Optimal Coverage in Wireless Sensor Network using Augmented Nature-Inspired Algorithm

           One of the difficult problems that must be carefully considered before any network configuration is getting the best possible network coverage. The amount of redundant information that is sensed is decreased due to optimal network coverage, which also reduces the restricted energy consumption of battery-powered sensors. WSN sensors can sense, receive, and send data concurrently. Along with the energy limitation, accurate sensors and non-redundant data are a crucial challenge for WSNs. To maximize the ideal coverage and reduce the waste of the constrained sensor battery lifespan, all these actions must be accomplished. Augmented Nature-inspired algorithm is showing promise as a solution to the crucial problems in “Wireless Sensor Networks” (WSNs), particularly those related to the reduced sensor lifetime. For “Wireless Sensor Networks” (WSNs) to provide the best coverage, we focus on algorithms that are inspired by Augmented Nature in this research. In wireless sensor networks, the cluster head is chosen using the Diversity-Driven Multi-Parent Evolutionary Algorithm. For Data encryption Improved Identity Based Encryption (IIBE) is used.  For centralized optimization and reducing coverage gaps in WSNs Time variant Particle Swarm Optimization (PSO) is used. The suggested model's metrics are examined and compared to various traditional algorithms. This model solves the reduced sensor lifetime and redundant information in Wireless Sensor Networks (WSNs) as well as will give real and effective optimum coverage to the Wireless Sensor Networks (WSNs)

An energy-aware and Q-learning-based area coverage for oil pipeline monitoring systems using sensors and Internet of Things

Pipelines are the safest tools for transporting oil and gas. However, the environmental effects and sabotage of hostile people cause corrosion and decay of pipelines, which bring financial and environmental damages. Today, new technologies such as the Internet of Things (IoT) and wireless sensor networks (WSNs) can provide solutions to monitor and timely detect corrosion of oil pipelines. Coverage is a fundamental challenge in pipeline monitoring systems to timely detect and resolve oil leakage and pipeline corrosion. To ensure appropriate coverage on pipeline monitoring systems, one solution is to design a scheduling mechanism for nodes to reduce energy consumption. In this paper, we propose a reinforcement learning-based area coverage technique called CoWSN to intelligently monitor oil and gas pipelines. In CoWSN, the sensing range of each sensor node is converted to a digital matrix to estimate the overlap of this node with other neighboring nodes. Then, a Q-learning-based scheduling mechanism is designed to determine the activity time of sensor nodes based on their overlapping, energy, and distance to the base station. Finally, CoWSN can predict the death time of sensor nodes and replace them at the right time. This work does not allow to be disrupted the data transmission process between sensor nodes and BS. CoWSN is simulated using NS2. Then, our scheme is compared with three area coverage schemes, including the scheme of Rahmani et al., CCM-RL, and CCA according to several parameters, including the average number of active sensor nodes, coverage rate, energy consumption, and network lifetime. The simulation results show that CoWSN has a better performance than other methods

Sensor Coverage Strategy in Underwater Wireless Sensor Networks

This paper mainly describes studies hydrophone placement strategy in a complex underwater environment model to compute a set of "good" locations where data sampling will be most effective. Throughout this paper it is assumed that a 3-D underwater topographic map of a workspace is given as input.Since the negative gradient direction is the fastest descent direction, we fit a complex underwater terrain to a differentiable function and find the minimum value of the function to determine the low-lying area of the underwater terrain.The hydrophone placement strategy relies on gradient direction algorithm that solves a problem of maximize underwater coverage: Find the maximize coverage set of hydrophone inside a 3-D workspace. After finding the maximize underwater coverage set, to better take into account the optimal solution to the problem of data sampling, the finite VC-dimension algorithm computes a set of hydrophone that satisfies hydroacoustic signal energy loss constraints. We use the principle of the maximize splitting subset of the coverage set and the ”dual” set of the coverage covering set, so as to find the hitting set, and finally find the suboptimal set (i.e., the sensor suboptimal coverage set).Compared with the random deployment algorithm, although the computed set of hydrophone is not guaranteed to have minimum size, the algorithm does compute with high network coverage quality

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area

A Novel Path Planning Optimization Algorithm for Semi-Autonomous UAV in Bird Repellent Systems Based in Particle Swarm Optimization

Bird damage to fruit crops causes significant monetary losses to farmers annually. The application of traditional bird repelling methods such as bird cannons and tree netting became inefficient in the long run, keeping high maintenance and reduced mobility. Due to their versatility, Unmanned Aerial Vehicles (UAVs) can be beneficial to solve this problem. However, due to their low battery capacity that equals low flight duration, it is necessary to evolve path planning optimization. A path planning optimization algorithm of UAVs based on Particle Swarm Optimization (PSO) is presented in this dissertation. This technique was used due to the need for an easy implementation optimization algorithm to start the initial tests. The PSO algorithm is simple and has few control parameters while maintaining a good performance. This path planning optimization algorithm aims to manage the drone's distance and flight time, applying optimization and randomness techniques to overcome the disadvantages of the traditional systems. The proposed algorithm's performance was tested in three study cases: two of them in simulation to test the variation of each parameter and one in the field to test the influence on battery management and height influence. All cases were tested in the three possible situations: same incidence rate, different rates, and different rates with no bird damage to fruit crops. The proposed algorithm presents promising results with an outstanding reduced average error in the total distance for the path planning obtained and low execution time. However, it is necessary to point out that the path planning optimization algorithm may have difficulty finding a suitable solution if there is a bad ratio between the total distance for path planning and points of interest. The field tests were also essential to understand the algorithm's behavior of the path planning algorithm in the UAV, showing that there is less energy discharged with fewer points of interest, but that do not correlates with the flight time. Also, there is no association between the maximum horizontal speed and the flight time, which means that the function to calculate the total distance for path planning needs to be adjusted.Anualmente, os danos causados pelas aves em pomares criam perdas monetárias significativas aos agricultores. A aplicação de métodos tradicionais de dispersão de aves, como canhões repelentes de aves e redes nas árvores, torna-se ineficiente a longo prazo, sendo ainda de alta manutenção e de mobilidade reduzida. Devido à sua versatilidade, os Veículos Aéreos Não Tripulados (VANT) podem ser benéficos para resolver este problema. No entanto, devido à baixa capacidade das suas baterias, que se traduz num baixo tempo de voo, é necessário otimizar o planeamento dos caminhos. Nesta dissertação, é apresentado um algoritmo de otimização para planeamento de caminhos para VANT baseado no Particle Swarm Optimization (PSO). Para se iniciarem os primeiros testes do algoritmo proposto, a técnica utilizada foi a supracitada devido à necessidade de um algoritmo de otimização fácil de implementar. O algoritmo PSO é simples e possuí poucos parâmetros de controlo, mantendo um bom desempenho. Este algoritmo de otimização de planeamento de caminhos propõe-se a gerir a distância e o tempo de voo do drone, aplicando técnicas de otimização e de aleatoriedade para superar a sua desvantagem relativamente aos sistemas tradicionais. O desempenho do algoritmo de planeamento de caminhos foi testado em três casos de estudo: dois deles em simulação para testar a variação de cada parâmetro e outro em campo para testar a capacidade da bateria. Todos os casos foram testados nas três situações possíveis: mesma taxa de incidência, taxas diferentes e taxas diferentes sem danos de aves. Os resultados apresentados pelo algoritmo proposto demonstram um erro médio muto reduzido na distância total para o planeamento de caminhos obtido e baixo tempo de execução. Porém, é necessário destacar que o algoritmo pode ter dificuldade em encontrar uma solução adequada se houver uma má relação entre a distância total para o planeamento de caminhos e os pontos de interesse. Os testes de campo também foram essenciais para entender o comportamento do algoritmo na prática, mostrando que há menos energia consumida com menos pontos de interesse, sendo que este parâmetro não se correlaciona com o tempo de voo. Além disso, não há associação entre a velocidade horizontal máxima e o tempo da missão, o que significa que a função de cálculo da distância total para o planeamento de caminhos requer ser ajustada

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

Energy Harvesting and Energy Storage Systems

This book discuss the recent developments in energy harvesting and energy storage systems. Sustainable development systems are based on three pillars: economic development, environmental stewardship, and social equity. One of the guiding principles for finding the balance between these pillars is to limit the use of non-renewable energy sources

Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications

This book is a Special Issue Reprint edited by Prof. Massimo Vitelli and Dr. Luigi Costanzo. It contains original research articles covering, but not limited to, the following topics: maximum power point tracking techniques; forecasting techniques; sizing and optimization of PV components and systems; PV modeling; reconfiguration algorithms; fault diagnosis; mismatching detection; decision processes for grid operators

Swarm Robotics

Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties