TY  - JOUR
A1  - Amirinasab, Mehdi
A1  - Shamshirband, Shahaboddin
A1  - Chronopoulos, Anthony Theodore
A1  - Mosavi, Amir
A1  - Nabipour, Narjes
T1  - Energy‐Efficient Method for Wireless Sensor Networks Low‐Power Radio Operation in Internet of Things
JF  - electronics
N2  - The radio operation in wireless sensor networks (WSN) in Internet of Things (IoT)applications is the most common source for power consumption. Consequently, recognizing and controlling the factors affecting radio operation can be valuable for managing the node power consumption. Among essential factors affecting radio operation, the time spent for checking the radio is of utmost importance for monitoring power consumption. It can lead to false WakeUp or idle listening in radio duty cycles and ContikiMAC. ContikiMAC is a low‐power radio duty‐cycle protocol in Contiki OS used in WakeUp mode, as a clear channel assessment (CCA) for checking radio status periodically. This paper presents a detailed analysis of radio WakeUp time factors of ContikiMAC. Furthermore, we propose a lightweight CCA (LW‐CCA) as an extension to ContikiMAC to reduce the Radio Duty‐Cycles in false WakeUps and idle listening though using dynamic received signal strength indicator (RSSI) status check time. The simulation results in the Cooja simulator show that LW‐CCA reduces about 8% energy consumption in nodes while maintaining up to 99% of the packet delivery rate (PDR).
KW  - Internet der Dinge
KW  - Internet of things
KW  - wireless sensor networks
KW  - ContikiMAC
KW  - energy efficiency
KW  - duty-cycles
KW  - clear channel assessments
KW  - fog computing
KW  - smart sensors
KW  - signal processing
KW  - received signal strength indicator
KW  - OA-Publikationsfonds2020
KW  - RSSI
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200213-40954
UR  - https://www.mdpi.com/2079-9292/9/2/320
VL  - 2020
IS  - volume 9, issue 2, 320
PB  - MDPI
ER  - 
TY  - JOUR
A1  - Shamshirband, Shahaboddin
A1  - Joloudari, Javad Hassannataj
A1  - GhasemiGol, Mohammad
A1  - Saadatfar, Hamid
A1  - Mosavi, Amir
A1  - Nabipour, Narjes
T1  - FCS-MBFLEACH: Designing an Energy-Aware Fault Detection System for Mobile Wireless Sensor Networks
JF  - Mathematics
N2  - Wireless sensor networks (WSNs) include large-scale sensor nodes that are densely distributed over a geographical region that is completely randomized for monitoring, identifying, and analyzing physical events. The crucial challenge in wireless sensor networks is the very high dependence of the sensor nodes on limited battery power to exchange information wirelessly as well as the non-rechargeable battery of the wireless sensor nodes, which makes the management and monitoring of these nodes in terms of abnormal changes very difficult. These anomalies appear under faults, including hardware, software, anomalies, and attacks by raiders, all of which affect the comprehensiveness of the data collected by wireless sensor networks. Hence, a crucial contraption should be taken to detect the early faults in the network, despite the limitations of the sensor nodes. Machine learning methods include solutions that can be used to detect the sensor node faults in the network. The purpose of this study is to use several classification methods to compute the fault detection accuracy with different densities under two scenarios in regions of interest such as MB-FLEACH, one-class support vector machine (SVM), fuzzy one-class, or a combination of SVM and FCS-MBFLEACH methods. It should be noted that in the study so far, no super cluster head (SCH) selection has been performed to detect node faults in the network. The simulation outcomes demonstrate that the FCS-MBFLEACH method has the best performance in terms of the accuracy of fault detection, false-positive rate (FPR), average remaining energy, and network lifetime compared to other classification methods.
KW  - Vernetzung
KW  - wireless sensor networks
KW  - machine learning
KW  - Funktechnik
KW  - Sensor
KW  - Maschinelles Lernen
KW  - Internet of Things
KW  - OA-Publikationsfonds2019
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200107-40541
UR  - https://www.mdpi.com/2227-7390/8/1/28
VL  - 2020
IS  - Volume 8, Issue 1, article 28
PB  - MDPI
ER  -