Hypervector Approximation of Complex Manifolds for Artificial Intelligence Digital Twins in Smart Cities

Abstract

Highlights: What are the main findings? The proposed AI approach is effective at hypervector approximation of complex manifolds in smart city settings. The Hyperseed algorithm can generate fine-grained local variations that can be tracked for anomalies and temporal changes, as well as incremental changes in dynamic data streams. What is the implication of the main finding? This approach can be integrated into AI digital twins that have to process complex manifolds of high-dimensional datasets and data streams generated by smart cities. The interplay between digital twins and novel AI approaches is crucial in unpacking the complexities of urban systems and shaping sustainable and resilient smart cities. The United Nations Sustainable Development Goal 11 aims to make cities and human settlements inclusive, safe, resilient and sustainable. Smart cities have been studied extensively as an overarching framework to address the needs of increasing urbanisation and the targets of SDG 11. Digital twins and artificial intelligence are foundational technologies that enable the rapid prototyping, development and deployment of systems and solutions within this overarching framework of smart cities. In this paper, we present a novel AI approach for hypervector approximation of complex manifolds in high-dimensional datasets and data streams such as those encountered in smart city settings. This approach is based on hypervectors, few-shot learning and a learning rule based on single-vector operation that collectively maintain low computational complexity. Starting with high-level clusters generated by the K-means algorithm, the approach interrogates these clusters with the Hyperseed algorithm that approximates the complex manifold into fine-grained local variations that can be tracked for anomalies and temporal changes. The approach is empirically evaluated in the smart city setting of a multi-campus tertiary education institution where diverse sensors, buildings and people movement data streams are collected, analysed and processed for insights and decisions.