Fully Connected Neural Networks Ensemble with Signal Strength Clustering for Indoor Localization in Wireless Sensor Networks

The paper introduces a method which improves localization accuracy of the signal strength fingerprinting approach. According to the proposed method, entire localization area is divided into regions by clustering the fingerprint database. For each region a prototype of the received signal strength is determined and a dedicated artificial neural network (ANN) is trained by using only those fingerprints that belong to this region (cluster). Final estimation of the location is obtained by fusion of the coordinates delivered by selected ANNs. Sensor nodes have to store only the signal strength prototypes and synaptic weights of the ANNs in order to estimate their locations. This approach significantly reduces the amount of memory required to store a received signal strength map. Various ANN topologies were considered in this study. Improvement of the localization accuracy as well as speed-up of learning process was achieved by employing fully connected neural networks. The proposed method was verified and compared against state-of-the-art localization approaches in realworld indoor environment by using both stationary andmobile sensor nodes

Intelligent evacuation management systems: A review

Crowd and evacuation management have been active areas of research and study in the recent past. Various developments continue to take place in the process of efficient evacuation of crowds in mass gatherings. This article is intended to provide a review of intelligent evacuation management systems covering the aspects of crowd monitoring, crowd disaster prediction, evacuation modelling, and evacuation path guidelines. Soft computing approaches play a vital role in the design and deployment of intelligent evacuation applications pertaining to crowd control management. While the review deals with video and nonvideo based aspects of crowd monitoring and crowd disaster prediction, evacuation techniques are reviewed via the theme of soft computing, along with a brief review on the evacuation navigation path. We believe that this review will assist researchers in developing reliable automated evacuation systems that will help in ensuring the safety of the evacuees especially during emergency evacuation scenarios

Particle Swarm Optimization for Interference Mitigation of Wireless Body Area Network: A Systematic Review

Wireless body area networks (WBAN) has now become an important technology in supporting services in the health sector and several other fields. Various surveys and research have been carried out massively on the use of swarm intelligent (SI) algorithms in various fields in the last ten years, but the use of SI in wireless body area networks (WBAN) in the last five years has not seen any significant progress. The aim of this research is to clarify and convince as well as to propose a answer to this problem, we have identified opportunities and topic trends using the particle swarm optimization (PSO) procedure as one of the swarm intelligence for optimizing wireless body area network interference mitigation performance. In this research, we analyzes primary studies collected using predefined exploration strings on online databases with the help of Publish or Perish and by the preferred reporting items for systematic reviews and meta-analysis (PRISMA) way. Articles were carefully selected for further analysis. It was found that very few researchers included optimization methods for swarm intelligence, especially PSO, in mitigating wireless body area network interference, whether for intra, inter, or cross-WBAN interference. This paper contributes to identifying the gap in using PSO for WBAN interference and also offers opportunities for using PSO both standalone and hybrid with other methods to further research on mitigating WBAN interference

Analysis on swarm robot coordination using fuzzy logic

In this paper, coordination among individual of swarm robot in communicating to maintain the safe distance between robots is analyzed. Each robot coordinates their movements to avoid obstacles and moving simultaneously. Evaluation of swarm robot performance is analyzed in this paper, namely: the coordination among robots to share information in safe distance determination. In controlling the coordination of motion, each robot has a sensor that provides several inputs about its surrounding environment. Fuzzy logic control in this paper allows uncertain input, and produces unlimited commands to control motion direction with speed settings according to environmental conditions. In this experiment, it is obtained that the size of the environment affects the coordination of robots

Analysis on swarm robot coordination using fuzzy logic

In this paper, coordination among individual of swarm robot in communicating to maintain the safe distance between robots is analyzed. Each robot coordinates their movements to avoid obstacles and moving simultaneously. Evaluation of swarm robot performance is analyzed in this paper, namely: the coordination among robots to share information in safe distance determination. In controlling the coordination of motion, each robot has a sensor that provides several inputs about its surrounding environment. Fuzzy logic control in this paper allows uncertain input, and produces unlimited commands to control motion direction with speed settings according to environmental conditions. In this experiment, it is obtained that the size of the environment affects the coordination of robots

Accelerating ant colony optimization by using local search

This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2015.Cataloged from PDF version of thesis report.Includes bibliographical references (page 42-45).Optimization is very important fact in terms of taking decision in mathematics, statistics, computer science and real life problem solving or decision making application. Many different optimization techniques have been developed for solving such functional problem. In order to solving various problem computer Science introduce evolutionary optimization algorithm and their hybrid. In recent years, test functions are using to validate new optimization algorithms and to compare the performance with other existing algorithm. There are many Single Object Optimization algorithm proposed earlier. For example: ACO, PSO, ABC. ACO is a popular optimization technique for solving hard combination mathematical optimization problem. In this paper, we run ACO upon five benchmark function and modified the parameter of ACO in order to perform SBX crossover and polynomial mutation. The proposed algorithm SBXACO is tested upon some benchmark function under both static and dynamic to evaluate performances. We choose wide range of benchmark function and compare results with existing DE and its hybrid DEahcSPX from other literature are also presented here.Nabila TabassumMaruful HaqueB. Computer Science and Engineerin

Models for Efficient Automated Site Data Acquisition

Accurate and timely data acquisition for tracking and progress reporting is essential for efficient management and successful project delivery. Considerable research work has been conducted to develop methods utilizing automated site data acquisition for tracking and progress reporting. However, these developments are challenged by: the dynamic and noisy nature of construction jobsites; the indoor localization accuracy; and the data processing and extraction of actionable information. Limited research work attempted to study and develop customized design of wireless sensor networks to meet the above challenges and overcome limitations of utilizing off-the-shelf technologies. The objective of this research is to study, design, configure and develop fully customized automated site data acquisition models, with a special focus on near real-time automated tracking and control of construction operations embracing cutting edge innovations in wireless and remote sensing technologies. In this context, wireless and remote sensing technologies are integrated in two customized prototypes to monitor and collect data from construction jobsites. This data is then processed and mined to generate meaningful and actionable information. The developed prototypes are expected to have wider scope of applications in construction management, such as improving construction safety, monitoring the condition of civil infrastructure and reducing energy consumption in buildings. Two families of prototypes were developed in this research; Sensor Aided GPS (SA-GPS) prototype, which is designed and developed for tracking outdoor construction operations such as earthmoving; and Self-Calibrated Wireless Sensor Network (SC-WSN), which is designed for indoor localization and tracking of construction resources (labor, materials and equipment). These prototypes along with their hardware and software are encapsulated in a computational framework. The framework houses a set of algorithms coded in C# to enable efficient data processing and fusion that support tracking and progress reporting. Both the hardware prototypes and software algorithms were progressively tested, evaluated and re-designed using Rapid Prototyping approach. The validation process of the developed prototypes encompasses three steps; (1) simulation to validate the prototypes’ design virtually using MATLAB, (2) laboratory experiments to evaluate prototypes’ functionality in real time, and (3) testing on scaled case studies after fine-tuning the prototype design based on the results obtained from the first two steps. The SA-GPS prototype consists of a microcontroller equipped with GPS module as well as a number of sensors such as accelerometer, barometric pressure sensor, Bluetooth proximity and strain gauges. The results of testing the developed SA-GPS prototype on scaled construction jobsite indicated that it was capable of estimating project progress within 3% mean absolute percentage error and 1% standard deviation on 16 trials, in comparison to the standalone GPS which had approximately 12% mean absolute percentage error and 2% standard deviation. The SC-WSN prototype incorporates two main features. The first is the use of the Kalman filtering and smoothing for the RSSI signal to provide more stable and predictable signal for estimating the distance between a reader and a tag. The second is the use of a developed dynamic path-loss model which continually optimizes its parameters to cope with the dynamically changing construction environment using Particle Swarm Optimization (PSO) algorithm. The laboratory testing indicated the improvement in location estimation, where the produced location estimates using SC_WSN had an average error of 0.66m in comparison to 1.67m using the raw RSSI signal. Also the results indicated 60% accuracy improvement in estimating locations using the developed dynamic model. The developed prototypes are not only expected to reduce the risk of project cost and duration overruns by timely and early detection of deviations from project plan, but also enables project managers to observe and oversee their project’s status in near real-time. It is expected that the accuracy of the developed hardware, can be achieved on large-scale real construction projects. This is attributed to the fact that the developed prototype does not require any scalable improvements on its hardware technology, nor does it require any additional computational changes to its developed algorithms and software

Evolutionary Robot Swarms Under Real-World Constraints

Tese de doutoramento em Engenharia Electrotécnica e de Computadores, na especialidade de Automação e Robótica, apresentada ao Departamento de Engenharia Electrotécnica e de Computadores da Faculdade de Ciências e Tecnologia da Universidade de CoimbraNas últimas décadas, vários cientistas e engenheiros têm vindo a estudar as estratégias provenientes da natureza. Dentro das arquiteturas biológicas, as sociedades que vivem em enxames revelam que agentes simplistas, tais como formigas ou pássaros, são capazes de realizar tarefas complexas usufruindo de mecanismos de cooperação. Estes sistemas abrangem todas as condições necessárias para a sobrevivência, incorporando comportamentos de cooperação, competição e adaptação. Na “batalha” sem fim em prol do progresso dos mecanismos artificiais desenvolvidos pelo homem, a ciência conseguiu simular o primeiro comportamento em enxame no final dos anos oitenta. Desde então, muitas outras áreas, entre as quais a robótica, beneficiaram de mecanismos de tolerância a falhas inerentes da inteligência coletiva de enxames. A área de investigação deste estudo incide na robótica de enxame, consistindo num domínio particular dos sistemas robóticos cooperativos que incorpora os mecanismos de inteligência coletiva de enxames na robótica. Mais especificamente, propõe-se uma solução completa de robótica de enxames a ser aplicada em contexto real. Nesta ótica, as operações de busca e salvamento foram consideradas como o caso de estudo principal devido ao nível de complexidade associado às mesmas. Tais operações ocorrem tipicamente em cenários dinâmicos de elevadas dimensões, com condições adversas que colocam em causa a aplicabilidade dos sistemas robóticos cooperativos. Este estudo centra-se nestes problemas, procurando novos desafios que não podem ser ultrapassados através da simples adaptação da literatura da especialidade em algoritmos de enxame, planeamento, controlo e técnicas de tomada de decisão. As contribuições deste trabalho sustentam-se em torno da extensão do método Particle Swarm Optimization (PSO) aplicado a sistemas robóticos cooperativos, denominado de Robotic Darwinian Particle Swarm Optimization (RDPSO). O RDPSO consiste numa arquitetura robótica de enxame distribuída que beneficia do particionamento dinâmico da população de robôs utilizando mecanismos evolucionários de exclusão social baseados na sobrevivência do mais forte de Darwin. No entanto, apesar de estar assente no caso de estudo do RDPSO, a aplicabilidade dos conceitos aqui propostos não se encontra restrita ao mesmo, visto que todos os algoritmos parametrizáveis de enxame de robôs podem beneficiar de uma abordagem idêntica. Os fundamentos em torno do RDPSO são introduzidos, focando-se na dinâmica dos robôs, nos constrangimentos introduzidos pelos obstáculos e pela comunicação, e nas suas propriedades evolucionárias. Considerando a colocação inicial dos robôs no ambiente como algo fundamental para aplicar sistemas de enxames em aplicações reais, é assim introduzida uma estratégia de colocação de robôs realista. Para tal, a população de robôs é dividida de forma hierárquica, em que são utilizadas plataformas mais robustas para colocar as plataformas de enxame no cenário de forma autónoma. Após a colocação dos robôs no cenário, é apresentada uma estratégia para permitir a criação e manutenção de uma rede de comunicação móvel ad hoc com tolerância a falhas. Esta estratégia não considera somente a distância entre robôs, mas também a qualidade do nível de sinal rádio frequência, redefinindo assim a sua aplicabilidade em cenários reais. Os aspetos anteriormente mencionados estão sujeitos a uma análise detalhada do sistema de comunicação inerente ao algoritmo, para atingir uma implementação mais escalável do RDPSO a cenários de elevada complexidade. Esta elevada complexidade inerente à dinâmica dos cenários motivaram a ultimar o desenvolvimento do RDPSO, integrando para o efeito um mecanismo adaptativo baseado em informação contextual (e.g., nível de atividade do grupo). Face a estas considerações, o presente estudo pode contribuir para expandir o estado-da-arte em robótica de enxame com algoritmos inovadores aplicados em contexto real. Neste sentido, todos os métodos propostos foram extensivamente validados e comparados com alternativas, tanto em simulação como com robôs reais. Para além disso, e dadas as limitações destes (e.g., número limitado de robôs, cenários de dimensões limitadas, constrangimentos reais limitados), este trabalho contribui ainda para um maior aprofundamento do estado-da-arte, onde se propõe um modelo macroscópico capaz de capturar a dinâmica inerente ao RDPSO e, até certo ponto, estimar analiticamente o desempenho coletivo dos robôs perante determinada tarefa. Em suma, esta investigação pode ter aplicabilidade prática ao colmatar a lacuna que se faz sentir no âmbito das estratégias de enxames de robôs em contexto real e, em particular, em cenários de busca e salvamento.Over the past decades, many scientists and engineers have been studying nature’s best and time-tested patterns and strategies. Within the existing biological architectures, swarm societies revealed that relatively unsophisticated agents with limited capabilities, such as ants or birds, were able to cooperatively accomplish complex tasks necessary for their survival. Those simplistic systems embrace all the conditions necessary to survive, thus embodying cooperative, competitive and adaptive behaviours. In the never-ending battle to advance artificial manmade mechanisms, computer scientists simulated the first swarm behaviour designed to mimic the flocking behaviour of birds in the late eighties. Ever since, many other fields, such as robotics, have benefited from the fault-tolerant mechanism inherent to swarm intelligence. The area of research presented in this Ph.D. Thesis focuses on swarm robotics, which is a particular domain of multi-robot systems (MRS) that embodies the mechanisms of swarm intelligence into robotics. More specifically, this Thesis proposes a complete swarm robotic solution that can be applied to real-world missions. Although the proposed methods do not depend on any particular application, search and rescue (SaR) operations were considered as the main case study due to their inherent level of complexity. Such operations often occur in highly dynamic and large scenarios, with harsh and faulty conditions, that pose several problems to MRS applicability. This Thesis focuses on these problems raising new challenges that cannot be handled appropriately by simple adaptation of state-of-the-art swarm algorithms, planning, control and decision-making techniques. The contributions of this Thesis revolve around an extension of the Particle Swarm Optimization (PSO) to MRS, denoted as Robotic Darwinian Particle Swarm Optimization (RDPSO). The RDPSO is a distributed swarm robotic architecture that benefits from the dynamical partitioning of the whole swarm of robots by means of an evolutionary social exclusion mechanism based on Darwin’s survival-of-the-fittest. Nevertheless, although currently applied solely to the RDPSO case study, the applicability of all concepts herein proposed is not restricted to it, since all parameterized swarm robotic algorithms may benefit from a similar approach The RDPSO is then proposed and used to devise the applicability of novel approaches. The fundamentals around the RDPSO are introduced by focusing on robots’ dynamics, obstacle avoidance, communication constraints and its evolutionary properties. Afterwards, taking the initial deployment of robots within the environment as a basis for applying swarm robotics systems into real-world applications, the development of a realistic deployment strategy is proposed. For that end, the population of robots is hierarchically divided, wherein larger support platforms autonomously deploy smaller exploring platforms in the scenario, while considering communication constraints and obstacles. After the deployment, a way of ensuring a fault-tolerant multi-hop mobile ad hoc communication network (MANET) is introduced to explicitly exchange information needed in a collaborative realworld task execution. Such strategy not only considers the maximum communication range between robots, but also the minimum signal quality, thus refining the applicability to real-world context. This is naturally followed by a deep analysis of the RDPSO communication system, describing the dynamics of the communication data packet structure shared between teammates. Such procedure is a first step to achieving a more scalable implementation by optimizing the communication procedure between robots. The highly dynamic characteristics of real-world applications motivated us to ultimate the RDPSO development with an adaptive strategy based on a set of context-based evaluation metrics. This thesis contributes to the state-of-the-art in swarm robotics with novel algorithms for realworld applications. All of the proposed approaches have been extensively validated in benchmarking tasks, in simulation, and with real robots. On top of that, and due to the limitations inherent to those (e.g., number of robots, scenario dimensions, real-world constraints), this Thesis further contributes to the state-of-the-art by proposing a macroscopic model able to capture the RDPSO dynamics and, to some extent, analytically estimate the collective performance of robots under a certain task. It is the author’s expectation that this Ph.D. Thesis may shed some light into bridging the reality gap inherent to the applicability of swarm strategies to real-world scenarios, and in particular to SaR operations.FCT - SFRH/BD /73382/201