Wireless energy transfer in sensor networks with adaptive, limited knowledge protocols

We investigate the problem of efficient wireless energy transfer in Wireless Rechargeable Sensor Networks (WRSNs). In such networks a special mobile entity (called the Mobile Charger) traverses the network and wirelessly replenishes the energy of sensor nodes. In contrast to most current approaches, we envision methods that are distributed, adaptive and use limited network information. We propose three new, alternative protocols for efficient charging, addressing key issues which we identify, most notably (i) to what extent each sensor should be charged, (ii) what is the best split of the total energy between the charger and the sensors and (iii) what are good trajectories the Mobile Charger should follow. One of our protocols (LRP) performs some distributed, limited sampling of the network status, while another one (RTP) reactively adapts to energy shortage alerts judiciously spread in the network. We conduct detailed simulations in uniform and non-uniform network deployments, using three different underlying routing protocol families. In most cases, both our charging protocols significantly outperform known state of the art methods, while their performance gets quite close to the performance of the global knowledge method (GKP) we also provide. (C) 2014 Elsevier B.V. All rights reserved

Modelled Testbeds: Visualizing and Augmenting Physical Testbeds with Virtual Resources

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 extrap- olation techniques towards realistic value assignment for virtual sensor measurements. K-fold cross validation is performed in a dataset compris- ing of nearly 300'000 measurements of temperature, illuminance and hu- midity sensors collected from the physical sensors of the Syndesi testbed, in order to evaluate the accuracy of the methods. We obtain strong re- sults including Mean Absolute Percentage Error (MAPE) levels below 7%

Crowdsourced Edge: a Novel Networking Paradigm for the Collaborative Community

Edge computing established paradigms are prone to implicate solely powerful server-like edge nodes, in static or semistatic topologies, in their edge network. Moreover, in all cases the network ownership resides with some centrally-controlled entity. 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 vis-a-vis the existing edge computing concretisations. Alternative to the rigid, businessoriented, existing approaches, the Crowdsourced Edge promotes a vendor-free collaborative edge computing ecosystem, capable of handling extreme dynamicity and heterogeneity while ensuring data privacy and data ownership for its users all along the way