Energy-efficient Internet of Things monitoring with low-capacity devices

The Internet of Things (IoT) allows users to gather data from the physical environment. While sensors in public spaces are already widely used, users are reluctant to deploy sensors for shared data at their homes. The deployment of IoT nodes at the users premises presents privacy issues regarding who can access to their data once it is sent to the Cloud which the users cannot control. In this paper we present an energy-efficient and low cost solution for environmental monitoring at the users home. Our system is built completely with open source components and is easy to reproduce. We leverage the infrastructure and trust of a community network to store and control the access to the monitored data. We tested our solution during several months on different low-capacity single board computers (SBC) and it showed to be stable. Our results suggest that this solution could become a permanently running service in SBCs at the users homes.Peer ReviewedPostprint (author's final draft

Image Classification on IoT Edge Devices: Profiling and Modeling

With the advent of powerful, low-cost IoT systems, processing data closer to where the data originates, known as edge computing, has become an increasingly viable option. In addition to lowering the cost of networking infrastructures, edge computing reduces edge-cloud delay, which is essential for mission-critical applications. In this thesis, we show the feasibility and study the performance of image classification using IoT devices. Specifically, we explore the relationships between various factors of image classification algorithms that may affect energy consumption such as dataset size, image resolution, algorithm type, algorithm phase, and device hardware. Our experiments show a strong, positive linear relationship between three predictor variables, namely model complexity, image resolution, and dataset size, with respect to energy consumption. In addition, in order to provide a means of predicting the energy consumption of an edge device performing image classification, we investigate the usage of three machine learning algorithms using the data generated from our experiments. The performance as well as the trade offs for using linear regression, Gaussian process, and random forests are discussed and validated. Our results indicate that the random forest model outperforms the two former algorithms, with an R-squared value of 0.95 and 0.79 for two different validation datasets

Energy-efficient Internet of Things monitoring with low-capacity devices

The Internet of Things (IoT) allows users to gather data from the physical environment. While sensors in public spaces are already widely used, users are reluctant to deploy sensors for shared data at their homes. The deployment of IoT nodes at the users premises presents privacy issues regarding who can access to their data once it is sent to the Cloud which the users cannot control. In this paper we present an energy-efficient and low cost solution for environmental monitoring at the users home. Our system is built completely with open source components and is easy to reproduce. We leverage the infrastructure and trust of a community network to store and control the access to the monitored data. We tested our solution during several months on different low-capacity single board computers (SBC) and it showed to be stable. Our results suggest that this solution could become a permanently running service in SBCs at the users homes.Peer Reviewe

A City in Common: Explorations on Sustained Community Engagement with Bottom-up Civic Technologies

Large technology companies and city councils are increasingly developing smart city programmes: augmenting urban environments with smart and ubiquitous computing devices, to transform how cities are run. At a smaller scale, communities of citizens are appropriating technologies to tackle matters of concern and to effect positive change from the bottom-up. HCI researchers are also deploying civic technology in the wild, sometimes collaborating with these communities, in the pursuit of both scientific and societal impact. However, little is known about how impactful they have been, and the extent to which they have meaningfully engaged communities in the long term. The goal of this PhD is to identify the factors that can guide the design and deployment of engaging, sustainable and impactful civic technology interventions, from the perspective of the communities that they are intended to benefit. Three case studies are presented: an ethnographic study of an existing civic technology, and two design and evaluation studies of novel interventions. A set of themes was derived from the studies that highlight factors that are positively associated to engagement, sustainability and impact. Based on these themes and on experience from deploying interventions, a framework was developed and validated. It comprises six key phases: identification of matters of concern, framing, co-design of community technologies, deployment, orchestration, and evaluation. In line with a new wave of civically engaged HCI and participatory methods, the framework puts people at the heart of socio-technical innovation and technology in the service of the common good by fostering the development of a commons: a pool of community managed resources. Using this approach, the thesis explores how researchers, entrepreneurs, artists, city councils and communities can collaborate to address community issues using digital technologies. It further suggests how citizens can be supported to develop skills that will allow them to appropriate the intervention for their own situated purposes