Towards Collection of Smart City Data for Cloud Storage Using UAVs

The article describes the methodology and process of collecting smart city data using drones for cities that do not have a sufficiently developed infrastructure. For storage and subsequent analysis of data, a cloud server is required; TUC DriveCloud is presented as an example of such a server in the article. Traffic analysis and building inspection are described as examples of drone data collection tasks. The advantages and disadvantages of collecting data using a thermal imaging camera are also discussed using the example of the problem of detecting and tracking the movement of people

Smart streetlights: a feasibility study

The world's cities are growing. The effects of population growth and urbanisation mean that more people are living in cities than ever before, a trend set to continue. This urbanisation poses problems for the future. With a growing population comes more strain on local resources, increased traffic and congestion, and environmental decline, including more pollution, loss of green spaces, and the formation of urban heat islands. Thankfully, many of these stressors can be alleviated with better management and procedures, particularly in the context of road infrastructure. For example, with better traffic data, signalling can be smoothed to reduce congestion, parking can be made easier, and streetlights can be dimmed in real time to match real-world road usage. However, obtaining this information on a citywide scale is prohibitively expensive due to the high costs of labour and materials associated with installing sensor hardware. This study investigated the viability of a streetlight-integrated sensor system to affordably obtain traffic and environmental information. This investigation was conducted in two stages: 1) the development of a hardware prototype, and 2) evaluation of an evolved prototype system. In Stage 1 of the study, the development of the prototype sensor system was conducted over three design iterations. These iterations involved, in iteration 1, the live deployment of the prototype system in an urban setting to select and evaluate sensors for environmental monitoring, and in iterations 2 and 3, deployments on roads with live and controlled traffic to develop and test sensors for remote traffic detection. In the final iteration, which involved controlled passes of over 600 vehicle, 600 pedestrian, and 400 cyclist passes, the developed system that comprised passive-infrared motion detectors, lidar, and thermal sensors, could detect and count traffic from a streetlight-integrated configuration with 99%, 84%, and 70% accuracy, respectively. With the finalised sensor system design, Stage 1 showed that traffic and environmental sensing from a streetlight-integrated configuration was feasible and effective using on-board processing with commercially available and inexpensive components. In Stage 2, financial and social assessments of the developed sensor system were conducted to evaluate its viability and value in a community. An evaluation tool for simulating streetlight installations was created to measure the effects of implementing the smart streetlight system. The evaluation showed that the on-demand traffic-adaptive dimming enabled by the smart streetlight system was able to reduce the electrical and maintenance costs of lighting installations. As a result, a 'smart' LED streetlight system was shown to outperform conventional always-on streetlight configurations in terms of financial value within a period of five to 12 years, depending on the installation's local traffic characteristics. A survey regarding the public acceptance of smart streetlight systems was also conducted and assessed the factors that influenced support of its applications. In particular, the Australia-wide survey investigated applications around road traffic improvement, streetlight dimming, and walkability, and quantified participants' support through willingness-to-pay assessments to enable each application. Community support of smart road applications was generally found to be positive and welcomed, especially in areas with a high dependence on personal road transport, and from participants adversely affected by spill light in their homes. Overall, the findings of this study indicate that our cities, and roads in particular, can and should be made smarter. The technology currently exists and is becoming more affordable to allow communities of all sizes to implement smart streetlight systems for the betterment of city services, resource management, and civilian health and wellbeing. The sooner that these technologies are embraced, the sooner they can be adapted to the specific needs of the community and environment for a more sustainable and innovative future

Pedestrian soft biometrics recognition using deep learning on thermal images in smart cities

With technological advancement and the rise of the Internet of Things, our society is becoming more interconnected than ever before. Our computers and devices are getting smaller, and their computing power and memory has been increasing. These advances coupled with the leaps in artificial intelligence caused by the deep learning revolution in recent yearshave led to an increasingly rising interest in the field of pervasive intelligence. Intelligence in the environment has been used in smart homes in order to bring assistance to semi-autonomous people by performing activity recognition based on sensor data. As technology keeps improving, we may start to investigate the extension of assistive technologies beyond the boundaries of smart homes and into our smart cities. In order to bring assistance to semi-autonomous people, the first step is to be able to recognize profiles of vulnerable people. In order to leverage technology and artificial intelligence to make our cities smarter, safer and more accessible, this thesis investigates the use of environmental sensors such as thermal cameras to perform pedestrian soft biometrics recognition (age, gender and mobility) in the city. In this thesis, the process of building prototypes from scratch in order to collect thermal gait data in the city is explored, and the use and optimization of deep learning algorithms to perform soft biometrics recognition, as well as the feasibility of implementing these algorithms on limited resource boards are explored. The use of unprocessed thermal images allows a higher degree of privacy for the citizens, and it is novel in the field of human profile recognition. This thesis aims to set the foundation of future work, both in the field of thermal images-based soft biometrics recognition and pervasive intelligence in our cities in order to make them smarter, and move towards an interconnected society. Les progrès technologiques et le développement de l’Internet des Objets nous mènent vers une société de plus en plus interconnectée. Nos ordinateurs et nos appareils deviennent de plus en plus petits et leur puissance de calcul et leur mémoire ne cesse de s’améliorer. Ces avancées combinées aux récents progrès dans le domaine de l’intelligence artificielle avec la révolution de l’apprentissage profond ont mené à un intérêt grandissant dans le domaine de l’intelligence ambiante. L’intelligence ambiante a été utilisée dans le domaine des maisons intelligentes sous forme de reconnaissance d’activités, permettant d’assister les personnes semi-autonomes en utilisant des données collectées par des capteurs. Alors que le progrès technologique continue, nous arrivons à un point où l’hypothèse d’étendre ces stratégies d’assistance des maisons aux villes intelligentes devient de plus en plus réaliste. Afin d’étendre cette assistance aux villes, la première étape est d’identifier les personnes vulnérables, qui sont celles qui pourraient bénéficier de cette assistance. Dans le but d’utiliser la technologie pour rendre nos villes plus intelligentes, plus sûres et plus accessibles, cette thèse explore l’utilisation de capteurs environnementaux tels que des caméras thermiques pour effectuer de la reconnaissance de profils dans la ville (âge, genre et mobilité). Dans cette thèse, le processus de construction de prototypes pour récolter des données thermales dans la ville est présenté, et l’utilisation ainsi que l’optimisation d’algorithmes d’apprentissage profond pour la reconnaissance de profils est explorée. L’implémentation des algorithmes sur un système embarqué est également abordée. L’utilisation d’images thermiques garantit un plus grand degré d’anonymat pour les citoyens que l’utilisation de caméras RGB, et cette thèse représente les premiers travaux de reconnaissance de profils multiples en utilisant uniquement des images thermiques sans pré-traitement. Cette thèse a pour objectif de poser les bases pour des travaux futurs dans le domaine de la reconnaissance de profils en utilisant des images thermiques, ainsi que dans le domaine de l’intelligence ambiante dans nos villes, afin de les rendre plus intelligentes et de se diriger vers une société interconnectée