Towards developing a machine learning-metaheuristic-enhanced energy-sensitive routing framework for the internet of things

Seyfollahi, Ali; Taami, Tania; Ghaffari, Ali

doi:10.1016/j.micpro.2022.104747

Cited by 28 publications

(11 citation statements)

References 40 publications

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“…Currently, metaheuristic algorithms seen a substantial history to achieve the optimal solution in fine-tuning machine learning algorithms and solving continuous optimization problems [27,28]. They have gained great popularity in different application domains such as smart grid applications [29][30][31], Green Building Energy-Optimization Systems (GBEOSs) [32], Electric Vehicles (EVs) [33][34][35], the Internet of Things (IoT) [36][37][38][39], the military domain [40][41][42], structural health monitoring applications [43,44], Resilient Cyber-Physical Systems (RCPSs) [45,46], robotics [47,48], etc. With the growth of solar energy systems and their various design and control aspects seen as complex problems, where metaheuristics are the best candidates for addressing them, PV cell parameter identification is becoming a wide research discipline, which involves artificial intelligence and bio-inspired optimization algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

An Accurate Metaheuristic Mountain Gazelle Optimizer for Parameter Estimation of Single- and Double-Diode Photovoltaic Cell Models

Abbassi,

Saidi,

Urooj

et al. 2023

Mathematics

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Accurate parameter estimation is crucial and challenging for the design and modeling of PV cells/modules. However, the high degree of non-linearity of the typical I–V characteristic further complicates this task. Consequently, significant research interest has been generated in recent years. Currently, this trend has been marked by a noteworthy acceleration, mainly due to the rise of swarm intelligence and the rapid progress of computer technology. This paper proposes a developed Mountain Gazelle Optimizer (MGO) to generate the best values of the unknown parameters of PV generation units. The MGO mimics the social life and hierarchy of mountain gazelles in the wild. The MGO was compared with well-recognized recent algorithms, which were the Grey Wolf Optimizer (GWO), the Squirrel Search Algorithm (SSA), the Differential Evolution (DE) algorithm, the Bat–Artificial Bee Colony Optimizer (BABCO), the Bat Algorithm (BA), Multiswarm Spiral Leader Particle Swarm Optimization (M-SLPSO), the Guaranteed Convergence Particle Swarm Optimization algorithm (GCPSO), Triple-Phase Teaching–Learning-Based Optimization (TPTLBO), the Criss-Cross-based Nelder–Mead simplex Gradient-Based Optimizer (CCNMGBO), the quasi-Opposition-Based Learning Whale Optimization Algorithm (OBLWOA), and the Fractional Chaotic Ensemble Particle Swarm Optimizer (FC-EPSO). The experimental findings and statistical studies proved that the MGO outperformed the competing techniques in identifying the parameters of the Single-Diode Model (SDM) and the Double-Diode Model (DDM) PV models of Photowatt-PWP201 (polycrystalline) and STM6-40/36 (monocrystalline). The RMSEs of the MGO on the SDM and the DDM of Photowatt-PWP201 and STM6-40/36 were 2.042717 ×10−3, 1.387641 ×10−3, 1.719946 ×10−3, and 1.686104 ×10−3, respectively. Overall, the identified results highlighted that the MGO-based approach featured a fast processing time and steady convergence while retaining a high level of accuracy in the achieved solution.

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Section: Literature Reviewmentioning

confidence: 99%

An Accurate Metaheuristic Mountain Gazelle Optimizer for Parameter Estimation of Single- and Double-Diode Photovoltaic Cell Models

Abbassi,

Saidi,

Urooj

et al. 2023

Mathematics

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“…The scalability of nodes needs to be verified by forming a global architecture. The solution should address different aspects of the problem [25].…”

Section: A Iot Challengesmentioning

confidence: 99%

An Energy‐Aware Technique for Task Allocation in the Internet of Things using Krill Herd Algorithm

Miao¹,

Xu²,

Chen³

et al. 2023

IJACSA

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The Internet of Things (IoT) is an innovative technology that connects the digital and physical worlds as well as allows physical devices capable of different capacities to share resources to accomplish tasks. Most IoT objects have limited battery life and are heterogeneous. Assignment of these objects is, therefore, extremely challenging. Energy consumption and reliability are the primary objectives of task allocation algorithms. We present an optimization solution to the IoT task allocation problem based on the krill herd algorithm. The algorithm increases the energy efficiency and stability of the network while providing a reliable task allocation solution. An extensive test of the proposed algorithm has been conducted using the MATLAB simulator. Compared to the most relevant method in the literature, our algorithm provides a higher level of energy efficiency.

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“…Seyfollahi et al 22 had suggested a machine learning‐based enhanced energy‐sensitive routing framework. The proposed protocol uses a four‐phase approach for fuzzy clustering, utilizing support vector regression (SVR) and time series approaches to forecast energy consumption, choosing the best cluster heads while taking energy and centralization factors into consideration, and using the ant colony optimization (ACO) algorithm to route and transmit data from every sensor to and from CH to the sink.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel energy‐efficient routing protocol for industrial WSN using hybrid COOT‐LS algorithm with LSTM‐based DOM prediction

Ravikumar,

Kumar

2023

Int J Communication

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SummaryIndustrial wireless sensor networks (IWSNs) are very important to improve and simplify the way to manage, monitor, and control industrial factories. IWSNs have many benefits, but because of their vulnerability to extremely complex and variable industrial contexts, they also limit some of the potential that is currently available and present difficulties on numerous fronts. The proposed work develops energy‐efficient routing technique to minimize the overall energy consumption. Heterogeneous mobile nodes are randomly deployed in the experimental region; then, region‐based grid formation is done in the proposed method. In each grid, the base node is selected utilizing the parameters like residual energy and distance of mobile nodes. Hybrid COOT‐HOA optimization is used in the proposed method for selecting the optimal base node in each grid. A deep learning‐based machine learning algorithm called long short‐term memory (LSTM) is utilized to predict the future direction of movement (DOM) of each mobile node in the grid. Estimation of signal parameters via rotational invariance technique (ESPRIT) is utilized to find an active zone from the sender node to direction of base node. Then, the sender node transmits the data to its nearest node in the active region. This proposed energy‐efficient routing algorithm is tested with several metrics which attains better performance like 94% packet delivery ratio, 7% packet loss, average residual energy of 9.5 J, and 3.4 Mbps throughput. Thus, the energy‐efficient routing protocol used in the proposed approach transfers the data in an energy‐efficient manner for IWSN.

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Towards developing a machine learning-metaheuristic-enhanced energy-sensitive routing framework for the internet of things

Cited by 28 publications

References 40 publications

An Accurate Metaheuristic Mountain Gazelle Optimizer for Parameter Estimation of Single- and Double-Diode Photovoltaic Cell Models

An Accurate Metaheuristic Mountain Gazelle Optimizer for Parameter Estimation of Single- and Double-Diode Photovoltaic Cell Models

An Energy‐Aware Technique for Task Allocation in the Internet of Things using Krill Herd Algorithm

A novel energy‐efficient routing protocol for industrial WSN using hybrid COOT‐LS algorithm with LSTM‐based DOM prediction

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