A GA-based simulation system for WMNs: comparison analysis for different number of flows, client distributions, DCF and EDCA functions

In this paper, we compare the performance of Distributed Coordination Function (DCF) and Enhanced Distributed Channel Access (EDCA) for normal and uniform distributions of mesh clients considering two Wireless Mesh Network (WMN) architectures. As evaluation metrics, we consider throughput, delay, jitter and fairness index metrics. For simulations, we used WMN-GA simulation system, ns-3 and Optimized Link State Routing. The simulation results show that for normal distribution, the throughput of I/B WMN is higher than Hybrid WMN architecture. For uniform distribution, in case of I/B WMN, the throughput of EDCA is a little bit higher than Hybrid WMN. However, for Hybrid WMN, the throughput of DCF is higher than EDCA. For normal distribution, the delay and jitter of Hybrid WMN are lower compared with I/B WMN. For uniform distribution, the delay and jitter of both architectures are almost the same. However, in the case of DCF for 20 flows, the delay and jitter of I/B WMN are lower compared with Hybrid WMN. For I/B architecture, in case of normal distribution the fairness index of DCF is higher than EDCA. However, for Hybrid WMN, the fairness index of EDCA is higher than DCF. For uniform distribution, the fairness index of few flows is higher than others for both WMN architectures.Peer ReviewedPostprint (author's final draft

Assessing the Quality of Mobile Graphical User Interfaces Using Multi-Objective Optimization

Aesthetic defects are a violation of quality attributes that are symptoms of bad interface design programming decisions. They lead to deteriorating the perceived usability of mobile user interfaces and negatively impact the Users eXperience (UX) with the mobile app. Most existing studies relied on a subjective evaluation of aesthetic defects depending on end-users feedback, which makes the manual evaluation of mobile user interfaces human-centric, time-consuming, and error-prone. Therefore, recent studies have dedicated their effort to focus on the definition of mathematical formulas that each targets a specific structural quality of the interface. As the UX is tightly dependent on the user profile, the combi-nation and calibration of quality attributes, formulas, and users characteristics, when defining a defect, is not straightforward. In this context, we propose a fully automated framework which combines literature quality attributes with the users profile to identify aesthetic defects of MUI. More precisely, we consider the mobile user interface evaluation as a multi-objective optimization problem where the goal is to maximize the number of detected violations while minimizing the detection complexity of detection rules and enhancing the interfaces overall quality in means

Dynamic electronic asset allocation comparing genetic algorithm with particle swarm optimization

Indiana University-Purdue University Indianapolis (IUPUI)The contribution of this research work can be divided into two main tasks: 1) implementing this Electronic Warfare Asset Allocation Problem (EWAAP) with the Genetic Algorithm (GA); 2) Comparing performance of Genetic Algorithm to Particle Swarm Optimization (PSO) algorithm. This research problem implemented Genetic Algorithm in C++ and used QT Data Visualization for displaying three-dimensional space, pheromone, and Terrain. The Genetic algorithm implementation maintained and preserved the coding style, data structure, and visualization from the PSO implementation. Although the Genetic Algorithm has higher fitness values and better global solutions for 3 or more receivers, it increases the running time. The Genetic Algorithm is around (15-30\%) more accurate for asset counts from 3 to 6 but requires (26-82\%) more computational time. When the allocation problem complexity increases by adding 3D space, pheromones and complex terrains, the accuracy of GA is 3.71\% better but the speed of GA is 121\% slower than PSO. In summary, the Genetic Algorithm gives a better global solution in some cases but the computational time is higher for the Genetic Algorithm with than Particle Swarm Optimization

Swarming Reconnaissance Using Unmanned Aerial Vehicles in a Parallel Discrete Event Simulation

Current military affairs indicate that future military warfare requires safer, more accurate, and more fault-tolerant weapons systems. Unmanned Aerial Vehicles (UAV) are one answer to this military requirement. Technology in the UAV arena is moving toward smaller and more capable systems and is becoming available at a fraction of the cost. Exploiting the advances in these miniaturized flying vehicles is the aim of this research. How are the UAVs employed for the future military? The concept of operations for a micro-UAV system is adopted from nature from the appearance of flocking birds, movement of a school of fish, and swarming bees among others. All of these natural phenomena have a common thread: a global action resulting from many small individual actions. This emergent behavior is the aggregate result of many simple interactions occurring within the flock, school, or swarm. In a similar manner, a more robust weapon system uses emergent behavior resulting in no weakest link because the system itself is made up of simple interactions by hundreds or thousands of homogeneous UAVs. The global system in this research is referred to as a swarm. Losing one or a few individual unmanned vehicles would not dramatically impact the swarms ability to complete the mission or cause harm to any human operator. Swarming reconnaissance is the emergent behavior of swarms to perform a reconnaissance operation. An in-depth look at the design of a reconnaissance swarming mission is studied. A taxonomy of passive reconnaissance applications is developed to address feasibility. Evaluation of algorithms for swarm movement, communication, sensor input/analysis, targeting, and network topology result in priorities of each model\u27s desired features. After a thorough selection process of available implementations, a subset of those models are integrated and built upon resulting in a simulation that explores the innovations of swarming UAVs

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

A Review on Swarm Intelligence Based Routing Approaches

The principles of bio-inspired or swarm intelligence algorithms can be effectively used to achieve optimal solutions in routing for complex and dynamic wireless sensor networks or body area networks. As the name indicates, it is a field that is inspired by natural living beings like ants, bees, fishes, etc. Studies have proved that the routing protocols based on such bio-inspired methods perform better in terms of energy efficiency, reliability, adaptivity, scalability, and robustness. The general classification of routing protocols is classical-based and swarm-based routing protocols, where both the protocols were specifically categorized as data-centric, location-aware, hierarchical and network flow, and QoS aware protocols. In this paper, an evocative taxonomy and comparison of various swarm-based routing algorithms are presented. A brief discussion about the sensor network design and the major factors that influence the routing is also discussed. The comparative analysis of the selected swarm-based protocols is also done with respect to routing characteristics like query based, route selection, energy efficiency, and path selection. From the review, it is observed that the selection of a routing protocol is application dependent. This paper will be helpful to the researchers as a reference on bio-inspired algorithms for new protocol designs and also for the proper selection of routing protocols according to the type of applications

Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement

Volume measurement plays an important role in the production and processing of food products. Various methods have been proposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction comes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction have a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs volume measurements using random points. Monte Carlo method only requires information regarding whether random points fall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a computer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with heuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images. Monte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from binary images. The experimental results show that the proposed method provided high accuracy and precision compared to the water displacement method. In addition, the proposed method is more accurate and faster than the space carving method

Multidisciplinary Design Optimization for Space Applications

Multidisciplinary Design Optimization (MDO) has been increasingly studied in aerospace engineering with the main purpose of reducing monetary and schedule costs. The traditional design approach of optimizing each discipline separately and manually iterating to achieve good solutions is substituted by exploiting the interactions between the disciplines and concurrently optimizing every subsystem. The target of the research was the development of a flexible software suite capable of concurrently optimizing the design of a rocket propellant launch vehicle for multiple objectives. The possibility of combining the advantages of global and local searches have been exploited in both the MDO architecture and in the selected and self developed optimization methodologies. Those have been compared according to computational efficiency and performance criteria. Results have been critically analyzed to identify the most suitable optimization approach for the targeted MDO problem