Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Auto id-labs-kaist-research-2014

2,300 views

Published on

Published in: Technology
  • Be the first to comment

  • Be the first to like this

Auto id-labs-kaist-research-2014

  1. 1. TEMPLATE DESIGN © 2008 www.PosterPresentations.com Auto-ID Labs The Leading Academic Research Network on the Internet of Things, GS1 Research Partner Auto-ID Labs – Future Proofing of GS1 Introduction to GS1 •GS1 is an international not-for-profit association with Member Organizations in over 110 countries. GS1 is dedicated to the design and implementation of global standards and solutions to improve the efficiency and visibility of supply and demand chains globally and across sectors. The GS1 system of standards is the most widely used supply chain standards system in the world. This 40-years-old global organization’s main activity is the development of the GS1 System, a series of standards designed to improve supply chain management as follows: •Global Unique Identifier standard and guideline •Electronic data interchange standard and guideline •GS1 Global Registry connecting business stakeholder •RFID standards and services for increased visibility and efficiency •The Auto-ID Labs are the leading global research network of academic laboratories in the field of Internet of Things. In 1999, the Internet of Things was first coined by Kevin Ashton who cofounded the Auto-ID Center at the MIT. The labs comprise six of the world’s most renowned research universities located on three different continents. The labs believe that the next generation of the Internet of Things can revolutionize global commerce and provide previously unrealizable consumer benefits. As a primary research partner of GS1, The Auto-ID Labs has proofed the future of GS1 by developing open standards for supply chain visibility and providing strategic guidance for several flagship projects. •The Auto-ID Labs Centers MIT Disruptive IoT Applications Cambridge Linked-data and Semantic IoT ETH Zurich New Business Models and Consumer Empowerment Fudan RFID core technology KAIST Scalable IoT Architectures KEIO The network for IoT Applications Auto-ID Lab at KAIST •Auto-ID Lab at KAIST will leverage the Internet of Things technologies through collaboration with the world's best IoT laboratories and participation in international standardization processes led by GS1/EPCglobal. Furthermore, personnel exchanges and research partnerships with other Auto-ID Labs will allow KAIST to cultivate more internationalized talent. •Office •Global Office in Brussels (Belgium) •GS1 AISBL, Blue Tower, Avenue Louise, 326 BE 1050 •Local Offices over 110 countries GS1: http://www.gs1.org/ •Event •GS1 Global Forums •GS1 Board Meeting •GS1 Advisory Council Meeting …. •CO-CHAIRS Prof. Sanjay Sarma MIT Prof. Elgar Fleisch ETH Zurich Kevin Ashton Auto-ID Labs: http://autoidlabs.org/ •Member & Research Area Research Director Name: Prof. Kim, Daeyoung RESL Lab: http://resl.kaist.ac.kr Area: IoT Platform, IoT Connectivity Professor Name: Prof. Lee, Sang-gug NICE Lab: http://nice.kaist.ac.kr Area: Nano integrated Circuit design Professor Name: Prof. Kwang-Jo, Kim CAIS Lab: http://caislab.kaist.ac.kr/html/main.html Area: Cryptology, Information security Professor Name: Prof. Rho, Jae-Jeung MIKeS Lab: http://mikes.kaist.ac.kr Area: Business Auto-ID Lab at KAIST: http://autoidlab.kaist.ac.kr Associate Research Director Name: Prof. Moon, Junghoon Contact: moonj@snu.ac.kr Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
  2. 2. TEMPLATE DESIGN © 2008 www.PosterPresentations.com Open Language for Internet of Things http://oliot.org Overview Passive Tags (e.g., passive tags, barcode) Sensor & Actuator Networks (e.g., ZigBee, 6LoWPAN, Mobile phone, BLE, AllJoyn, lwM2M etc.) Active Tags (e.g., Wireless ID and Sensor Networks) RFID Middleware LLRP LLRP Sensor & actuator protocols Sensor & actuator protocols Domain-specific capturing application Domain-specific accessing applications Sensor Interface Sensor interface EPC Information Service (static and dynamic information) ALE Actuation Interface Sensor & Actuator Middleware Object Name Service Discovery Service ZigBee 6LoWPAN/ CoAP MQTT Web service-* REST Other Comm. RFID stream processing Logical RFID reader Reader Management Sensor stream processing Sensor & actuator Management ID-Sensor stream processing Open Language for Internet of Things (Oliot) is to build a ID-based framework to identify, capture, and share information of smart things. • International standard based open-source IoT infrastructure platform • Based on GS1 EPCglobal standard architecture • Providing complete implementations of latest GS1 EPCglobal Architecture Framework • Oliot is a spin-off project of open-source EPCglobal implementation, Fosstrak(fosstrak.org) Scope of Oliot Project History of Oliot Project Oliot project is composed of following core components, • Smart things’ control and data acquisition (with Oliot-LLRP) • ID & sensor stream processing (with Oliot-F&C or ALE) • Smart thing information service (with Oliot-EPCIS) • Object name service (with Oliot-ONS) • Discovery service (with Oliot-DS) And additionally includes, • Cloud-based smart things repository using Apache Cassandra DB • Real-time big data processing using Apache Storm Complete Implementations of EPCglobal Framework Oliot Next & Release Plan • EPC Sensor Network (EPCSN), since 2005 to 2011 • Expand GS1 EPCglobal Network to integrate various sensor network protocols • Adaptation of Zigbee, 6LoWPAN to LLRP protocol • Complex Event Processing, etc. • Smart Thing Information Service (STIS), since 2011 to 2014 • Successor of EPC Sensor Network • Integrate 6LoWPAN/CoAP/Obix protocol to middleware (without LLRP adaptation) • Interoperable with EU FP7 IoT6 project • GS1 EPCglobal Network on the Cloud for Groceries Trace Framework, since 2013 • Complete Implementation of latest GS1 EPCglobal framework • EPCIS Enhanced with NOSQL DB • Cloud Support • ELFIN: Enhanced LLRP-enabling Framework for the INternet of Things • Geo-discovery Service • ONS 2.0.1 implemented • Oliot 1.0 (Current version) • Complete implementation of latest GS1 EPCglobal framework • Run on any clouds that supports MySQL and Cassandra • Oliot 1.0 – Current • Oliot 1.1 – 4Q 2014 • Provide EPCIS 1.1, strengthened to support food industry • Oliot 2.0 – 2015 • Intensively support Internet of Things • Integration with EPCSN and STIS project • Support various connectivity such as 6LoWPAN, BLE, etc. • Support various protocols such as MQTT, AllJoyn, etc. • Oliot LLRP • Enhanced LLRP-Enabling Framework for Internet of Things (ELFIN) • Support adaptation of various kinds of connectivity and protocols • Oliot F&C (ALE) • Process stream-based raw data from various devices, and generate refined high-level events following GS1 standard • Oliot EPCIS • Repository that stores EPCIS events and Master data. • Adopts Cassandra NoSQL DB for scalability and performance • Oliot ONS • Service look-up system on top of DNS. • Looks up and returns services related to given EPC • Oliot DS • Finds physical location of the product with given EPC • Miscellaneous Extensions (ongoing) • Flow-based load balancing and migration for EPC network • Real-time stream data processing of EPCglobal based IoT Environment Above implementations are available on http://oliot.org Δt Interacting with smart things Sensor stream processing & actuation Search & discovery Distributed storage on cloud infrastructure N..1 N..1 N..1 Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
  3. 3. TEMPLATE DESIGN © 2008 www.PosterPresentations.com SNAIL: Sensor Networks for an All-IP worLd Introduction Software Architecture Overview Demonstration Hardware Platform • SNAIL Sensor Node Hardware Platform •6LoWPAN over 802.15.4 IP-based WSN  An IP-based Wireless Sensor Networks platform  Important Features • Global IP-interconnection for constrained devices • Interoperability between IPv4/v6 domains and the IEEE 802.15.4, BLE(Bluetooth Low Energy) • Fully compatible with IETF 6LoWPAN WG, IETF ROLL WG, and IETF CoRE WG standards • Supports mobility, web enablement, time synchronization, and secure communication(using SSL and DTLS) • On-going work: Routing protocols, Service discovery, Network Management, Plug-and-play, Device Management, Low Power Connectivity, and Security. • Internet of Things • Regarding the Internet of Things, 6LoWPAN has been a very successful network standard in connecting constrained things to the Internet. 6LoWPAN standard provides end-to-end IPv6 communication to physical things and seamless access to them from the Internet. • WEST – Web-enabled Smart Tags is a new generation of smart tags that leverage 6LoWPAN network standard to enable access to tags’ data from the Internet. WEST tags feature web access with rich web experience to every tag. Global IP Interconnection • SNAIL Gateway Hardware Platform (6LoWPAN Edge Router) •6LoWPAN over BLE • SNAIL Gateway Software Platform • SNAIL Sensor Node Software Platform Processor TI MSP430F5438 (16-bit RISC Architecture) • System Clock : Up to 18-MHz • Flash: 256KB • RAM: 16KB • 12 Bit ADC • 4 USCIs RF transceiver TI CC2520 • IEEE 802.15.4 compliant DSSS baseband modem • Data rate: 250kbps • RF freq. range: 2394-2507MHz Sensors • Temperature • Humidity • Compass sensor • sensor • 3-axis accelerometer • 2-axis analog gyrometer Operation Mode • Plan A: 6LoWPAN over 6BLE(Bluetooth Low Energy) • Plan B: 6LoWPAN over Bluetooth Communication Processor Raspberry Pi • Broadcom BCM2835 SoC full HD multimedia applications processor • 700 MHz Low Power ARM1176JZ-F Applications Processor • Flash: MicroSD • RAM: 512 MB SDRAM @ 400 MHz GPU • Dual Core VideoCore IV® Multimedia Co- Processor Interface • USB2.0 x 2, 10/100mb Ethernet RJ45 Supported Sensors • SPO2, Breathing, Body temperature, ECG, Glucometer, GSR, Blood pressure, EMG, Accelerometer RF transceiver TI CC2520 • IEEE 802.15.4 compliant DSSS baseband modem • Data rate: 250kbps • RF freq. range: 2394-2507MHz Features APP • Web Server(HTTP) • HTML5 WebSocket Proxy • WSCoAP Daemon • SSL NET/TRN • TCP/UDP Stack • IPv6, ICMPv6, MIPv6, NEMO, Neighbor Discovery, Route-over Routing(RPL) • IP Adaptatoin • SNAIL Services • Mobility management • Load balancing • Global time synchronization MAC • Ethernet • Wifi • IEEE 802.15.4 PHY/MAC • BLE(Bluetooth Low Energy Features App • Lightweight Web Server(HTTP) • CoAP Server with DTLS(Datagram Transport Layer Security) • Lightweight SSL(Secure Socket Layer) NET/TRN • Lightweight TCP/UDP • Lightweight IPv6, ICMPv6, MIPv6, NEMO • Neighbor Discovery • Route-over Routing(RPL) • IP Adaptation • Services • Mobility management • Load balancing • Global time synchronization MAC • IEEE 802.15.4 PHY/MAC • BLE(Bluetooth Low Energy) Raspberry Pi Ra- spberry Pi CC 2540 dongle CC 2540 dongle • Related IETF Working Group • 6LoWPAN/6lo: RFC6282/4919, Defines IPv6 IoT connectivity for 802.15.4 and other constrained devices • ROLL: Routing over low power and lossy network • DICE: DTLS in the constrained Environments • CoRE: Constrained RESTful Environments Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
  4. 4. TEMPLATE DESIGN © 2008 www.PosterPresentations.com BuddyThing Cloud System: IoT Browsing as a Service Overview  BuddyThing cloud system encompasses product manufactures, service developers and end-users of the Web of Things eco system. IoT Browsing as a Service Rich WoT Apps Mashup Apps Constrained device Web browser Constrained device Cloud Backend App Builder/ Deployer Services ThingDNS ThingID ThingProxy ThingSocial WoT Apps JS CSS HTML Images, audio, video Provides rich web contents and WoT services AppInit  BuddyThing Cloud  Developers can deploy the WoT App on the BuddyThing Cloud and its resources are managed and served under the GS1 code based domain name.  By minimizing interactions with physical things except vital data transfer, BuddyThing cloud reduce overhead on physical things. Mashup WoT App Domain User Smith App 0000000002. 06614141999997.app.iot.kr Domain Marry App 0000000003. 06614141999997.app.iot.kr Domain Domain Ambulance App 0000000001. 06614141999997.app.iot.kr John App 0000000004. 06614141999997.app.iot.kr Domain Patient Browsing App 1234567890. 06614141999996.iot.kr Domain  Any web app can access other web app’s resource using domain based URL.  Users can access WoT apps via GS1 code based domain name. GS1 code for WoT Service  Every WoT service has their own GS1 code presented through its domain name.  Service-Class: Services in the same class can share web contents such as files.  Service-Serial: It gives different context even though it is in the same class.  The doctor can browse patient’s health signals using the patient browsing app which is mashup app.  Patient browsing app consists of patient apps whose resources are health signal graph.  Mashup  WoT Mashup App Examples Patient Browsing App GS1 code based Domains Develop WoT Service Developer Mash up! Users BuddyThing Cloud Physical Things  Smart-home app mashups any smart things in user’s home.  Any mashup app such as Bed room app, Kitchen app can be part of other mashup app like Smart-home app. Smart-home App I want to manage services of my products. Domains up! BuddyThing Cloud Things Domain User Smith App 0000000002. 06614141999997.app.iot.kr Domain Marry App 0000000003. 06614141999997.app.iot.kr Domain Domain Ambulance App 0000000001. 06614141999997.app.iot.kr John App 0000000004. 06614141999997.app.iot.kr Domain Patient Browsing App 1234567890. 06614141999996.iot.kr Domain GS1 code for Service User Service Developer Product Manufacturer I want to make service with more functions of other services I want to get safe services of my smart thing.  Based on the interface, Service GS1 code, BuddyThing provides capabilities to meet requirements of each actor of WoT eco system. (01)00614141999996 (21)1234567890 Company Prefix Item Reference Serial GS1 code: Service-Serial 1234567890. 00614141999996.app.iot.kr Service-Class Web App Server domain Domain: Smith Ambulance Doctor's Google Glass John Marry Patient Browsing App John Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
  5. 5. TEMPLATE DESIGN © 2008 www.PosterPresentations.com Versatile Internet of Things Application on Mobile Dynamic Service Composition Framework Architecture Problem & Approach Overview •Developer should decide explicit binding at implementation time •Other devices cannot interact with Normal App without binding •User feels difficult to use external things fit on their purpose Composition UI Demonstration •Connectivity Provider : abstraction for connectivity to various smart things having heterogeneous protocols such as GATT(BLE) or UPnP(SSDP). •Object Abstraction Layer : smart things discovery, virtualized object management and bundle management (by using OSGi framework) •Composition Layer : carries out the service composition by parsing the authoring information that is defined by user at run-time and receiving the reference of bundles from the object abstraction layer. •IoTApp-API : offers standard interfaces for smart-thing’s group such as bulb, sensor, camera and etc. Also offers API for the various functions such as the things discovery, retrieve virtualized object and etc. •Application logic bundle and service bundle repositories : warehouses that provide the bundles corresponding to authoring information. •Object Name Service : retrieve discovered smart-thing’s information by using the ID of the smart things. Normal Application Case Versatile IoT-App Case IoT Mashup as a Service •Composition UI shows the list of service bundles provided by the discovered smart-things and application logic bundles. •This plays the role of delivering the authoring information to the composition layer. •User could decide explicit binding at run-time. •All other devices can be connected at run-time by user’s authoring •User feels comfortable to use external things fit on their purpose Problem Approach App-logic bundles are listed (by developer) discovered device’s services are listed Implemented on Node-red Description about bundles Generating assembly information as Json format. •In our demonstration the brightness and color of the lights are changed in accordance with state of people. Heart rate and movement values of user will be input-parameters that change brightness and color of lights. List Discovered Device & Service 2 Service Composition 6 a b c UPnP, TCP/IP GATT(BLE) Device Discovery 1 Download Needed Bundle 5 d Service Launched 7 generate Assembly Info… 4 Authoring… 3 •A new class of cloud-based IoT Mashup service model •Consists of •Thing •Software •Computing Resource •We assume that Mashup service is composed with software components at run- time upon a dynamically allocated computation resource, processing data from things to produce output Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
  6. 6. TEMPLATE DESIGN © 2008 www.PosterPresentations.com The SeaHaven project The Visual Sensor Networks platform for Internet of Things Overview Prototypes and Demo Applications •The major demo application is focused on surveillance category •Vision based event detection and sensor based event detection •Visual sensor node streams data to the cloud and cloud runs algorithms to detect events and give feedback to the registered feedback interface and event viewer •Visual sensor node performs a feedback actuator in voice feedback We are living in a world of camera everywhere and camera on everything. According to the report "iSuppli, Image Sensor Market Tracker, 2011", more than 2,500 million units of CMOS image sensor will be distributed on the market which includes various type of consumer electronics claim to be digital convergence. And we also have plenty of legacy sensors over the world already and many of those are already on mature level to be used in everyday life. We profit from image sensors and legacy sensors as well to make a multi-dimensional context data which will make machine more clever than ever. The visual BigData processing cloud archives and process visual contact data and legacy sensor data as well. By processing multi-dimensional and spatio-temporal fused data, we make machine to understand the visual perception and make camera node intelligence evolving site by site. Platform compliance architectures Representing Algorithms •Multi-tiered architecture visual sensor node •S/W stack on Linux with standard interfaces •Preliminary event processing is done on 1st tier •Camera and sensor cloud streamer sends multi sensor data over the cloud •Multi-tier H/W to make extremely power saving architecture •1st tier microcontroller node performs sensor preprocessing •2nd tier camera node performs streaming •Basic detection, recognition algorithms are implied on every pipeline •Multi sensor fusion to process higher level context •Event and process hierarchy discovery through scale space representation •Analysis leads to the cause of events and causality between events •Event transition analysis on probability based measure •Bigdata analysis aided prediction on next move of specific event of interest •Zero configuration sensor and cloud network •Security enhanced sensor to cloud and cloud to user data stream by platform level VPN •Fully modularized streamer design to meet scalability requirement of expanding services in the future •Multi-sensor fusion service as a container architecture which makes fully pluggable service architecture •RESTful API for diverse sensor devices •Scalable sensor interface to adopt zillions of sensor streams •Unified filesystem to archive visual data over distributed and multi-zone geo located storage service Sensor S/W architecture Cloud architecture Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
  7. 7. TEMPLATE DESIGN © 2008 www.PosterPresentations.com GPGPU enabled HPC Cloud Platform Overview CPU GPU Less core (4-8cores) Thousands of cores Each core is complex Simple core Coarse grain parallelism Fine grain parallelism Sequential computing Parallel Computing Domains: Synthesis, Compiling, Data-dependent application Domains: Simulation, Graphic processing,… • Recently, HPC users are interested in running HPC applications on Cloud computing since they are considering Cloud computing as an alternative to dedicated supercomputers. • In addition, GPGPU is now one of the most efficient way to boost up scientific applications. Many HPC applications got better performance by using GPU programming models such as CUDA and OpenCL. The number of SCs using GPU/coprocessor in Top 500 Physical Machine Hypervisor Operating System Physical Hardware ... CPU RAM Virtual Machine (VM) GPGPU Application Operating System Emulated Hardware FrontEnd ... vCPU vRAM vNetwork Virtual Machine (VM) GPGPU Application Operating System Emulated Hardware FrontEnd ... vCPU vRAM vNetwork Virtual Machine (VM) GPGPU Application Operating System Emulated Hardware FrontEnd ... vCPU vRAM vNetwork Physical Machine Hypervisor BackEnd Operating System Physical Hardware ... CPU RAM GPU GPU Driver Virtual network • No1, No2 Supercomputers are also using many GPUs/coprocessors as accelerators. • By using GPUs and Coprocessors, performance of Supercomputer is increasing very fast. • The number of Supercomputers using GPUs/coprocessors has kept increasing. H/W GPU Server Node x2 Single Node - Intel Xeon E5 CPU x2[8] - NVIDIA Tesla K20[5] - SSD 256GB - RAM 64GB Interconnect - Infiniband S/W Ubuntu 12.04 LTS x64 OpenStack “Havana”[9] rCUDA for Ubuntu 11.10 x64 GPU Resource Scheduling on HPC Cloud Platform • Previous Cloud platforms only consider CPU/RAM/Disk as shared resources • In GPGPU enabled HPC Cloud platform, we need to consider GPUs as new cloud resource • Scheduling GPU resource in Initial VM allocation and Dynamic run time is important issue • We suggest Centralized/Distributed GPU resource scheduling on GPGPU HPC Cloud platform Scheduler GPU CPU GPU CPU GPU CPU GPU CPU VM VM VM VM VM VM VM VM VM Initial Placement Migration User log , Business activity logs , etc . Internet contents , SNS , etc . Everyday objects Multimedia ( video , audio ) , etc . Big Internet of Things Data Scientific Applications In order to implement smart world, we want to gather all data in the real world. However, it is difficult to process the data on time, because the data is generated quickly and has features of big data like a huge volume and various format. Engineers and researchers want more powerful computing capability and performance. So, cloud computing & hybrid system based on accelerator like GPU is spotlighted in IoT and HPC filed to improve processing performance, save money and energy. We meet diverse research issues as follows for convergence of two technology. • Implement GPU Virtualization on Cloud System • Use Virtualized GPU resource efficiently • Maximize GPU utilization rate. • Minimize overhead and latency caused by virtualization • Dynamically allocate virtualized GPU resource on HPC cloud platform • Process Big data using GPGPU HPC Cloud platform Our goal is the realization of GPGPU enabled HPC Cloud platform in order to enhance the computing process of scientific applications as well as foster the growth of IoT world. Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea Trend of Technology Platform & Research
  8. 8. TEMPLATE DESIGN © 2008 www.PosterPresentations.com Cognitive Radio Network for Future IoT Connectivity Motivation & Application area Architecture Overview Platform testing result Demonstration • Hardware: USRP N210 in used with daughterboard RFX2400 and VERT2450 antenna (as shown on the left) in order to operate in 802.15.4 spectrum band. Current application processor is Intel Core based running on PC. Aiming target is to use low-cost, low-power controlling processor such as ARM board (A15, A7). • OS: Ubuntu 10.04, UHD Driver for USRP N210 controlling and GNU Radio v3.6 (most suitable with current configuration of hardware and software) • Physical layer: 802.15.4 PHY layer implemented in GNU Radio to stimulate and control signal transceiver • MAC layer : Slow-hopping MAC protocol for Coordinator-based Cognitive Radio Network, which utilizes multiple unlicensed channels to improve the aggregate throughput. SDR Platform OS Ubuntu GNU Radio UHD Driver H/W USRP N210 FPGA Xilinx® Spartan® 3A-DSP3400 RFX2400 PC / ARM board Intel / ARM processor GB Ethernet interface Chip modulation O-QPSK PHY parameter: channel numbers, PHY 802.15.4 PHY channel spacing, Tx rates ... Bootstraping Multichannel Operation MAC SHCS MAC Cooperative sensing Self-coexistence Primitive funtions Transmitter Receiver Spectrum sensing SUC Jammer User2 User 1 Coordinator 0 10 20 30 40 50 60 70 80 90 100 2405M 2410M 2415M 2420M 2425M 2430M 2435M 2440M 2445M 2450M 2455M 2460M 2465M 2470M • Software-defined Radio (SDR): System where the functions of modern-day radio systems are implemented and defined in software. • Cognitive Radio (CR): An intelligent radio that can be programmed and configured dynamically. Its transceiver is designed to use the best wireless channel in its vicinity RF/IF conversion circuit FPGA User App Basic Software-defined Radio diagram • Universal Software Radio Peripheral (USRP): low-cost, high-quality software defined radio systems; enable users worldwide to address a broad range of research, academic, industrial and defense application • Equipment: 4 sets of SDR platform (1 set: Coordinator, 2 sets: User 1/2, 1 set: Jammer) Operating channels Packet receiving rate Ad-hoc CR for dynamic spectrum access USRP N210 platform • Motivation: • The shortage of spectrum resources will become the bottleneck of the IoT development in the near future. Apply CR to IoT will meet the increasing demand of frequency • Researching PNT tactical data link using CR and SDR technology. • Fuse the future PNT tactical network based technology such as Multi-mode, radio positioning, battlefield situational awareness, cognitive radio, and so on. • Application area: a basic platform for multi-mode PNT network testbed that utilizes radio waves and GPS positioning adaptively according to the surrounding radio environment. • Operation: • Coordinator performs spectrum sensing • If Jammer activity is not detected, Coordinator will send out an active beacon on the current hop • If Jammer activity is detected, Coordinator will change the channel according to the common hopping sequence • Jammer changes operating channel randomly and makes that channel busy • User 1 and User 2, after joining network by common hopping sequence, will decide whether to send the data using the free channel based on receiving active beacon. • Channels of IEEE 802.15.4 and the overlapping with 802.11 spectrum • Packet receiving rate on 802.15.4 channels affected by the interference • Testing packets with CC2420EM packet sniffer • USRP N210 specification: • Spartan 3A-DSP 3400 FPGA • 1 MB High-Speed SRAM • Modular Architecture: DC-6 GHz • Dual 100 MS/s, 14-bit ADC • Dual 400 MS/s, 16-bit DAC • 25 mHz Resolution DDC/DUC • Fully-Coherent MIMO Capability • Gigabit Ethernet Interface to Host • Auxiliary Analog and Digital I/O SDR Platform using USRP N210 RFX2400 daughterboard & VERT2450 antenna • RFX2400 specification: • Full duplex transceiver • Operation range 2.3 – 2.9GHz • Power output of 50 mW • Noise figure of 8 dB • VERT2450 specification: • Omni-directional vertical • 3dBi Gain Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
  9. 9. TEMPLATE DESIGN © 2008 www.PosterPresentations.com GS1 Digital •The GS1 Digital is a new GS1 “Share” technology for communicating the GS1 GTIN and other keys and attributes in computer-readable formats across the World Wide Web. •The GS1 Digital includes standards and guidelines for companies to publish product data on web pages that allow their consumers to more effectively search for, compare, buy, share information about, and get the most out of the products and services that meet their needs. Schick quattro titanium razor Schick quattro titanium razor Missing retailers Missing reviews Different image Different name Different highlight Different pricing However, what can you find on Internet now? Search engines often return different or incomplete shopping results Digital makes people more smarter Change the way consumers access and use information Category hierarchy: Create standard hierarchy to structure on-line commerce sites (GPC) Trusted source of data: create database of trusted product attributes to facilitate one-to-many communication and consumer analytics (GS1 Source) Unique online product identifier: Use GTINs to improve accuracy in online/mobile search and prevent digital out of stocks (GTIN+ on the Web) #1 #2 #3 Enabling the ‘Digital’ Revolution with GS1 Digital and GS1 Standards Consumer specifies Product/Service and refines search using GPC attribute-value pairs Might also specify: budget, urgency buy locally / online GPC & att. -val. GTIN Store Location GeoSPARQL can calculate distances between points Price Mass, Volume, Nutritional Info etc. from B2B (GDSN) Trade Item Master Data Product Image & Description Start here! Convenient package(s) of information The Offer Milk £1 1hr 1km Map human-readable keyword(s) to Product category identifier (GPC) Contextual filters are shown for product category User constraints are specified Information about matching products and services •Improved accuracy and completeness of online search, resulting in fewer digital out-of-stocks, lower SEO costs, and higher sales •Better ability for ads to target web pages about a specific product •Improved online identification, enabling easier / more complete aggregation of third party content (e.g., reviews, photos) Future Digital Commerce using GS1 Digital •GS1 Digital official homepage http://www.gs1.org/digital •GS1 Digital @ University of Cambridge Auto-ID labs http://www.autoidlabs.org.uk/GS1Digital/index.html GS1 Digital Current Digital Commerce Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
  10. 10. TEMPLATE DESIGN © 2008 www.PosterPresentations.com On-going IoT Systems Projects Health-care/Medical EcoSystem Bridge Operation and Management System KKAAIISSTT__ssttuu11 KKiim Name: KAIST_stu1 Kim Dept. of CS Heart Rate: 23 Speed: 1 m/s Predicted Disease CCTV2 - Heart Attack - Symptoms . Discomfort, pressure, heaviness, or pain in the chest . Discomfort radiating to the back . Rapid or irregular heartbeats Nearest Hospitals - (Notified) - - - CCTV1 Multi-Vision Display EEG biotelemetry EECCGG SSeennssoorr Cloud Computing FFiittbbiitt sseennssoorr GGPPSS SSaatteelllliittee KAIST Clinic CCCCTTVV Machine Learning Biig Anaallyyttiiccss Heart rate ecg, emg, gsr, temparature Dr.M Project • Development of Smart Mobile Health/Medicare Solution • Connect medical sensors with doctors/patients through Internet • Store/process/access medical sensing data based on Cloud Computing and Big Data Analysis • Test-bed (Show room) Construction in KAIST • Patient Location tracking, Health/Medicare data monitoring, Big data processing, Alarm for emergency • Web based real-time patient browsing, Real-time Health/Medicare data monitoring, Lightweight 6Lo over IEEE 802.15.4/BLE communication ffiittbbiitt stick-EECCGG SSeennssoorr -on EEG biotelemetry stick Heart Rate Sensor Smaarrtt SSeennssoorrss 6LoWPAN-ble Multiple-App Overload One Background Gateway Serv. Ecg, Emg, Gsr, Temperature Smart Agriculture Internet SNAIL Border Router (6LBR) SNAIL Node (6LN) SNAIL Node (6LN) SNAIL Node (6LN) SNAIL Node (6LN) Btle link Btle link Btle link Btle link Cloud Computing Scientific Big Data Visualization User Interaction Health Big Data Analysis Health Big Data Provision Social Interaction Remote Medical Service 6Lo Standard based Lightweight/Low Power IPv6 over IEEE 802.15.4/BLE (SNAIL) GS1 Standard based Cloud/Big Data Platform Collecting/Storing/Processing Health/Medicare Information (Oliot) Eagle Eye Service Patient Browsing Service GPU Computing Resource based Big Data Cloud Platform • Distributed Parallel Processing using GPU Computing Resource Virtualization for Fast Processing Huge amount of Big Data • Cloud based Platform to Improve Big Data Processing Efficiency and System Availability Oliot with Big Data Processing over IaaS Cloud • Retrieving Sensing Data from Yeongjong Grand Bridge in Incheon • Big Data Processing and Cloud Computing using the Bridge Data • Data Analysis/Visualization, Information Searching/Discovering • Scalable System to Process Massive Data Stream • Collaboration with Stanford University for Bridge Operation and Management Application GS1 Standard based Smart Agriculture Distribution Logistics System • Development of Standard Protocols for Smart Agriculture Networks • IP based Wired/Wireless Integrated Smart Agriculture Network platform • Analysis of GS1 ID System for Distribution Logistics Management of farm products • Design of GS1 Standard based Distribution Logistics Architecture IT Convergence Technologies for Farm Produce Optimization Object Naming Service (ONS) EPC Information Service(EPCIS) Filtering and Collection (F&C) 2002:8ff8:6a89::8ff8:6a89 2002:8ff8:6a6c::8ff8:6a6c 2002:8ff8:6a87::8ff8:6a87 Data fusion Pattern recognition Machine learning Damage Detection Risk Alarm Δt Sensor stream Processing & actuation Distributed Storage Interacting with Smart Things Search & discovery Δt Collector 1 Collector 4 Collector 2 Collector 3 frangible parts Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea

×