Code Generation for Wireless Devices
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												  Internet of Things Meets Brain-Computer Interface: a Unified Deep Learning Framework for Enabling Human-Thing Cognitive InteractivityJOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 1 Internet of Things Meets Brain-Computer Interface: A Unified Deep Learning Framework for Enabling Human-Thing Cognitive Interactivity Xiang Zhang, Student Member, IEEE, Lina Yao, Member, IEEE, Shuai Zhang, Student Member, IEEE, Salil Kanhere, Member, IEEE, Michael Sheng, Member, IEEE, and Yunhao Liu, Fellow, IEEE Abstract—A Brain-Computer Interface (BCI) acquires brain signals, analyzes and translates them into commands that are relayed to actuation devices for carrying out desired actions. With the widespread connectivity of everyday devices realized by the advent of the Internet of Things (IoT), BCI can empower individuals to directly control objects such as smart home appliances or assistive robots, directly via their thoughts. However, realization of this vision is faced with a number of challenges, most importantly being the issue of accurately interpreting the intent of the individual from the raw brain signals that are often of low fidelity and subject to noise. Moreover, pre-processing brain signals and the subsequent feature engineering are both time-consuming and highly reliant on human domain expertise. To address the aforementioned issues, in this paper, we propose a unified deep learning based framework that enables effective human-thing cognitive interactivity in order to bridge individuals and IoT objects. We design a reinforcement learning based Selective Attention Mechanism (SAM) to discover the distinctive features from the input brain signals. In addition, we propose a modified Long Short-Term Memory (LSTM) to distinguish the inter-dimensional information forwarded from the SAM. To evaluate the efficiency of the proposed framework, we conduct extensive real-world experiments and demonstrate that our model outperforms a number of competitive state-of-the-art baselines.
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												  A Comparative Study on Operating System for Wireless Sensor NetworksICACSIS 2011 ISBN: 978-979-1421-11-9 A Comparative Study on Operating System for Wireless Sensor Networks Thang Vu Chien, Hung Nguyen Chan, and Thanh Nguyen Huu Thai Nguyen University of Information and Communication Technology Quyet Thang Commune, Thai Nguyen City, Vietnam E-mail: [email protected], [email protected], [email protected] Abstract—Wireless Sensor Networks (WSNs) them work correctly and effectively without a conflict have been the subject of intensive research over the in support of the specific applications for which they last years. WSNs consist of a large number of are designed. Each sensor node needs an OS that can sensor nodes, and are used for various applications control the hardware, provide hardware abstraction to such as building monitoring, environment control, application software, and fill in the gap between wild-life habitat monitoring, forest fire detection, applications and the underlying hardware. industry automation, military, security, and health- The basic functionalities of an OS include resource care. Operating system (OS) support for WSNs abstractions for various hardware devices, interrupt plays a central role in building scalable distributed management and task scheduling, concurrency control, applications that are efficient and reliable. Over and networking support. Based on the services the years, we have seen a variety of operating provided by the OS, application programmers can systems (OSes) emerging in the sensor network conveniently use high-level application programming community to facilitate developing WSN interfaces (APIs) independent of the underlying applications. In this paper, we present OS for WSNs. We begin by presenting the major issues for hardware.
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												  The Internet of Things (Iot): an OverviewUpdated February 12, 2020 The Internet of Things (IoT): An Overview The Internet of Things (IoT) is a system of interrelated incorporation of IIoT and analytics is viewed by experts as devices connected to a network and/or to one another, the Fourth Industrial Revolution, or 4IR. exchanging data without necessarily requiring human-to- machine interaction. In other words, IoT is a collection of Internet of Medical Things (IoMT): The healthcare field electronic devices that can share information among has begun incorporating IoT, creating the Internet of themselves. Examples include smart factories, smart home Medical Things (IoMT). These devices, such as heart devices, medical monitoring devices, wearable fitness monitors and pace makers, collect and send patient health trackers, smart city infrastructures, and vehicular statistics over various networks to healthcare providers for telematics. Potential issues for Congress include regulation, monitoring, analysis, and remote configuration. At a digital privacy, and data security as discussed below. personal health level, wearable IoT devices, such as fitness trackers and smart watches, can track a user’s physical IoT Characteristics activities, basic vital data, and sleeping patterns. According IoT devices are often called “smart” devices because they to a 2019 survey by Pew Research, about one-in-five have sensors and can conduct complex data analytics. IoT Americans uses a smart watch or fitness tracker. devices collect data using sensors and offer services to the user based on the analyses of that data and according to Smart Cities: IoT devices and systems in the utilities, user-defined parameters. For example, a smart refrigerator transportation, and infrastructure sectors may be grouped uses sensors (e.g., cameras) to inventory stored items and under the category of “smart city.” Utilities can use IoT to can alert the user when items run low based on image create “smart” grids and meters for electricity, water, and recognition analyses.
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												  Graphical Product-Line Configuration of Nesc-Based Sensor Network Applications Using Feature ModelsGRAPHICAL PRODUCT-LINE CONFIGURATION OF NESC-BASED SENSOR NETWORK APPLICATIONS USING FEATURE MODELS by MATTHIAS NIEDERHAUSEN Diplom, University of Applied Sciences, Frankfurt, Germany, 2007 A THESIS submitted in partial fulfillment of the requirements for the degree MASTER OF SCIENCE Department of Computing and Information Sciences College of Engineering KANSAS STATE UNIVERSITY Manhattan, Kansas 2008 Approved by: Major Professor John Hatcliff Copyright Matthias Niederhausen 2008 Abstract Developing a wireless sensor network application includes a variety of tasks, such as coding of the implementation, designing the architecture and assessing availability of hardware components, that provide necessary capabilities. Before compiling an application, the developer has to con- figure the selection of hardware components and set up required parameters. One has to choose from among a variety of configurations regarding communication parameters, such as frequency, channel, subnet identifier, transmission power, etc.. This configuration step also includes setting up parameters for the selection of hardware components, such as a specific hardware platform, which sensor boards and programmer boards to be used or the use of optional services and more. Reasoning about a proper selection of configuration parameters is often very difficult, since there are a lot of dependencies among these parameters which may rule out some other options. The developer has to know about all these constraints in order to pick a valid configuration. Unfor- tunately, the existing makefile approach that comes with nesC is poorly organized and does not capture important compatibility constraints. The configuration of a particular nesC application is distributed in multiple makefiles. There- fore a developer has to look at multiple files to make sure all necessary parameter are set up correctly for compiling a specific application.
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												  Fog Computing: a Platform for Internet of Things and AnalyticsFog Computing: A Platform for Internet of Things and Analytics Flavio Bonomi, Rodolfo Milito, Preethi Natarajan and Jiang Zhu Abstract Internet of Things (IoT) brings more than an explosive proliferation of endpoints. It is disruptive in several ways. In this chapter we examine those disrup- tions, and propose a hierarchical distributed architecture that extends from the edge of the network to the core nicknamed Fog Computing. In particular, we pay attention to a new dimension that IoT adds to Big Data and Analytics: a massively distributed number of sources at the edge. 1 Introduction The “pay-as-you-go” Cloud Computing model is an efficient alternative to owning and managing private data centers (DCs) for customers facing Web applications and batch processing. Several factors contribute to the economy of scale of mega DCs: higher predictability of massive aggregation, which allows higher utilization with- out degrading performance; convenient location that takes advantage of inexpensive power; and lower OPEX achieved through the deployment of homogeneous compute, storage, and networking components. Cloud computing frees the enterprise and the end user from the specification of many details. This bliss becomes a problem for latency-sensitive applications, which require nodes in the vicinity to meet their delay requirements. An emerging wave of Internet deployments, most notably the Internet of Things (IoTs), requires mobility support and geo-distribution in addition to location awareness and low latency. We argue that a new platform is needed to meet these requirements; a platform we call Fog Computing [1]. We also claim that rather than cannibalizing Cloud Computing, F. Bonomi R.
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												  The Internet of Things: Impact on Public Safety CommunicationsCybersecurity and Infrastructure Security Agency The Internet of Things March 2019 The Internet of Things: Impact on Public Safety Communications The Internet of Things (IoT) is the network of physical devices and connectivity that enables objects to connect to one another, to the Internet, and exchange data amongst themselves.1, 2 IoT allows connected devices to be sensed or controlled remotely across network infrastructures, creating opportunities for more direct, cross-platform integration and improved efficiencies for the transfer of data between devices. IoT presents undeniable implications for public safety IoT goes beyond simply connecting communications. In turn, comprehensively addressing the objects to the Internet; it allows ever-growing IoT environment presents a unique challenge to physical objects to intelligently self- service providers, equipment manufacturers, and consumers. identify and communicate with other Harnessing network architecture changes and equipping devices, creating a new model of everyday objects to be IoT-enabled will allow public safety information sharing with a variety of stakeholders to maximize existing infrastructure investments potential applications. and provide near-real time decision support experiences that can change how they operate. IoT Benefits IoT-enabled devices can provide numerous benefits to public safety, as shown in Table 1. For example, a traffic accident response team could use the data collected from a variety of Internet-connected devices ― such as the involved vehicles (e.g.,
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												  Home Automation System Using Google Assistant© 2021 JETIR June 2021, Volume 8, Issue 6 www.jetir.org (ISSN-2349-5162) HOME AUTOMATION SYSTEM USING GOOGLE ASSISTANT MUDASIR M, NEHA V, NIHAAL THATHIR K, PAVAN YADAV M, POLLARPU SREERAMULU, SMITHA PATIL B.Tech. Student, Department of Computer Science and Engineering, Presidency University, Bangalore, India B.Tech. Student, Department of Computer Science and Engineering, Presidency University, Bangalore, India B.Tech. Student, Department of Computer Science and Engineering, Presidency University, Bangalore, India B.Tech. Student, Department of Computer Science and Engineering, Presidency University, Bangalore, India B.Tech. Student, Department of Computer Science and Engineering, Presidency University, Bangalore, India Assistant Professor, Department of Computer Science and Engineering, Presidency University, Bangalore, India Abstract: Nowadays Technology keeps on upgrading. The idea behind Google assistant-controlled Home automation is to control home devices with voice. On the market there are many devices available to do that, but making our own is awesome. In this project, the Google assistant requires voice commands. Adafruit account which is a cloud based free IoT web server used to create virtual switches, is linking to IFTTT website abbreviated as “If This Than That” which is used to create if else conditional statements. The voice commands for Google assistant have been added through IFTTT website. In this home automation, as the user gives commands to the Google assistant, Home appliances like Bulb, Fan and Motor etc., can be controlled accordingly. The commands given through the Google assistant are decoded and then sent to the microcontroller, the microcontroller in turn control the relays connected to it. The device connected to the respective relay can be turned ON or OFF as per the users request to the Google Assistant.
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												  FPGA BASED RECONFIGURABLE BODY AREA NETWORK USING Nios II and UclinuxFPGA BASED RECONFIGURABLE BODY AREA NETWORK USING Nios II AND uClinux A Thesis Submitted to the College of Graduate Studies and Research in Partial Fulfillment of the Requirements for the Degree of Master of Science in the Department of Electrical and Computer Engineering University of Saskatchewan by Anthony Voykin Saskatoon, Saskatchewan, Canada c Copyright Anthony Voykin, April 2013. All rights reserved. Permission to Use In presenting this thesis in partial fulfillment of the requirements for a Postgraduate degree from the University of Saskatchewan, it is agreed that the Libraries of this University may make it freely available for inspection. Permission for copying of this thesis in any manner, in whole or in part, for scholarly purposes may be granted by the professors who supervised this thesis work or, in their absence, by the Head of the Department of Electrical and Computer Engineering or the Dean of the College of Graduate Studies and Research at the University of Saskatchewan. Any copying, publication, or use of this thesis, or parts thereof, for financial gain without the written permission of the author is strictly prohibited. Proper recognition shall be given to the author and to the University of Saskatchewan in any scholarly use which may be made of any material in this thesis. Request for permission to copy or to make any other use of material in this thesis in whole or in part should be addressed to: Head of the Department of Electrical and Computer Engineering 57 Campus Drive University of Saskatchewan Saskatoon, Saskatchewan, Canada S7N 5A9 i Acknowledgments I would like to thank my advisors Professor Ron Bolton and Professor Francis Bui for providing me with guidance and support necessary to complete my thesis work.
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												  Shared Sensor Networks Fundamentals, Challenges, Opportunities, Virtualization Techniques, Comparative Analysis, Novel Architecture and TaxonomyJournal of Sensor and Actuator Networks Review Shared Sensor Networks Fundamentals, Challenges, Opportunities, Virtualization Techniques, Comparative Analysis, Novel Architecture and Taxonomy Nahla S. Abdel Azeem 1, Ibrahim Tarrad 2, Anar Abdel Hady 3,4, M. I. Youssef 2 and Sherine M. Abd El-kader 3,* 1 Information Technology Center, Electronics Research Institute (ERI), El Tahrir st, El Dokki, Giza 12622, Egypt; [email protected] 2 Electrical Engineering Department, Al-Azhar University, Naser City, Cairo 11651, Egypt; [email protected] (I.T.); [email protected] (M.I.Y.) 3 Computers & Systems Department, Electronics Research Institute (ERI), El Tahrir st, El Dokki, Giza 12622, Egypt; [email protected] 4 Department of Computer Science & Engineering, School of Engineering and Applied Science, Washington University in St. Louis, St. Louis, MO 63130, 1045, USA; [email protected] * Correspondence: [email protected] Received: 19 March 2019; Accepted: 7 May 2019; Published: 15 May 2019 Abstract: The rabid growth of today’s technological world has led us to connecting every electronic device worldwide together, which guides us towards the Internet of Things (IoT). Gathering the produced information based on a very tiny sensing devices under the umbrella of Wireless Sensor Networks (WSNs). The nature of these networks suffers from missing sharing among them in both hardware and software, which causes redundancy and more budget to be used. Thus, the appearance of Shared Sensor Networks (SSNs) provides a real modern revolution in it. Where it targets making a real change in its nature from domain specific networks to concurrent running domain networks. That happens by merging it with the technology of virtualization that enables the sharing feature over different levels of its hardware and software to provide the optimal utilization of the deployed infrastructure with a reduced cost.
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												  Atomicity and Visibility in Tiny Embedded SystemsAtomicity and Visibility in Tiny Embedded Systems John Regehr Nathan Cooprider David Gay University of Utah, School of Computing Intel Research Berkeley {regehr, coop}@cs.utah.edu [email protected] Abstract bool flag = false; // interrupt handler Visibility is a property of a programming language’s void __vector_5 (void) memory model that determines when values stored by { one concurrent computation become visible to other atomic flag = true; computations. Our work exploits the insight that in } nesC, a C-like language with explicit atomicity, the traditional way of ensuring timely visibility—volatile void wait_for_interrupt(void) variables—can be entirely avoided. This is advan- { tageous because the volatile qualifier is a notorious bool done = false; do { source of programming errors and misunderstandings. atomic if (!flag) done = true; Furthermore, the volatile qualifier hurts performance } while (!done); by inhibiting many more optimizations than are neces- } sary to ensure visibility. In this paper we extend the semantics of nesC’s atomic statements to include a visi- bility guarantee, we show two ways that these semantics Figure 1. nesC code containing a visibility er- can be implemented, and we also show that our better ror implementation has no drawbacks in terms of resource usage. Until now, nesC has left it to programmers to en- 1. Introduction sure visibility by judicious use of C’s volatile qual- ifier. In this paper we investigate the idea of strength- ening nesC’s memory model such that the semantics of The nesC [5] language is a C dialect designed for pro- atomic are augmented with a visibility guarantee. This gramming embedded wireless sensor network nodes.
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												  Overview of Wireless Sensor Network (WSN) SecurityOverview of Wireless Sensor Network (WSN) Security Aparicio Carranza Heesang Kim Xiao Lin Chen NYC College of Technology NYC College of Technology NYC College of Technology 186 Jay Street – V626 186 Jay Street 186 Jay Street Brooklyn, NY, USA Brooklyn, NY, USA Brooklyn NY, USA 718 – 260 – 5897 703 – 232 – 2001 917 – 285 - 5155 [email protected] [email protected] xiaolin,[email protected] ABSTRACT of different transmission schemes, allowing the connection Our civilization has entered an era of big data networking. to be made between different models. A router is a good Massive amounts of data is being converted into bits for example of a gateway. There are two common Wireless transmitting over the Internet and shared all over the world network topologies commonly used in WSN systems, they every second. The concept of “smart” devices has are “relay nodes” and “leaf nodes”. The relay network is a conquered preconceived notions of daily routines. broad network topology where the source and destination Smartphones and wearable device technologies have made are interconnected through some node. In these networks, humans to be continuously interconnected, making distance the source and destination cannot communicate directly a factor of no limitation. “Smart” systems are based on a with each other because the distance between the source single concept called the Internet of Things (IoT). IoT is a and destination is greater than the transmission range, due web of wirelessly networked devices embedded with to this an intermediate node needs to relay. The advantage electronic components and sensors that monitors physical of using the relay nodes is that the information can travel and environmental conditions and acquires data to be long distances even when the caller and recipient are far shared with other systems.
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												  Predictive Analysis of 3D Reram-Based PUF for Securing the Internet of ThingsPredictive Analysis of 3D ReRAM-based PUF for Securing the Internet of Things Jeeson Kim Hussein Nili School of Engineering Electrical and Computer Engineering RMIT University University of California Santa Barbara Melbourne, Australia Santa Barbara, USA [email protected] [email protected] Gina C. Adam Nhan Duy Truong Electrical and Computer Engineering School of Engineering University of California Santa Barbara RMIT University Santa Barbara, USA Melbourne, Australia gina [email protected] [email protected] Dmitri B. Strukov Omid Kavehei Electrical and Computer Engineering Electrical and Information Engineering University of California Santa Barbara The University of Sydney Santa Barbara, USA Sydney, Australia [email protected] [email protected] Abstract—In recent years, an explosion of IoT devices challenge [2, 3]. Widely used traditional cryptographic and its use leads threats to the privacy and security solutions, for example, advanced encryption standard concerns of individual users and merchandises. As one (AES) and elliptic curve cryptography (ECC), can be of promising solutions, physical unclonable function (PUF) has been extensively studied. This paper investigates quality used for both the integrity and the authentication of of randomness in the first generation of 3D analog ReRAM exchanging data and messages. PUF primitives using measured and gathered data from IoT hardware anti-counterfeiting, integrated circuit fabricated ReRAM crossbars. This study is significant as (IC) trust and physical tampering are also critical the randomness quality of a PUF directly relates to its tasks [4]. In 2014, defense advanced research projects resilience against various model-building attacks, includ- ing machine learning attack.