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Adaptive fuzzy-based node communication performance prediction with hybrid heuristic Cluster Head selection framework in WSN using enhanced K-means clustering mechanism

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JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS 2024年第3期16卷 309-335页

作者： Ayyappan, Asha Arunachalam, Rajesh Kumar, Manivel Lenin Rajalakshmi Engn Coll Dept Elect & Commun Engn Mevalurkuppam 602105 Tamil Nadu India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Elect & Commun Engn Chennai 602105 Tamil Nadu India Vignan Inst Technol & Sci Dept Elect & Commun Engn Hyderabad 508284 Telangana India

The "Wireless Sensor Networks (WSN)" has gained a lot of interest among research scholars and has been utilized in various advanced applications in distinct fields. Along with the load balancing techniques, the clustering scheme also prolongs the network's overall lifespan. The "Cluster Head (CH)" performs the task of load balancing between the nodes in the "Clustering algorithm";hence, the CH selection procedure is regarded as a critical task in the case of the clustering algorithms. Depending on the CH selection and cluster nodes, the rate of energy consumed by these CHs will be reduced in the wireless sensor. CH selection is a promising solution for the transmission of information within various parameters. Thus, CH selection leads to an increase in the duration of the system and a reduction in the energy utilization by the nodes. Therefore, an "optimization- based CH selection" mechanism in WSN is developed in this paper along with an enhanced node communication performance prediction strategy to provide better communication between the "Sensor Nodes (SNs)" with limited energy expenditure. The node's communication performance is predicted using the Adaptive Fuzzy, in which metrics such as bit rate, latency, throughput, loss, and packet delivery ratio are specified as the input to the network. Here, the parameters within the fuzzy network are tuned with the help of the recommended "Hybrid Position of Heap and African Buffalo Optimization (HP-HABO)". Then, to perform efficient node clustering, the "Optimal K-Means Clustering (OKMC)" approach is executed and the CHs are formed using the developed HP-HABO. The objective function depends on the constraints like energy, distance, and predicted communication performance attained by forming these CHs. The performance of the developed CH selection mechanism is verified by analyzing the experimental outcome of the proposed technique with different optimization algorithms and previous works concerning the objective constra

关键词： Wireless sensor network node communication performance prediction Hybrid Position of Heap and African Buffalo Optimization adaptive fuzzy Optimal K-Means Clustering multi-objective function

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An optimal dispatch strategy of off-grid park integrated energy system considering source volatility

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ENERGY REPORTS 2024年 12卷 1597-1607页

作者： Zhao, Yuyang Yang, Zhen Guo, Yingjun Song, Haiyun Sun, Hexu Hebei Univ Sci & Technol Sch Elect Engn Shijiazhuang 050018 Peoples R China Hebei Engn Lab Wind Power & PV Coupling Hydrogen P Shijiazhuang 050018 Peoples R China

An off-grid integrated energy system (IES) with hydrogen storage at park-level is proposed, utilizing wind, solar and natural gas as the main energy supply to replace fossil fuels, in order to overcome the insufficient consideration of energy source conversion and information exchange in the traditional energy system. Firstly, an IES schematic consisted with typical devices is proposed, and all the components are modelled in detail. Then, based on the renewable energy (RE) input requirements and the facility parameters, an optimal dispatch model for multi-energy conversion based on hydrogen storage has been demonstrated. In addition, an improved particle swarm optimization algorithm is also introduced to improve the strategy. Combined with the environmental characteristics, the multi-objective solution with the lowest economic cost and environmental cost, the highest energy supply reliability and energy efficiency as the optimization objectives is determined. Finally, a case study is conducted using the practical data of the Chongli Large-Scale Wind-Solar Complementary Coupled Hydrogen Production System Application Demonstration Project to validate the feasibility of the study under real conditions. The result shows that the RE consumption rate is greater than 99 % under the premise of considering the environmental cost and ensuring the operation reliability, which can effectively improve the operation economy and user economy of the power grid

关键词： Park integrated energy system multi-objective function Particle swarm optimization Hydrogen energy System optimal dispatch

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Optimal load balancing strategy-based centralised sensor for a WSN-based cloud-IoT framework using a hybrid meta-heuristic strategy

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INTERNATIONAL JOURNAL OF AUTONOMOUS AND ADAPTIVE COMMUNICATIONS SYSTEMS 2024年第3期17卷 247-271页

作者： Yogaraja, G. S. R. Thippeswamy, M. N. Venkatesh, K. SJC Inst Technol Dept Informat Sci & Engn Chickaballapur 562101 India Ramaiah Inst Technol Dept Comp Sci & Engn AI&ML & Cyber Secur Bangalore 560054 Karnataka India Nitte Meenakshi Inst Technol Dept Comp Sci & Engn Bangalore 560064 India

This paper is to implement a load balancing centralised server to control the wireless sensor networks (WSN) connected to internet of things (IoT) and cloud. The WSN gathers data pertaining to diverse applications and transfers it to a centralised server in the cloud through the IoT channel. Sever controls the routing of each node in the WSN through an optimal load balancing strategy. A hybrid meta-heuristic algorithm with the Forest-Cat Optimisation Algorithm (F-COA) is introduced for accomplishing a centralised load balanced strategy in the communication system. The fundamental constraints used in the proposed models are clustering parameters like distance between nodes, energy, and delay, load balancing parameters like response time, turnaround time, server load, and QoS parameters like resource utilisation, execution time, and throughput. The experimental results present superior performance through multi-objective optimisation when compared to the other approaches in terms of different constraints.

关键词： IoT internet of things cloud computing WSN wireless sensor networks optimal routing F-COA Forest-Cat Optimisation Algorithm load balancing multi-objective function

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Evaluation method of renewable energy absorptive capacity based on Monte Carlo

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INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES 2024年第6期46卷 603页

作者： He, Jinding Qin, Wenchao Yunnan Elect Power Dispatching Control Ctr Kunming Yunnan Peoples R China

Because the traditional assessment method of renewable energy absorptive capacity has the problems of low assessment accuracy and long assessment time, a Monte Carlo-based assessment method of renewable energy absorptive capacity is proposed. First, build a renewable energy absorptive capacity evaluation system, obtain the evaluation indicators, then analyse the renewable energy wind output characteristics, extract the characteristics of renewable energy absorptive capacity and then set the maximum renewable energy absorptive capacity, the system power balance, the minimum conventional power technology output, and the minimum production cost as the optimisation objectives to establish a multi-objective function for evaluation. Finally, under the constraint conditions, the objective function is solved by Monte Carlo method, and the solution is the evaluation result. The simulation results show that the proposed method has higher accuracy and shorter evaluation time for renewable energy absorptive capacity evaluation.

关键词： Monte Carlo renewable energy absorptive capacity wind output multi-objective function

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Hybrid weight optimized deep learning for autism and visual sensory processing disorder detection: A heuristic strategy on medical domain

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2024年第1期18卷 533-559页

作者： Dedgaonkar, Suruchi Sachdeo, Rajneeshkaur MIT ADT Univ Inst Informat Technol Sch EngnSch Engn Dept Comp Sci & Engn Pune Maharashtra India Inst Informat Technol Fac Vishwakarma Pune Maharashtra India MIT ADT Univ MIT Sch Engn Dept Comp Sci & Engn Pune Maharashtra India

An Autism Spectrum Disorder (ASD) affected individual has several difficulties with social-emotional cues. The existing model is observed with emotional face processing in adolescents and ASD and Typical Development (TD) by utilizing various body parameters. Scanning facial expressions is the initial task, and recognizing the face's sensitivity to different emotional expressions is the next complex task. To address this shortcoming, in this work, a new autism and visual Sensory Processing Disorder (SPD) detection model for supporting healthcare applications by processing facial expressions and sensory data of heart rate and body temperature. Here, initially, the individual data regarding facial emotions and other body parameters like heart rate and body temperature are collected from various subjects. Then, the selection of optimal features is executed by a hybrid algorithm named Density Factor-based Artificial Bee Honey Badger Optimization (DF-ABHBO), where the most essential features are attained and fed to the detection phase. The optimal feature selection is made by resolving the fitness function with constraints like correlation, data variance, and cosine similarity for inter and intra-class. Finally, the autism and visual SPD detection are performed through a Hybrid Weight Optimized Deep Neural Recurrent Network (HWODNRN), where the hyperparameter and weights of "Deep Neural Network (DNN) and Recurrent Neural Network (RNN)" are optimized with the developed DF-ABHBO technique. From the result analysis, the accuracy and F1-score rate of the offered DF-ABHBO-HWODNRN method have attained 96% and 93%. The findings obtained from the simulations of the designed system achieve better performance.

关键词： Autism and visual sensory processing disorder density factor-based artificial bee honey badger optimization hybrid weight optimized deep neural recurrent network multi-objective function optimal feature selection

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Incorporating quantum key distribution and reinforcement learning for secure and efficient smart grid advanced metering infrastructure

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OPTICAL AND QUANTUM ELECTRONICS 2024年第6期56卷 932-932页

作者： Beula, G. Starlin Franklin, S. Wilfred Arunachala Coll Engn Women Elect & Commun Engn Nagercoil Tamil Nadu India CSI Inst Technol Elect & Commun Engn Kanyakumari Tamil Nadu India

The Smart Grid AMI is at the forefront of modernizing energy management for efficient energy distribution and maintaining grid reliability. However, the seamless flow of data within AMI introduces critical security challenges, necessitating innovative approaches to ensure secure data transmission. This paper addresses these challenges by proposing a Quantum Key Distribution (QKD) based on multi-objective Optimization (MOO) and Reinforcement Learning (RL) framework for secure data transmission within the Smart Grid AMI. Primarily, we formulate the routing problem as a MOO challenge with four primary objectives namely, energy efficiency, latency minimization, reliability, and security to capture the essence of efficient and secure data transmission. Further, we introduce an RL agent based on the Proximal Policy Optimization (PPO) algorithm for robust policy learning. The RL agent by exploring diverse routing actions and receiving rewards optimizes the routing decisions within the network environment based on the objectives specified in the multi-objective function (MOF). A novel MOF quantifies trade-offs between security and performance metrics, integrating QKD-based security metrics with traditional optimization objectives. This function guides the RL agent in making informed routing decisions. Through extensive simulations using the NS-2 simulator in a realistic Smart Grid AMI environment, the proposed approach obtained energy consumption of 750 J, latency of 55 ms, and security level of 96% and also revealed significant improvements compared to conventional techniques widely employed for secured communication in AMI networks. Overall, the simulation results exhibited that the proposed method showed outstanding performance by achieving energy efficiency of 0.95, latency reduction of 0.92, reliability improvement of 0.94, and security enhancement of 0.96. Overall, this integration offers a pioneering solution to address the evolving security challenges in the Smart G

关键词： Smart grid AMI Security QKD Data transmission multi-objective function

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Application of the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) in a Two-Echelon Cold Supply Chain

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SYSTEMS 2025年第3期13卷 206-206页

作者： Acerce, Asli Denizhan, Berrin Sakarya Univ Engn Fac Dept Ind Engn TR-54050 Sakarya Turkiye

A two-stage cold supply chain manages the transportation, storage, and distribution of temperature-sensitive products like frozen food, fresh/green products, and pharmaceuticals, which makes it costly. It consists of three key elements: a supplier, a warehouse, and multiple customers. Procurement planning can be conducted for various products, and this study assumes the transport of a fresh/green product with gradually decreasing quality due to its perishable nature. In a two-stage cold supply chain, multiple objective functions can be defined, including cost minimization, product quality optimization, and transportation/storage condition optimization. We developed a mathematical model to optimize these objectives, incorporating two specific functions, cost minimization and product age reduction, to ensure efficient supply chain performance. Traditional solution methods often struggle with multi-objective mathematical models due to their complexity. Therefore, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), a Genetic Algorithm-based approach, was applied to solve the model efficiently. NSGA-II optimized planning for a 7-day period under specific demand conditions, ensuring better resource allocation. The results showed that NSGA-II was better than traditional methods at making decisions and routing efficiently in the two-stage cold supply chain. This led to much better outcomes, with lower costs, less waste, and better product quality throughout the process.

关键词： two-echelon inventory system cold supply chain multi-objective function meta-heuristic algorithm NSGA-II

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An optimal load distribution method for distributed energy systems based on the improved particle swarm optimisation

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INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES 2024年第6期46卷 651页

作者： Liu, Junjun Changshu Inst Technol Changshu Jiangsu Peoples R China Jiangsu Key Lab Elevator Intelligent Safety Changshu Jiangsu Peoples R China

In order to solve the problem of large load variance and high distribution scheme cost after the distributed energy system is integrated into the traditional large power grid, an optimal load distribution method for distributed energy systems based on the improved particle swarm optimisation algorithm is proposed in this paper. Firstly, four output models of the distributed energy system are established. With the minimum cost and the minimum system load variance as the objectives, a multi-objective function model is constructed. Considering the power limit, generation power limit and other restrictions, constraints are constructed to complete the construction of the optimal load distribution model of the distributed energy system. Finally, the PSO algorithm is introduced to update the optimal particles in the solution space through different iterative processes. Combined with quantum theory, the PSO algorithm is optimised to obtain the optimal load distribution scheme. The results show that the cost of the distribution scheme obtained by this method can be reduced by more than 30,000 Yuan, and its load variance value is smaller, so the method has certain research value.

关键词： improved particle swarm optimisation distributed energy system output model multi-objective function constraints load distribution

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multi-objective secure aware workflow scheduling algorithm in cloud computing based on hybrid optimization algorithm

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WEB INTELLIGENCE 2023年第4期21卷 385-405页

作者： Reddy, G. Narendrababu Kumar, S. Phani G Narayanamma Inst Technol & Sci Hyderabad 500104 Telangana India GITAM Univ Sch Technol Hyderabad 502329 Telangana India

Cloud computing provides the on-demand service of the user with the use of distributed physical machines, in which security has become a challenging factor while performing various tasks. Several methods were developed for the cloud computing workflow scheduling based on optimal resource allocation;still, the security consideration and efficient allocation of the workflow are challenging. Hence, this research introduces a hybrid optimization algorithm based on multi-objective workflow scheduling in the cloud computing environment. The Regressive Whale Water Tasmanian Devil Optimization (RWWTDO) is proposed for the optimal workflow scheduling based on the multi-objective fitness function with nine various factors, like Predicted energy, Quality of service (QoS), Resource utilization, Actual task running time, Bandwidth utilization, Memory capacity, Make span equivalent of the total cost, Task priority, and Trust. Besides, secure data transmission is employed using the triple data encryption standard (3DES) to acquire enhanced security for workflow scheduling. The method's performance is evaluated using the resource utilization, predicted energy, task scheduling cost, and task scheduling time and acquired the values of 1.00000, 0.16587, 0.00041, and 0.00314, respectively.

关键词： Security cloud computing workflow scheduling multi-objective function virtual machine

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Unmanned Aerial Vehicles Surveillance Routing as a multi-objective Team Orienteering Problem 2nd

Unmanned Aerial Vehicles Surveillance Routing as a Multi-obj...

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2nd Decision Science Alliance International Summer Conference-DSA ISC

作者： Agud-Albesa, Lucia Garrido, Neus Llorens, Almudena Oltra-Crespo, Sandra Univ Politecn Valencia Dept Appl Math Alcoy 03801 Spain Univ Politecn Valencia Inst Matemat Multidisciplinar Valencia 46022 Spain Univ Politecn Valencia Technol Inst Informat Alcoy 03801 Spain

ISBN: (纸本)9783031782404;9783031782411

In the team orienteering problem a fixed fleet of vehicles (i.e. unmanned aerial vehicles, self-driving vehicles) leaves the initial depot and, until reaching the destination, the fleet accumulates the rewards associated with each customer it visits, providing different routes that satisfy the time constraints. Although in these problems it is not mandatory to visit all customers, in this work we add the feature of prioritizing some of them. The objective of the paper is to identify the solutions of some real problems with these variations of the team orienteering problem scheme. Also, to obtain high quality solutions combining the bias-randomized heuristics with the weighted average method and simulation to maximize both values, reward obtained and priority customers visited. A small variation in the method is presented in order to obtain comparable or similar magnitudes in the two values that we want to maximize. In addition, we approach the problem from two different perspectives: considering time as a constant value and another more realistic approach considering time with dynamic values. To verify the performance of the proposed algorithms, different examples are executed and the results are shown graphically. To simplify decision making when maximizing a function with two objectives, Pareto frontiers are used in this work.

关键词： Team Orienteering Problem multi-objective function Weighted Average Method Unmanned Aerial Vehicles

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