Browsing by Subject "mobile computing"

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  • Emenike, Hilary; Dar, Farooq; Liyanage, Mohan; Sharma, Rajesh; Zuniga, Agustin; Hoque, Mohammad Ashraful; Radeta, Marko; Nurmi, Petteri; Flores, Huber (IEEE, 2021)
    International Conference on Pervasive Computing and Communications
    We contribute MIDAS as a novel sensing solution for characterizing everyday objects using thermal dissipation. MIDAS takes advantage of the fact that anytime a person touches an object, it results in heat transfer. By capturing and modeling the dissipation of the transferred heat, e.g., through the decrease in the captured thermal radiation, MIDAS can characterize the object and determine its material. We validate MIDAS through extensive empirical benchmarks and demonstrate that MIDAS offers an innovative sensing modality that can recognize a wide range of materials - with up to 83% accuracy - and generalize to variations in the people interacting with objects.
  • Shaw, Peter; Mikusz, Mateusz; Nurmi, Petteri; Davies, Nigel (ACM, 2019)
    Internet of Things (IoT) devices are becoming increasingly ubiquitous in our everyday environments. While the number of devices and the degree of connectivity is growing, it is striking that as a society we are increasingly unaware of the locations and purposes of such devices. Indeed, much of the IoT technology being deployed is invisible and does not communicate its presence or purpose to the inhabitants of the spaces within which it is deployed. In this paper, we explore the potential benefits and challenges of constructing IoT maps that record the location of IoT devices. To illustrate the need for such maps, we draw on our experiences from multiple deployments of IoT systems.
  • Zuniga , Agustin; Flores, Huber; Lagerspetz, Eemil; Tarkoma, Sasu; Manner, Jukka; Hui, Pan; Nurmi, Petteri (International World Wide Web Conferences Steering Committee, 2019)
    We contribute by quantifying the effect of network latency and battery consumption on mobile app performance and retention, i.e., user’s decisions to continue or stop using apps. We perform our analysis by fusing two large-scale crowdsensed datasets collected by piggybacking on information captured by mobile apps. We find that app performance has an impact in its retention rate. Our results demonstrate that high energy consumption and high latency decrease the likelihood of retaining an app. Conversely, we show that reducing latency or energy consumption does not guarantee higher likelihood of retention as long as they are within reasonable standards of performance. However, we also demonstrate that what is considered reasonable depends on what users have been accustomed to, with device and network characteristics, and app category playing a role. As our second contribution, we develop a model for predicting retention based on performance metrics. We demonstrate the benefits of our model through empirical benchmarks which show that our model not only predicts retention accurately, but generalizes well across application categories, locations and other factors moderating the effect of performance.