On-demand fuzzy clustering and ant-colony optimisation based mobile data collection in wireless sensor network

In a wireless sensor network (WSN), sensor nodes collect data from the environment and transfer this data to an end user through multi-hop communication. This results in high energy dissipation of the devices. Thus, balancing of energy consumption is a major concern in such kind of network. Appropriate cluster head (CH) selection may provide to be an efficient way to reduce the energy dissipation and prolonging the network lifetime in WSN. This paper has adopted the concept of fuzzy if-then rules to choose the cluster head based on certain fuzzy descriptors. To optimise the fuzzy membership functions, Particle Swarm Optimisation (PSO) has been used to improve their ranges. Moreover, recent study has confirmed that the introduction of a mobile collector in a network which collects data through short-range communications also aids in high energy conservation. In this work, the network is divided into clusters and a mobile collector starts from the static sink or base station and moves through each of these clusters and collect data from the chosen cluster heads in a single-hop fashion. Mobility based on Ant-Colony Optimisation (ACO) has already proven to be an efficient method which is utilised in this work. Additionally, instead of performing clustering in every round, CH is selected on demand. The performance of the proposed algorithm has been compared with some existing clustering algorithms. Simulation results show that the proposed protocol is more energy-efficient and provides better packet delivery ratio as compared to the existing protocols for data collection obtained through Matlab Simulations

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

Efficient CPU-Optimized Parameter Estimation for Modeling Fish Schooling Behavior in Large Particle Systems

The schooling behavior of fish can be studied through simulations involving a large number of interacting particles. In such systems, each individual particle is guided by behavior rules, which include aggregation towards a centroid, collision avoidance, and direction alignment. The movement vector of each particle may be expressed as a linear combination of behaviors, with unknown parameters that define a trade-off among several behavioral constraints. A fitness function for collective schooling behavior encompasses all individual particle parameters. For a large number of interacting particles in a complex environment, heuristic methods, such as evolutionary algorithms, are used to optimize the fitness function, ensuring that the resulting decision rule preserves collective behavior. However, these algorithms exhibit slow convergence, making them inefficient in terms of CPU time cost. This paper proposes a CPU-efficient iterative (Cluster, Partition, Refine -- CPR) algorithm for estimating decision rule parameters for a large number of interacting particles. In the first step, we employ the K-Means (unsupervised learning) algorithm to cluster candidate solutions. Then, we partition the search space using Voronoi tessellation over the defined clusters. We assess the quality of each cluster based on the fitness function, with the centroid of their Voronoi cells representing the clusters. Subsequently, we refine the search space by introducing new cells into a number of identified well-fitting Voronoi cells. This process is repeated until convergence. A comparison of the performance of the CPR algorithm with a standard Genetic Algorithm reveals that the former converges faster than the latter. We also demonstrate that the application of the CPR algorithm results in a schooling behavior consistent with empirical observations.Comment: 10page

Assessing the Performance of a Particle Swarm Optimization Mobility Algorithm in a Hybrid Wi-Fi/LoRa Flying Ad Hoc Network

Research on Flying Ad-Hoc Networks (FANETs) has increased due to the availability of Unmanned Aerial Vehicles (UAVs) and the electronic components that control and connect them. Many applications, such as 3D mapping, construction inspection, or emergency response operations could benefit from an application and adaptation of swarm intelligence-based deployments of multiple UAVs. Such groups of cooperating UAVs, through the use of local rules, could be seen as network nodes establishing an ad-hoc network for communication purposes. One FANET application is to provide communication coverage over an area where communication infrastructure is unavailable. A crucial part of a FANET implementation is computing the optimal position of UAVs to provide connectivity with ground nodes while maximizing geographic span. To achieve optimal positioning of FANET nodes, an adaptation of the Particle Swarm Optimization (PSO) algorithm is proposed. A 3D mobility model is defined by adapting the original PSO algorithm and combining it with a fixed-trajectory initial flight. A Long Range (LoRa) mesh network is used for air-to-air communication, while a Wi-Fi network provides air-to-ground communication to several ground nodes with unknown positions. The optimization problem has two objectives: maximizing coverage to ground nodes and maintaining an end-to-end communication path to a control station, through the UAV mesh. The results show that the hybrid mobility approach performs similarly to the fixed trajectory flight regarding coverage, and outperforms fixed trajectory and PSO-only algorithms in both path maintenance and overall network efficiency, while using fewer UAVs

Mobile Robots

The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations