Abstract
The Internet of Things (IoT) marks a phase transition in the evolution of the Internet, distinguished by a massive connectivity and the interaction with the physical world. The organic evolution of IoT requires the consideration of three dimensions: scale, organization, and context. These dimensions are particularly relevant in Ultra Large Scale Systems (ULSS), of which autonomous vehicles is a prime example. Fog Computing is well positioned to support contextual awareness and communication, critical for ULSS. The design and orchestration of ULSS require fresh approaches, new organizing principles. A recent paper proposed Hierarchical Emergent Behaviors (HEB), an architecture that builds on established concepts of emergent behaviors and hierarchical decomposition and organization. HEB’s local rules induce emergent behaviors, i.e., useful behaviors not explicitly programmed. In this chapter we take a first step to validate HEB concepts through the study of two basic self-driven car “primitives”: exiting a platoon formation, and maneuvering in anticipation of obstacles beyond the range of on-board sensors. Fog nodes provide the critical contextual information required.
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Acknowledgements
Damian Roca work was supported by a Doctoral Scholarship provided by Fundación La Caixa. This work has been supported by the Spanish Government (Severo Ochoa grants SEV2015-0493) and by the Spanish Ministry of Science and Innovation (contracts TIN2015-65316-P).
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Roca, D., Milito, R., Nemirovsky, M., Valero, M. (2018). Tackling IoT Ultra Large Scale Systems: Fog Computing in Support of Hierarchical Emergent Behaviors. In: Rahmani, A., Liljeberg, P., Preden, JS., Jantsch, A. (eds) Fog Computing in the Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-319-57639-8_3
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