Review on Swarm Intelligence Optimization Techniques for Obstacle-Avoidance Localization in Wireless Sensor Networks

Wireless sensor network (WSN) is an evolving research topic with potential applications. In WSN, the nodes are spatially distributed and determining the path of transmission high challenging. Localization eases the path determining process between source and destination. The article, describes the localization techniques based on wireless sensor networks. Sensor network has been made viable by the convergence of Micro Electro- Mechanical Systems technology. The mobile anchor is used for optimizing the path planning location-aware mobile node. Two optimization algorithms have been used for reviewing the performacne. They are Grey Wolf Optimizer(GWO) and Whale Optimization Algorithm(WOA). The results show that WOA outperforms in maximizing the localization accuracy

Review on Swarm Intelligence Optimization Techniques for Obstacle-Avoidance Localization in Wireless Sensor Networks

Wireless sensor network (WSN) is an evolving research topic with potential applications. In WSN, the nodes are spatially distributed and determining the path of transmission high challenging. Localization eases the path determining process between source and destination. The article, describes the localization techniques based on wireless sensor networks. Sensor network has been made viable by the convergence of Micro Electro- Mechanical Systems technology. The mobile anchor is used for optimizing the path planning location-aware mobile node. Two optimization algorithms have been used for reviewing the performacne. They are Grey Wolf Optimizer(GWO) and Whale Optimization Algorithm(WOA). The results show that WOA outperforms in maximizing the localization accuracy

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

Design and Performance Analysis of Genetic Algorithms for Topology Control Problems

In this dissertation, we present a bio-inspired decentralized topology control mechanism, called force-based genetic algorithm (FGA), where a genetic algorithm (GA) is run by each autonomous mobile node to achieve a uniform spread of mobile nodes and to provide a fully connected network over an unknown area. We present a formal analysis of FGA in terms of convergence speed, uniformity at area coverage, and Lyapunov stability theorem. This dissertation emphasizes the use of mobile nodes to achieve a uniform distribution over an unknown terrain without a priori information and a central control unit. In contrast, each mobile node running our FGA has to make its own movement direction and speed decisions based on local neighborhood information, such as obstacles and the number of neighbors, without a centralized control unit or global knowledge. We have implemented simulation software in Java and developed four different testbeds to study the effectiveness of different GA-based topology control frameworks for network performance metrics including node density, speed, and the number of generations that GAs run. The stochastic behavior of FGA, like all GA-based approaches, makes it difficult to analyze its convergence speed. We built metrically transitive homogeneous and inhomogeneous Markov chain models to analyze the convergence of our FGA with respect to the communication ranges of mobile nodes and the total number of nodes in the system. The Dobrushin contraction coefficient of ergodicity is used for measuring convergence speed for homogeneous and inhomogeneous Markov chain models of our FGA. Furthermore, convergence characteristic analysis helps us to choose the nearoptimal values for communication range, the number of mobile nodes, and the mean node degree before sending autonomous mobile nodes to any mission. Our analytical and experimental results show that our FGA delivers promising results for uniform mobile node distribution over unknown terrains. Since our FGA adapts to local environment rapidly and does not require global network knowledge, it can be used as a real-time topology controller for commercial and military applications

An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots

The remoteness and hazards that are inherent to the operating environments of space infrastructures promote their need for automated robotic inspection. In particular, micrometeoroid and orbital debris impact and structural fatigue are common sources of damage to spacecraft hulls. Vibration sensing has been used to detect structural damage in spacecraft hulls as well as in structural health monitoring practices in industry by deploying static sensors. In this paper, we propose using a swarm of miniaturized vibration-sensing mobile robots realizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evolutionary algorithm niching techniques to deliver the task of enumeration and localization of an a priori unknown number of vibration sources on a simplified 2.5D spacecraft surface. Our algorithm is deployed on a swarm of simulated cm-scale wheeled robots. These are guided in their inspection task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three performance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the inspection task given a 75% inspection coverage threshold. We find that our swarm is able to successfully localize the present so

Multi-Agent Systems

This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019

Evolution of collective behaviors for a real swarm of aquatic surface robots

Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers.info:eu-repo/semantics/publishedVersio