20,475 research outputs found
Pervasive intelligent routing in content centric delay tolerant networks
This paper introduces a Swarm-Intelligence based Routing protocol (SIR) that aims to efficiently route information in content centric Delay Tolerant Networks (CCDTN) also dubbed pocket switched networks. First, this paper formalizes the notion of optimal path in CCDTN and introduces an original and efficient algorithm to process these paths in dynamic graphs. The properties and some invariant features of these optimal paths are analyzed and derived from several real traces. Then, this paper shows how optimal path in CCDTN can be found and used from a fully distributed swarm-intelligence based approach of which the global intelligent behavior (i.e. shortest path discovery and use) emerges from simple peer to peer interactions applied during opportunistic contacts. This leads to the definition of the SIR routing protocol of which the consistency, efficiency and performances are demonstrated from intensive representative simulations
Studying the effect of multisource Darwinian particle swarm optimization in search and rescue missions
Robotic Swarm Intelligence is considered one of the hottest topics within the robotics research eld nowadays, for its major contributions to di erent elds of life from hobbyists, makers and expanding to military applications. It has also proven to be more effective and effcient than other robotic approaches targeting the same problem. Within this research, we targeted to test the hypothesis that using more than a single starting/ seeding point for a swarm to explore an unknown environment will yield better solutions, routes and cover more area of the search space within context of Search and Rescue applications domain. We tested such hypothesis via extending existing Particle swarm optimization techniques for search and rescue operations (i.e. Robotic Darwinian Particle Swarm Optimization and we split the swarm into smaller groups that start exploration from di erent seed positions, then took the convergence time average for di erent runs of simulations and recorded the results for quanti cation. The results presented in this work con rms the hypothesis we started with, and gives insight to how the number of robots contributing in the experiments a ect the quality of the results. This work also shows a direct correlation between the swarm size and the search space
A field-based computing approach to sensing-driven clustering in robot swarms
Swarm intelligence leverages collective behaviours emerging from interaction and activity of several “simple” agents to solve problems in various environments. One problem of interest in large swarms featuring a variety of sub-goals is swarm clustering, where the individuals of a swarm are assigned or choose to belong to zero or more groups, also called clusters. In this work, we address the sensing-based swarm clustering problem, where clusters are defined based on both the values sensed from the environment and the spatial distribution of the values and the agents. Moreover, we address it in a setting characterised by decentralisation of computation and interaction, and dynamicity of values and mobility of agents. For the solution, we propose to use the field-based computing paradigm, where computation and interaction are expressed in terms of a functional manipulation of fields, distributed and evolving data structures mapping each individual of the system to values over time. We devise a solution to sensing-based swarm clustering leveraging multiple concurrent field computations with limited domain and evaluate the approach experimentally by means of simulations, showing that the programmed swarms form clusters that well reflect the underlying environmental phenomena dynamics
Physics-Based Swarm Intelligence for Disaster Relief Communications
This study explores how a swarm of aerial mobile vehicles can provide network
connectivity and meet the stringent requirements of public protection and
disaster relief operations. In this context, we design a physics-based
controlled mobility strategy, which we name the extended Virtual Force Protocol
(VFPe), allowing self-propelled nodes, and in particular here unmanned aerial
vehicles, to fly autonomously and cooperatively. In this way, ground devices
scattered on the operation site may establish communications through the
wireless multi-hop communication routes formed by the network of aerial nodes.
We further investigate through simulations the behavior of the VFPe protocol,
notably focusing on the way node location information is disseminated into the
network as well as on the impact of the number of exploration nodes on the
overall network performance.Comment: in International Conference on Ad Hoc Networks and Wireless, Jul
2016, Lille, Franc
The impact of agent density on scalability in collective systems : noise-induced versus majority-based bistability
In this paper, we show that non-uniform distributions in swarms of agents have an impact on the scalability of collective decision-making. In particular, we highlight the relevance of noise-induced bistability in very sparse swarm systems and the failure of these systems to scale. Our work is based on three decision models. In the first model, each agent can change its decision after being recruited by a nearby agent. The second model captures the dynamics of dense swarms controlled by the majority rule (i.e., agents switch their opinion to comply with that of the majority of their neighbors). The third model combines the first two, with the aim of studying the role of non-uniform swarm density in the performance of collective decision-making. Based on the three models, we formulate a set of requirements for convergence and scalability in collective decision-making
Particle Swarm Optimization Based Source Seeking
Signal source seeking using autonomous vehicles is a complex problem. The
complexity increases manifold when signal intensities captured by physical
sensors onboard are noisy and unreliable. Added to the fact that signal
strength decays with distance, noisy environments make it extremely difficult
to describe and model a decay function. This paper addresses our work with
seeking maximum signal strength in a continuous electromagnetic signal source
with mobile robots, using Particle Swarm Optimization (PSO). A one to one
correspondence with swarm members in a PSO and physical Mobile robots is
established and the positions of the robots are iteratively updated as the PSO
algorithm proceeds forward. Since physical robots are responsive to swarm
position updates, modifications were required to implement the interaction
between real robots and the PSO algorithm. The development of modifications
necessary to implement PSO on mobile robots, and strategies to adapt to real
life environments such as obstacles and collision objects are presented in this
paper. Our findings are also validated using experimental testbeds.Comment: 13 pages, 12 figure
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