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RISS 인기검색어
- 검색키워드
- 저자 : ( Remesh Babu K R ) (검색결과 2 건)
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( Remesh Babu K R ),( Preetha K G ),( Saritha S ),( Rinil K R ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.9
Internet of Things (IoT) connects several objects with embedded sensors and they are capable of exchanging information between devices to create a smart environment. IoT smart devices have limited resources, such as batteries, computing power, and bandwidth, but comprehensive sensing causes severe energy restrictions, lowering data quality. The main objective of the proposal is to build a hybrid protocol which provides high data quality and reduced energy consumption in IoT sensor network. The hybrid protocol gives a flexible and complete solution for sensor selection problem. It selects a subset of active sensor nodes in the network which will increase the data quality and optimize the energy consumption. Since the unused sensor nodes switch off during the sensing phase, the energy consumption is greatly reduced. The hybrid protocol uses Dijkstra’s algorithm for determining the shortest path for sensing data and Ant colony inspired variable path selection algorithm for selecting active nodes in the network. The missing data due to inactive sensor nodes is reconstructed using enhanced belief propagation algorithm. The proposed hybrid method is evaluated using real sensor data and the demonstrated results show significant improvement in energy consumption, data utility and data reconstruction rate compared to other existing methods.
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Price Forecasting on a Large Scale Data Set using Time Series and Neural Network Models
Preetha K G,K R Remesh Babu,Sangeetha U,Rinta Susan Thomas,Saigopika,Shalon Walter,Swapna Thomas 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.12
Environment, price, regulation, and other factors influence the price of agricultural products, which is a social signal of product supply and demand. The price of many agricultural products fluctuates greatly due to the asymmetry between production and marketing details. Horticultural goods are particularly price sensitive because they cannot be stored for long periods of time. It is very important and helpful to forecast the price of horticultural products which is crucial in designing a cropping plan. The proposed method guides the farmers in agricultural product production and harvesting plans. Farmers can benefit from long-term forecasting since it helps them plan their planting and harvesting schedules. Customers can also profit from daily average price estimates for the short term. This paper study the time series models such as ARIMA, SARIMA, and neural network models such as BPN, LSTM and are used for wheat cost prediction in India. A large scale available data set is collected and tested. The results shows that since ARIMA and SARIMA models are well suited for small-scale, continuous, and periodic data, the BPN and LSTM provide more accurate and faster results for predicting well weekly and monthly trends of price fluctuation.
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