A METHOD FOR FORECASTING WEATHER CONDITION BY USING ARTIFICIAL NEURAL NETWORK ALGORITHM

This article presents a method to forecast and make decision on weather condition. In most of the cities around the world, people try to decide on leisure activities on their spare time but weather condition would not be suitable for them. By this fact, we suggest a solution to solve this problem with ANN. Therefore, users of our proposed method can organize their daily life in accordance with weather condition. Artificial Neural Network (ANN) is one of the popular research subjects in computer science, thus, this paper aims to familiarize the reader with ANN. In our proposed method, at first, people can organize weather condition, and then the program suggest whether the time is suitable for them or not on chosen hour of day. In ANN, we discuss about neuron that have relation with performance. Mean Square Error (MSE) is the key issue for the performance of our method. At the end, the simulation results show that relation between Neuron and MSE is applicable for daily usage

Metaheuristic Algorithms in IoT: Optimized Edge Node Localization

In this study, a new hybrid method is proposed by using the advantages of Grey Wolf Optimizer (GWO) and Moth-Flame Optimization (MFO) algorithms. The proposed hybrid metaheuristic algorithm tries to find the near-optimal solution with high efficiency by using the advantage of both algorithms. At the same time, the shortcomings of each will be eliminated. The proposed algorithm is used to solve the edge computing node localization problem, which is one of the important problems on the Internet of Things (IoT) systems, with the least error rate. This algorithm has shown a successful performance in solving this problem with a smooth and efficient position update mechanism. It was also applied to 30 famous benchmark functions (CEC2015 and CEC2019) to prove the accuracy and general use of the proposed method. It has been proven from the results that it is the best algorithm with a success rate of 54% and 57%, respectively

MAP-ACO: An efficient protocol for multi-agent pathfinding in real-time WSN and decentralized IoT systems

Efficient energy consumption is one of the main problems in wireless sensor networks routing protocols. Since the sensor nodes have limited battery level and memory space, it is important to manage these resources efficiently. Although there are studies in this subject in recent years, it is lacking in concurrent and real-time environments with multi-agents. The importance of this issue is increasing more especially for decentralized IoT systems. This paper presents a novel routing protocol based on ant colony optimization for multi-agents that manages network resources adequately in real-time conditions. The proposed method is used, both to find the next destination of ants, and to manage pheromone update and evaporation rate operators. This method takes into account some key parameters such as remaining energy, buffer size, traffic rate, and distance when selecting the next destination under different conditions. The proposed method finds the optimal paths with low energy consumption thereby prolonging the network lifetime in concurrent and parallel conditions. The simulation results of the proposed method have given good results, in terms of network lifetime and energy consumption, when compared with other ant colony optimization (ACO)-based routing protocols

I-GWO and Ex-GWO: improved algorithms of the Grey Wolf Optimizer to solve global optimization problems

In this paper, two novel meta-heuristic algorithms are introduced to solve global optimization problems inspired by the Grey Wolf Optimizer (GWO) algorithm. In the GWO algorithm, wolves are likely to be located in regions close to each other. Therefore, as they catch the hunt (approaching the solution), they may create an intensity in the same or certain regions. In this case, the mechanism to prevent the escape of the hunt may not work well. First, the proposed algorithm is the expanded model of the GWO algorithm that is called expanded Grey Wolf Optimizer. In this method, the same as GWO, alpha, beta, and delta play the role of the main three wolves. However, the next wolves select and update their positions according to the previous and the first three wolves in each iteration. Another proposed algorithm is based on the incremental model and is, therefore, called incremental Grey Wolf Optimizer. In this method, each wolf updates its own position based on all the wolves selected before it. There is the possibility of finding solutions (hunts) quicker than according to other algorithms in the same category. However, they may not always guarantee to find a good solution because of their act dependent on each other. Both algorithms focus on exploration and exploitation. In this paper, the proposed algorithms are simulated over 33 benchmark functions and the related results are compared with well-known optimization algorithms. The results of the proposed algorithms seem to be good solutions for various problems. © 2019, Springer-Verlag London Ltd., part of Springer Nature

HEEL: A new clustering method to improve wireless sensor network lifetime

In wireless sensor networks, some resources such as memory and energy are limited. In recent years, there has been an increasing interest in improving network lifetime. Node energy plays an important role in the network lifetime. Along with this remarkable growth in wireless sensor networks, however, there is an increasing concern over network lifetime. The principal purpose of this study is to develop an understanding of the effects of other parameters on selecting a cluster head. The methodological approach taken in this study is a mixed methodology typically based on the node's energy. The authors have operated four parameters to select the cluster head: Node energy, the energy of the node's neighbours, number of hops and number of links to neighbours. Each of these parameters has an impact in selecting the cluster head. They accurately observed hop size, energy of each sensor node, average energy of sensor neighbours, links to sensor nodes (HEEL) has better improvements in comparison of Node ranked Low Energy Adaptive Clustering Hierarchy (Nr-LEACH), Modified Low Energy Adaptive Clustering Hierarchy (ModLEACH), Low Energy Adaptive Clustering Hierarchy-B (LEACH-B), Low Energy Adaptive Clustering Hierarchy (LEACH), Power-Efficient Gathering in Sensor Information System (PEGASIS), energy-aware clustering scheme with transmission power control for sensor networks (EACLE) and hybrid energy efficient distributed clustering (HEED) algorithms in possible case of network lifetime and throughput

Designing a dynamic protocol for real-time Industrial Internet of Things-based applications by efficient management of system resources

Wireless sensor networks have gained the attention of researchers from various fields due to increased applicability. This has thus led to rapid development in the field. However, these networks still suffer from various challenges and limitations. These range from computation and processing power and available energy to mention but a few. These problems are even much more pronounced in some areas of the field such as real-time Internet of Things-based applications. In this study, a dynamic protocol that efficiently utilizes the available resources is proposed. The protocol employs five developed algorithms that aid the data transmission, neighbor, and optimal path finding processes. The protocol can be utilized in, but not limited to, real-time large-data streaming applications. The protocol is implemented on sensor nodes that are custom made by our research team. In this article, a structure that enables the sensor devices to communicate with each other over their local network or Internet as required in order to preserve the available resources is defined. Both theoretical and experimental result analyses of the entire protocol in general and individual algorithms are also performed. © The Author(s) 2019

Optimal characterization of a microwave transistor using grey wolf algorithms

Modern time microwave stages require low power consumption, low size, low-noise amplifier (LNA) designs with high-performance measures. These demands need a single transistor LNA design, which is a challenging multi-objective, multi-dimensional optimization problem that requires solving objectives with non-linear feasible design target space, that can only be achieved by optimally selecting the source (Z(S)) and load (Z(L)) terminations. Meta-heuristic algorithms (MHAs) have been extensively used as a search and optimization method in many problems in the field of science, commerce, and engineering. Since feasible design target space (FDTS) of an LNA transistor (NE3511S02 biased at VDS = 2 V and IDS = 7 mA) is a multi-objective multi-variable optimization problem the MHA can be considered as a suitable choice. Three different types of grey wolf variants inspired algorithms had been applied to the LNA FDTS problem to obtain the optimal source and load terminations that satisfies the required performance measures of the aimed LNA design. Furthermore, the obtained results are justified via the use of the Electromagnetic Simulator tool AWR. As a result, an efficient optimization method for optimal determination of Z(S) and Z(L) terminations of a high-performance LNA design had been achieved

Maximizing Coverage and Maintaining Connectivity in WSN and Decentralized Iot: An Efficient Metaheuristic-Based Method for Environment-Aware Node Deployment

The node deployment problem is a non-deterministic polynomial time (NP-hard). This study proposes a new and efficient method to solve this problem without the need for predefined circumstances about the environments independent of terrain. The proposed method is based on a metaheuristic algorithm and mimics the grey wolf optimizer (GWO) algorithm. In this study, we also suggested an enhanced version of the GWO algorithm to work adaptively in such problems and named it Mutant-GWO (MuGWO). Also, the suggested model ensures connectivity by generating topology graphs and potentially supports data transmission mechanisms. Therefore, the proposed method based on MuGWO can enhance resources utilization, such as reducing the number of nodes, by maximizing the coverage rate and maintaining the connectivity. While most studies assume classical rectangle uniform environments, this study also focuses on custom (environmentaware) maps in line with the importance and requirements of the real world. The motivation of supporting custom maps by this study is that environments can consist of custom shapes with prioritized and critical areas. In this way, environment awareness halts the deployment of nodes in undesired regions and averts resource waste. Besides, novel multi-purpose fitness functions of the proposed method satisfy a convenient approach to calculate costs instead of using complicated processes. Accordingly, this method is suitable for large-scale networks thanks to the capability of the distributed architecture and the metaheuristic-based approach. This study justifies the improvements in the suggested model by presenting comparisons with a Deterministic Grid-based approach and the Original GWO. Moreover, this method outperforms the fruit fly optimization algorithm, bat algorithm (BA), Optimized BA, harmony search, and improved dynamic deployment technique based on genetic algorithm methods in declared scenarios in literature, considering the results of simulations

Optimal data transmission and pathfinding for WSN and decentralized IoT systems using I-GWO and Ex-GWO algorithms

Efficient resource use is a very important issue in wireless sensor networks and decentralized IoT-based systems. In this context, a smooth pathfinding mechanism can achieve this goal. However, since this problem is a Non-deterministic Polynomial-time (NP-hard) problem type, metaheuristic algorithms can be used. This article proposes two new energy-efficient routing methods based on Incremental Grey Wolf Optimization (I-GWO) and Expanded Grey Wolf Optimization (Ex-GWO) algorithms to find optimal paths. Moreover, in this study, a general architecture has been proposed, making it possible for many different metaheuristic algorithms to work in an adaptive manner as well as these algorithms. In the proposed methods, a new fitness function is defined to determine the next hop based on some parameters such as residual energy, traffic, distance, buffer size and hop size. These parameters are important measurements in subsequent node selections. The main purpose of these methods is to minimize traffic, improve fault tolerance in related systems, and increase reliability and lifetime. The two metaheuristic algorithms mentioned above are used to find the best values ​​for these parameters. The suggested methods find the best path of any length for the path between any source and destination node. In this study, no ready dataset was used, and the established network and system were run in the simulation environment. As a result, the optimal path has been discovered in terms of the minimum cost of the best paths obtained by the proposed methods. These methods can be very useful in decentralized peer-to-peer and distributed systems. The metrics for performance evaluation and comparisons are i) network lifetime, ii) the alive node ratio in the network, iii) the packet delivery ratio and lost data packets, iv) routing overhead, v) throughput, and vi) convergence behavior. According to the results, the proposed methods generally choose the most suitable and efficient ways with minimum cost. These methods are compared with Genetic Algorithm Based Routing (GAR), Artificial Bee Colony Based routing (ABCbased), Multi-Agent Protocol based on Ant Colony Optimization (MAP-ACO), and Wireless Sensor Networks based on Grey Wolf optimizer. (GWO-WSN) algorithms. The simulation results show that the proposed methods outperform the others

Hybrid Algorithms Based On Combining Reinforcement Learning And Metaheuristic Methods To Solve Global Optimization Problems

This paper introduces three hybrid algorithms that help in solving global optimization problems using reinforcement learning along with metaheuristic methods. Using the algorithms presented, the search agents try to find a global optimum avoiding the local optima trap. Compared to the classical metaheuristic approaches, the proposed algorithms display higher success in finding new areas as well as exhibiting a more balanced performance while in the exploration and exploitation phases. The algorithms employ reinforcement agents to select an environment based on predefined actions and tasks. A reward and penalty system is used by the agents to discover the environment, done dynamically without following a predetermined model or method. The study makes use of Q-Learning method in all three metaheuristic algorithms, so-called RLI-GWO, RLEx-GWO, and RLWOA algorithms, so as to check and control exploration and exploitation with Q-Table. The Q-Table values guide the search agents of the metaheuristic algorithms to select between the exploration and exploitation phases. A control mechanism is used to get the reward and penalty values for each action. The algorithms presented in this paper are simulated over 30 benchmark functions from CEC 2014, 2015 and the results obtained are compared with well-known metaheuristic and hybrid algorithms (GWO, RLGWO, I-GWO, Ex-GWO, and WOA). The proposed methods have also been applied to the inverse kinematics of the robot arms problem. The results of the used algorithms demonstrate that RLWOA provides better solutions for relevant problems. (C) 2021 Elsevier B.V. All rights reserved