Accelerated collection of sensor data by mobility-enabled topology ranks

We study the problem of fast and energy-efficient data collection of sensory data using a mobile sink, in wireless sensor networks in which both the sensors and the sink move. Motivated by relevant applications, we focus on dynamic sensory mobility and heterogeneous sensor placement. Our approach basically suggests to exploit the sensor motion to adaptively propagate information based on local conditions (such as high placement concentrations), so that the sink gradually “learns” the network and accordingly optimizes its motion. Compared to relevant solutions in the state of the art (such as the blind random walk, biased walks, and even optimized deterministic sink mobility), our method significantly reduces latency (the improvement ranges from 40% for uniform placements, to 800% for heterogeneous ones), while also improving the success rate and keeping the energy dissipation at very satisfactory level

VIVO: a Secure, Privacy-Preserving, and Real-Time Crowd-Sensing Framework for the Internet of Things

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 crowd-sensed 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

Crowdcloud: A Crowdsourced System for Cloud Infrastructure

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 wellconnected, 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

The Paradigm of Crowdsourced Systems

Title: The Paradigm of Crowdsourced Systems Abstract: High acceptance rates of truly personal, portable devices such as smartphones and smart gadgets, along with the successful introduction of DIY computer platforms, like Arduino's and Raspberry Pi's, have lead to an unprecedented abundance of well-connected and well-equipped devices. Crowdsourced Systems is a new system paradigm that seeks to exploit the high availability of such devices and thus change the way data is generated, processed and consumed. In this talk, we will discuss this new paradigm, the challenges and opportunities it poses, review real-world use-cases and present relative on-going standardization efforts. Short CV: Dr. Constantinos Marios Angelopoulos is Lecturer in Computing at Bournemouth University (U.K.) specializing in future and emerging paradigms of computer networks and distributed systems. He is also the Lead Editor of the ITU-T Work Item on Crowdsourced Systems; co-author of the ITU-T Technical Report on “Artificial Intelligence in IoT” and the Vocabulary Co-rapporteur for ITU-T SG20. In the past, he has worked for three years as a postdoctoral researcher at University of Geneva (CH) under the prestigious Swiss Government Excellence Scholarship for Foreign Researchers