The document describes an Internet of Things (IoT) application framework that allows for dynamic service composition on mobile devices. The framework addresses issues with traditional IoT applications by allowing services to be composed at runtime rather than requiring explicit binding during implementation. This enables interaction between devices and applications without pre-defined bindings. The framework uses a service registry to discover IoT services and a composition engine to dynamically combine services into applications on mobile devices based on user needs and context.

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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

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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

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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

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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

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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

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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

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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

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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

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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. TEMPLATE DESIGN © 2008
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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