A pulse sensor interface design for FPGA based multisensor health monitoring platform

The FPGA-based platform is critical for producing an inexpensive early validation platform design. In past years, sensor nodes based on the FPGA platform have been proposed to be IoT low-end devices. In this study, we present the FPGA based IoT low-end reconfigurable pulse sensor interface design that can be integrated with a multi-sensor healthcare platform to monitor a human pulse vital sign and be able to distinguish between user normal, Bradycardia, or Tachycardia heart rate. The pulse sensor interface is implemented by VHDL programming and FPGA technology. The designed pulse sensor peripheral interface is reliable and reconfigurable. It can collect vital body signs with the accuracy of a 15 nanoseconds period. The peripheral in FPGAs embedded system has been tested by placing the biosensor on the user’s fingertips. The BPM can be updated every 15 seconds

An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics

Near-sensor data analytics is a promising direction for IoT endpoints, as it minimizes energy spent on communication and reduces network load - but it also poses security concerns, as valuable data is stored or sent over the network at various stages of the analytics pipeline. Using encryption to protect sensitive data at the boundary of the on-chip analytics engine is a way to address data security issues. To cope with the combined workload of analytics and encryption in a tight power envelope, we propose Fulmine, a System-on-Chip based on a tightly-coupled multi-core cluster augmented with specialized blocks for compute-intensive data processing and encryption functions, supporting software programmability for regular computing tasks. The Fulmine SoC, fabricated in 65nm technology, consumes less than 20mW on average at 0.8V achieving an efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to 25MIPS/mW in software. As a strong argument for real-life flexible application of our platform, we show experimental results for three secure analytics use cases: secure autonomous aerial surveillance with a state-of-the-art deep CNN consuming 3.16pJ per equivalent RISC op; local CNN-based face detection with secured remote recognition in 5.74pJ/op; and seizure detection with encrypted data collection from EEG within 12.7pJ/op.Comment: 15 pages, 12 figures, accepted for publication to the IEEE Transactions on Circuits and Systems - I: Regular Paper

System Synthesis for Networks of Programmable Blocks

The advent of sensor networks presents untapped opportunities for synthesis. We examine the problem of synthesis of behavioral specifications into networks of programmable sensor blocks. The particular behavioral specification we consider is an intuitive user-created network diagram of sensor blocks, each block having a pre-defined combinational or sequential behavior. We synthesize this specification to a new network that utilizes a minimum number of programmable blocks in place of the pre-defined blocks, thus reducing network size and hence network cost and power. We focus on the main task of this synthesis problem, namely partitioning pre-defined blocks onto a minimum number of programmable blocks, introducing the efficient but effective PareDown decomposition algorithm for the task. We describe the synthesis and simulation tools we developed. We provide results showing excellent network size reductions through such synthesis, and significant speedups of our algorithm over exhaustive search while obtaining near-optimal results for 15 real network designs as well as nearly 10,000 randomly generated designs.Comment: Submitted on behalf of EDAA (http://www.edaa.com/

Design techniques to enhance low-power wireless communication soc with reconfigurability and wake up radio

Nowadays, Internet of things applications are increasing, and each end-node has more demanding requirements such as energy efficiency and speed. The thesis proposes a heterogeneous elaboration unit for smart power applications, that consists of an ultra-low-power microcontroller coupled with a small (around 1k equivalent gates) soft-core of embedded FPGA. This digital system is implemented in 90-nm BCD technology of STMicroelectronics, and through the analysis presented in this thesis proves to have good performance in terms of power consumption and latency. The idea is to increase the system performance exploiting the embedded FPGA to managing smart power tasks. For the intended applications, a remarkable computational load is not required, it is just required the implementation of simple finite state machines, since they are event-driven applications. In this way, while the microcontroller deals with other system computations such as high-level communications, the eFPGA can efficiently manage smart power applications. An added value of the proposed elaboration unit is that a soft-core approach is applied to the whole digital system including the eFPGA, and hence, it is portable to different technologies. On the other hand, the configurability improvement has a straightforward drawback of about a 20–27% area overhead. The eFPGA usage to manage smart power applications, allows the system to reduce the required energy per task from about 400 to around 800 times compared to a processor implementation. The eFPGA utilization improves also the latency performance of the system reaching from 8 to 145 times less latency in terms of clock cycles. The thesis also introduces the architecture of a nano-watt wake-up radio integrated circuit implemented in 90-nm BCD technology of STMicroelectronics. The wake-up radio is an auxiliary always-on radio for medium-range applications that allows the IoT end-nodes to drastically reduce the power consumption during the node idle-listening communication phase

A Java application to display temperature, humidity and luminosity in a Wireless Sensor Network

The aim of this thesis is to develop a Java application, using Android platform, to display in a mobile device the values taken by a Wireless Sensor Network (WSN). For this purpose, in this papers, firstly I give a general vision of what the Wireless Sensor Networks are. In particular, I wished to provide a comprehensive analysis of the main component of the WSN: the sensor node. I describe its hardware and software structural. The second part of the thesis is dedicated to the implementation of the application. It is listed which software were useful to the development and it explained how the application has been built. In the last part of the thesis it is shown the graphical result of the work obtainedope

A PUF-and biometric-based lightweight hardware solution to increase security at sensor nodes

Security is essential in sensor nodes which acquire and transmit sensitive data. However, the constraints of processing, memory and power consumption are very high in these nodes. Cryptographic algorithms based on symmetric key are very suitable for them. The drawback is that secure storage of secret keys is required. In this work, a low-cost solution is presented to obfuscate secret keys with Physically Unclonable Functions (PUFs), which exploit the hardware identity of the node. In addition, a lightweight fingerprint recognition solution is proposed, which can be implemented in low-cost sensor nodes. Since biometric data of individuals are sensitive, they are also obfuscated with PUFs. Both solutions allow authenticating the origin of the sensed data with a proposed dual-factor authentication protocol. One factor is the unique physical identity of the trusted sensor node that measures them. The other factor is the physical presence of the legitimate individual in charge of authorizing their transmission. Experimental results are included to prove how the proposed PUF-based solution can be implemented with the SRAMs of commercial Bluetooth Low Energy (BLE) chips which belong to the communication module of the sensor node. Implementation results show how the proposed fingerprint recognition based on the novel texture-based feature named QFingerMap16 (QFM) can be implemented fully inside a low-cost sensor node. Robustness, security and privacy issues at the proposed sensor nodes are discussed and analyzed with experimental results from PUFs and fingerprints taken from public and standard databases.Ministerio de Economía, Industria y Competitividad TEC2014-57971-R, TEC2017-83557-

A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones

Fully-autonomous miniaturized robots (e.g., drones), with artificial intelligence (AI) based visual navigation capabilities are extremely challenging drivers of Internet-of-Things edge intelligence capabilities. Visual navigation based on AI approaches, such as deep neural networks (DNNs) are becoming pervasive for standard-size drones, but are considered out of reach for nanodrones with size of a few cm${}^\mathrm{2}$. In this work, we present the first (to the best of our knowledge) demonstration of a navigation engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based visual navigation. To achieve this goal we developed a complete methodology for parallel execution of complex DNNs directly on-bard of resource-constrained milliwatt-scale nodes. Our system is based on GAP8, a novel parallel ultra-low-power computing platform, and a 27 g commercial, open-source CrazyFlie 2.0 nano-quadrotor. As part of our general methodology we discuss the software mapping techniques that enable the state-of-the-art deep convolutional neural network presented in [1] to be fully executed on-board within a strict 6 fps real-time constraint with no compromise in terms of flight results, while all processing is done with only 64 mW on average. Our navigation engine is flexible and can be used to span a wide performance range: at its peak performance corner it achieves 18 fps while still consuming on average just 3.5% of the power envelope of the deployed nano-aircraft.Comment: 15 pages, 13 figures, 5 tables, 2 listings, accepted for publication in the IEEE Internet of Things Journal (IEEE IOTJ

Runtime Hardware Reconfiguration in Wireless Sensor Networks for Condition Monitoring

The integration of miniaturized heterogeneous electronic components has enabled the deployment of tiny sensing platforms empowered by wireless connectivity known as wireless sensor networks. Thanks to an optimized duty-cycled activity, the energy consumption of these battery-powered devices can be reduced to a level where several years of operation is possible. However, the processing capability of currently available wireless sensor nodes does not scale well with the observation of phenomena requiring a high sampling resolution. The large amount of data generated by the sensors cannot be handled efficiently by low-power wireless communication protocols without a preliminary filtering of the information relevant for the application. For this purpose, energy-efficient, flexible, fast and accurate processing units are required to extract important features from the sensor data and relieve the operating system from computationally demanding tasks. Reconfigurable hardware is identified as a suitable technology to fulfill these requirements, balancing implementation flexibility with performance and energy-efficiency. While both static and dynamic power consumption of field programmable gate arrays has often been pointed out as prohibitive for very-low-power applications, recent programmable logic chips based on non-volatile memory appear as a potential solution overcoming this constraint. This thesis first verifies this assumption with the help of a modular sensor node built around a field programmable gate array based on Flash technology. Short and autonomous duty-cycled operation combined with hardware acceleration efficiently drop the energy consumption of the device in the considered context. However, Flash-based devices suffer from restrictions such as long configuration times and limited resources, which reduce their suitability for complex processing tasks. A template of a dynamically reconfigurable architecture built around coarse-grained reconfigurable function units is proposed in a second part of this work to overcome these issues. The module is conceived as an overlay of the sensor node FPGA increasing the implementation flexibility and introducing a standardized programming model. Mechanisms for virtual reconfiguration tailored for resource-constrained systems are introduced to minimize the overhead induced by this genericity. The definition of this template architecture leaves room for design space exploration and application- specific customization. Nevertheless, this aspect must be supported by appropriate design tools which facilitate and automate the generation of low-level design files. For this purpose, a software tool is introduced to graphically configure the architecture and operation of the hardware accelerator. A middleware service is further integrated into the wireless sensor network operating system to bridge the gap between the hardware and the design tools, enabling remote reprogramming and scheduling of the hardware functionality at runtime. At last, this hardware and software toolchain is applied to real-world wireless sensor network deployments in the domain of condition monitoring. This category of applications often require the complex analysis of signals in the considered range of sampling frequencies such as vibrations or electrical currents, making the proposed system ideally suited for the implementation. The flexibility of the approach is demonstrated by taking examples with heterogeneous algorithmic specifications. Different data processing tasks executed by the sensor node hardware accelerator are modified at runtime according to application requests

Deploying RIOT operating system on a reconfigurable Internet of Things end-device

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e ComputadoresThe Internet of Everything (IoE) is enabling the connection of an infinity of physical objects to the Internet, and has the potential to connect every single existing object in the world. This empowers a market with endless opportunities where the big players are forecasting, by 2020, more than 50 billion connected devices, representing an 8 trillion USD market. The IoE is a broad concept that comprises several technological areas and will certainly, include more in the future. Some of those already existing fields are the Internet of Energy related with the connectivity of electrical power grids, Internet of Medical Things (IoMT), for instance, enables patient monitoring, Internet of Industrial Things (IoIT), which is dedicated to industrial plants, and the Internet of Things (IoT) that focus on the connection of everyday objects (e.g. home appliances, wearables, transports, buildings, etc.) to the Internet. The diversity of scenarios where IoT can be deployed, and consequently the different constraints associated to each device, leads to a heterogeneous network composed by several communication technologies and protocols co-existing on the same physical space. Therefore, the key requirements of an IoT network are the connectivity and the interoperability between devices. Such requirement is achieved by the adoption of standard protocols and a well-defined lightweight network stack. Due to the adoption of a standard network stack, the data processed and transmitted between devices tends to increase. Because most of the devices connected are resource constrained, i.e., low memory, low processing capabilities, available energy, the communication can severally decrease the device’s performance. Hereupon, to tackle such issues without sacrificing other important requirements, this dissertation aims to deploy an operating system (OS) for IoT, the RIOT-OS, while providing a study on how network-related tasks can benefit from hardware accelerators (deployed on reconfigurable technology), specially designed to process and filter packets received by an IoT device.O conceito Internet of Everything (IoE) permite a conexão de uma infinidade de objetos à Internet e tem o potencial de conectar todos os objetos existentes no mundo. Favorecendo assim o aparecimento de novos mercados e infinitas possibilidades, em que os grandes intervenientes destes mercados preveem até 2020 a conexão de mais de 50 mil milhões de dispositivos, representando um mercado de 8 mil milhões de dólares. IoE é um amplo conceito que inclui várias áreas tecnológicas e irá certamente incluir mais no futuro. Algumas das áreas já existentes são: a Internet of Energy relacionada com a conexão de redes de transporte e distribuição de energia à Internet; Internet of Medical Things (IoMT), que possibilita a monotorização de pacientes; Internet of Industrial Things (IoIT), dedicada a instalações industriais e a Internet of Things (IoT), que foca na conexão de objetos do dia-a-dia (e.g. eletrodomésticos, wearables, transportes, edifícios, etc.) à Internet. A diversidade de cenários à qual IoT pode ser aplicado, e consequentemente, as diferentes restrições aplicadas a cada dispositivo, levam à criação de uma rede heterogénea composto por diversas tecnologias de comunicação e protocolos a coexistir no mesmo espaço físico. Desta forma, os requisitos chave aplicados às redes IoT são a conectividade e interoperabilidade entre dispositivos. Estes requisitos são atingidos com a adoção de protocolos standard e pilhas de comunicação bem definidas. Com a adoção de pilhas de comunicação standard, a informação processada e transmitida entre dispostos tende a aumentar. Visto que a maioria dos dispositivos conectados possuem escaços recursos, i.e., memória reduzida, baixa capacidade de processamento, pouca energia disponível, o aumento da capacidade de comunicação pode degradar o desempenho destes dispositivos. Posto isto, para lidar com estes problemas e sem sacrificar outros requisitos importantes, esta dissertação pretende fazer o porting de um sistema operativo IoT, o RIOT, para uma solução reconfigurável, o CUTE mote. O principal objetivo consiste na realização de um estudo sobre os benefícios que as tarefas relacionadas com as camadas de rede podem ter ao serem executadas em hardware via aceleradores dedicados. Estes aceleradores são especialmente projetados para processar e filtrar pacotes de dados provenientes de uma interface radio em redes IoT periféricas