GreenCrowd: Toward a Holistic Algorithmic Crowd Charging Framework

Crowd charging represents an alternative peer-to-peer energy replenishment option for mobile users to align with the circular economy paradigm. Following this option, users bound by finite resource capacity utilize the energy from external to the crowd wireless or wired energy sources (such as shared chargers), and internal to the crowd energy sources (such as mobile devices, via wireless power transfer). If designed carefully, such utilization can boost the energy availability of users and provide energy ubiquitously to their devices for making them functional for longer. This article proposes the GreenCrowd framework, introducing a privacy-by-design in the digital domain crowd charging process, the architecture of which incorporates multiple crowd-* components, such as online social information exploitation, algorithmic battery aging mitigation, user reward mechanisms, and advanced decision making. The primary aim of article is to present the technological and applicative requirements and constraints of GreenCrowd, and provide practical evidence on its feasibility

Editing Team: Markus Fiedler

and contributions of the FIA community on the important research topics that should be addressed for the Framework Programme 8 research programmes broadly grouped around three main concerns; economic and business interests; societal interests and challenges; technical disruptions and capabilities. The contents of this roadmap originate with the community of researchers working on all aspects of the Future Internet and meet to share and discuss ideas through the Future Internet Assembly through an open consultation of research projects who participate in FIA. This roadmap is primarily concerned with identifying research that can be carried out in the second half of this decade and which will have an impact in 2020 and beyond. By ‘impact ’ we mean will result in products, services, systems, capabilities, that come to market and are available and deployed in that timeframe The approach adopted in this report is to integrate contributions across the entire space of future Internet research with the aim of bringing out the vision for how and where the Internet will make a significant difference in the future and identifying the broad challenges and gaps, and identifying the solutions and research needs in the future. In this report we have summarised and grouped ideas with the aim of identifying the strong themes and consistent challenges that emerge looking acros

From MANET to people-centric networking: Milestones and open research challenges

In this paper, we discuss the state of the art of (mobile) multi-hop ad hoc networking with the aim to present the current status of the research activities and identify the consolidated research areas, with limited research opportunities, and the hot and emerging research areas for which further research is required. We start by briefly discussing the MANET paradigm, and why the research on MANET protocols is now a cold research topic. Then we analyze the active research areas. Specifically, after discussing the wireless-network technologies, we analyze four successful ad hoc networking paradigms, mesh networks, opportunistic networks, vehicular networks, and sensor networks that emerged from the MANET world. We also present an emerging research direction in the multi-hop ad hoc networking field: people centric networking, triggered by the increasing penetration of the smartphones in everyday life, which is generating a people-centric revolution in computing and communications

Synergistic user ↔ context analytics

Various flavours of a new research field on (socio-)physical or personal analytics have emerged, with the goal of deriving semanticallyrich insights from people’s low-level physical sensing combined with their (online) social interactions. In this paper, we argue for more comprehensive data sources, including environmental and application-specific data, to better capture the interactions between users and their context, in addition to those among users. We provide some example use cases and present our ongoing work towards a synergistic analytics platform: a testbed based on mobile crowdsensing and IoT, a data model for representing the different sources of data and their connections, and a prediction engine for analyzing the data and producing insights