With the rapid technological advancements and the ever-evolving complex systems, the identification and integration of the components and resources for the functioning of multi-agent systems (MAS) are crucial tasks. However, difficulties arise due to the complexity of not having reference frameworks that normalize their implementation. Therefore, in this survey, we propose the FC-MAS (Framework-Components in Multi-Agent System) model as a conceptual framework designed to simplify comprehension and standardization in incorporating the required functions and components for the deployment and operation of MAS in engineering applications. This model comprises five abstract layers, each of which serves a specific purpose and encompasses the details and resources required to operate MAS. Furthermore, we propose a structured workflow for centralized and distributed MAS schemes with a set of related activities that integrate the fundamental steps and stages for the successful implementation of MAS. Finally, this work discusses potential directions for future research, including a deeper exploration of essential components, the establishment of terminology standards across various domains, and the refinement of the proposed model to enhance its applicability and relevance across a broader spectrum of contexts.

Sparse resource allocation to shape a network dynamical process is studied. Specifically, we consider allocating limited distributed control resources among a subset of a network's nodes, to minimize the dominant eigenvalue of a linear dynamical spread process associated with the network. Structural characterizations of the closed-loop dynamics at the optimum are obtained. These results are then used to 1) develop constructive algorithms for optimal resource allocation, 2) identify limits on the control performance, and 3) understand the relationship between the network's graph and the optimal resource profile. While the focus here is on a simplified linear model, an exploratory study of the design's applicability to realistic stochastic and nonlinear spread processes is undertaken, via simulation examples. As a whole, this study advances a research thrust on disease spread control in networks, toward the realistic paradigm that control resources can only be allocated at a subset of network locations.

We examine linear network dynamics in which an input can be applied at only one network component and measurements can only be made at one (in general different) component. The infinite-zero and finite-zero structures of these dynamics are characterised explicitly in terms of the network's graph matrix, using the special coordinate basis for linear system. These algebraic characterisations are then used to identify relationships between features of the network's graph topology and the state matrix of its finite zero dynamics.

Unmanned Aerial Vehicles (UAVs) are versatile, adapting hardware and software for research. They are vital for remote monitoring, especially in challenging settings such as volcano observation with limited access. In response, economical computer vision systems provide a remedy by processing data, boosting UAV autonomy, and assisting in maneuvering. Through the application of these technologies, researchers can effectively monitor remote areas, thus improving surveillance capabilities. Moreover, flight controllers employ onboard tools to gather data, further enhancing UAV navigation during surveillance tasks. For energy efficiency and comprehensive coverage, this paper introduces a budget-friendly prototype aiding UAV navigation, minimizing effects on endurance. The prototype prioritizes improved maneuvering via the integrated landing and obstacle avoidance system (LOAS). Employing open-source software and MAVLink communication, these systems underwent testing on a Pixhawk-equipped quadcopter. Programmed on a Raspberry Pi onboard computer, the prototype includes a distance sensor and basic camera to meet low computational and weight demands.Tests occurred in controlled environments, with systems performing well in 90% of cases. The Pixhawk and Raspberry Pi documented quad actions during evasive and landing maneuvers. Results prove the prototype’s efficacy in refining UAV navigation. Integrating this cost-effective, energy-efficient model holds promise for long-term mission enhancement—cutting costs, expanding terrain coverage, and boosting surveillance capabilities.