An IoT ecosystem for the implementation of scalable wireless home automation systems at smart city level

Lohokare, Jay; Dani, Reshul; Rajurkar, Ajit; Apte, Ameya

doi:10.1109/tencon.2017.8228095

Cited by 19 publications

(5 citation statements)

References 14 publications

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“…Previous studies have shown a significant impact from the integration of several big data technologies. Lohokare et al proposed a scalable framework for home automation in smart cities [ 38 ]. The proposed framework utilized Apache Kafka as a message broker to handle incoming IoT data and MongoDB to store the sensor data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Previous studies have shown significant benefits from several big data technologies in processing and storing large volumes of data quickly, such as with the application of Apache Kafka [ 21 , 22 , 23 , 24 , 25 , 26 ], Apache Storm [ 27 , 28 , 29 , 30 , 31 ], and NoSQL MongoDB [ 32 , 33 , 34 , 35 , 36 , 37 ]. Previous studies showed significant advantages from the integration of big data technologies such as reducing the processing time for home automation systems [ 38 ], providing effective and efficient solutions for processing IoT-generated data for smart cities [ 39 ], and handling large amounts of smart environmental data in real-time [ 40 ]. The aforementioned big data technologies have been integrated in data processing systems, resulting in significant advantages due to processing large amounts of streaming spatiotemporal data [ 41 ] as well as processing massive amounts of manufacturing sensor data efficiently [ 42 ].…”

Section: Introductionmentioning

confidence: 99%

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Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

Syafrudin

Alfian

Fitriyani

et al. 2018

Sensors

280

103

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With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management. Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process. In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed. Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed. The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type. The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process. Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively. The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea. The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process. Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input. The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

Syafrudin

Alfian

Fitriyani

et al. 2018

Sensors

280

103

View full text Add to dashboard Cite

show abstract

“…According to authors in [52], the smart city incorporates communication networks for connecting major city appliances and services for remote control, monitoring, and access of the residence from within or outside the premises. The major benefits associated with smart city automation systems are healthy living with its deployment in healthcare systems, reduction in energy consumption, and home safety and security.…”

Section: Artificial Intelligence Enabled Intrusion Detection For Iot ...mentioning

confidence: 99%

Privacy and Security Enhancement of Smart Cities using Hybrid Deep Learning-enabled Blockchain

Awotunde,

Gaber,

Prasad

et al. 2023

SCPE

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The emergence of the Internet of Things (IoT) accelerated the implementation of various smart city applications and initiatives. The rapid adoption of IoT-powered smart cities is faced by a number of security and privacy challenges that hindered their application in areas such as critical infrastructure. One of the most crucial elements of any smart city is safety. Without the right safeguards, bad actors can quickly exploit weak systems to access networks or sensitive data. Security issues are a big worry for smart cities in addition to safety issues. Smart cities become easy targets for attackers attempting to steal data or disrupt services if they are not adequately protected against cyberthreats like malware or distributed denial-of-service (DDoS) attacks. Therefore, in order to safeguard their systems from potential threats, businesses must employ strong security protocols including encryption, authentication, and access control measures. In order to ensure that their network traffic remains secure, organizations should implement powerful network firewalls and intrusion detection systems (IDS). This article proposes a blockchain-supported hybrid Convolutional Neural Network (CNN) with Kernel Principal Component Analysis (KPCA) to provide privacy and security for smart city users and systems. Blockchain is used to provide trust, and CNN enabled with KPCA is used for classifying threats. The proposed solution comprises three steps, preprocessing, feature selection, and classification. The standard features of the datasets used are converted to a numeric format during the preprocessing stage, and the result is sent to KPCA for feature extraction. Feature extraction reduces the dimensionality of relevant features before it passes the resulting dataset to the CNN to classify and detect malicious activities. Two prominent datasets namely ToN-IoT and BoT-IoT were used to measure the performance of this anticipated method compared to its best rivals in the literature. Experimental evaluation results show an improved performance in terms of threat prediction accuracy, and hence, increased security, privacy, and maintainability of IoT-enabled smart cities.

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“…Arduino board consists of different type of hardware, all boards have 14 digital input output pins except Mega2560 Arduino that has 54 pins which has same working as the raspberry pi GPIO pins [25]. The relay support for switching is given to Arduino digital input output pins that are connected with household's devices.…”

Section: B Aurdinomentioning

confidence: 99%

Smart Home Automation Using IOT and its Low Cost Implementation

Shah¹,

Mahmood²

2020

IJEM

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The use of automation is emerging with the help of internet providing possibility of objects to work itself. With recent advancements to the fast speed internet, IoT will be playing a vital role in our daily tasks in present and future. IoT is offering feasibility and effectiveness to the system that are based upon it. These modern technologies are creating comfort and standard way of living because of its time, energy and cost efficiency. In this modern world, where things are going to be on our finger tips, our daily household appliances will also be controlled with our smartphones. This will allow us to manage the usage smartly, and can help in building of an eco-friendly environment. This paper will conduct a study based on how household appliances may be automated smartly with software applications that are integrated with hardware board. It presents the complete architecture of the system with its working capabilities. Also, it explains the internal mechanism of the system which mainly considers the software application and hardware board interaction. As we know the smart home automation is a costly process, so in this paper we would be looking at its low-cost implementation.

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An IoT ecosystem for the implementation of scalable wireless home automation systems at smart city level

Cited by 19 publications

References 14 publications

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

Privacy and Security Enhancement of Smart Cities using Hybrid Deep Learning-enabled Blockchain

Smart Home Automation Using IOT and its Low Cost Implementation

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