Large-scale agent-based simulation and crowd sensing with mobile agents

Modelling and simulation of social interaction and networks are of high interest in multiple disciplines and fields of application ranging from fundamental social sciences to smart city management. Future smart city infrastructures and management are characterised by adaptive and self-organising control using real-world sensor data. In this work, humans are considered as sensors. Virtual worlds, e.g., simulations and games, are commonly closed and rely on artificial social behaviour and synthetic sensor information generated by the simulator program or using data collected off-line by surveys. In contrast, real worlds have a higher diversity. Agent-based modelling relies on parameterised models. The selection of suitable parameter sets is crucial to match real-world behaviour. In this work, a framework combining agent-based simulation with crowd sensing and social data mining using mobile agents is introduced. The crowd sensing via chat bots creates augmented virtuality and reality by augmenting the simulated worlds with real-world interaction and vice versa. The simulated world interacts with real-world environments, humans, machines, and other virtual worlds in real-time. Among the mining of physical sensors (e.g., temperature, motion, position, and light) of mobile devices like smartphones, mobile agents can perform crowd sensing by participating in question–answer dialogues via a chat blog (provided by smartphone Apps or integrated into WEB pages and social media). Additionally, mobile agents can act as virtual sensors (offering data exchanged with other agents) and create a bridge between virtual and real worlds. The ubiquitous usage of digital social media has relevant impact on social interaction, mobility, and opinion-making, which has to be considered. Three different use-cases demonstrate the suitability of augmented agent-based simulation for social network analysis using parameterised behavioural models and mobile agent-based crowd sensing. This paper gives a rigorous overview and introduction of the challenges and methodologies used to study and control large-scale and complex socio-technical systems using agent-based methods.

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PARALLEL AGENT-BASED SIMULATION ON A CLUSTER OF WORKSTATIONS

Parallel Processing Letters ◽

10.1142/s0129626403001562 ◽

2003 ◽

Vol 13 (04) ◽

pp. 629-641 ◽

Cited By ~ 5

Author(s):

Konstantin Popov ◽

Mahmoud Rafea ◽

Fredrik Holmgren ◽

Per Brand ◽

Vladimir Vlassov ◽

...

Keyword(s):

Web Sites ◽

Large Scale ◽

Parallel Implementation ◽

Simulation Models ◽

Agent Based Simulation ◽

Time Step ◽

Agent Based ◽

Simulation Step ◽

Cluster Of Workstations ◽

Concurrent Simulation

We discuss a parallel implementation of an agent-based simulation. Our approach allows to adapt a sequential simulator for large-scale simulation on a cluster of workstations. We target discrete-time simulation models that capture the behavior of Web users and Web sites. Web users are connected with each other in a graph resembling the social network. Web sites are also connected in a similar graph. Users are stateful entities. At each time step, they exhibit certain behaviour such as visiting bookmarked sites, exchanging information about Web sites in the "word-of-mouth" style, and updating bookmarks. The real-world phenomena of emerged aggregated behavior of the Internet population is studied. The system distributes data among workstations, which allows large-scale simulations infeasible on a stand-alone computer. The model properties cause traffic between workstations proportional to partition sizes. Network latency is hidden by concurrent simulation of multiple users. The system is implemented in Mozart that provides multithreading, dataflow variables, component-based software development, and network-transparency. Currently we can simulate up to 106 Web users on 104 Web sites using a cluster of 16 computers, which takes few seconds per simulation step, and for a problem of the same size, parallel simulation offers speedups between 11 and 14.

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Large scale multi-agent-based simulation using NetLogo for the multi-robot exploration problem

2013 11th IEEE International Conference on Industrial Informatics (INDIN) ◽

10.1109/indin.2013.6622904 ◽

2013 ◽

Cited By ~ 3

Author(s):

Ionel Muscalagiu ◽

Cosmina Illes ◽

Horia Emil Popa

Keyword(s):

Large Scale ◽

Agent Based Simulation ◽

Agent Based ◽

Multi Agent ◽

Multi Robot

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A large-scale, agent-based simulation of metropolitan freight movements with passenger and freight market interactions

Procedia Computer Science ◽

10.1016/j.procs.2020.03.157 ◽

2020 ◽

Vol 170 ◽

pp. 771-778 ◽

Cited By ~ 2

Author(s):

Monique Stinson ◽

Joshua Auld ◽

Abolfazl (Kouros) Mohammadian

Keyword(s):

Large Scale ◽

Agent Based Simulation ◽

Agent Based ◽

Market Interactions ◽

Freight Market

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Agent-based simulation for large-scale emergency response

ACM Computing Surveys ◽

10.1145/2379776.2379784 ◽

2012 ◽

Vol 45 (1) ◽

pp. 1-51 ◽

Cited By ~ 47

Author(s):

Glenn I. Hawe ◽

Graham Coates ◽

Duncan T. Wilson ◽

Roger S. Crouch

Keyword(s):

Emergency Response ◽

Large Scale ◽

Agent Based Simulation ◽

Agent Based

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An Initial Implementation of Multiagent Simulation of Travel Behavior for a Medium-Sized City in China

Mathematical Problems in Engineering ◽

10.1155/2014/980623 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 6

Author(s):

Chengxiang Zhuge ◽

Chunfu Shao ◽

Jian Gao ◽

Meng Meng ◽

Weiyang Xu

Keyword(s):

Survey Data ◽

Travel Behavior ◽

Input Data ◽

Large Scale ◽

Simulation Tool ◽

Preparation Process ◽

Agent Based Simulation ◽

Agent Based ◽

Multiagent Simulation ◽

Step Model

Since the traditional four-step model is so simple that it cannot solve complex modern transportation problems, microsimulation is gradually applied for transportation planning and some researches indicate that it is more compatible and realistic. In this paper, a framework of agent-based simulation of travel behavior is proposed, which is realized by MATSim, a simulation tool developed for large-scale agent-based simulation. MATSim is currently developed and some of its models are under training, so a detailed introduction of simulation structure and preparation of input data will be presented. In practice, the preparation process differs from one to another in different simulation projects because the available data for simulation is various. Thus, a simulation of travel behavior under a condition of limited available survey data will be studied based on MATSim; furthermore, a medium-sized city in China will be taken as an example to check whether agent-based simulation of travel behavior can be successfully applied in China.

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