Engineering friendly tool to estimate battery life of a wireless sensor node

Bag, Gargi; Pang, Zhibo; Johansson, Morgan; Min, Xiaoyu; Zhu, Shaoling

doi:10.1016/j.jii.2016.11.001

Cited by 14 publications

(6 citation statements)

References 12 publications

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“…One of the relevant aspects is that the numerical data of the evaluated parameters of the manufactured products, in addition to the operation of equipment, systems and industrial machinery, can be obtained in real time with industry 4.0 applications, one of them being the internet of things This application contemplates from obtaining numerical data and storing them in a database that includes the protection of the data in a cloud, to avoid the use of USB memories (which are rarely used today), or sending them by mail (which is sometimes made a bit difficult by the servers) (Deloitte, 2015). This process also involves the use of electronic sensors, which are the ones that obtain the information from numerical data or measurement levels and send it to a cloud computing system (Bag et al, 2016). Industry 4.0 is recognized as the Fourth Industrial Revolution, made up of innovative technologies, with intelligent systems that can develop a wide variety of activities in industrial processes, substituting labor and avoiding the generation of errors and the presence of products.…”

Section: Literary Reviewmentioning

confidence: 99%

Industry 4.0 Analysis as Differentiating Factor in Innovation: The Case of an Automotive Industry Using Technology with Optical Sensors to Optimize the Visibility in Cars

Valdés Hernández,

Arcos Vega,

López Hernández

2024

Journal of Technology Management & Innovation

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An investigation was made in an automotive industry considered as industry 4.0 in the city of Tijuana, Baja California, which is the northwest zone of the Mexican Republic, evaluating optical sensors to optimize the visibility in cars, as the internet of things (IOT) process, to improve the productivity and quality yielding in the manufacturing areas of this industry evaluated. The main objective of this scientific study was the analysis of the operational yielding of electronic sensors used in the industrial operations to count, control and obtain and safeguarding numerical data of parameters of the sensors evaluated as numerical in a data base in a cloud and in a computer system, as an innovation technology. This investigation shows also a correlation analysis of the implementation of innovation system in industrial process of the industry where was made the scientific study, with the evaluation of the operative yielding as innovation with the productivity and quality indices. The correlation analysis was made with the Spearmen method. This was made to determine the levels of impact on the functionality of these electronic sensors on the productivity and quality indices that led to the economic profit factor, as an aspect of innovation, technology and economy in this evaluated industry. The evaluations were with the MatLab software, using statistical methods as the Cronbach's Alpha coefficient and the Spearmen used to evaluate the reliability of the instrument as a questionnaire, with five managers of the industry evaluated about the use of these electronic sensors. The principal objective of this investigation was to determine the principal reason to use the industry 4.0 tools as IOT to improve industrial process in an automotive industry that is a relevant industry in the worldwide. Results shows the importance of the use of this type of industry 4.0 tool.

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Section: Literary Reviewmentioning

confidence: 99%

Industry 4.0 Analysis as Differentiating Factor in Innovation: The Case of an Automotive Industry Using Technology with Optical Sensors to Optimize the Visibility in Cars

Valdés Hernández,

Arcos Vega,

López Hernández

2024

Journal of Technology Management & Innovation

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“…A tool is proposed as a power utilisation model to measure the battery's lifetime in IoT devices by merely assessing the details with respect to the expected network traffic and the feasible level of interferences. The work explains various techniques that can reduce the effect of various components which are used for the consumption of power [29]. Some senor nodes have been powered using solar energy, which is a leading technology in IoT for harvesting the energy.…”

Section: Related Workmentioning

confidence: 99%

Predictive model for battery life in IoT networks

Maddikunta

Srivastava

Gadekallu

et al. 2020

IET Intelligent Trans Sys

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The internet of things (IoT) is prominently used in the present world. Although it has vast potential in several applications, it has several challenges in the real-world. One of the most important challenges is conservation of battery life in devices used throughout IoT networks. Since many IoT devices are not rechargeable, several steps to conserve the battery life of an IoT network can be taken using the early prediction of battery life. In this study, a machine learning based model implementing a random forest regression algorithm is used to predict the battery life of IoT devices. The proposed model is experimented on 'Beach Water Quality -Automated Sensors' data set generated from sensors in an IoT network from the city of Chicago, USA. Several pre-processing techniques like normalisation, transformation and dimensionality reduction are used in this model. The proposed model achieved a 97% predictive accuracy. The results obtained proved that the proposed model performs better than other state-of-art regression algorithms in preserving the battery life of IoT devices.

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“…The I Avg calculated in the above equation is used to calculate the expected lifetime of the sensor node. As given in [14], the lifetime of the battery of the sensor node in days γ is given as γ = C 24 I Avg , where C is the capacity of the battery in mAh. Table IV shows the average current consumption and the expected lifetime of the sensor node for Shewhart test for different number of piggybacks p = 0, 1, 4.…”

Section: Prediction Of Battery Lifetimementioning

confidence: 99%

“…Table IV shows the average current consumption and the expected lifetime of the sensor node for Shewhart test for different number of piggybacks p = 0, 1, 4. These calculations have been done as presented in [14] and [15]. It can be seen from Table IV that Shewhart based data reduction schemes (with p = 0, 1, 4) significantly increase the lifetime as compared to the scheme "Without Shewhart", which is basically transmitting all the values from the node to the sink.…”

Section: Prediction Of Battery Lifetimementioning

confidence: 99%

Improving Accuracy of the Shewhart-based Data-Reduction in IoT Nodes using Piggybacking

Shastri

Jain

Chaudhari

et al. 2019

2019 IEEE 5th World Forum on Internet of Things (WF-IoT)

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This paper proposes the use of Shewhart test to reduce the number of data-transmissions in IoT networks. It is shown to outperform the widely-used least mean square (LMS) based data reduction method in terms of the number of data-transmissions, implementation complexity and mean square error (MSE) in prediction of time-series data at the sink node based on the partial transmissions of the measured time-series data from the sensor node. The paper also proposes the use of piggybacking and interpolation to further reduce the MSE of the estimated time-series data at the sink node without increasing the number of packet transmissions. The timeseries data used for the comparison of data reduction algorithms is a set of measured temperature values in indoor and outdoor scenarios for four days using customdesigned wireless sensor nodes. To express the effectiveness of the piggybacked transmissions on battery lifetime, the total current consumption of the sensor node is measured for different number of piggybacks and corresponding battery lifetime is estimated. It is shown that the proposed piggyback approach significantly reduces the MSE at the cost of slight decrease in battery-lifetime.

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Engineering friendly tool to estimate battery life of a wireless sensor node

Cited by 14 publications

References 12 publications

Industry 4.0 Analysis as Differentiating Factor in Innovation: The Case of an Automotive Industry Using Technology with Optical Sensors to Optimize the Visibility in Cars

Industry 4.0 Analysis as Differentiating Factor in Innovation: The Case of an Automotive Industry Using Technology with Optical Sensors to Optimize the Visibility in Cars

Predictive model for battery life in IoT networks

Improving Accuracy of the Shewhart-based Data-Reduction in IoT Nodes using Piggybacking

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