Abstract

Dynamic control software reconfiguration for the Internet of Things (IoT) and cyber-physical systems (CPS) is crucial for adaptable and efficient automation. This paper presents a knowledge-driven architecture enabling dynamic device reconfiguration using the Web Ontology Language (OWL) and Terse Triple Language (TTL) formats. Key components include a capability ontology, session-type information for sequencing and concurrent operations, and an Integrated Development Environment (IDE) for automated control design. The capability ontology standardizes machine capabilities, facilitating device integration based on their capabilities, while session-type information ensures correct sequencing and synchronization of machine functions. The IDE platform supports dynamic reconfiguration by automating device selection, control strategy formulation, and system adjustments across diverse use cases. The architecture has been validated in real-world scenarios, including smart meeting rooms, warehouse automation, and energy management, showing a reduction in manual configuration time (up to 50%), development time (86% in some cases), and error rates (30%). Benchmarking results indicate faster code generation (40% improvement) and efficient component integration across different CPS environments. Challenges like computational complexity, scalability, and integration with existing systems highlight limitations. Future research will explore further optimizations and broader applicability to ensure low-latency, high-accuracy, and seamless integration in complex CPS. This work advances dynamic control software reconfiguration by providing a flexible solution that enhances CPS reliability and efficiency through a knowledgedriven approach.

Abstract

Developing control software for Cyber-Physical Systems (CPS) and industrial automation faces challenges like hardware integration, real-time responsiveness, and high reliability. Traditional environments often lack domain-specific knowledge, leading to inefficiencies. This paper introduces the Knowledge-aided Integrated Development Environment (K-IDE), which integrates domain knowledge into the software lifecycle, improving efficiency, consistency, and maintainability. K-IDE uses domain-specific languages (DSLs) and knowledge graphs to automate design and code generation. Key findings show that K-IDE significantly reduces development time and errors compared to traditional IDEs, proving effective in robotics and industrial automation. K-IDE bridges the gap between abstract designs and concrete implementations, offering a robust solution for modern control software development.

Abstract

Remote Labs refer to an end-to-end system, including hardware and software built to access scientific equipment and resources remotely. The software platform built for such purposes needs to be robust enough to handle the communication of inputs and outputs between the client and the hardware nodes with minimal latency and simultaneously provide a seamless user experience. This paper highlights the importance of scalability in Remote Labs and presents multi-user multiplexing as a solution, to essentially provide users with concurrent access to the hardware node for experiments which can generate outputs instantaneously. The paper discusses the inefficiency of existing web-based Remote Labs with 4-layered architectures and proposes the use of WebSocket with a 3-layer software architecture to enhance user experience, accelerate input-output communication and implement multi-user multiplexing. To showcase the effectiveness of the proposed architecture over existing implementations using Blynk IoT platform as the middleware, a comprehensive communication pipeline was developed from scratch to perform Kirchhoff’s Voltage Law (KVL) experiment remotely.

Abstract

This paper presents a knowledge-driven approach for automated synthesis of controllers in three different use-cases. The approach addresses the engineering challenge posed by Industrie 4.0, which requires fast, reliable, and flexible integration of multiple heterogeneous hardware and software components. Manual design approaches are not scalable for large systems due to their complexity. The proposed approach captures resource-capability knowledge and uses a reasoning-based synthesis mechanism to compose a controller design for a plant goal. The approach uses domain-specific languages (DSLs) to describe the components, their interfaces, and capabilities. The generated control designs are executable codes that implement the control strategy. The proposed approach reduces the average engineering time by 70% and generates on an average 60% of the executable code in each use-case. The

Abstract

One of the key challenges for a novice engineer in a product company is to comprehend the product sufficiently and quickly. It can take anywhere from six months to several years for them to attain mastery but they need to start delivering results much before. SaaS (Software-as-a-Service) products have sophisticated system architecture which adds to the time and effort of understanding them. On the other hand, time available to new hires for product understanding continues to be short and getting shorter, given the pressure to deliver more in less time. Constructivist theory views learning as a personal process in which the learner constructs new knowledge for themselves. Building and refining a mental model is the key way in which they learn, similar to how the brain operates. This paper presents an approach to improve system comprehension process by using a system model that a) acts as a transitional object to aid and refine the mental model of the learner, and b) captures the current understanding of the dynamics of the software system in a way that can be reasoned with and simulated. We have adapted discrete systems modeling techniques and used a transition system as a lightweight modeling language. Such a model can be used by novice engineers during their product ramp-up phase to build a model of the software system that captures their knowledge of the system and aid their mental model. The paper also presents a learning approach in which the learners create and refine these models iteratively using the available and newly uncovered knowledge about the software system. We hypothesize that by leveraging this modeling

Abstract

Software-as-a-Service (SaaS) product companies have brought in significant changes in how we build software from architecture and engineering process perspective. SaaS products are large, dis- tributed software systems hosted in cloud and built using collabo- rating services (or micro-services). The software releases happen in days and weeks, necessitating an agile development process. Novice engineers (those who join the company fresh from college) need to become comfortable with complex systems and proficient in agile delivery with high quality, otherwise they fall behind in productivity. The paper posits that, to be successful at these SaaS product companies, the novice engineers need good modeling and design skills. While this has been for all software development, the changes driven by SaaS products have made this need more acute. Such skills will allow them to capture their feature behaviors (in context of their understanding of the larger product) in an implementation- independent manner and any knowledge gaps can be identified and bridged by their collaborators. We propose a modeling language that is easy for them to learn and use, and which has characteristics suitable for the kind of engineering work they need to do in their early years in a SaaS product company. This modeling language is based on the notion of Transition Systems. The paper demonstrates the usage and value of this language by creating a model for a real feature. The modeling language is quite general and transcends abstraction boundaries. We also present a modeling process that should be used with this language for better results. This is a short position paper that presents an idea about a new modeling language for a specific purpose (helping novice engineers design well at SaaS product companies). Validation studies for the language and the design process is a work in progress and the results will be shared in a full paper later.

Abstract

Remote Triggered Labs (RTL) are helpful for students to work on laboratory experiments virtually anytime, anywhere. Such setups can facilitate distance learning and are helpful during pandemics. In this paper, the use of Computer Vision (CV) is demonstrated for RTL experiments. For this, a use-case of the Conservation of Mechanical Energy experiment is considered. A CV-based approach is used to estimate an object’s velocity whose setup primarily consists of a microprocessor, a camera and infrared (IR) sensors. The experiment is recorded, and various CV techniques are employed to estimate the object’s velocity. This paper also compares a CV-based and an IR sensor-based approach to estimate the object’s velocity. Linear regression applied to the CV-based implementation resulted in an optimal mean-squared error (MSE), nearly 10 times better than IR-based implementation.

Abstract

An engineer in a product company is expected to design a good solution to a computing problem (Design skill) and articulate the solution well (Expression skill). We expect an industry-ready student (final year student or a fresh campus hire) as well to demonstrate both these skills when working on simple problems assigned to them. This paper reports on the results when we tested a cohort of participants (N=16) for these two skills. We created two participant groups from two different tiers of college, one from a Tier 1 college (who were taking an advanced elective course), and another from Tier 2 colleges (who had been hired for internship in a SaaS product company). We gave them a simple design problem and evaluated the quality of their design and expression. Design quality was evaluated along three design principles of Abstraction, Decomposition, and Precision (adapted from the Software Engineering Book of Knowledge). Expression quality was evaluated using criteria we developed for our study that is based on the diversity and density of the expressions used in the articulation. We found the students lacking in design and expression skills. Specifically, a) they struggled with abstraction as a design principle, b) they did not use enough modes of expressions to articulate their design, and c) they did not use enough formal notations (UML, equations, relations, etc.). We also found significant difference in the performance between the two participant groups.

Abstract

No Results Found

Abstract

Control systems operate industrial plants to accomplish stakeholder objectives like achieving production targets, complying with environmental ordinances, handling faults, etc. Such stakeholder objectives get realised by identifying and executing valid control actions on the plant’s control system. E.g., to achieve fault management a command is fired to place machines in a fault mode when the plant is under an error state. Arriving at such control actions is a non-trivial task demanding a detailed understanding of the plant’s structure and behaviour. Besides, it is also essential to verify the consequences of such control actions relative to other cross-cutting objectives and plant behaviour. E.g., to fulfil fault management objectives, the action to set machines in fault mode may affect production goals due to the machine unavailability. Hence, validation of control actions is vital before executing them using the actual plant’s control system. With digital twin technologies (DT), it is now possible to verify the implications of such control actions against a plant’s behaviour and objectives in a simulated environment without affecting the actual plant operations. DTs get developed autonomously as one-off solutions to simulate and validate plant control actions in the current state of practice, demanding high efforts and domain expertise. Our paper proposes a knowledge-driven approach enabling automation in DT development. The result of our approach is an auto-generated digital twin that pro-actively mimics the plant’s control system behaviour and helps with the validation of control actions before their execution. We use this approach to build three fault management DTs in a power plant. The application of our approach significantly reduces the manual efforts and development time to build such DTs.