The widespread adoption of truly portable, smart devices and Do-It-Yourself computing platforms by the general public has enabled the rise of new network and system paradigms. This abundance of well-connected, well-equipped, affordable devices, when combined with crowdsourcing methods, enables the development of systems with the aid of the crowd. In this work, we introduce the paradigm of Crowdsourced Systems, systems whose constituent infrastructure, or a significant part of it, is pooled from the general public by following crowdsourcing methodologies. We discuss the particular distinctive characteristics they carry and also provide their ''canonical'' architecture. We exemplify the paradigm by also introducing Crowdcloud, a crowdsourced cloud infrastructure where crowd members can act both as cloud service providers and cloud service clients. We discuss its characteristic properties and also provide its functional architecture. The concepts introduced in this work underpin recent advances in the areas of mobile edge/fog computing and co-designed/cocreated systems.
Smartphones are a key enabling technology in the Internet of Things (IoT) for gathering crowd-sensed data. However, collecting crowd-sensed data for research is not simple. Issues related to device heterogeneity, security, and privacy have prevented the rise of crowd-sensing platforms for scientific data collection. For this reason, we implemented VIVO, an open framework for gathering crowdsensed Big Data for IoT services, where security and privacy are managed within the framework. VIVO introduces the enrolled crowd-sensing model, which allows the deployment of multiple simultaneous experiments on the mobile phones of volunteers. The collected data can be accessed both at the end of the experiment, as in traditional testbeds, as well as in real-time, as required by many Big Data applications. We present here the VIVO architecture, highlighting its advantages over existing solutions, and four relevant real-world applications running on top of VIVO.
Abstract. Testbed facilities play a major role in the study and evolution of emerging technologies, such as those related to the Internet of Things. In this work we introduce the concept of modelled testbeds, which are 3D interactive representations of physical testbeds where the addition of virtual resources mimicking the physical ones is made possible thanks to back-end infrastructure. We present the architecture of the Syndesi testbed, deployed at the premises of University of Geneva, which was used for the prototype modelled testbed. We investigate several extrapolation techniques towards realistic value assignment for virtual sensor measurements. K-fold cross validation is performed in a dataset comprising of nearly 300'000 measurements of temperature, illuminance and humidity sensors collected from the physical sensors of the Syndesi testbed, in order to evaluate the accuracy of the methods. We obtain strong results including Mean Absolute Percentage Error (MAPE) levels below 7%.
Edge computing established paradigms are prone to implicate solely powerful server-like edge nodes, in static or semistatic topologies, of centrally-controlled edge networks. In this paper, leveraging upon recent technological advancements and trends, we introduce a novel networking paradigm employing resources provided by independent crowd peers, within a zone of local proximity, to establish collaborative networks for edge computing. We call this paradigm the Crowdsourced Edge. We detail the architecture and characteristics of this novel paradigm, highlighting its unique characteristics and specific challenges, while also positioning it visa -vis the existing edge computing concretisations. Finally, we demonstrate the Crowdsourced Edge functionality by presenting an ongoing use case regarding a videoenhanced object search.
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