arXiv subjects

Prem Prakash Jayaraman

Publications and source records attributed to Prem Prakash Jayaraman.

At least 19 recordsLinked to original sources

arXiv2026

HMB-GAN: Hybrid Multi-Bézier GAN for Vector Shape Synthesis

We explore the use of hybrid quantum-classical generative adversarial networks for synthesising CAD-ready vector geometries. Unlike prior work that operates in rasterised or single-Bézier domains, we introduce HMB-GAN (Hybrid Multi-Bézier GAN), an end-to-end differentiable generative framework that constructs closed shapes through stitched multi-segment Bézier representations with geometric continuity enforced by construction. We compare a quantum-enhanced generator with a classical generator within this architecture and evaluate them across point cloud distribution metrics and geometric shape statistics. Results show that despite faster convergence, a reduction in model parameter count, and slightly improved performance on point cloud metrics, the quantum generator suffers from excessive simulator overhead and thus classically-simulated evaluation suffers from hardware constraints. These results demonstrate the feasibility of modelling structured geometries through hybrid quantum architectures whilst highlighting contemporary hardware limitations.

cs.LG↗

arXiv2026

Assisting Mission-Critical Traffic Flows with Active Queue Management in Industrial Internet of Things

Mission-critical Industrial Internet of Things (IIoT) traffic flows require bounded network latency and jitter guarantees to ensure the safe functioning of critical industrial infrastructure. These flows are typically communicated via commodity network routers with conventional First-In-First-Out (FIFO) buffers. FIFO has proven to be the culprit of the well-known bufferbloat phenomenon, and the deployment of Active Queue Management (AQM) schemes have demonstrated significant performance improvements for latency-sensitive applications over the Internet in the IT domain. However, the bufferbloat phenomenon and the efficacy of AQM schemes have not been studied in IIoT-based OT domain. In this paper, we propose the use of AQM as a lightweight and non-intrusive mechanism for assisting mission-critical traffic flows in IIoT networks. Our experimental results demonstrated that multi-queue AQM schemes provide substantial flow isolation and capacity sharing benefits, and significantly improve the performance of mission-critical traffic flows under network pressure. We further provide deployment recommendations based on our experimental insights.

cs.NI↗

arXiv2026

FacProcessTwin: An LLM-Based System for Process Twin Development

Process twins provide real-time representations of entire production processes. By capturing how process steps interact, rather than monitoring a single machine in isolation as an asset-based digital twin does, they have the potential to drive efficiency gains across the whole process. However, developing a process twin is costly. It requires accurately modelling the entire production process: its process steps, the equipment and product-specific settings each step uses, and its process variations. The resulting model must then be bound to live operational data. We present FacProcessTwin, a system that leverages a large language model (LLM) to reduce this development time, building a process twin from a plant's process documentation and natural-language input from an operator. FacProcessTwin generates this complete process model and then automatically binds its process steps to live operational data. The generated model and its data bindings are rendered as an interactive process diagram through which manufacturing personnel can monitor and correct the system's autonomous decisions, such as resolving uncertainty at safety-critical binding steps. We evaluate FacProcessTwin through a real-world case study of an Australian food manufacturer, covering 16 production process flows that span chilled, frozen, and aseptic shelf-stable product categories and include process variations within the same product. The results show that FacProcessTwin generates these process models accurately (a mean F1 of 95.2% against ground truth) and builds each twin in roughly a sixth of the manual time. Its human-in-the-loop governance then keeps the safety-critical bindings correct: at ambiguous tags where a single-pass baseline silently mis-binds 75.0% of the time, FacProcessTwin defers to the operator and mis-binds none.

cs.SE↗

arXiv2026

FactoryLLM: A Safe and Open-Source AI Playground for Evaluating LLMs in Smart Factories

Fault diagnostics and recovery in smart factories is challenging because critical information is dispersed across manuals of multiple machines which are interconnected through the manufacturing process. Large Language Models (LLMs) can provide a promising approach. In this paper, we propose FactoryLLM, a safe and open-source AI playground designed for evaluating different LLM-based retrieval-augmented generation (RAG) models by analysing documents from multiple machines across the manufacturing process. FactoryLLM enables the user to configure the LLM, and assess performance when reasoning over multiple documents, through a dual evaluation setup using both RAGAS and NVIDIA's LLM-as-a-Judge metrics. FactoryLLM is safe because it allows users to run local or open-source LLMs without sharing sensitive industrial data, providing a controlled environment for experimentation. We demonstrate the efficacy of FactoryLLM through a case study which involves an Autonomous Intelligent Vehicle and its Mobile Planner software, evaluating three LLMs across 30 maintenance queries derived from approximately 600 pages of cross-machine documentation. The results suggest that FactoryLLM is effective in cross-machine document reasoning: every model achieved a groundedness score above 0.88. The full code and documentation for community to test FactoryLLM with their manufacturing specific scenarios are publicly available.

cs.AI↗

arXiv2025

DCMF: A Dynamic Context Monitoring and Caching Framework for Context Management Platforms

The rise of context-aware IoT applications has increased the demand for timely and accurate context information. Context is derived by aggregating and inferring from dynamic IoT data, making it highly volatile and posing challenges in maintaining freshness and real-time accessibility. Caching is a potential solution, but traditional policies struggle with the transient nature of context in IoT (e.g., ensuring real-time access for frequent queries or handling fast-changing data). To address this, we propose the Dynamic Context Monitoring Framework (DCMF) to enhance context caching in Context Management Platforms (CMPs) by dynamically evaluating and managing context. DCMF comprises two core components: the Context Evaluation Engine (CEE) and the Context Management Module (CMM). The CEE calculates the Probability of Access (PoA) using parameters such as Quality of Service (QoS), Quality of Context (QoC), Cost of Context (CoC), timeliness, and Service Level Agreements (SLAs), assigning weights to assess access likelihood. Based on this, the CMM applies a hybrid Dempster-Shafer approach to manage Context Freshness (CF), updating belief levels and confidence scores to determine whether to cache, evict, or refresh context items. We implemented DCMF in a Context-as-a-Service (CoaaS) platform and evaluated it using real-world smart city data, particularly traffic and roadwork scenarios. Results show DCMF achieves a 12.5% higher cache hit rate and reduces cache expiry by up to 60% compared to the m-CAC technique, ensuring timely delivery of relevant context and reduced latency. These results demonstrate DCMF's scalability and suitability for dynamic context-aware IoT environments.

cs.DB↗

arXiv2024

Achieving Observability on Fog Computing with the use of open-source tools

Fog computing can provide computational resources and low-latency communication at the network edge. But with it comes uncertainties that must be managed in order to guarantee Service Level Agreements. Service observability can help the environment better deal with uncertainties, delivering relevant and up-to-date information in a timely manner to support decision making. Observability is considered a superset of monitoring since it uses not only performance metrics, but also other instrumentation domains such as logs and traces. However, as Fog Computing is typically characterised by resource-constrained nodes and network uncertainties, increasing observability in fog can be risky due to the additional load injected into a restricted environment. There is no work in the literature that evaluated fog observability. In this paper, we first outline the challenges of achieving observability in a Fog environment, based on which we present a formal definition of fog observability. Subsequently, a real-world Fog Computing testbed running a smart city use case is deployed, and an empirical evaluation of fog observability using open-source tools is presented. The results show that under certain conditions, it is viable to provide observability in a Fog Computing environment using open-source tools, although it is necessary to control the overhead modifying their default configuration according to the application characteristics.

cs.DC↗

arXiv2024

A Hybrid Approach to Monitor Context Parameters for Optimising Caching for Context-Aware IoT Applications

Internet of Things (IoT) has seen a prolific rise in recent times and provides the ability to solve several key challenges faced by our societies and environment. Data produced by IoT provides a significant opportunity to infer context that is key for IoT applications to make decisions/actuations. Context Management Platform (CMP) is a middleware to facilitate the exchange and management of such context information among IoT applications. In this paper, we propose a novel approach to monitoring context freshness as a key metric, to improving the CMP's caching performance to support the real-time context needs of IoT applications. Our proposed hybrid algorithm uses Analytic Hierarchy Process (AHP) and Sliding Window technique to ensure the most relevant (as needed by the IoT applications) context information is cached. By continuously monitoring and prioritizing context attributes, the strategy adapts to IoT environment changes, keeping cached context fresh and reliable. Through experimental evaluation and using mock data obtained from a real-world mobile IoT scenario in section~\ref{use case}, we demonstrate that the proposed algorithm can substantially enhance context cache performance, by monitoring the context attributes in real time.

cs.DC↗

arXiv2024

DeepHeteroIoT: Deep Local and Global Learning over Heterogeneous IoT Sensor Data

Internet of Things (IoT) sensor data or readings evince variations in timestamp range, sampling frequency, geographical location, unit of measurement, etc. Such presented sequence data heterogeneity makes it difficult for traditional time series classification algorithms to perform well. Therefore, addressing the heterogeneity challenge demands learning not only the sub-patterns (local features) but also the overall pattern (global feature). To address the challenge of classifying heterogeneous IoT sensor data (e.g., categorizing sensor data types like temperature and humidity), we propose a novel deep learning model that incorporates both Convolutional Neural Network and Bi-directional Gated Recurrent Unit to learn local and global features respectively, in an end-to-end manner. Through rigorous experimentation on heterogeneous IoT sensor datasets, we validate the effectiveness of our proposed model, which outperforms recent state-of-the-art classification methods as well as several machine learning and deep learning baselines. In particular, the model achieves an average absolute improvement of 3.37% in Accuracy and 2.85% in F1-Score across datasets

cs.LG↗

arXiv2022

ExpFinder: An Ensemble Expert Finding Model Integrating $N$-gram Vector Space Model and $μ$CO-HITS

Finding an expert plays a crucial role in driving successful collaborations and speeding up high-quality research development and innovations. However, the rapid growth of scientific publications and digital expertise data makes identifying the right experts a challenging problem. Existing approaches for finding experts given a topic can be categorised into information retrieval techniques based on vector space models, document language models, and graph-based models. In this paper, we propose $\textit{ExpFinder}$, a new ensemble model for expert finding, that integrates a novel $N$-gram vector space model, denoted as $n$VSM, and a graph-based model, denoted as $\textit{$μ$CO-HITS}$, that is a proposed variation of the CO-HITS algorithm. The key of $n$VSM is to exploit recent inverse document frequency weighting method for $N$-gram words and $\textit{ExpFinder}$ incorporates $n$VSM into $\textit{$μ$CO-HITS}$ to achieve expert finding. We comprehensively evaluate $\textit{ExpFinder}$ on four different datasets from the academic domains in comparison with six different expert finding models. The evaluation results show that $\textit{ExpFinder}$ is a highly effective model for expert finding, substantially outperforming all the compared models in 19% to 160.2%.

cs.IR↗

arXiv2021

An AI-based Solution for Enhancing Delivery of Digital Learning for Future Teachers

There has been a recent and rapid shift to digital learning hastened by the pandemic but also influenced by ubiquitous availability of digital tools and platforms now, making digital learning ever more accessible. An integral and one of the most difficult part of scaling digital learning and teaching is to be able to assess learner's knowledge and competency. An educator can record a lecture or create digital content that can be delivered to thousands of learners but assessing learners is extremely time consuming. In the paper, we propose an Artificial Intelligence (AI)-based solution namely VidVersityQG for generating questions automatically from pre-recorded video lectures. The solution can automatically generate different types of assessment questions (including short answer, multiple choice, true/false and fill in the blank questions) based on contextual and semantic information inferred from the videos. The proposed solution takes a human-centred approach, wherein teachers are provided the ability to modify/edit any AI generated questions. This approach encourages trust and engagement of teachers in the use and implementation of AI in education. The AI-based solution was evaluated for its accuracy in generating questions by 7 experienced teaching professionals and 117 education videos from multiple domains provided to us by our industry partner VidVersity. VidVersityQG solution showed promising results in generating high-quality questions automatically from video thereby significantly reducing the time and effort for educators in manual question generation.

cs.CY↗

arXiv2021

An IIoT machine model for achieving consistency in product quality in manufacturing plants

Consistency in product quality is of critical importance in manufacturing. However, achieving a target product quality typically involves balancing a large number of manufacturing attributes. Existing manufacturing practices for dealing with such complexity are driven largely based on human knowledge and experience. The prevalence of manual intervention makes it difficult to perfect manufacturing practices, underscoring the need for a data-driven solution. In this paper, we present an Industrial Internet of Things (IIoT) machine model which enables effective monitoring and control of plant machinery so as to achieve consistency in product quality. We present algorithms that can provide product quality prediction during production, and provide recommendations for machine control. Subsequently, we perform an experimental evaluation of the proposed solution using real data captured from a food processing plant. We show that the proposed algorithms can be used to predict product quality with a high degree of accuracy, thereby enabling effective production monitoring and control.

cs.LG↗

arXiv2021

Evaluating Sensor Data Quality in Internet ofThings Smart Agriculture Applications

The unprecedented growth of Internet of Things (IoT) and its applications in areas such as Smart Agriculture compels the need to devise newer ways for evaluating the quality of such applications. While existing models for application quality focus on the quality experienced by the end-user (captured using likert scale), IoT applications have minimal human involvement and rely on machine to machine communication and analytics to drive decision via actuations. In this paper, we first present a conceptual framework for the evaluation of IoT application quality. Subsequently, we propose, develop and validate via empirical evaluations a novel model for evaluating sensor data quality that is a key component in assessing IoT application quality. We present an implementation of the sensor data quality model and demonstrate how the IoT sensor data quality can be integrated with a Smart Agriculture application. Results of experimental evaluations conducted using data from a real-world testbed concludes the paper.

eess.SP↗

arXiv2021

CorrDetector: A Framework for Structural Corrosion Detection from Drone Images using Ensemble Deep Learning

In this paper, we propose a new technique that applies automated image analysis in the area of structural corrosion monitoring and demonstrate improved efficacy compared to existing approaches. Structural corrosion monitoring is the initial step of the risk-based maintenance philosophy and depends on an engineer's assessment regarding the risk of building failure balanced against the fiscal cost of maintenance. This introduces the opportunity for human error which is further complicated when restricted to assessment using drone captured images for those areas not reachable by humans due to many background noises. The importance of this problem has promoted an active research community aiming to support the engineer through the use of artificial intelligence (AI) image analysis for corrosion detection. In this paper, we advance this area of research with the development of a framework, CorrDetector. CorrDetector uses a novel ensemble deep learning approach underpinned by convolutional neural networks (CNNs) for structural identification and corrosion feature extraction. We provide an empirical evaluation using real-world images of a complicated structure (e.g. telecommunication tower) captured by drones, a typical scenario for engineers. Our study demonstrates that the ensemble approach of \model significantly outperforms the state-of-the-art in terms of classification accuracy.

cs.CV↗

arXiv2019

The Role of Big Data Analytics in Industrial Internet of Things

Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational- and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT.We identify and discuss the indispensable challenges that remain to be addressed as future research directions as well.

cs.CY↗

arXiv2018

Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions

Emerging technologies like the Internet of Things (IoT) require latency-aware computation for real-time application processing. In IoT environments, connected things generate a huge amount of data, which are generally referred to as big data. Data generated from IoT devices are generally processed in a cloud infrastructure because of the on-demand services and scalability features of the cloud computing paradigm. However, processing IoT application requests on the cloud exclusively is not an efficient solution for some IoT applications, especially time-sensitive ones. To address this issue, Fog computing, which resides in between cloud and IoT devices, was proposed. In general, in the Fog computing environment, IoT devices are connected to Fog devices. These Fog devices are located in close proximity to users and are responsible for intermediate computation and storage. Fog computing research is still in its infancy, and taxonomy-based investigation into the requirements of Fog infrastructure, platform, and applications mapped to current research is still required. This paper starts with an overview of Fog computing in which the definition of Fog computing, research trends, and the technical differences between Fog and cloud are reviewed. Then, we investigate numerous proposed Fog computing architecture and describe the components of these architectures in detail. From this, the role of each component will be defined, which will help in the deployment of Fog computing. Next, a taxonomy of Fog computing is proposed by considering the requirements of the Fog computing paradigm. We also discuss existing research works and gaps in resource allocation and scheduling, fault tolerance, simulation tools, and Fog-based microservices. Finally, by addressing the limitations of current research works, we present some open issues, which will determine the future research direction.

cs.DC↗

arXiv2018

A Unified Knowledge Representation and Context-aware Recommender System in Internet of Things

Within the rapidly developing Internet of Things (IoT), numerous and diverse physical devices, Edge devices, Cloud infrastructure, and their quality of service requirements (QoS), need to be represented within a unified specification in order to enable rapid IoT application development, monitoring, and dynamic reconfiguration. But heterogeneities among different configuration knowledge representation models pose limitations for acquisition, discovery and curation of configuration knowledge for coordinated IoT applications. This paper proposes a unified data model to represent IoT resource configuration knowledge artifacts. It also proposes IoT-CANE (Context-Aware recommendatioN systEm) to facilitate incremental knowledge acquisition and declarative context driven knowledge recommendation.

cs.DC↗

arXiv2016

Analytics-as-a-Service in a Multi-Cloud Environment through Semantically enabled Hierarchical Data Processing

A large number of cloud middleware platforms and tools are deployed to support a variety of Internet of Things (IoT) data analytics tasks. It is a common practice that such cloud platforms are only used by its owners to achieve their primary and predefined objectives, where raw and processed data are only consumed by them. However, allowing third parties to access processed data to achieve their own objectives significantly increases integration, cooperation, and can also lead to innovative use of the data. Multicloud, privacy-aware environments facilitate such data access, allowing different parties to share processed data to reduce computation resource consumption collectively. However, there are interoperability issues in such environments that involve heterogeneous data and analytics-as-a-service providers. There is a lack of both - architectural blueprints that can support such diverse, multi-cloud environments, and corresponding empirical studies that show feasibility of such architectures. In this paper, we have outlined an innovative hierarchical data processing architecture that utilises semantics at all the levels of IoT stack in multicloud environments. We demonstrate the feasibility of such architecture by building a system based on this architecture using OpenIoT as a middleware, and Google Cloud and Microsoft Azure as cloud environments. The evaluation shows that the system is scalable and has no significant limitations or overheads.

cs.NI↗

arXiv2016

The Schema Editor of OpenIoT for Semantic Sensor Networks

Ontologies provide conceptual abstractions over data, in domains such as the Internet of Things, in a way that sensor data can be harvested and interpreted by people and applications. The Semantic Sensor Network (SSN) ontology is the de-facto standard for semantic representation of sensor observations and metadata, and it is used at the core of the open source platform for the Internet of Things, OpenIoT. In this paper we present a Schema Editor that provides an intuitive web interface for defining new types of sensors, and concrete instances of them, using the SSN ontology as the core model. This editor is fully integrated with the OpenIoT platform for generating virtual sensor descriptions and automating their semantic annotation and registration process.

cs.AI↗