M
Mario Di Francesco
Researcher at Aalto University
Publications - 81
Citations - 5425
Mario Di Francesco is an academic researcher from Aalto University. The author has contributed to research in topics: Wireless sensor network & Key distribution in wireless sensor networks. The author has an hindex of 24, co-authored 74 publications receiving 4614 citations. Previous affiliations of Mario Di Francesco include University of Pisa & Helsinki Institute for Information Technology.
Papers
More filters
Journal ArticleDOI
Energy conservation in wireless sensor networks: A survey
TL;DR: This paper breaks down the energy consumption for the components of a typical sensor node, and discusses the main directions to energy conservation in WSNs, and presents a systematic and comprehensive taxonomy of the energy conservation schemes.
Journal ArticleDOI
Data Collection in Wireless Sensor Networks with Mobile Elements: A Survey
TL;DR: This article defines WSNs with MEs and provides a comprehensive taxonomy of their architectures, based on the role of the MEs, and provides an extensive survey of the related literature.
Journal ArticleDOI
Edge Computing for the Internet of Things: A Case Study
TL;DR: An experimental evaluation of edge computing and its enabling technologies in a selected use case represented by mobile gaming shows that edge computing is necessary to meet the latency requirements of applications involving virtual and augmented reality.
Journal ArticleDOI
A Comprehensive Analysis of the MAC Unreliability Problem in IEEE 802.15.4 Wireless Sensor Networks
TL;DR: It is found that, with a more appropriate MAC parameters setting, it is possible to mitigate the problem of unreliability of IEEE 802.15.4 WSNs and achieve a delivery ratio up to 100%, at least in the scenarios considered in this paper.
Proceedings ArticleDOI
Adaptive configuration of lora networks for dense IoT deployments
TL;DR: FLoRa, an open-source framework for end-to-end LoRa simulations in OMNeT++, is developed and the Adaptive Data Rate (ADR) mechanism built into LoRa is implemented to dynamically manage link parameters for scalable and efficient network operations.