Operating systems for Internet of Things low-end devices: analysis and benchmarking

In the era of the Internet of Things (IoT), billions of wirelessly connected embedded devices rapidly became part of our daily lives. As a key tool for each Internet-enabled object, embedded operating systems (OSes) provide a set of services and abstractions which eases the development and speedups the deployment of IoT solutions at scale. This article starts by discussing the requirements of an IoT-enabled OS, taking into consideration the major concerns when developing solutions at the network edge, followed by a deep comparative analysis and benchmarking on Contiki-NG, RIOT, and Zephyr. Such OSes were considered as the best representative of their class considering the main key-points that best define an OS for resource-constrained IoT devices: low-power consumption, real-time capabilities, security awareness, interoperability, and connectivity. While evaluating each OS under different network conditions, the gathered results revealed distinct behaviors for each OS feature, mainly due to differences in kernel and network stack implementations.This work has been supported by national funds through FCT - Fundação para a Ciência e a Tecnologia within the Project Scope: UID/CEC/00319/2019

Building real-time embedded applications on QduinoMC: a web-connected 3D printer case study

Single Board Computers (SBCs) are now emerging with multiple cores, ADCs, GPIOs, PWM channels, integrated graphics, and several serial bus interfaces. The low power consumption, small form factor and I/O interface capabilities of SBCs with sensors and actuators makes them ideal in embedded and real-time applications. However, most SBCs run non-realtime operating systems based on Linux and Windows, and do not provide a user-friendly API for application development. This paper presents QduinoMC, a multicore extension to the popular Arduino programming environment, which runs on the Quest real-time operating system. QduinoMC is an extension of our earlier single-core, real-time, multithreaded Qduino API. We show the utility of QduinoMC by applying it to a specific application: a web-connected 3D printer. This differs from existing 3D printers, which run relatively simple firmware and lack operating system support to spool multiple jobs, or interoperate with other devices (e.g., in a print farm). We show how QduinoMC empowers devices with the capabilities to run new services without impacting their timing guarantees. While it is possible to modify existing operating systems to provide suitable timing guarantees, the effort to do so is cumbersome and does not provide the ease of programming afforded by QduinoMC.http://www.cs.bu.edu/fac/richwest/papers/rtas_2017.pdfAccepted manuscrip

AND THE STATE-OF-THE-ART

Our goal in this article is to give an overview of the broad are

Nanoprocessors: Configurable Hardware Accelerators for Embedded Systems

Today's consumer market is driven by technology innovations. Many technologies that were not available a few years ago are quickly bing adopted into common use. Equipment for these services requires microprocessors inside and can be regarded as embedded systems. Embedded systems are computer systems that are well hidden inside devices. At the time of design, much is known about the operating conditions and requirements. Embedded systems are designed to meet these requirements at a minimal cost. To improve efficiency and throughput, real-time operating systems (RTOSs) can be used. However, RTOSs can create overhead in systems. Using hardware accelerators can significantly reduce overhead. In this work, we survey the major overhead in embedded systems and identify and analyze some of them in details. We then purpose and discuss nanoprocessors, as configurable hardware accelerators, to lower this system overhead. Our simulation result shows that nanoprocessors can improve system performance at a nomial cost

Predictive power management for multi-core processors

textEnergy consumption by computing systems is rapidly increasing due to the growth of data centers and pervasive computing. In 2006 data center energy usage in the United States reached 61 billion kilowatt-hours (KWh) at an annual cost of 4.5 billion USD [Pl08]. It is projected to reach 100 billion KWh by 2011 at a cost of 7.4 billion USD. The nature of energy usage in these systems provides an opportunity to reduce consumption. Specifically, the power and performance demand of computing systems vary widely in time and across workloads. This has led to the design of dynamically adaptive or power managed systems. At runtime, these systems can be reconfigured to provide optimal performance and power capacity to match workload demand. This causes the system to frequently be over or under provisioned. Similarly, the power demand of the system is difficult to account for. The aggregate power consumption of a system is composed of many heterogeneous systems, each with a unique power consumption characteristic. This research addresses the problem of when to apply dynamic power management in multi-core processors by accounting for and predicting power and performance demand at the core-level. By tracking performance events at the processor core or thread-level, power consumption can be accounted for at each of the major components of the computing system through empirical, power models. This also provides accounting for individual components within a shared resource such as a power plane or top-level cache. This view of the system exposes the fundamental performance and power phase behavior, thus making prediction possible. This dissertation also presents an extensive analysis of complete system power accounting for systems and workloads ranging from servers to desktops and laptops. The analysis leads to the development of a simple, effective prediction scheme for controlling power adaptations. The proposed Periodic Power Phase Predictor (PPPP) identifies patterns of activity in multi-core systems and predicts transitions between activity levels. This predictor is shown to increase performance and reduce power consumption compared to reactive, commercial power management schemes by achieving higher average frequency in active phases and lower average frequency in idle phases.Electrical and Computer Engineerin

Securing Real-Time Internet-of-Things

Modern embedded and cyber-physical systems are ubiquitous. A large number of critical cyber-physical systems have real-time requirements (e.g., avionics, automobiles, power grids, manufacturing systems, industrial control systems, etc.). Recent developments and new functionality requires real-time embedded devices to be connected to the Internet. This gives rise to the real-time Internet-of-things (RT-IoT) that promises a better user experience through stronger connectivity and efficient use of next-generation embedded devices. However RT- IoT are also increasingly becoming targets for cyber-attacks which is exacerbated by this increased connectivity. This paper gives an introduction to RT-IoT systems, an outlook of current approaches and possible research challenges towards secure RT- IoT frameworks

Efficient and Reliable Task Scheduling, Network Reprogramming, and Data Storage for Wireless Sensor Networks

Wireless sensor networks (WSNs) typically consist of a large number of resource-constrained nodes. The limited computational resources afforded by these nodes present unique development challenges. In this dissertation, we consider three such challenges. The first challenge focuses on minimizing energy usage in WSNs through intelligent duty cycling. Limited energy resources dictate the design of many embedded applications, causing such systems to be composed of small, modular tasks, scheduled periodically. In this model, each embedded device wakes, executes a task-set, and returns to sleep. These systems spend most of their time in a state of deep sleep to minimize power consumption. We refer to these systems as almost-always-sleeping (AAS) systems. We describe a series of task schedulers for AAS systems designed to maximize sleep time. We consider four scheduler designs, model their performance, and present detailed performance analysis results under varying load conditions. The second challenge focuses on a fast and reliable network reprogramming solution for WSNs based on incremental code updates. We first present VSPIN, a framework for developing incremental code update mechanisms to support efficient reprogramming of WSNs. VSPIN provides a modular testing platform on the host system to plug-in and evaluate various incremental code update algorithms. The framework supports Avrdude, among the most popular Linux-based programming tools for AVR microcontrollers. Using VSPIN, we next present an incremental code update strategy to efficiently reprogram wireless sensor nodes. We adapt a linear space and quadratic time algorithm (Hirschberg\u27s Algorithm) for computing maximal common subsequences to build an edit map specifying an edit sequence required to transform the code running in a sensor network to a new code image. We then present a heuristic-based optimization strategy for efficient edit script encoding to reduce the edit map size. Finally, we present experimental results exploring the reduction in data size that it enables. The approach achieves reductions of 99.987% for simple changes, and between 86.95% and 94.58% for more complex changes, compared to full image transmissions - leading to significantly lower energy costs for wireless sensor network reprogramming. The third challenge focuses on enabling fast and reliable data storage in wireless sensor systems. A file storage system that is fast, lightweight, and reliable across device failures is important to safeguard the data that these devices record. A fast and efficient file system enables sensed data to be sampled and stored quickly and batched for later transmission. A reliable file system allows seamless operation without disruptions due to hardware, software, or other unforeseen failures. While flash technology provides persistent storage by itself, it has limitations that prevent it from being used in mission-critical deployment scenarios. Hybrid memory models which utilize newer non-volatile memory technologies, such as ferroelectric RAM (FRAM), can mitigate the physical disadvantages of flash. In this vein, we present the design and implementation of LoggerFS, a fast, lightweight, and reliable file system for wireless sensor networks, which uses a hybrid memory design consisting of RAM, FRAM, and flash. LoggerFS is engineered to provide fast data storage, have a small memory footprint, and provide data reliability across system failures. LoggerFS adapts a log-structured file system approach, augmented with data persistence and reliability guarantees. A caching mechanism allows for flash wear-leveling and fast data buffering. We present a performance evaluation of LoggerFS using a prototypical in-situ sensing platform and demonstrate between 50% and 800% improvements for various workloads using the FRAM write-back cache over the implementation without the cache

Scheduling Techniques for Operating Systems for Medical and IoT Devices: A Review

Software and Hardware synthesis are the major subtasks in the implementation of hardware/software systems. Increasing trend is to build SoCs/NoC/Embedded System for Implantable Medical Devices (IMD) and Internet of Things (IoT) devices, which includes multiple Microprocessors and Signal Processors, allowing designing complex hardware and software systems, yet flexible with respect to the delivered performance and executed application. An important technique, which affect the macroscopic system implementation characteristics is the scheduling of hardware operations, program instructions and software processes. This paper presents a survey of the various scheduling strategies in process scheduling. Process Scheduling has to take into account the real-time constraints. Processes are characterized by their timing constraints, periodicity, precedence and data dependency, pre-emptivity, priority etc. The affect of these characteristics on scheduling decisions has been described in this paper

Cost effective technology applied to domotics and smart home energy management systems

Premio extraordinario de Trabajo Fin de Máster curso 2019/2020. Máster en Energías Renovables DistribuidasIn this document is presented the state of art for domotics cost effective technologies available on market nowadays, and how to apply them in Smart Home Energy Management Systems (SHEMS) allowing peaks shaving, renewable management and home appliance controls, always in cost effective context in order to be massively applied. Additionally, beyond of SHEMS context, it will be also analysed how to apply this technology in order to increase homes energy efficiency and monitoring of home appliances. Energy management is one of the milestones for distributed renewable energy spread; since renewable energy sources are not time-schedulable, are required control systems capable of the management for exchanging energy between conventional sources (power grid), renewable sources and energy storage sources. With the proposed approach, there is a first block dedicated to show an overview of Smart Home Energy Management Systems (SMHEMS) classical architecture and functional modules of SHEMS; next step is to analyse principles which has allowed some devices to become a cost-effective technology. Once the technology has been analysed, it will be reviewed some specific resources (hardware and software) available on marked for allowing low cost SHEMS. Knowing the “tools” available; it will be shown how to adapt classical SHEMS to cost effective technology. Such way, this document will show some specific applications of SHEMS. Firstly, in a general point of view, comparing the proposed low-cost technology with one of the main existing commercial proposals; and secondly, developing the solution for a specific real case.En este documento se aborda el estado actual de la domótica de bajo coste disponible en el mercado actualmente y cómo aplicarlo en los sistemas inteligentes de gestión energética en la vivienda (SHEMS) permitiendo el recorte de las puntas de demanda, gestión de energías renovables y control de electrodomésticos, siempre en el contexto del bajo coste, con el objetivo de lograr la máxima difusión de los SHEMS. Adicionalmente, más allá del contexto de la tecnología SHEMS, se analizará cómo aplicar esta tecnología para aumentar la eficiencia energética de los hogares y para la supervisión de los electrodomésticos. La gestión energética es uno de los factores principales para lograr la difusión de las energías renovables distribuidas; debido a que las fuentes de energía renovable no pueden ser planificadas, se requieren sistemas de control capaces de gestionar el intercambio de energía entre las fuentes convencionales (red eléctrica de distribución), energías renovables y dispositivos de almacenamiento energético. Bajo esta perspectiva, este documento presenta un primer bloque en el que se exponen las bases de la arquitectura y módulos funcionales de los sistemas inteligentes de gestión energética en la vivienda (SHEMS); el siguiente paso será analizar los principios que han permitido a ciertos dispositivos convertirse en dispositivos de bajo coste. Una vez analizada la tecnología, nos centraremos en los recursos (hardware y software) existentes que permitirán la realización de un SHEMS a bajo coste. Conocidas las “herramientas” a nuestra disposición, se mostrará como adaptar un esquema SHEMS clásico a la tecnología de bajo coste. Primeramente, comparando de modo genérico la tecnología de bajo coste con una de las principales propuestas comerciales de SHEMS, para seguidamente desarrollar la solución de bajo coste a un caso específico real