A metaheuristic optimization approach for energy efficiency in the IoT networks

Iwendi, Celestine; Maddikunta, Praveen Kumar Reddy; Gadekallu, Thippa Reddy; Lakshmanna, Kuruva; Bashir, Ali Kashif; Piran, Md. Jalil

doi:10.1002/spe.2797

Cited by 180 publications

(99 citation statements)

References 40 publications

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“…The authors also use a Gradient Boosting Machine algorithm, which is embedded in the framework and performs better than the existing ML algorithms. Most of the existing smart systems use the combination of IoT [19]- [21] embedded with the power of ML algorithms so that the systems perform to their fullest capacity efficiently. The authors in [22] analyze various IoT-based ML modes, which are used in the different domains such as healthcare, smart city, and vehicle-vehicle communication in smart cites.…”

Section: Literature Surveymentioning

confidence: 99%

A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

et al. 2020

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The grid denotes the electric grid which consists of communication lines, control stations, transformers, and distributors that aids in supplying power from the electrical plant to the consumers. Presently, the electric grid constitutes humongous power production units which generates millions of megawatts of power distributed across several demographic regions. There is a dire need to efficiently manage this power supplied to the various consumer domains such as industries, smart cities, household and organizations. In this regard, a smart grid with intelligent systems is being deployed to cater the dynamic power requirements. A smart grid system follows the Cyber-Physical Systems (CPS) model, in which Information Technology (IT) infrastructure is integrated with physical systems. In the scenario of the smart grid embedded with CPS, the Machine Learning (ML) module is the IT aspect and the power dissipation units are the physical entities. In this research, a novel Multidirectional Long Short-Term Memory (MLSTM) technique is being proposed to predict the stability of the smart grid network. The results obtained are evaluated against other popular Deep Learning approaches such as Gated Recurrent Units (GRU), traditional LSTM and Recurrent Neural Networks (RNN). The experimental results prove that the MLSTM approach outperforms the other ML approaches. INDEX TERMS Multidirectional long short-term memory (MLSTM), machine learning (ML), smart grid (SG), cyber physical systems (CPS).

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Section: Literature Surveymentioning

confidence: 99%

A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

et al. 2020

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“…This factor aims minimizing the energy consumption of sensors in the IoT network in order to increase in the network lifetime. Iwendi et al [117] selected the most appropriate Cluster Header (KH) in the IoT network to optimize energy consumption, considering many factors such as residual energy, cost function, and proposed a hybrid algorithm consisting of Simulated Annealing (SA) and Whale Optimization Algorithm (WOA). The authors state that based on the comparison results obtained from the proposed model and other optimization algorithms (Artificial Bee Colony algorithm, Genetic Algorithm, Adaptive Gravity Search algorithm, WOA), the proposed method yields superior results.…”

Section: Networking Layermentioning

confidence: 99%

Analysis of factors affecting IoT-based smart hospital design

2020

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Currently, rapidly developing digital technological innovations affect and change the integrated information management processes of all sectors. The high efficiency of these innovations has inevitably pushed the health sector into a digital transformation process to optimize the technologies and methodologies used to optimize healthcare management systems. In this transformation, the Internet of Things (IoT) technology plays an important role, which enables many devices to connect and work together. IoT allows systems to work together using sensors, connection methods, internet protocols, databases, cloud computing, and analytic as infrastructure. In this respect, it is necessary to establish the necessary technical infrastructure and a suitable environment for the development of smart hospitals. This study points out the optimization factors, challenges, available technologies, and opportunities, as well as the system architecture that come about by employing IoT technology in smart hospital environments. In order to do that, the required technical infrastructure is divided into five layers and the system infrastructure, constraints, and methods needed in each layer are specified, which also includes the smart hospital’s dimensions and extent of intelligent computing and real-time big data analytic. As a result of the study, the deficiencies that may arise in each layer for the smart hospital design model and the factors that should be taken into account to eliminate them are explained. It is expected to provide a road map to managers, system developers, and researchers interested in optimization of the design of the smart hospital system.

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“…The spider monkey optimization algorithm (SMO) is one of the metaheuristic methods [41,[44][45][46]] based on the spider monkey's social behavior, adopting the fission and fusion swarm intelligence tactic for foraging [47]. Spider monkeys usually live in a swarm of 40 to 50 members.…”

Section: Spider Monkey Optimization Algorithmmentioning

confidence: 99%

SMO-DNN: Spider Monkey Optimization and Deep Neural Network Hybrid Classifier Model for Intrusion Detection

et al. 2020

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The enormous growth in internet usage has led to the development of different malicious software posing serious threats to computer security. The various computational activities carried out over the network have huge chances to be tampered and manipulated and this necessitates the emergence of efficient intrusion detection systems. The network attacks are also dynamic in nature, something which increases the importance of developing appropriate models for classification and predictions. Machine learning (ML) and deep learning algorithms have been prevalent choices in the analysis of intrusion detection systems (IDS) datasets. The issues pertaining to quality and quality of data and the handling of high dimensional data is managed by the use of nature inspired algorithms. The present study uses a NSL-KDD and KDD Cup 99 dataset collected from the Kaggle repository. The dataset was cleansed using the min-max normalization technique and passed through the 1-N encoding method for achieving homogeneity. A spider monkey optimization (SMO) algorithm was used for dimensionality reduction and the reduced dataset was fed into a deep neural network (DNN). The SMO based DNN model generated classification results with 99.4% and 92% accuracy, 99.5%and 92.7% of precision, 99.5% and 92.8% of recall and 99.6%and 92.7% of F1-score, utilizing minimal training time. The model was further compared with principal component analysis (PCA)-based DNN and the classical DNN models, wherein the results justified the advantage of implementing the proposed model over other approaches.

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A metaheuristic optimization approach for energy efficiency in the IoT networks

Cited by 180 publications

References 40 publications

A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

Analysis of factors affecting IoT-based smart hospital design

SMO-DNN: Spider Monkey Optimization and Deep Neural Network Hybrid Classifier Model for Intrusion Detection

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