scholarly journals Appling an Improved Method Based on ARIMA Model to Predict the Short-Term Electricity Consumption Transmitted by the Internet of Things (IoT)

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ni Guo ◽  
Wei Chen ◽  
Manli Wang ◽  
Zijian Tian ◽  
Haoyue Jin
Keyword(s):  
Internet Of Things ◽  
Arima Model ◽  
Electricity Consumption ◽  
Support Vector ◽  
Small Scale ◽  
The Internet ◽  
Short Term ◽  
Prediction Horizon ◽  
The Mean ◽  
The Internet Of Things

The rapid development of the Internet of Things (IoT) has brought a data explosion and a new set of challenges. It has been an emergency to construct a more robust and precise model to predict the electricity consumption data collected from the Internet of Things (IoT). Accurately forecasting the electricity consumption is a crucial technology for the planning of the energy resource which could lead to remarkable conservation of the building electricity consumption. This paper is focused on the electricity consumption forecasting of an office building with a small-scale dataset, and 117 daily electricity consumption of the building are involved in the dataset, among which 89 values are selected as the training dataset and the remaining 28 values as the testing dataset. The hybrid model ARIMA (autoregression integrated moving average)-SVR (support vector regression) is proposed to predict the electricity consumption with different prediction horizons ranging from 1 day to 28 days. The model performances are assessed by three evaluation indicators, respectively, are the mean squared error (MSE), the root mean square error (RMSE), and the mean absolute percentage error (MAPE). The proposed model ARIMA-SVR is compared with the other four models, respectively, are the ARIMA, ARIMA-GBR (gradient boosting regression), LSTM (long short-term memory), and GRU (gated recurrent unit) models. The experiment result shows that the ARIMA-SVR model has lower prediction errors when the prediction horizon is within 20 days, and the ARIMA model is better when the prediction horizon is in the interval of 20 to 28 days. The provided method ARIMA-SVR has higher flexibility, and it is a great choice for electricity consumption prediction with more accurate results.

CLOUD BASED INTERNET OF THINGS FOR SMART CONNECTED OBJECTS

2019 ◽  
Vol 01 (02) ◽  
pp. 31-39 ◽  
Author(s):  
Duraipandian M. ◽  
Vinothkanna R.
Keyword(s):  
Energy Consumption ◽  
Internet Of Things ◽  
Smart Home ◽  
Electricity Consumption ◽  
Considerable Improvement ◽  
The Internet ◽  
The Internet Of Things ◽  
At Home

The paper proposing the cloud based internet of things for the smart connected objects, concentrates on developing a smart home utilizing the internet of things, by providing the embedded labeling for all the tangible things at home and enabling them to be connected through the internet. The smart home proposed in the paper concentrates on the steps in reducing the electricity consumption of the appliances at the home by converting them into the smart connected objects using the cloud based internet of things and also concentrates on protecting the house from the theft and the robbery. The proposed smart home by turning the ordinary tangible objects into the smart connected objects shows considerable improvement in the energy consumption and the security provision.

From Users’ Intentions to IF-THEN Rules in the Internet of Things

2021 ◽  
Vol 39 (4) ◽  
pp. 1-33
Author(s):  
Fulvio Corno ◽  
Luigi De Russis ◽  
Alberto Monge Roffarello
Keyword(s):  
Internet Of Things ◽  
Smart Devices ◽  
The Internet ◽  
Short Term ◽  
Real World Data ◽  
Original Intention ◽  
Recommendation Accuracy ◽  
High Level ◽  
The Internet Of Things

In the Internet of Things era, users are willing to personalize the joint behavior of their connected entities, i.e., smart devices and online service, by means of trigger-action rules such as “IF the entrance Nest security camera detects a movement, THEN blink the Philips Hue lamp in the kitchen.” Unfortunately, the spread of new supported technologies makes the number of possible combinations between triggers and actions continuously growing, thus motivating the need of assisting users in discovering new rules and functionality, e.g., through recommendation techniques. To this end, we present , a semantic Conversational Search and Recommendation (CSR) system able to suggest pertinent IF-THEN rules that can be easily deployed in different contexts starting from an abstract user’s need. By exploiting a conversational agent, the user can communicate her current personalization intention by specifying a set of functionality at a high level, e.g., to decrease the temperature of a room when she left it. Stemming from this input, implements a semantic recommendation process that takes into account ( a ) the current user’s intention , ( b ) the connected entities owned by the user, and ( c ) the user’s long-term preferences revealed by her profile. If not satisfied with the suggestions, then the user can converse with the system to provide further feedback, i.e., a short-term preference , thus allowing to provide refined recommendations that better align with the original intention. We evaluate by running different offline experiments with simulated users and real-world data. First, we test the recommendation process in different configurations, and we show that recommendation accuracy and similarity with target items increase as the interaction between the algorithm and the user proceeds. Then, we compare with other similar baseline recommender systems. Results are promising and demonstrate the effectiveness of in recommending IF-THEN rules that satisfy the current personalization intention of the user.

scholarly journals SISTEM INFORMASI PERAWATAN TANAMAN BERBASIS INTERNET OF THINGS DI TAMAN BALAD CIPARAY KABUPATEN BANDUNG

2021 ◽  
pp. 1-9
Author(s):  
Rosmalina Rosma ◽  
Yaya Suharya ◽  
Megantari Suhendar
Keyword(s):  
Information Systems ◽  
Internet Of Things ◽  
Water Consumption ◽  
Large Scale ◽  
Business Owners ◽  
Small Scale ◽  
The Internet ◽  
Sensor Structure ◽  
Care Information ◽  
The Internet Of Things

Most people in Indonesia usually have plants at their homes, places of business and so on. Balad is a place of business, which has a minimalist garden on the second floor. The limited land owned by Balad has made business owners take advantage of the existing land conditions to raise crops on a small scale. The garden is usually planted with a variety of plants to beautify and make the gardens in Balad cool. Plants grown by business owners in order to grow properly must have adequate water consumption and adequate lighting. The provision of water or watering and lighting to plants is one of the important things to keep the plants alive. Seeing this condition, business owners must do regular watering so that these plants get sufficient water consumption. Nowadays everyone has their own preferences, the same applies to business owners in Balad, so that sometimes they are forgotten to care for plants due to limited time. Information systems on plant care based on the Internet of Things help in collecting information related to conditions such as humidity, temperature, soil fertility, and plant inspection that can be controlled via a smartphone using the internet network. Internet of Things makes use of plant owners to connect with their residence or place of business from anywhere and anytime. The remote sensor structure using Microcontroller ESP8266 is used to monitor the condition of plants in the Balad park, of course, to see conditions remotely. Designing Plant Care Information Systems based on the Internet of Things, can reduce costs and update productivity standards in maintaining small-scale plants and if needed can be developed on a large scale

An information security analysis method of Internet of things based on balanced double SVM

2020 ◽  
Vol 39 (6) ◽  
pp. 8633-8642
Author(s):  
Zhu, Hongwei ◽  
Wang, Xuesong
Keyword(s):  
Support Vector Machine ◽  
Information Security ◽  
Internet Of Things ◽  
Security Analysis ◽  
Reference Value ◽  
Support Vector ◽  
The Internet ◽  
Analysis Method ◽  
Network Intrusion ◽  
The Internet Of Things

With the continuous progress of social science and technology, the development of the Internet of things is growing. With the development of Internet of things, security problems emerge in endlessly. During the period of COVID-19, the Internet of Things have been widely used to fight virus outbreak. However, the most serious security problem of the Internet of things is network intrusion. This paper proposes a balanced quadratic support vector machine information security analysis method for Internet of things. Compared with the traditional support vector machine Internet of things security analysis method, this method has a higher accuracy, and can shorten the detection time, with efficient and powerful characteristics. The method proposed in this paper has certain reference value to the Internet of things network intrusion problem. It provides better security for the Internet of things during the protection period of covid-19.

Application of feature extraction method based on support vector machine in Internet of things

2020 ◽  
Vol 39 (6) ◽  
pp. 8623-8632
Author(s):  
Tang Lin
Keyword(s):  
Support Vector Machine ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Smart Home ◽  
Rapid Development ◽  
Support Vector ◽  
The Internet ◽  
Machine Model ◽  
Network Intrusion ◽  
The Internet Of Things

Although much less fatal than the Ebola and previous SARS virus epidemics, the current coronavirus outbreak (COVID-19) has spread to more people in more countries in a much shorter time frame. With the rapid development of the Internet of things, it has played an important role to track/monitor transmission movements throughout the population. The technology infrastructure between mobile devices, wearable devices and sensors, smart home device makes it possible to readily deploy solutions to monitor and collect data and perform analysis to ensure policy make intelligent, rapid decisions. This research combines AOL and Support Vector Machine to form the Internet of things cycle through smart home. The parameters of Support Vector Machine model are optimized by ALO algorithm, which shortens the learning time and improves the performance of classifier. Then, the algorithm of ALO is used to optimize the Support Vector Machine intrusion detection method and agent technology, and the intrusion detection model is established. Experimental results show that the combination of these two can effectively reduce the false alarm rate of network intrusion.

scholarly journals Implementation of The Internet of Things for Monitoring The Company's Electrical Power Consumption

2021 ◽  
Vol 2 (1) ◽  
pp. 16-22
Author(s):  
Riklan Kango ◽  
Suhaedi Suhaedi ◽  
Fadli Awal Hasanuddin
Keyword(s):  
Internet Of Things ◽  
Power Consumption ◽  
Real Time ◽  
Monitoring System ◽  
Electricity Consumption ◽  
The Internet ◽  
Voltage Measurement ◽  
Monitoring Method ◽  
Internet Of Things Technology ◽  
The Internet Of Things

This research aims to design an electric panel monitoring system using the Internet of Things technology in company buildings so that consumers can monitor real-time electricity consumption. The energy consumption monitoring method that we propose uses PM2100 by implementing a real-time monitoring function of the power consumption of a 3-phase electric panel. The monitoring system implementation results show that the value is very close to measuring the digital multimeter measuring instrument. The monitoring system produces a current measurement accuracy of 97.38% with an error of 2.62%, while the 3-phase voltage measurement error is 0.616%. This system design helps companies obtain information faster to be considered data to improve efficiency in the Company.

scholarly journals An Identity Authentication Method of a MIoT Device Based on Radio Frequency (RF) Fingerprint Technology

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1213 ◽  
Author(s):  
Qiao Tian ◽  
Yun Lin ◽  
Xinghao Guo ◽  
Jin Wang ◽  
Osama AlFarraj ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Wireless Communication ◽  
Radio Frequency ◽  
Success Rate ◽  
Mobile Internet ◽  
Support Vector ◽  
The Internet ◽  
Communication Device ◽  
Rf Fingerprint ◽  
The Internet Of Things

With the continuous development of science and engineering technology, our society has entered the era of the mobile Internet of Things (MIoT). MIoT refers to the combination of advanced manufacturing technologies with the Internet of Things (IoT) to create a flexible digital manufacturing ecosystem. The wireless communication technology in the Internet of Things is a bridge between mobile devices. Therefore, the introduction of machine learning (ML) algorithms into MIoT wireless communication has become a research direction of concern. However, the traditional key-based wireless communication method demonstrates security problems and cannot meet the security requirements of the MIoT. Based on the research on the communication of the physical layer and the support vector data description (SVDD) algorithm, this paper establishes a radio frequency fingerprint (RFF or RF fingerprint) authentication model for a communication device. The communication device in the MIoT is accurately and efficiently identified by extracting the radio frequency fingerprint of the communication signal. In the simulation experiment, this paper introduces the neighborhood component analysis (NCA) method and the SVDD method to establish a communication device authentication model. At a signal-to-noise ratio (SNR) of 15 dB, the authentic devices authentication success rate (ASR) and the rogue devices detection success rate (RSR) are both 90%.

Sign in / Sign up

Export Citation Format

Share Document