes, do not efficiently address anomalous readings, which leads to slow and imprecise localization, while increasing energy consumption. Moreover, they are application specific, making them less adaptable to other sensing tasks. Therefore, this work proposes a comprehensive and novel deep learning-based system that — (a) repopulates missing data from inactive sensors, (b) detects and rectifies anomalous data, and (c) instantly localizes a target. The efficacy of the proposed approach is validated in a radioactive environment and compared to existing benchmark. The results show that the proposed approach achieves precise, rapid, and efficient localization by achieving 5 times better accuracy, 50 times faster speeds, and 3 times lesser energy consumption.
AB - The use of Internet of Things (IoT) in environment monitoring has led to the development of Smart Environmental Monitoring (SEM) paradigm. Target or source localization, that determines the underlying cause of environmental occurrences, is an important aspect of SEM. To prevent an environmental event in becoming a potential disaster, swift and early localization of its source is crucial. Target localization is performed with the help of IoT sensors, which are typically deployed in hazardous environments and are not easily serviceable, making energy efficiency a requirement for SEM systems. Furthermore, IoT sensors may provide anomalous readings due to low battery, low sensor sensitivity, faults, or malicious attacks. This alters the decision-making process and leads to inaccurate localization of the source. Hence, detecting and dealing with anomalies, while utilizing a limited number of sensor nodes, are key factors in addressing reliability and energy efficiency issues. The current works are iterative in nature, employs redundant nodes, do not efficiently address anomalous readings, which leads to slow and imprecise localization, while increasing energy consumption. Moreover, they are application specific, making them less adaptable to other sensing tasks. Therefore, this work proposes a comprehensive and novel deep learning-based system that — (a) repopulates missing data from inactive sensors, (b) detects and rectifies anomalous data, and (c) instantly localizes a target. The efficacy of the proposed approach is validated in a radioactive environment and compared to existing benchmark. The results show that the proposed approach achieves precise, rapid, and efficient localization by achieving 5 times better accuracy, 50 times faster speeds, and 3 times lesser energy consumption.
KW - Anomalies
KW - Autoencoder
KW - CNN
KW - Deep learning
KW - IoT
KW - Localization
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85148581523&partnerID=8YFLogxK
U2 - 10.1016/j.iot.2023.100713
DO - 10.1016/j.iot.2023.100713
M3 - Article
AN - SCOPUS:85148581523
SN - 2542-6605
VL - 22
JO - Internet of Things (Netherlands)
JF - Internet of Things (Netherlands)
M1 - 100713
ER -