Abstract-In this letter, we study the performance of Network Coding (NC)-aided cooperative communications in large scale networks, where the relays are able to harvest energy emitted by wireless transmissions. In particular, we derive theoretical expressions for key network performance metrics, i.e., the probability of successful data exchange and the network lifetime gain. The proposed analytical expressions are verified via extensive Monte Carlo simulations, demonstrating the potential benefits of the energy harvested by the wireless transmissions.

In the new era of connectivity, marked by the explosive number of wireless electronic devices and the need for smart and pervasive applications, Machine-to-Machine (M2M) communications are an emerging technology that enables the seamless device interconnection without the need of human interaction. The use of M2M technology can bring to life a wide range of mHealth applications, with considerable benefits for both patients and healthcare providers. Many technological challenges have to be met, however, to ensure the widespread adoption of mHealth solutions in the future. In this context, we aim to provide a comprehensive survey on M2M systems for mHealth applications from a wireless communication perspective. An end-to-end holistic approach is adopted, focusing on different communication aspects of the M2M architecture. Hence, we first provide a systematic review of Wireless Body Area Networks (WBANs), which constitute the enabling technology at the patient's side, and then discuss end-to-end solutions that involve the design and implementation of practical mHealth applications. We close the survey by identifying challenges and open research issues, thus paving the way for future research opportunities.

Abstract-As large-scale dense and often randomly deployed wireless sensor networks (WSNs) become widespread, local information exchange between co-located sets of nodes may play a significant role in handling the excessive traffic volume. Moreover, to account for the limited life-span of the wireless devices, harvesting the energy of the network transmissions provides significant benefits to the lifetime of such networks. In this paper, we study the performance of communication in dense networks with wireless energy harvesting (WEH)-enabled sensor nodes. In particular, we examine two different communication scenarios (direct and cooperative) for data exchange and we provide theoretical expressions for the probability of successful communication. Then, considering the importance of lifetime in WSNs, we employ state-of-the-art WEH techniques and realistic energy converters, quantifying the potential energy gains that can be achieved in the network. Our analytical derivations, which are validated by extensive Monte-Carlo simulations, highlight the importance of WEH in dense networks and identify the trade-offs between the direct and cooperative communication scenarios.

The increasing interest for reliable generation of large scale scenes and objects has facilitated several real-time applications. Although the resolution of the new generation geometry scanners are constantly improving, the output models, are inevitably noisy, requiring sophisticated approaches that remove noise while preserving sharp features. Moreover, we no longer deal exclusively with individual shapes, but with entire scenes resulting in a sequence of 3D surfaces that are affected by noise with different characteristics due to variable environmental factors (e.g., lighting conditions, orientation of the scanning device). In this work, we introduce a novel coarse-to-fine graph spectral processing approach that exploits the fact that the sharp features reside in a low dimensional structure hidden in the noisy 3D dataset. In the coarse step, the mesh is processed in parts, using a model based Bayesian learning method that identifies the noise level in each part and the subspace where the features lie. In the feature-aware fine step, we iteratively smooth face normals and vertices, while preserving geometric features. Extensive evaluation studies carried out under a broad set of complex noise patterns verify the superiority of our approach as compared to the state-of-the-art schemes, in terms of reconstruction quality and computational complexity.

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Ambient Assisted Living (AAL) technologies constitute a new paradigm that promises quality\ud of life enhancements in chronic-care patients and elderly people. From a communication perspective,\ud they involve heterogeneous deployments of body and ambient sensors in compex, multihop topologies.\ud Such networks can significantly benefit from the application of cooperative schemes based on network\ud coding, where random linear combinations of the original data packets are transmitted in order to\ud exploit diversity. Nevertheless, network coordination is sometimes required to obtain the full potential\ud of these schemes, especially in the presence of channel errors, requiring the design of efficient, reliable\ud and versatile Medium Access Control (MAC) protocols. Motivated by the recent advances in cloud\ud computing, we investigate the possibility of transferring the network coordination to the cloud while\ud maintaining the data exchange and storage at a local data plane. Hence, we design a general framework\ud for the development of cloud-assisted protocols for AAL applications and propose a high-performance\ud and error-resilient MAC scheme with cloud capabilities.Peer ReviewedPostprint (author’s final draft

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