Andrew Jennings’s research while affiliated with La Trobe University and other places

Highlights What are the main findings? Smart Cities as Hyper-Connected Digital Environments generate large and diverse data streams and repositories that do not consistently translate into insights and decisions. A Responsible AI Hyper-Automation framework with Generative AI agents is developed and evaluated to address these complex challenges. What are the implications of the main findings? The developed AI framework is effective when grounded on five core technical capabilities with an independent cognitive engine for hyper-automated agentic AI that feeds into human-in-the-loop processes. The framework provides a prototypical setting for university cities of the future to provide direction, guidance, and standards for sustainable and safe smart cities of the future. Abstract Smart cities are Hyper-Connected Digital Environments (HCDEs) that transcend the boundaries of natural, human-made, social, virtual, and artificial environments. Human activities are no longer confined to a single environment as our presence and interactions are represented and interconnected across HCDEs. The data streams and repositories of HCDEs provide opportunities for the responsible application of Artificial Intelligence (AI) that generates unique insights into the constituent environments and the interplay across constituents. The translation of data into insights poses several complex challenges originating in data generation and then propagating through the computational layers to decision outcomes. To address these challenges, this article presents the design and development of a Hyper-Automated AI framework with Generative AI agents for sustainable smart cities. The framework is empirically evaluated in the living lab setting of a ‘University City of the Future’. The developed AI framework is grounded on the core capabilities of acquisition, preparation, orchestration, dissemination, and retrospection, with an independent cognitive engine for hyper-automation of these AI capabilities using Generative AI. Hyper-automation output feeds into a human-in-the-loop process prior to decision-making outcomes. More broadly, this framework aims to provide a validated pathway for university cities of the future to take up the role of prototypes that deliver evidence-based guidelines for the development and management of sustainable smart cities.

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. Abstract 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.

Despite the increasing prevalence of artificial intelligence (AI) ethics frameworks, the practical application of these frameworks in industrial settings remains limited. This limitation is further augmented in energy systems by the complexity of systems composition and systems operation for energy generation, distribution, and supply. The primary reason for this limitation is the gap between the conceptual notion of ethics principles and the technical performance of AI applications in energy systems. For instance, trust is featured prominently in ethics frameworks but pertains to limited relevance for the robust operation of a smart grid. In this paper, we propose a lifecycle approach for AI ethics that aims to address this gap. The proposed approach consists of four phases: design, development, operation, and evaluation. All four phases are supported by a central AI ethics repository that gathers and integrates the primary and secondary dimensions of ethical practice, including reliability, safety, and trustworthiness, from design through to evaluation. This lifecycle approach is closely aligned with the operational lifecycle of energy systems, from design and production through to use, maintenance, repair, and overhaul, followed by shutdown, recycling, and replacement. Across these lifecycle stages, an energy system engages with numerous human stakeholders, directly with designers, engineers, users, trainers, operators, and maintenance technicians, as well as indirectly with managers, owners, policymakers, and community groups. This lifecycle approach is empirically evaluated in the complex energy system of a multi-campus tertiary education institution where the alignment between ethics and technical performance, as well as the human-centric application of AI, are demonstrated.

Big Data is steadily expanding beyond the boundaries of its foundational constructs of three primary Vs, Volume, Velocity and Variety, and two secondary Vs, Veracity and Value. The advent of 5G networks, Edge computing and IoT technologies has transformed Big Data into this modern context. With these new manifestations of Big Data, the focus is not only on the data itself but on the context that it applies to its immediate environment as well as the human and societal perception of this context. It is increasingly challenging for conventional AI algorithms to process and transform this data, analyse and visualise a broad spectrum of insights, and then formulate the explainability of such insights in terms of bias, transparency, safety, ethics, and causality. Self-structuring Artificial Intelligence (SSAI) addresses the limitations of conventional AI by adapting to the inherent structure of the data, incrementally learning and abstracting from this structure. SSAI has not been investigated in a cloud-based setting for generating explainable insights from these new types of Big Data. In this paper we propose a cloud-based architecture for explainable Big Data analytics using SSAI in highly-connected 5G and Edge computing environments. The proposed architecture is empirically evaluated on a commercial scale Big Data use case of Smart Grid for Smart Cities. The results of these experiments confirm the functionality and effectiveness of the proposed architecture.

Internet-of-Things (IoT) technologies have been steadily adopted and embedded into energy infrastructure following the rapid transformation of energy grids through distributed consumption, renewables generation, and battery storage. The data streams produced by such energy IoT infrastructure can be extracted, processed, analyzed, and synthesized for informed decision-making that delivers optimized grid operations, reduced costs, and net-zero carbon emissions. However, the voluminous nature of such data streams leads to an equally large number of analysis outcomes that have proven ineffective in decision-making by energy grid operators. This gap can be addressed by introducing artificial intelligence (AI) chatbots, or more formally conversational agents, to proactively assist human operators in analyzing and identifying decision opportunities in energy grids. In this research, we draw upon the recent success of generative AI for optimized AI chatbots with natural language understanding and generation capabilities for the complex information needs of energy IoT infrastructure and net-zero emissions. The proposed approach for optimized generative AI chatbots is composed of six core modules: Intent Classifier, Knowledge Extractor, Database Retriever, Cached Hierarchical Vector Storage, Secure Prompting, and Conversational Interface with Language Generator. We empirically evaluate the proposed approach and the optimized generative AI chatbot in the real-world setting of an energy IoT infrastructure deployed at a large, multi-campus tertiary education institution. The results of these experiments confirm the contribution of generative AI chatbots in simplifying the complexity of energy IoT infrastructure for optimized grid operations and net-zero carbon emissions.

Building energy baseline models, particularly machine learning-based models, are a core aspect in the evaluation of building energy performance to identify inefficient energy consumption behavior. In smart city design, energy planners and decision makers require comprehensive information on energy consumption across diverse building types as well as comparisons between different types of buildings. However, there is no comprehensive study of baseline modeling across the main building types to help identify factors that influence the performance of different machine learning algorithms for baseline modeling. Therefore, the goal of this paper is to review and analyze energy consumption behavior and evaluate the prediction performance and interpretability of machine learning-based baseline modeling techniques across major building types. The results have shown that the Extreme Gradient Boosting Machine (XGBoost) model is the most accurate baseline modeling method for all building types. Time-related factors, especially the week of the year and the day of the week, have the most impact on energy consumption across all building types. This study is presented as a useful resource for smart city energy managers to help in choosing and setting up appropriate methodologies for better operational effectiveness and efficiencies when designing and planning smart energy systems.

Crowd monitoring is a primary function in diverse industrial domains, such as smart cities, public transport, and public safety. Recent advancements in low-energy devices and rapid connectivity have enabled the generation of real-time data streams suitable for crowd-monitoring applications. Crowd forecasting is typically achieved using deep learning models that learn the evolving nature of data streams. The computational complexity, execution time, and opaqueness are inherent challenges of deep learning models that also overlook the latent relationships between multiple real-time data streams for improved accuracy. To address these challenges, we propose the global ensemble echo state network approach for explainable crowd forecasting using multiple WiFi data streams. This approach replaces the random input mapping layer with a clustering layer, allowing the network to learn input projections on cluster centroids. It incorporates an ensemble readout comprising a stack of reservoir layers that provide model explainability. It also learns multiple related time series in parallel to construct a global model that leverage latent relationships across the data streams. This approach was empirically evaluated in a multicampus, mixed-use tertiary education setting. The results of which confirm the effectiveness and interpretability of the proposed approach for industrial applications of crowd forecasting.