Performance of Temperature and Humidity Sensors with WSN Mesh Topology

Zainuri, Akhmad; Yuwono, Rudy; Arief, Syukri; Ghadafi, Mohammad

doi:10.1166/asl.2019.13189

Cited by 2 publications

(2 citation statements)

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“…In recent years, wireless sensor networks (WSNs) have achieved an accelerated increase in deployment. WSNs are widely utilized in scenarios such as smart homes [1], vehicular ad hoc networks [2][3][4], industrial Internet of ings [5], and monitoring environments [6][7][8]. e sensor devices in WSNs are responsible for sensing real-time data and transmitting the sensed data to control center for data analysis and intelligent control.…”

Section: Introductionmentioning

confidence: 99%

EPPSA: Efficient Privacy-Preserving Statistical Aggregation Scheme for Edge Computing-Enhanced Wireless Sensor Networks

Tao

Kong

et al. 2022

Security and Communication Networks

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In edge computing-enhanced wireless sensor networks (WSNs), multidimensional data aggregation can optimize the utilization of computation resources for data collection. How to improve the efficiency of data aggregation has gained considerable attention in both academic and industrial fields. This article proposes a new efficient privacy-preserving statistical aggregation scheme (EPPSA) for WSNs, in which statistical data can be calculated without exposing the total number of sensor devices to control center. The EPPSA scheme supports multiple statistical aggregation functions, including arithmetic mean, quadratic mean, weighted mean, and variance. Furthermore, the EPPSA scheme adopts the modified Montgomery exponentiation algorithms to improve the aggregation efficiency in the edge aggregator. The performance evaluation shows that the EPPSA scheme gets higher aggregation efficiency and lower communication load than the existing statistical aggregation schemes.

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Section: Introductionmentioning

confidence: 99%

EPPSA: Efficient Privacy-Preserving Statistical Aggregation Scheme for Edge Computing-Enhanced Wireless Sensor Networks

Tao

Kong

et al. 2022

Security and Communication Networks

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“…Batteries are used in many daily life activities, such as in portable telecommunication equipments, multimedia devices, hybrid electric vehicles (HEV) [1], electric vehicles (EV) [2,3], energy harvesting, base-transceiver station (BTS) in the telecommunication system [4], wearable computing devices [5], wireless sensor network (WSN) [6,7] and energy storage in smartgrid networks [8,9]. A battery in a system must be used properly in order to reach its lifetime as long as possible.…”

Section: Introductionmentioning

confidence: 99%

VRLA battery state of health estimation based on charging time

Zainuri¹,

Wibawa²,

Rusli³

et al. 2019

TELKOMNIKA

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Battery state of health (SoH) is an important parameter of the battery's ability to store and deliver electrical energy. Various methods have been so far developed to calculate the battery SoH, such as through the calculation of battery impedance or battery capacity using Kalman Filter, Fuzzy theory, Probabilistic Neural Network, adaptive hybrid battery model, and Double Unscented Kalman Filtering (D-UKF) algorithm. This paper proposes an approach to estimate the value of battery SoH based on the charging time measurement. The results of observation and measurements showed that a new and used batteries would indicate different charging times. Unhealthy battery tends to have faster charging and discharging time. The undertaken analysis has been focused on finding out the relationship between the battery SoH and the charging time range. To validate the results of this proposed approach, the use of battery capacity method has been considered as comparison. It can be concluded that there is a strong correlation between the two discussed SoH estimation methods, confirming that the proposed method is feasible as an alternative SoH estimation method to the widely known battery capacity method. The correlation between the charging-disharging times of healthy and unhealthy batteries is very prospective to develop a battery charger in the future with a prime advantage of not requiring any sensor for the data acquisition.

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Performance of Temperature and Humidity Sensors with WSN Mesh Topology

Cited by 2 publications

References 0 publications

EPPSA: Efficient Privacy-Preserving Statistical Aggregation Scheme for Edge Computing-Enhanced Wireless Sensor Networks

EPPSA: Efficient Privacy-Preserving Statistical Aggregation Scheme for Edge Computing-Enhanced Wireless Sensor Networks

VRLA battery state of health estimation based on charging time

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