A Lightweight Blockchain and Fog-enabled Secure Remote Patient Monitoring System

IoT has enabled the rapid growth of smart remote healthcare applications. These IoT-based remote healthcare applications deliver fast and preventive medical services to patients at risk or with chronic diseases. However, ensuring data security and patient privacy while exchanging sensitive medical data among medical IoT devices is still a significant concern in remote healthcare applications. Altered or corrupted medical data may cause wrong treatment and create grave health issues for patients. Moreover, current remote medical applications' efficiency and response time need to be addressed and improved. Considering the need for secure and efficient patient care, this paper proposes a lightweight Blockchain-based and Fog-enabled remote patient monitoring system that provides a high level of security and efficient response time. Simulation results and security analysis show that the proposed lightweight blockchain architecture fits the resource-constrained IoT devices well and is secure against attacks. Moreover, the augmentation of Fog computing improved the responsiveness of the remote patient monitoring system by 40%.Comment: 32 pages, 13 figures, 5 tables, accepted by Elsevier "Internet of Things; Engineering Cyber Physical Human Systems" journal on January 9, 202

Mr.Wolf: An Energy-Precision Scalable Parallel Ultra Low Power SoC for IoT Edge Processing

This paper presents Mr. Wolf, a parallel ultra-low power (PULP) system on chip (SoC) featuring a hierarchical architecture with a small (12 kgates) microcontroller (MCU) class RISC-V core augmented with an autonomous IO subsystem for efficient data transfer from a wide set of peripherals. The small core can offload compute-intensive kernels to an eight-core floating-point capable of processing engine available on demand. The proposed SoC, implemented in a 40-nm LP CMOS technology, features a 108-mu W fully retentive memory (512 kB). The IO subsystem is capable of transferring up to 1.6 Gbit/s from external devices to the memory in less than 2.5 mW. The eight-core compute cluster achieves a peak performance of 850 million of 32-bit integer multiply and accumulate per second (MMAC/s) and 500 million of 32-bit floating-point multiply and accumulate per second (MFMAC/s) -1 GFlop/s-with an energy efficiency up to 15 MMAC/s/mW and 9 MFMAC/s/mW. These building blocks are supported by aggressive on-chip power conversion and management, enabling energy-proportional heterogeneous computing for always-on IoT end nodes improving performance by several orders of magnitude with respect to traditional single-core MCUs within a power envelope of 153 mW. We demonstrated the capabilities of the proposed SoC on a wide set of near-sensor processing kernels showing that Mr. Wolf can deliver performance up to 16.4 GOp/s with energy efficiency up to 274 MOp/s/mW on real-life applications, paving the way for always-on data analytics on high-bandwidth sensors at the edge of the Internet of Things

A Regional Innovation Impact Assessment Framework for universities

This report provides a framework to assess the impact of universities on their regional innovation ecosystem. The policy context for this work is provided by: a) the Renewed EU agenda for higher education which argued that universities do not attain their full potential; and b) the report by the High Level Group chaired by Pascal Lamy which called for an additional funding stream to support universities to modernise and increase their innovation impact. This report explores what the assessment framework underpinning such an innovation performance based funding instrument could look like. However, it acknowledges that the final form of such a framework would heavily depend on the regional, national or EU level instrument through which it is implemented. The report proposes a system in which universities draft a case study supported by indicators, through which they present evidence of their contribution to regional innovation. It identifies four impact categories and identifies a list of associated indicators. In this "narrative with numbers the universities can both explain how they reach this impact and contextualise their performance with reference to the development level of their region.JRC.B.7-Knowledge for Finance, Innovation and Growt