Towards the world smallest wireless sensor nodes with low power consumption for ‘Green’ sensor networks

Author(s):  
J. Lu ◽  
H. Okada ◽  
T. Itoh ◽  
R. Maeda ◽  
T. Harada
2021 ◽  
Vol 11 (4) ◽  
pp. 2836-2849
Author(s):  
K. Raghava Rao ◽  
D. Sateesh Kumar ◽  
Mohiddin Shaw ◽  
V. Sitamahalakshmi

Now a days IoT technologies are emerging technology with wide range of applications. Wireless sensor networks (WSNs) are plays vital role in IoT technologies. Construction of wireless sensor node with low-power radio link and high-speed processors is an interesting contribution for wireless sensor networks and IoT applications. Most of WSNs are furnished with battery source that has limited lifetime. The maximum operations of these networks require more power utility. Nevertheless, improving network efficiency and lifetime is a curtail issue in WSNs. Designing a low powered wireless sensor networks is a major challenges in recent years, it is essential to model its efficiency and power consumption for different applications. This paper describes power consumption model based on LoRa and Zigbee protocols, allows wireless sensor nodes to monitor and measure power consumption in a cyclic sleeping scenario. Experiential results reveals that the designed LoRa wireless sensor nodes have the potential for real-world IoT application with due consideration of communicating distance, data packets, transmitting speed, and consumes low power as compared with Zigbee sensor nodes. The measured sleep intervals achieved lower power consumption in LoRa as compared with Zigbee. The uniqueness of this research work lies in the review of wireless sensor node optimization and power consumption of these two wireless sensor networks for IoT applications.


2021 ◽  
Vol 7 ◽  
pp. e780
Author(s):  
Mostafa Ibrahim Labib ◽  
Mohamed ElGazzar ◽  
Atef Ghalwash ◽  
Sarah Nabil AbdulKader

Wireless sensor networks connect a set of highly flexible wireless devices with small weight and size. They are used to monitor and control the environment by organizing the acquired data at a central device. Constructing fully connected networks using low power consumption sensors, devices, and protocols is one of the main challenges facing wireless sensor networks, especially in places where it is difficult to establish wireless networks in a normal way, such as military areas, archaeological sites, agricultural districts, construction sites, and so on. This paper proposes an approach for constructing and extending Bi-Directional mesh networks using low power consumption technologies inside various indoors and outdoors architectures called “an adaptable Spider-Mesh topology”. The use of ESP-NOW protocol as a communication technology added an advantage of longer communication distance versus a slight increase of consumed power. It provides 15 times longer distance compared to BLE protocol while consuming only twice as much power. Therefore, according to our theoretical and experimental comparisons, the proposed approach could provide higher network coverage while maintaining an acceptable level of power consumption.


2014 ◽  
Vol 14 (6) ◽  
pp. 2035-2041 ◽  
Author(s):  
Jian Lu ◽  
Hironao Okada ◽  
Toshihiro Itoh ◽  
Takeshi Harada ◽  
Ryutaro Maeda

2020 ◽  
Vol 10 (1) ◽  
pp. 6
Author(s):  
Swagat Bhattacharyya ◽  
Steven Andryzcik ◽  
David W. Graham

The wireless sensor nodes used in a growing number of remote sensing applications are deployed in inaccessible locations or are subjected to severe energy constraints. Audio-based sensing offers flexibility in node placement and is popular in low-power schemes. Thus, in this paper, a node architecture with low power consumption and in-the-field reconfigurability is evaluated in the context of an acoustic vehicle detection and classification (hereafter “AVDC”) scenario. The proposed architecture utilizes an always-on field-programmable analog array (FPAA) as a low-power event detector to selectively wake a microcontroller unit (MCU) when a significant event is detected. When awoken, the MCU verifies the vehicle class asserted by the FPAA and transmits the relevant information. The AVDC system is trained by solving a classification problem using a lexicographic, nonlinear programming algorithm. On a testing dataset comprising of data from ten cars, ten trucks, and 40 s of wind noise, the AVDC system has a detection accuracy of 100%, a classification accuracy of 95%, and no false alarms. The mean power draw of the FPAA is 43 μ W and the mean power consumption of the MCU and radio during its validation and wireless transmission process is 40.9 mW. Overall, this paper demonstrates that the utilization of an FPAA-based signal preprocessor can greatly improve the flexibility and power consumption of wireless sensor nodes.


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