| 1. |
- Angelopoulos, Constantinos Marios, et al.
(författare)
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Improving sensor network performance with wireless energy transfer
- 2015
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Ingår i: International Journal of Ad Hoc and Ubiquitous Computing. - : InderScience Publishers. - 1743-8225 .- 1743-8233. ; 20:3, s. 159-171
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Tidskriftsartikel (refereegranskat)abstract
- Through recent technology advances in the field of wireless energy transmission wireless rechargeable sensor networks have emerged. In this new paradigm for wireless sensor networks a mobile entity called mobile charger (MC) traverses the network and replenishes the dissipated energy of sensors. In this work we first provide a formal definition of the charging dispatch decision problem and prove its computational hardness. We then investigate how to optimise the trade-offs of several critical aspects of the charging process. In the light of these optimisations, we study the impact of the charging process to the network lifetime for three characteristic underlying routing protocols. Finally, we propose a mobile charging protocol that locally adapts the circular trajectory of the MC to the energy dissipation rate of each sub-region of the network. We compare this protocol against several MC trajectories by a detailed experimental evaluation. The derived findings demonstrate significant performance gains.
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| 2. |
- Luceri, Luca, et al.
(författare)
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VIVO: A secure, privacy-preserving, and real-time crowd-sensing framework for the Internet of Things
- 2018
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Ingår i: Pervasive and Mobile Computing. - : Elsevier BV. - 1574-1192. ; 49, s. 126-138
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Tidskriftsartikel (refereegranskat)abstract
- 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. (C) 2018 Elsevier B.V. All rights reserved.
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| 3. |
- Ziouvelou, Xenia, et al.
(författare)
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Crowd-driven IoT/IoE ecosystems : a multidimensional approach
- 2017
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Ingår i: Beyond the Internet of Things. - Cham : Springer International Publishing. - 9783319507569 - 9783319507583 ; , s. 341-375
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Bokkapitel (refereegranskat)abstract
- During the past few years an astonishing paradigm shift has occurred; user-driven, open and collaborative innovation practices have emerged and an increasing number of users mutually collaborate by openly communicating their ideas, sharing best practices, and creating new knowledge across various sectors. These online, distributed crowd-driven networks leverage the network effects so as to harness the collective power and intelligence. Recently, there is an increasing interest in mobile crowd sensing (MCS) in the context of IoT/IoE which leverages both the power and the wisdom of the crowd in order to observe, measure and make sense of particular phenomena, such as environmental ones, using user-owned mobile and wearable devices. However, in order to ensure the success of such ecosystems, a number of diverse criteria need to be considered. As such this paper provides a framework, which supports the use of multiple perspectives (holistic approach) for the design of crowd-driven ecosystems. The proposed framework utilises three key perspectives: technical, business and end-user (people), in order to describe, analyse and finally design crowd-driven IoT/IoE ecosystems. In addition, this chapter examines the proposed model, in the context of IoT Lab, as a best practice crowd-driven IoT ecosystem, in order to explain how these perspectives can be used to promote ecosystem success.
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