scholarly journals Application of NSGA-II to Obtain the Charging Current-Time Tradeoff Curve in Battery Based Underwater Wireless Sensor Nodes

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5324
Author(s):  
Daniel Rodríguez Rodríguez García ◽  
Juan-A. Montiel-Nelson ◽  
Tomás Bautista ◽  
Javier Sosa
Keyword(s):  
Power Consumption ◽  
Search Strategy ◽  
Sensor Nodes ◽  
Nsga Ii ◽  
Wireless Sensor Nodes ◽  
Current Time ◽  
Time Tradeoff ◽  
Charging Current ◽  
Bisection Search ◽  
Tradeoff Curve

In this paper, a novel application of the Nondominated Sorting Genetic Algorithm II (NSGA II) is presented for obtaining the charging current–time tradeoff curve in battery based underwater wireless sensor nodes. The selection of the optimal charging current and times is a common optimization problem. A high charging current ensures a fast charging time. However, it increases the maximum power consumption and also the cost and complexity of the power supply sources. This research studies the tradeoff curve between charging currents and times in detail. The design exploration methodology is based on a two nested loop search strategy. The external loop determines the optimal design solutions which fulfill the designers’ requirements using parameters like the sensor node measurement period, power consumption, and battery voltages. The inner loop executes a local search within working ranges using an evolutionary multi-objective strategy. The experiments proposed are used to obtain the charging current–time tradeoff curve and to exhibit the accuracy of the optimal design solutions. The exploration methodology presented is compared with a bisection search strategy. From the results, it can be concluded that our approach is at least four times better in terms of computational effort than a bisection search strategy. In terms of power consumption, the presented methodology reduced the required power at least 3.3 dB in worst case scenarios tested.

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.

A Novel Algorithm to Improve Network Lifetime of Wireless Sensor Networks

2013 ◽  
Vol 433-435 ◽  
pp. 599-602
Author(s):  
Rui Ma ◽  
Yan Cheng Liu ◽  
Chuan Wang
Keyword(s):  
Network Lifetime ◽  
Search Strategy ◽  
Hybrid Genetic Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Sensor Nodes ◽  
Sensor Cover ◽  
Simulation Results ◽  
Local Search Strategy ◽  
Novel Algorithm

One approach to extend the network lifetime is to divide the deployed sensors into disjoint subsets of sensors, or sensor covers, such that each sensor cover can cover all targets and work by turns. The more sensor covers can be found, the longer sensor network lifetime can be prolonged.This study propose a novel hybrid genetic algorithm (NHGA) comprising both basic generic operations with a fitness-improving local-search strategy to divide all wireless sensor nodes into a maximum number of disjoint set covers (DSCs). The simulation results show that NHGA outperforms the existing methods by generating more disjoint set covers and prolongs network lifetime.

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.

Sign in / Sign up

Export Citation Format

Share Document