Effect of Data Validation Schemes on the Energy Consumptions of Edge Device in IoT /WSN

Alduais, Nayef Abdulwahab Mohammed; Abdullah, Jiwa; Jamil, Ansar; Audah, Lukman; Alias, Rozlan

doi:10.1109/iwcmc.2018.8450460

Cited by 5 publications

(9 citation statements)

References 11 publications

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“…The data produced by these three types of sensors were collected by Intel Berkeley Research Laboratory (IBRL). In [16], the authors proposed a power dissipation model to determine fault detection cost at the edge device level. The proposed model was used to assess the impact of various data validation schemes on the power consumption of edge devices.…”

Section: Related Workmentioning

confidence: 99%

An Efficient IoT-based Smart Water Meter System of Smart City Environment

AL-Madhrahi¹,

Alduais²,

Abdullah³

et al. 2021

IJACSA

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Water is a precious need of our lives. Due to the rapid population and urbanization, water usage monitoring is a significant problem facing our society. One solution is to control, analyze, and reduce the water consumption of the houses. The emerging of the Internet of Things (IoT) concept lately in our lives has offered the opportunity to establish water usageefficient smart devices, systems and applications for buildings and cities. Many studies have suggested designing an IoT-based smart meter system; however, the IoT sensor node has limited studies, especially in battery life. Therefore, this study aims to implement and analyze an efficient data collection algorithm for IoT-based smart metering applications in consideration with energy consumption. The system items used are Arduino Uno, Wi-Fi-ESP8266, and water flow sensors. The applied algorithm is an efficient data collection algorithm for water meter (EDCDWM) to reduce the number of packet transmissions. Arduino performed this system's implementation, while the simulation and analysis performed by MATLAB R2019b. The average percentage of energy saved by the applied algorithms of EDCDWM absolute change; and EDCDWM with relative differences in all nodes are around 60% and 93%, respectively.

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Section: Related Workmentioning

confidence: 99%

An Efficient IoT-based Smart Water Meter System of Smart City Environment

AL-Madhrahi¹,

Alduais²,

Abdullah³

et al. 2021

IJACSA

View full text Add to dashboard Cite

show abstract

“…In WSN/IoT, the sensing data error detection approaches can be divided into two types, namely: centralized error discovery method and distributed error discovery method. Most existing error detection approaches use periodic batch testing at a central location, possibly a cluster head or a fusion center [25]. A useful background overview of the current outlier detection methods for WSN can be found in [25]- [28].…”

Section: ) Reduce the Number Of Transmitted Incorrect Datamentioning

confidence: 99%

“…Most existing error detection approaches use periodic batch testing at a central location, possibly a cluster head or a fusion center [25]. A useful background overview of the current outlier detection methods for WSN can be found in [25]- [28]. In addition, recent work [25] introduced a novel mathematical model for assessing the impact of different data verification systems on energy dissipation in the edge device.…”

Section: ) Reduce the Number Of Transmitted Incorrect Datamentioning

confidence: 99%

“…A useful background overview of the current outlier detection methods for WSN can be found in [25]- [28]. In addition, recent work [25] introduced a novel mathematical model for assessing the impact of different data verification systems on energy dissipation in the edge device. The One Class Quarter Sphere Support Vector Machine (OCSVM) was used in [29] to create an anomaly discovery algorithm.…”

Section: ) Reduce the Number Of Transmitted Incorrect Datamentioning

confidence: 99%

“…According to [25], the IoT sensor node energy consumption will be affected by a mechanism that is used to detect and remove the incorrect data during data collection for IoT real-time application. The cost of the error detection and transmissions E DV −Phase during the validation phase based on the approaches applied is defined as in Eq.…”

Section: B Numerical Examplementioning

confidence: 99%

See 2 more Smart Citations

RDCM: An Efficient Real-Time Data Collection Model for IoT/WSN Edge With Multivariate Sensors

2019

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In the application of the Internet of Things (IoT), a sensor board depends on a battery that has a limited lifetime to function. Furthermore, the IoT sensor board with multivariate sensors influences the battery lifetime , since there are additional data transmissions that must be supported by the board causing it to drain the battery much faster than the sensor board with one sensor. The main aim of this paper is to increase the battery life of the IoT sensor node. To do so, this paper proposes an efficient real-time data collection model for multivariate sensors in IoT/WSN applications named RDCM. The general structure of RDCM is composed of two main levels: the IoT sensor board level and the fusion center level. The IoT sensor board level is implemented in real time by all the IoT sensor boards simultaneously in each cycle and fusion center level is executed by the fusion center. The IoT sensor board level includes various stages as follows: check the physical conditions of the IoT edge device (board) stage and update data strategy stage, data validation stage, and sensed data reduction stage. The average of the total percentage of energy saved by the application of RDCM to real-time data sets injected with various percentages of errors for all nodes is 98%. In summary, the RDCM has a very high performance in terms of energy consumption compared with other algorithms. This paper concludes with the limitation of the current study and some further research opportunities.

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Trust Evaluation Model in IoT Environment: A Comprehensive Survey

2023

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The Internet of Things (IoT) is a rapidly evolving field that provides seamless connections to a physical object, making it part of a smart environment. To fully realize the potential power of the connections between these objects in IoT, trust between them is critical. Conventional security measures are not sufficient to provide comprehensive security for this smart world. Trust is used to reduce the risk of insecurity when nodes are connected to the Internet. In an IoT environment, various trust models have been proposed for Wireless Sensor Networks (WSNs) and Radio Frequency Identification (RFID). Nevertheless, these are not fully adapted to the dynamic and uncertain environment of the intelligent IoT. Therefore, this article reviews the characteristics of recent works in IoT trust models. A comprehensive study has been conducted on the classification of trust models. A set of factors are discussed, such as trust characteristics, trust architecture, trust distribution, trust aggregation technique, trust model type, and attack type. In addition, this paper provides readers with an understanding of the current existing trust model and directs future works to propose new models that satisfy all characteristics that should be considered when developing a trust model. Finally, some research challenges and directions are identified.INDEX TERMS Internet of Things, wireless sensor network, radio frequency identification, trust model.

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Effect of Data Validation Schemes on the Energy Consumptions of Edge Device in IoT /WSN

Cited by 5 publications

References 11 publications

An Efficient IoT-based Smart Water Meter System of Smart City Environment

An Efficient IoT-based Smart Water Meter System of Smart City Environment

RDCM: An Efficient Real-Time Data Collection Model for IoT/WSN Edge With Multivariate Sensors

Trust Evaluation Model in IoT Environment: A Comprehensive Survey

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