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Bhupesh Mishra
Department of Computer Science, University of Bradford, Bradford BD7 1DP, UK

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Journal article
Published: 04 May 2021 in IoT
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A key aspect of the development of Smart Cities involves the efficient and effective management of resources to improve liveability. Achieving this requires large volumes of sensors strategically deployed across urban areas. In many cases, however, it is not feasible to install devices in remote and inaccessible areas, resulting in incomplete data coverage. In such situations, citizens can often play a crucial role in filling this data collection gap. A popular complimentary science to traditional sensor-based data collection is to design Citizen Science (CS) activities in collaboration with citizens and local communities. Such activities are also designed with a feedback loop where the Citizens benefit from their participation by gaining a greater sense of awareness of their local issues while also influencing how the activities can align best with their local contexts. The participation and engagement of citizens are vital and yet often a real challenge in ensuring the long-term continuity of CS projects. In this paper, we explore engagement factors, factors that help keeping engagement high, in technology-centric CS projects where technology is a key enabler to support CS activities. We outline a literature review of exploring and understanding various motivational and engagement factors that influence the participation of citizens in technology-driven CS activities. Based on this literature, we present a mobile-based flood monitoring citizen science application aimed at supporting data collection activities in a real-world CS project as part of an EU project. We discuss the results of a user evaluation of this app, and finally discuss our findings within the context of citizens’ engagement.

ACS Style

Muhammad Ali; Bhupesh Mishra; Dhavalkumar Thakker; Suvodeep Mazumdar; Sydney Simpson. Using Citizen Science to Complement IoT Data Collection: A Survey of Motivational and Engagement Factors in Technology-Centric Citizen Science Projects. IoT 2021, 2, 275 -309.

AMA Style

Muhammad Ali, Bhupesh Mishra, Dhavalkumar Thakker, Suvodeep Mazumdar, Sydney Simpson. Using Citizen Science to Complement IoT Data Collection: A Survey of Motivational and Engagement Factors in Technology-Centric Citizen Science Projects. IoT. 2021; 2 (2):275-309.

Chicago/Turabian Style

Muhammad Ali; Bhupesh Mishra; Dhavalkumar Thakker; Suvodeep Mazumdar; Sydney Simpson. 2021. "Using Citizen Science to Complement IoT Data Collection: A Survey of Motivational and Engagement Factors in Technology-Centric Citizen Science Projects." IoT 2, no. 2: 275-309.

Journal article
Published: 13 November 2020 in Smart Cities
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Traditional Artificial Intelligence (AI) technologies used in developing smart cities solutions, Machine Learning (ML) and recently Deep Learning (DL), rely more on utilising best representative training datasets and features engineering and less on the available domain expertise. We argue that such an approach to solution development makes the outcome of solutions less explainable, i.e., it is often not possible to explain the results of the model. There is a growing concern among policymakers in cities with this lack of explainability of AI solutions, and this is considered a major hindrance in the wider acceptability and trust in such AI-based solutions. In this work, we survey the concept of ‘explainable deep learning’ as a subset of the ‘explainable AI’ problem and propose a new solution using Semantic Web technologies, demonstrated with a smart cities flood monitoring application in the context of a European Commission-funded project. Monitoring of gullies and drainage in crucial geographical areas susceptible to flooding issues is an important aspect of any flood monitoring solution. Typical solutions for this problem involve the use of cameras to capture images showing the affected areas in real-time with different objects such as leaves, plastic bottles etc., and building a DL-based classifier to detect such objects and classify blockages based on the presence and coverage of these objects in the images. In this work, we uniquely propose an Explainable AI solution using DL and Semantic Web technologies to build a hybrid classifier. In this hybrid classifier, the DL component detects object presence and coverage level and semantic rules designed with close consultation with experts carry out the classification. By using the expert knowledge in the flooding context, our hybrid classifier provides the flexibility on categorising the image using objects and their coverage relationships. The experimental results demonstrated with a real-world use case showed that this hybrid approach of image classification has on average 11% improvement (F-Measure) in image classification performance compared to DL-only classifier. It also has the distinct advantage of integrating experts’ knowledge on defining the decision-making rules to represent the complex circumstances and using such knowledge to explain the results.

ACS Style

Dhavalkumar Thakker; Bhupesh Kumar Mishra; Amr Abdullatif; Suvodeep Mazumdar; Sydney Simpson. Explainable Artificial Intelligence for Developing Smart Cities Solutions. Smart Cities 2020, 3, 1353 -1382.

AMA Style

Dhavalkumar Thakker, Bhupesh Kumar Mishra, Amr Abdullatif, Suvodeep Mazumdar, Sydney Simpson. Explainable Artificial Intelligence for Developing Smart Cities Solutions. Smart Cities. 2020; 3 (4):1353-1382.

Chicago/Turabian Style

Dhavalkumar Thakker; Bhupesh Kumar Mishra; Amr Abdullatif; Suvodeep Mazumdar; Sydney Simpson. 2020. "Explainable Artificial Intelligence for Developing Smart Cities Solutions." Smart Cities 3, no. 4: 1353-1382.

Original article
Published: 24 February 2020 in Journal of Reliable Intelligent Environments
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Event monitoring is an essential application of Smart City platforms. Real-time monitoring of gully and drainage blockage is an important part of flood monitoring applications. Building viable IoT sensors for detecting blockage is a complex task due to the limitations of deploying such sensors in situ. Image classification with deep learning is a potential alternative solution. However, there are no image datasets of gullies and drainages. We were faced with such challenges as part of developing a flood monitoring application in a European Union-funded project. To address these issues, we propose a novel image classification approach based on deep learning with an IoT-enabled camera to monitor gullies and drainages. This approach utilises deep learning to develop an effective image classification model to classify blockage images into different class labels based on the severity. In order to handle the complexity of video-based images, and subsequent poor classification accuracy of the model, we have carried out experiments with the removal of image edges by applying image cropping. The process of cropping in our proposed experimentation is aimed to concentrate only on the regions of interest within images, hence leaving out some proportion of image edges. An image dataset from crowd-sourced publicly accessible images has been curated to train and test the proposed model. For validation, model accuracies were compared considering model with and without image cropping. The cropping-based image classification showed improvement in the classification accuracy. This paper outlines the lessons from our experimentation that have a wider impact on many similar use cases involving IoT-based cameras as part of smart city event monitoring platforms.

ACS Style

Bhupesh Kumar Mishra; Dhavalkumar Thakker; Suvodeep Mazumdar; Daniel Neagu; Marian Gheorghe; Sydney Simpson. A novel application of deep learning with image cropping: a smart city use case for flood monitoring. Journal of Reliable Intelligent Environments 2020, 6, 51 -61.

AMA Style

Bhupesh Kumar Mishra, Dhavalkumar Thakker, Suvodeep Mazumdar, Daniel Neagu, Marian Gheorghe, Sydney Simpson. A novel application of deep learning with image cropping: a smart city use case for flood monitoring. Journal of Reliable Intelligent Environments. 2020; 6 (1):51-61.

Chicago/Turabian Style

Bhupesh Kumar Mishra; Dhavalkumar Thakker; Suvodeep Mazumdar; Daniel Neagu; Marian Gheorghe; Sydney Simpson. 2020. "A novel application of deep learning with image cropping: a smart city use case for flood monitoring." Journal of Reliable Intelligent Environments 6, no. 1: 51-61.