Cognitive IoT Systems via Adaptive Swarm Intelligence
Abstract
We present a novel paradigm called Adaptive Swarm Intelligence (ASI) where heterogeneous devices (or agents) engage in collaborative swarm computing for robust and adaptive real-time operation. ASI, a paradigm inspired by the collaborative and decentralized behavior of some systems in nature, finds application in a myriad of scenarios, in domains like the IoT, mobile computing and distributed systems. Examples include network cybersecurity, connected/autonomous cars, and other types of unmanned vehicles, like intelligent drone swarms. This is by no means an exhaustive list but it gives an indication of the many and diverse domains that can benefit from this paradigm. This paper presents a specific ASI case study for cooperative sensor fusion in prospective connected/autonomous vehicles, which constitutes the driving application of the IBM-led Efficient Programmability of Cognitive Heterogeneous Systems (EPOCHS) project under the DARPA DSSoC program. Due to the magnitude of EPOCHS, we focus on one specific piece of our project: the EPOCHS Reference Application (ERA) for multi-vehicle sensor fusion. We show characterization results on a x86 system that allow us to draft preliminary conclusions about ERAs performance characteristics and real-time needs. The paper briefly describes EPOCHS roadmap and future work.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 25, 2019
- Accession Number
- AD1076211
Entities
People
- Augusto Vega
- Pradip Bose
Organizations
- IBM Thomas J. Watson Research Center