Data Analytics and Cloud-Based Platform for Internet of Things Applications in Smart Cities

Ajay, P.; Sharma, Avinash; P, Santosh Reddy; Patwal, Pratap Singh; Gowda, Dankan

doi:10.1109/i4tech55392.2022.9952780

Cited by 80 publications

(3 citation statements)

References 17 publications

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“…In contrast, principal component analysis (PCA) has seen extensive use in e-nose technologies and meat rotting detection. A favorable outcome was reported [14][15][16][17][18][19][20][21][22]. Alternatively, principal component analysis may be used to reduce massive data sets to more manageable proportions.…”

Section: Literature Surveymentioning

confidence: 99%

A Technique Based on Ensemble Machine Learning for The Analysis of Electronic Nose Signals

Taneja

Rajamani

Shekhawat

et al. 2023

2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)

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Numerous applications have been documentedfor electronic nose (e-nose) devices, which are valued for their speed, affordability, and lack of invasiveness. The e-nose scheme is a useful instrument for analyzing volatile chemicals as a distinct 'fingerprint,' especially in the meat industry. The pattern identification algorithm's capacity to decode e-nose signals is crucial to the e-nose system's effectiveness in a wide variety of uses. However, positive results using ensemble approaches have been reported in a number of data types. In this study, an ensemble learning strategy is proposed for enose signal analysis, particularly for evaluating beef quality. Not only are learning algorithms and sensor array optimization using ensemble approaches. Ensemble FSA is created by combining three filter-based feature selection algorithms (FSAs)-reliefF, chi-square, and Gini index-for sensor array optimization. When using a single FSA on a collection of homogenous e-nose data for beef quality monitoring, it is common for the results to be inconsistent or unexpected. In addition, multi-class regression and categorization problems are handled by using ensemble learning techniques. We use bagging and boosting algorithms, shown here by Random Forest (RF) and Adaboost. Both a support vector machine (SVM) and a decision tree are used as independent learners, and their results are compared to those of the ensemble. Experiments show that our ensemble method is effective and generalizable for e-nose signal processing. Optimized sensor combination using filter-based FSA consistently outperforms previous methods in both classification and regression. Additionally, despite using a reduced sensor complement, Adaboost as a boosting algorithm yields the highest quality prediction.

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Section: Literature Surveymentioning

confidence: 99%

A Technique Based on Ensemble Machine Learning for The Analysis of Electronic Nose Signals

Taneja

Rajamani

Shekhawat

et al. 2023

2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)

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“…Its usage of heterogeneous nodes and knowledge of residual energy for cluster head selection are both advantages when dealing with high energy levels [17]. The chosen cluster head has the disadvantage of sending data to the sink in a single hop [18]. The main drawback is that it has a large information processing overhead.…”

Section: ░ 2 Literature Surveymentioning

confidence: 99%

Performance Analysis of Energy Efficiency and Security Solutions of Internet of Things Protocols

Itagi¹,

Gowda

Prasad

et al. 2023

IJEER

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The scientific and business communities are showing considerable interest in wireless sensor networks (WSN). The availability of low-cost, small-scale components like CPUs, radios, and sensors, which are often combined into a single chip, is crucial. Parallel to the evolution of WSNs, the concepts of the IoT have been evolving in recent years. Wireless communication technologies may play a significant role in the implementation of IoT, despite the fact that IoT does not need or require any particular technology for communication. WSN assisted IoT networks can drive several applications in many industries. The proposed research explores the possibility of enhancing energy efficiency in WSN-assisted IoTN by balancing various challenging sensor network performance metrics. The base station's current placement inside the sensing field is predetermined by the preexisting routing algorithms. Our study examines the impact of base station placement outside and within the prescribed sensing domains on energy consumption and network longevity. In addition, methods for transferring data from the distributed source sensor to the base station while minimizing energy consumption are investigated. In this preliminary study, we focus on developing an algorithm for WSN-Assisted IoTN that can balance network factors such as hop count, communication distance, and residual energy. To further optimize the routing route between local cluster heads and the base station, a novel network architecture is built based on the Ant-optimization model, which uses centroid routing to balance energy consumption among local clusters. An open-source Network Simulator (NS-3) is used to model the behaviour of the proposed routing protocols and compare them to comparable existing network protocols. All of the suggested protocols have the same fundamentals for creating networks, however they vary in terms of routing, optimization, and performance depending on the development effort under consideration.

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“…The device microprocessor acts as the brain of any embedded system. This section illustrate the previous research work related to the device microprocessor in the IoT domain [10]. In another hand and as per current work requirements, the distributed motion control application is selected as an important industrial topic for IoT integration process, so the research work related to both frequency measurement and distributed motion control is reviewed within current article, as proper frequency measurement results in proper motion control, along with illustrating the role of the computational hardware in PMDC motor control applications.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Microcomputer Edge Computing for Autonomous IoT Motion Control

Kamatchi

Agme

Premkumar

et al. 2023

IJRITCC

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Devices microprocessors, microcontrollers, and Field Programmable Gate Arrays (FPGA) play the core rule at the IoT edge level and it should be right provisioned. For proper controller performance, control algorithms should be implemented near the actuator eliminating the delay effects. In the IoT domain, this means to implement the mentioned algorithm at the edge level and prior data transmitting. The efficient IoT-enabled motion control can be obtained by considering two main factors; the first factor is from the actuator design point of view and the second factor is from the controller performance point of view. Therefore, in this article, the two mentioned factors are treated concerning the microprocessor rule and importance as a core for proper prototype design and as the main platform to implement the control algorithms. A comparison of controller performance indices for both prototypes is done using previously distributed motion control schemes and newly developed schemes after tuning the respective schemes gains in an optimal manner. The scheme with better behavior of both prototypes are selected for the IoT integration process, this scheme ensures optimal edge computing for the distributed motion control, making the implementation of all control computation take place at the IoT-edge level. As a result, the dynamic pipeline stages (DPS) based prototype gives better controller performance indices for most strategies, less power consumption, and optimally utilized resources encouraging the use of the microprocessors with reconfigurable components at the IoT-edge level.

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Data Analytics and Cloud-Based Platform for Internet of Things Applications in Smart Cities

Cited by 80 publications

References 17 publications

A Technique Based on Ensemble Machine Learning for The Analysis of Electronic Nose Signals

A Technique Based on Ensemble Machine Learning for The Analysis of Electronic Nose Signals

Performance Analysis of Energy Efficiency and Security Solutions of Internet of Things Protocols

Enhancing Microcomputer Edge Computing for Autonomous IoT Motion Control

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