Crowdsourcing and Simulation with Mobile Agents and the JavaScript Agent Machine
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Crowdsourcing and Simulation with Mobile Agents and the JavaScript Agent Machine

About the Book

Using Mobile Multi-Agent Systems, this book tackles the problem of unified and distributed computing in robust heterogeneous contexts, spanning from Internet Clouds to Sensor Networks. The operational gap between low-resource data processing units, such as single microchips embedded in materials, mobile devices, and generic computers including servers, should be closed by a unified agent behaviour model, agent processing platform architecture, and programming framework, supporting real-world deployment as well as simulation. Major features include robustness, scalability, self-organization, reconfiguration, adaptivity, and learning. This book provides a straightforward introduction to creating JavaScript agents using the JavaScript Agent Machine (JAM) requiring only a few lines of code. In a short amount of time, even beginners may develop robust multi-agent systems.

There are countless application areas, including sensor data processing, structural health monitoring, load monitoring of technical structures, distributed computing, distributed databases, and search, automated design, cloud-based manufacturing, mobile crowdsensing (MCS), and surveys. This book has a strong practical focus on MCS. MCS is a useful tool for data mining because it views people as sensors. In addition, agent-based simulation is addressed, finally coupled to real worlds using MCS and digital twin concepts.

With distinct objectives in mind, intelligence and smartness can be defined at various operational and processing levels. One component is the capacity to adapt and be reliable in the face of sensor, communication, node, and network failures without letting the accuracy and integrity of the information computed suffer.

Crowdsourcing and crowdsensing are elaborated in detail after a brief introduction to agent-based notions. If you are solely interested in the agent platform and its programming, you can skip this chapter. The platform is described in connection to the agent interaction and behaviour model. AgentsJS, a subset of JavaScript that is described in depth in a separate chapter, is used to program agents. Pre-compiled libraries and programs are added to the core programming interface, including a simulator. The simulator uses JAM and has the ability to connect to other JAM nodes, allowing for augmented simulation that incorporates the real world.

Finally, an extended example chapter shows various aspects of agent programming with AgentJS and JAM. The software is freely available from [](

This book is based on recent scientific work as well as on different lectures I have held at the University of Bremen and the University of Koblenz-Landau. The lectures address the design and deployment of multi-agent systems as well as mobile crowdsensing. Bachelor and master students in computer science, production engineering, and social sciences are the intended audience.

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    • JavaScript
    • Machine Learning
    • Sciences
    • Networking
    • Artificial Intelligence
    • Internet of Things
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About the Author

Stefan Bosse
Stefan Bosse
Stefan Bosse is teaching and researching as a Privatdozent at the University of Bremen, Department of Computer Science, and in the years 2018/2019 he was an interim professor at the University of Koblenz-Landau, Faculty Computer Science, Institute of Software Technologies.
He originally studied physics at the University of Bremen. He received a PhD/doctoral degree (Dr. rer. nat.) in physics in the year 2002 (topic "Advanced Optical Laser Measuring Techniques") at the University of Bremen, and the post-doctoral degree (Habilitation) and the Venia Legendi in Computer Science in the year 2016 at the University of Bremen with his habilitation (postdoctoral degree) "Unified Distributed Sensor and Environmental Information Processing with Multi-Agent Systems: Models, Platforms, and Technological Aspects".
At the University of Bremen and University Koblenz-Landau he teaches several courses in fundamental computer science, functional programming, and in selected advanced topics covering the design and programming of massive parallel and distributed systems, multi-agents systems and agent-based simulation, high-level synthesis of complex digital logic data processing systems, and material-integrated sensing systems with a high interdisciplinary background.
He is principle investigator and researcher in the DFG founded interegional and interdisciplinary Research Unit FOR3022 (Ultrasonic Monitoring of Fibre Metal Laminates Using Integrated Sensors), Subproject 4 (Automated Model-free Damage Diagnostic).
His main research area is distributed artificial intelligence in general, and in particular information processing in massive parallel and distributed systems using agent-based approaches combined with machine learning, and agent-based simulation. A broad range of fields of application and domains are addressed: Material Science, Materials Informatics, Smart Materials, IoT, Production Engineering, Social Science, Crowd Sensing, Geo Science.
He conducted projects in the internationally recognized ISIS Scientific Centre for Intelligent Sensorial Materials pushing interdisciplinary research closing the gap between technology and computer science, finally joining the ISIS council and publishing an internationally well regarded handbook on this topic.
He published about 100 journal and conference papers and acts as a reviewer and a guest editor for several international journals and is a member of a broad range of international conference programme and organizing committees.

Table of Contents

1. Introduction 2. Crowdsourcing . 3. Platforms and Agents 4. The Agent Model 5. JavaScript Agent Machine 6. AgentJS API 7. JAM for Users and Programmers 8. Performance 9. Simulation with SEJAM 10. Introduction to Augmented Simulation 11. Mobile Crowdsensing with JAM 12. Conclusion 13. Examples 14. References

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