scholarly journals Large-scale Experiment of Power Consumption Monitoring Using Wireless Sensor Nodes

2016 ◽  
Vol 52 (12) ◽  
pp. 698-706
Author(s):  
Akio SUZUKI ◽  
Jun FUJIMOTO ◽  
Toshihiro ITOH ◽  
Ryutaro MAEDA
Keyword(s):  
Power Consumption ◽  
Large Scale ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Sensor Nodes ◽  
Scale Experiment

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.

Research on Detection of Distribution and Collision of Wireless Sensor Nodes Based on Radio-Frequency Technique

2014 ◽  
Vol 602-605 ◽  
pp. 2089-2092
Author(s):  
Jin Cheng Zhou ◽  
Zhen Yang
Keyword(s):  
Energy Method ◽  
Strain Energy ◽  
Large Scale ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Detection Accuracy ◽  
Structural Components ◽  
Modal Strain Energy ◽  
Wireless Sensor Nodes ◽  
Modal Strain Energy Method

many factors need to be considered in layout and distribution of wireless sensor nodes; in addition to the general features of common monitoring sensors, such as economic factors, environmental factors, detection accuracy factor, etc, some particular features should be fully considered, such as intensity, large-scale rapid layout, early warning and monitoring of critical structural components and important areas, as well as its redundancy. Basing on the Modal Energy Method, the paper proposes an optimal sensor layout algorithm available in improvement of modal strain energy method, and such the method is simple and quick, with certain practical value.

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

Distributed Tree Routing Scheme for Large-Scale Wireless Sensor Networks

2013 ◽  
Vol 479-480 ◽  
pp. 783-787
Author(s):  
Yih Chuan Lin ◽  
Jia Hong Zhong
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Sensor Nodes ◽  
Routing Schemes ◽  
Routing Scheme ◽  
Tree Routing ◽  
Optimal Tree

This paper addresses the problem of how to make efficient energy consumption on routing communications among the set of wireless sensor nodes deployed randomly in a large-scale manner. Tree-based routing topology is considered in the study with regarding to its simplicity of routing process. To minimize the energy dissipation of tree-routing, centralized coordination schemes are usually used to construct an optimal tree routing topology. In this paper, a distributed-based tree routing protocol is proposed not only to improve the scalability of the centralized tree routing schemes but also handle the balance consumption among the sensor nodes. With computer simulation, the effectiveness of the proposed routing scheme is verified and shown to be useful in large-scale wireless sensor networks.

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.

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