Browsing by Author "Rana, O"

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    Automated license plate recognition for resource-constrained environments
    (MDPI, 2022) Padmasiri, H; Shashirangana, J; Meedeniya, D; Rana, O; Perera, C
    The incorporation of deep-learning techniques in embedded systems has enhanced the capabilities of edge computing to a great extent. However, most of these solutions rely on high-end hardware and often require a high processing capacity, which cannot be achieved with resource-constrained edge computing. This study presents a novel approach and a proof of concept for a hardware-efficient automated license plate recognition system for a constrained environment with limited resources. The proposed solution is purely implemented for low-resource edge devices and performed well for extreme illumination changes such as day and nighttime. The generalisability of the proposed models has been achieved using a novel set of neural networks for different hardware configurations based on the computational capabilities and low cost. The accuracy, energy efficiency, communication, and computational latency of the proposed models are validated using different license plate datasets in the daytime and nighttime and in real time. Meanwhile, the results obtained from the proposed study have shown competitive performance to the state-of-the-art server-grade hardware solutions as well.
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    PARROT: Interactive Privacy-Aware Internet of Things Application Design Tool
    (2023) Alhirabi, N; Beaumont, S; Llanos, JS; Meedeniya, D; Rana, O; Perera, C
    Internet of Things (IoT) applications typically collect and analyse personal data that is categorised as sensitive or special category of personal data. These data are subject to a higher degree of protection under data privacy laws. Regardless of legal requirements to support privacy practices, such as in Privacy by Design (PbD) schemes, these practices are not yet commonly followed by software developers. The difficulty of developing privacy-preserving applications emphasises the importance of exploring the problems developers face to embed privacy techniques, suggesting the need for a supporting tool. An interactive IoT application design tool – PARROT (PrivAcy by design tool foR inteRnet Of Things) – is presented. This tool helps developers to design privacy-aware IoT applications, taking account of privacy compliance during the design process and providing real-time feedback on potential privacy violations. A user study with 18 developers was conducted, comprising a semi-structured interview and a design exercise to understand how developers typically handle privacy within the design process. Collaboration with a privacy lawyer was used to review designs produced by developers to uncover privacy limitations that could be addressed by developing a software tool. Based on the findings, a proof-of-concept prototype of PARROT was implemented and evaluated in two controlled lab studies. The outcome of the study indicates that IoT applications designed with PARROT addressed privacy concerns better and managed to reduce several of the limitations identified. From a privacy compliance perspective, PARROT helps developers to address compliance requirements throughout the design and testing process. This is achieved by incorporating privacy specific design features into the IoT application from the beginning rather than retrospectively