A Novel IDS to Detect Multiple DoS Attacks with Network Lifetime Estimation Based on Learning-Based Energy Prediction Algorithm for Hierarchical WSN

2018 ◽  
pp. 1-17
Author(s):  
N. Dharini ◽  
N. Duraipandian ◽  
Jeevaa Katiravan
Keyword(s):  
Network Lifetime ◽  
Prediction Algorithm ◽  
Dos Attacks ◽  
Lifetime Estimation ◽  
Energy Prediction

scholarly journals Predict Electric Power Demand with Extended Goal Graph and Heterogeneous Mixture Modeling

Information ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 134 ◽  
Author(s):  
Noriyuki Kushiro ◽  
Ami Fukuda ◽  
Masatada Kawatsu ◽  
Toshihiro Mega
Keyword(s):  
Linear Regression ◽  
Energy Demand ◽  
Mixture Modeling ◽  
Data Partitioning ◽  
Machine Learning Algorithms ◽  
Prediction Algorithm ◽  
Heterogeneous Mixture ◽  
Energy Prediction ◽  
Consumption Data ◽  
Goal Graph

In this study, methods for predicting energy demand on hourly consumption data are established for realizing an energy management system for buildings. The methods consist of an energy prediction algorithm that automatically separates the datasets to partitions (gate) and creates a linear regression model (local expert) for each partition on the heterogeneous mixture modeling, and an extended goal graph that extracts candidates of variables both for data partitioning and for linear regression for the energy prediction algorithm. These methods were implemented as tools and applied to create the energy prediction model on two years' hourly consumption data for a building. We validated the methods by comparing accuracies with those of different machine learning algorithms applied to the same datasets.

scholarly journals Centralized Energy Prediction in Wireless Sensor Networks Leveraged by Software-Defined Networking

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5379
Author(s):  
Gustavo A. Nunez Segura ◽  
Cintia Borges Margi
Keyword(s):  
Wireless Sensor Networks ◽  
Markov Chain ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Performance ◽  
Resource Constraints ◽  
Software Defined Networking ◽  
Wireless Sensor ◽  
Prediction Algorithm ◽  
Energy Prediction

Resource Constraints in Wireless Sensor Networks are a key factor in protocols and application design. Furthermore, energy consumption plays an important role in protocols decisions, such as routing metrics. In Software-Defined Networking (SDN)-based networks, the controller is in charge of all control and routing decisions. Using energy as a metric requires such information from the nodes, which would increase packets traffic, impacting the network performance. Previous works have used energy prediction techniques to reduce the number of packets exchanged in traditional distributed routing protocols. We applied this technique in Software-Defined Wireless Sensor Networks (SDWSN). For this, we implemented an energy prediction algorithm for SDWSN using Markov chain. We evaluated its performance executing the prediction on every node and on the SDN controller. Then, we compared their results with the case without prediction. Our results showed that by running the Markov chain on the controller we obtain better prediction and network performance than when running the predictions on every node. Furthermore, we reduced the energy consumption for topologies up to 49 nodes for the case without prediction.

scholarly journals Lifetime Estimation of WSN with Enhanced Pairwise Directional Geographic Routing

2019 ◽  
Vol 8 (3) ◽  
pp. 5499-5504
Keyword(s):  
Network Lifetime ◽  
Performance Metrics ◽  
Research Work ◽  
Geographic Routing ◽  
Mobility Model ◽  
Directional Antenna ◽  
Lifetime Estimation ◽  
Random Waypoint

This research work proposes an enhanced pair-wise directional geographic routing (EPWDGR) technique using the directional antenna and compares it with the conventional pair-wise directional geographic routing (PWDGR) method that uses the Omni-directional antenna. PWDGR has two key limitations - minimum network lifetime and its use of static nodes. The EPWDGR technique aims to overcome these pitfalls by incorporating a directional antenna patch that requires lesser power, thereby increasing the network lifetime. The validations have been performed through simulations that use a random waypoint mobility model which is more practical. Varying performance metrics have been used for the estimation of network lifetime. The EPWDGR also solves the energy bottleneck problem at the nodes near the sink

A Distributed Vertex Cut-set Based Algorithm for Network Lifetime Estimation in Strip-based Wireless Sensor Networks

2010 ◽  
Vol 32 (11) ◽  
pp. 2599-2605
Author(s):  
Ming-juan Wu ◽  
Bao-xian Zhang ◽  
He-qing Huang ◽  
Hai-lin Wang ◽  
Hai-tao Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Wireless Sensor ◽  
Lifetime Estimation ◽  
Cut Set

scholarly journals Accurate Prediction of Hourly Energy Consumption in a Residential Building Based on the Occupancy Rate Using Machine Learning Approaches

2021 ◽  
Vol 11 (5) ◽  
pp. 2229
Author(s):  
Le Hoai My Truong ◽  
Ka Ho Karl Chow ◽  
Rungsimun Luevisadpaibul ◽  
Gokul Sidarth Thirunavukkarasu ◽  
Mehdi Seyedmahmoudian ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Deep Neural Network ◽  
Residential Building ◽  
Prediction Algorithm ◽  
Coefficient Of Determination ◽  
Learning Approaches ◽  
Mean Square ◽  
Occupancy Rate ◽  
Energy Prediction

In this paper, a novel deep neural network-based energy prediction algorithm for accurately forecasting the day-ahead hourly energy consumption profile of a residential building considering occupancy rate is proposed. Accurate estimation of residential load profiles helps energy providers and utility companies develop an optimal generation schedule to address the demand. Initially, a comprehensive multi-criteria analysis of different machine learning approaches used in energy consumption predictions was carried out. Later, a predictive micro-grid model was formulated to synthetically generate the stochastic load profiles considering occupancy rate as the critical input. Finally, the synthetically generated data were used to train the proposed eight-layer deep neural network-based model and evaluated using root mean square error and coefficient of determination as metrics. Observations from the results indicated that the proposed energy prediction algorithm yielded a coefficient of determination of 97.5% and a significantly low root mean square error of 111 Watts, thereby outperforming the other baseline approaches, such as extreme gradient boost, multiple linear regression, and simple/shallow artificial neural network.

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