Internet of Things (Iot) Operating Systems Management: Opportunities, Challenges, and Solution
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Internet of Things Meets Brain-Computer Interface: a Unified Deep Learning Framework for Enabling Human-Thing Cognitive Interactivity
JOURNAL 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. -
Comparison of Contemporary Real Time Operating Systems
ISSN (Online) 2278-1021 IJARCCE ISSN (Print) 2319 5940 International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 11, November 2015 Comparison of Contemporary Real Time Operating Systems Mr. Sagar Jape1, Mr. Mihir Kulkarni2, Prof.Dipti Pawade3 Student, Bachelors of Engineering, Department of Information Technology, K J Somaiya College of Engineering, Mumbai1,2 Assistant Professor, Department of Information Technology, K J Somaiya College of Engineering, Mumbai3 Abstract: With the advancement in embedded area, importance of real time operating system (RTOS) has been increased to greater extent. Now days for every embedded application low latency, efficient memory utilization and effective scheduling techniques are the basic requirements. Thus in this paper we have attempted to compare some of the real time operating systems. The systems (viz. VxWorks, QNX, Ecos, RTLinux, Windows CE and FreeRTOS) have been selected according to the highest user base criterion. We enlist the peculiar features of the systems with respect to the parameters like scheduling policies, licensing, memory management techniques, etc. and further, compare the selected systems over these parameters. Our effort to formulate the often confused, complex and contradictory pieces of information on contemporary RTOSs into simple, analytical organized structure will provide decisive insights to the reader on the selection process of an RTOS as per his requirements. Keywords:RTOS, VxWorks, QNX, eCOS, RTLinux,Windows CE, FreeRTOS I. INTRODUCTION An operating system (OS) is a set of software that handles designed known as Real Time Operating System (RTOS). computer hardware. Basically it acts as an interface The motive behind RTOS development is to process data between user program and computer hardware. -
The Internet of Things (Iot): an Overview
Updated 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. -
Graphical Product-Line Configuration of Nesc-Based Sensor Network Applications Using Feature Models
GRAPHICAL 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. -
AMNESIA 33: How TCP/IP Stacks Breed Critical Vulnerabilities in Iot
AMNESIA:33 | RESEARCH REPORT How TCP/IP Stacks Breed Critical Vulnerabilities in IoT, OT and IT Devices Published by Forescout Research Labs Written by Daniel dos Santos, Stanislav Dashevskyi, Jos Wetzels and Amine Amri RESEARCH REPORT | AMNESIA:33 Contents 1. Executive summary 4 2. About Project Memoria 5 3. AMNESIA:33 – a security analysis of open source TCP/IP stacks 7 3.1. Why focus on open source TCP/IP stacks? 7 3.2. Which open source stacks, exactly? 7 3.3. 33 new findings 9 4. A comparison with similar studies 14 4.1. Which components are typically flawed? 16 4.2. What are the most common vulnerability types? 17 4.3. Common anti-patterns 22 4.4. What about exploitability? 29 4.5. What is the actual danger? 32 5. Estimating the reach of AMNESIA:33 34 5.1. Where you can see AMNESIA:33 – the modern supply chain 34 5.2. The challenge – identifying and patching affected devices 36 5.3. Facing the challenge – estimating numbers 37 5.3.1. How many vendors 39 5.3.2. What device types 39 5.3.3. How many device units 40 6. An attack scenario 41 6.1. Other possible attack scenarios 44 7. Effective IoT risk mitigation 45 8. Conclusion 46 FORESCOUT RESEARCH LABS RESEARCH REPORT | AMNESIA:33 A note on vulnerability disclosure We would like to thank the CERT Coordination Center, the ICS-CERT, the German Federal Office for Information Security (BSI) and the JPCERT Coordination Center for their help in coordinating the disclosure of the AMNESIA:33 vulnerabilities. -
Fog Computing: a Platform for Internet of Things and Analytics
Fog 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. -
The Internet of Things: Impact on Public Safety Communications
Cybersecurity 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., -
FPGA BASED RECONFIGURABLE BODY AREA NETWORK USING Nios II and Uclinux
FPGA 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. -
Tinyos Meets Wireless Mesh Networks
TinyOS Meets Wireless Mesh Networks Muhammad Hamad Alizai, Bernhard Kirchen, Jo´ Agila´ Bitsch Link, Hanno Wirtz, Klaus Wehrle Communication and Distributed Systems, RWTH Aachen University, Germany [email protected] Abstract 2 TinyWifi We present TinyWifi, a nesC code base extending TinyOS The goal of TinyWifi is to enable direct execution of to support Linux powered network nodes. It enables devel- TinyOS applications and protocols on Linux driven network opers to build arbitrary TinyOS applications and protocols nodes with no additional effort. To achieve this, the Tiny- and execute them directly on Linux by compiling for the Wifi platform extends the existing TinyOS core to provide new TinyWifi platform. Using TinyWifi as a TinyOS plat- the exact same hardware independent functionality as any form, we expand the applicability and means of evaluation of other platform (see Figure 1). At the same time, it exploits wireless protocols originally designed for sensornets towards the customary advantages of typical Linux driven network inherently similar Linux driven ad hoc and mesh networks. devices such as large memory, more processing power and higher communication bandwidth. In the following we de- 1 Motivation scribe the architecture of each component of our TinyWifi Implementation. Although different in their applications and resource con- straints, sensornets and Wi-Fi based multihop networks share 2.1 Timers inherent similarities: (1) They operate on the same frequency The TinyOS timing functionality is based on the hardware band, (2) experience highly dynamic and bursty links due to timers present in current microcontrollers. A sensor-node radio interferences and other physical influences resulting in platform provides multiple realtime hardware timers to spe- unreliable routing paths, (3) each node can only communi- cific TinyOS components at the HAL layer - such as alarms, cate with nodes within its radio range forming a mesh topol- counters, and virtualization. -
Shared Sensor Networks Fundamentals, Challenges, Opportunities, Virtualization Techniques, Comparative Analysis, Novel Architecture and Taxonomy
Journal 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. -
Atomicity and Visibility in Tiny Embedded Systems
Atomicity 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. -
1921 Tulsa Race Riot Reconnaissance Survey
1921 Tulsa Race Riot Reconnaissance Survey Final November 2005 National Park Service U.S. Department of the Interior CONTENTS INTRODUCTION 1 Summary Statement 1 Bac.ground and Purpose 1 HISTORIC CONTEXT 5 National Persp4l<live 5 1'k"Y v. f~u,on' World War I: 1896-1917 5 World W~r I and Postw~r ( r.: 1!1t7' EarIV 1920,; 8 Tulsa RaCR Riot 14 IIa<kground 14 TI\oe R~~ Riot 18 AIt. rmath 29 Socilot Political, lind Economic Impa<tsJRamlt;catlon, 32 INVENTORY 39 Survey Arf!a 39 Historic Greenwood Area 39 Anla Oubi" of HiOlorK G_nwood 40 The Tulsa Race Riot Maps 43 Slirvey Area Historic Resources 43 HI STORIC GREENWOOD AREA RESOURCeS 7J EVALUATION Of NATIONAL SIGNIFICANCE 91 Criteria for National Significance 91 Nalional Signifiunce EV;1lu;1tio.n 92 NMiol\ill Sionlflcao<e An.aIYS;s 92 Inl~ri ly E~alualion AnalY'is 95 {"",Iu,ion 98 Potenl l~1 M~na~menl Strategies for Resource Prote<tion 99 PREPARERS AND CONSULTANTS 103 BIBUOGRAPHY 105 APPENDIX A, Inventory of Elltant Cultural Resoun:es Associated with 1921 Tulsa Race Riot That Are Located Outside of Historic Greenwood Area 109 Maps 49 The African American S«tion. 1921 51 TI\oe Seed. of c..taotrophe 53 T.... Riot Erupt! SS ~I,.,t Blood 57 NiOhl Fiohlino 59 rM Inva.ion 01 iliad. TIll ... 61 TM fighl for Standp''''' Hill 63 W.II of fire 65 Arri~.. , of the Statl! Troop< 6 7 Fil'lal FiOlrtino ~nd M~,,;~I I.IIw 69 jii INTRODUCTION Summary Statement n~sed in its history.