scholarly journals Towards a comprehensive power consumption model for wireless sensor nodes

2016 ◽  
Author(s):  
Marc Hesse ◽  
Michael Adams ◽  
Timm Hormann ◽  
Ulrich Ruckert
Keyword(s):  
Power Consumption ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Sensor Nodes ◽  
Consumption Model

Optimizing Power Consumption for the Wireless Sensor Node

2015 ◽  
Vol 738-739 ◽  
pp. 107-110
Author(s):  
Hui Lin
Keyword(s):  
Power Consumption ◽  
Radio Frequency ◽  
Power Saving ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Sensor Node ◽  
Wireless Sensor Nodes ◽  
Cycle Times ◽  
Current Consumption ◽  
Saving Effect

A Wireless Sensor Network is composed of sensor nodes powered by batteries. Thus, power consumption is the major challenge. In spite of so many research works discussing this issue from the aspects of network optimization and system design, so far not so many focus on optimizing power consumption of the Radio Frequency device, which consumes most of the energy. This paper describes the digital features of the Radio Frequency device used to optimize current consumption, and presents a practical approach to measure current consumption in static and dynamic scenarios in details, by which we evaluates the power saving effect. The results demonstrated that according to cycle times and application characteristics choosing appropriate features can prolong the lifetime of wireless sensor nodes.

Toward the World Smallest Wireless Sensor Nodes With Ultralow Power Consumption

2014 ◽  
Vol 14 (6) ◽  
pp. 2035-2041 ◽  
Author(s):  
Jian Lu ◽  
Hironao Okada ◽  
Toshihiro Itoh ◽  
Takeshi Harada ◽  
Ryutaro Maeda
Keyword(s):  
Power Consumption ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Sensor Nodes ◽  
The World ◽  
Ultralow Power

scholarly journals A Node Density Control Learning Method for the Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3428 ◽  
Author(s):  
Shumei Lou ◽  
Gautam Srivastava ◽  
Shuai Liu
Keyword(s):  
Internet Of Things ◽  
Power Consumption ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Learning Method ◽  
Mobile Nodes ◽  
Node Density ◽  
Wireless Sensor Nodes ◽  
Density Control ◽  
Control Learning

When examining density control learning methods for wireless sensor nodes, control time is often long and power consumption is usually very high. This paper proposes a node density control learning method for wireless sensor nodes and applies it to an environment based on Internet of Things architectures. Firstly, the characteristics of wireless sensors networks and the structure of mobile nodes are analyzed. Combined with the flexibility of wireless sensor networks and the degree of freedom of real-time processing and configuration of field programmable gate array (FPGA) data, a one-step transition probability matrix is introduced. In addition, the probability of arrival of signals between any pair of mobile nodes is also studied and calculated. Finally, the probability of signal connection between mobile nodes is close to 1, approximating the minimum node density at T. We simulate using a fully connected network identifying a worst-case test environment. Detailed experimental results show that our novel proposed method has shorter completion time and lower power consumption than previous attempts. We achieve high node density control as well at close to 90%.

scholarly journals An Acoustic Vehicle Detector and Classifier Using a Reconfigurable Analog/Mixed-Signal Platform

2020 ◽  
Vol 10 (1) ◽  
pp. 6
Author(s):  
Swagat Bhattacharyya ◽  
Steven Andryzcik ◽  
David W. Graham
Keyword(s):  
Power Consumption ◽  
Low Power ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Detection Accuracy ◽  
Mean Power ◽  
Wireless Sensor Nodes ◽  
Vehicle Class ◽  
Sensing Applications ◽  
The Mean

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.

scholarly journals Energy Efficiency Analysis of LoRa and ZigBee Protocols in Wireless Sensor Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 2836-2849
Author(s):  
K. Raghava Rao ◽  
D. Sateesh Kumar ◽  
Mohiddin Shaw ◽  
V. Sitamahalakshmi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Power Consumption ◽  
Low Power ◽  
Sensor Node ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Sensor Node ◽  
Wireless Sensor Nodes ◽  
Iot Applications

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.

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