The overall purpose of this paper is to develop a heterogeneous collaborative swarm of entities with varying capabilities operating within different levels of information clouds where knowledge is shared either between vehicles, between vehicles and base stations, or between different clouds. The entities, or agents, may be of robot form (ground, air or sea vehicles) or human form and will have...

We define a Markovian Agent Model (MAM) as an analytical model formed by a spatial collection of interacting Markovian Agents (MAs), whose properties and behavior can be evaluated by numerical techniques. MAMs have been introduced with the aim of providing a flexible and scalable framework for distributed systems of interacting objects, where both the local properties and the interactions may d...

This paper proposes a swarm-inspired data centre self-organizing and consolidation technique which aims at reducing the power demand in data centres while ensuring the workload execution within the established performance parameters. Each data centre server is managed by an intelligent agent that implements a bird’s migration-inspired behaviour to decide on the appropriate server consolidation ...

Swarm dynamics is the study of collections of agents that interact with one another without central control. In natural systems, insects, birds, fish and other large mammals function in larger units to increase the overall fitness of the individuals. Their behavior is coordinated through local interactions to enhance mate selection, predator detection, migratory route identification and so fort...

We explore the problem of resource allocation in a system made up of autonomous agents that can either carry out tasks individually or, when necessary, cooperate by forming physical connections with each other. We consider a group transport scenario that involves transporting broken robots to a repair zone. Some broken robots can be transported by an individual ‘rescue’ robot, whereas other bro...

Microrobotics has the potential to revolutionize many applications including targeted material delivery, assembly, and surgery. The same properties that promise breakthrough solutions—small size and large populations—present unique challenges for controlling motion. Robotic manipulation usually assumes intelligent agents, not particle systems manipulated by a global signal. To identify the key ...

This paper presents a novel and bio-inspired algorithm for distributed division of labour in swarms of artificial agents (e.g., autonomous underwater vehicles). The algorithm is inspired by division of labour via local interactions in social insects. The algorithm is successfully implemented in virtual agents and simulated robot swarms and demonstrates a high adaptivity in response to changes i...

Wireless Sensor Networks (WSN) consist of a large number of tiny sensor nodes that are usually randomly distributed over a geographical region. In order to reduce power consumption, battery operated sensors undergo cycles of sleeping active periods; furthermore, sensors may be located in hostile environments increasing their attitude to failure. As a result, the topology of the WSN may be varyi...

Most research into Swarm Intelligence explores swarms of autonomous robots or simulated agents. Little work, however, has been done on swarms of networked humans. This paper introduces UNU, an online platform that enables networked users to assemble in real-time swarms and tackle problems as an Artificial Swarm Intelligence (ASI). Modeled after biological swarms, UNU enables large groups of net...

Nowadays, swarm intelligence optimization has become an important optimization tool and wildly used in many fields of application. In contrast to many successful applications, the theoretical foundation is rather weak. Therefore, there are still many problems to be solved. One problem is how to quantify the performance of algorithm in finite time, that is, how to evaluate the solution quality g...