scholarly journals A Compact and Low Power RO PUF with High Resilience to the EM Side-Channel Attack and the SVM Modelling Attack of Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (2) ◽  
pp. 322 ◽  
Author(s):  
Yuan Cao ◽  
Xiaojin Zhao ◽  
Wenbin Ye ◽  
Qingbang Han ◽  
Xiaofang Pan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Low Power ◽  
Wireless Sensor ◽  
Side Channel ◽  
Side Channel Attack

Highly Integrated Ultra-Low Power RF Transceivers for Wireless Sensor Networks

2018 ◽  
pp. 14-1-14-23
Author(s):  
Brian P. Otis ◽  
Yuen Hui Chee ◽  
Richard Lu ◽  
Nathan M. Pletcher ◽  
Jan M. Rabaey ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Low Power ◽  
Wireless Sensor ◽  
Ultra Low Power ◽  
Rf Transceivers

scholarly journals Low-Power Scan Correlation-Aware Scan Cluster Reordering for Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6111
Author(s):  
Sangjun Lee ◽  
Kyunghwan Cho ◽  
Jihye Kim ◽  
Jongho Park ◽  
Inhwan Lee ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Low Power ◽  
Power Reduction ◽  
Wireless Sensor ◽  
Computational Time ◽  
Test Power ◽  
Significant Power ◽  
Highly Correlated ◽  
Scan Chain

Cryptographic circuits generally are used for applications of wireless sensor networks to ensure security and must be tested in a manufacturing process to guarantee their quality. Therefore, a scan architecture is widely used for testing the circuits in the manufacturing test to improve testability. However, during scan testing, test-power consumption becomes more serious as the number of transistors and the complexity of chips increase. Hence, the scan chain reordering method is widely applied in a low-power architecture because of its ability to achieve high power reduction with a simple architecture. However, achieving a significant power reduction without excessive computational time remains challenging. In this paper, a novel scan correlation-aware scan cluster reordering is proposed to solve this problem. The proposed method uses a new scan correlation-aware clustering in order to place highly correlated scan cells adjacent to each other. The experimental results demonstrate that the proposed method achieves a significant power reduction with a relatively fast computational time compared with previous methods. Therefore, by improving the reliability of cryptography circuits in wireless sensor networks (WSNs) through significant test-power reduction, the proposed method can ensure the security and integrity of information in WSNs.

scholarly journals Energy-Efficient Method for Wireless Sensor Networks Low-Power Radio Operation in Internet of Things

2020 ◽  
Author(s):  
Mehdi Amiri Nasab ◽  
Shahaboddin Shamshirband ◽  
Anthony Theodore Chronopoulos ◽  
Amir Mosavi ◽  
Narjes Nabipur
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Power Consumption ◽  
Low Power ◽  
Time Factors ◽  
Wireless Sensor ◽  
Common Source ◽  
Factors Affecting ◽  
Duty Cycles

The radio operation in wireless sensor networks (WSN) in the Internet of Things (IoT) applications are the most common source for power consumption. However, recognizing and controlling the factors affecting radio operation can be valuable for managing the node power consumption. ContikiMAC is a low-power Radio Duty-Cycle protocol in Contiki OS used in WakeUp mode, which is a clear channel assessment (CCA) to check radio status periodically. The time spent to check 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. This paper presents a detailed analysis of radio WakeUp time factors of ContikiMAC. Then, we propose lightweight CCA (LW-CCA) as an extension to ContikiMAC to reduce the percentage of Radio Duty-Cycles in false WakeUps and idle listenings by using dynamic received signal strength indicators (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).

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