Internet of Things
Kim Jonatan Wessel Bjørneset
kjbjorne@ifi.uio.no
LuLiu
lliu@ifi.uio.no
26/02/2016
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Kim
Master student at 1st year
Norwegian
Bachelor in programming and networking at IFI, UiO
Thesis: Security and privacy in smart electric grids and IoT
LuLiu
Master student of 1st year
Chinese
Master in programming and networks
Thesis: Big Data Analytics for PV Systems Real-time Monitoring
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Wi-Fi Enabled Sensors for Internet of Things:
A Practical Approach
Authors:
Serbulent Tozlu, Murat Senel, Wei Mao, and Abtin Keshavarzian, Robert Bosch LLC
Note:
All pictures used in these slides are from original article, and the Internet
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Introduction
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From home appliances and electronics to small battery powered devices
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Low powered Wi-Fi technology
This article evaluates three typical sensor application scenarios:
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Power consumption
Interference (and reliability)
Range performance
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IEEE 802.15.4 with 6LoWPAN adaptation layer
○ 6LoWPAN was developed for taking IP for wireless sensors
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Picture: wikipedia
Traditionally considered for sensor network applications:
■ ZigBee and other IEEE 802.15.4 based protocols
Low-power WiFi
○ Decreasing power consumption on transceivers
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A Stronger candidate - power efficient Wi-Fi components
■ Existing infrastructure
■ Cost savings
■ Years of battery time
■ IT personell already familiar
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System Model
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Network of WiFi enabled sensors
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Associated with an Access Point (AP)
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Basic operations:
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Initialization
Keep-alive Messages
Periodic Data Transmission
Event-triggered Data Transmission
Command Messages
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Initialization
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Keep-alive Messages
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Device reads sensor data periodically
Transmits data to a control unit
Event-triggered Data Transmission
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Communicates with the AP periodically
Periodic Data Transmission
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Sensor is powered up
Authentication with an AP and acquires an IP
Monitors the environment
Transmits a message upon a certain event
Command Messages
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Query, Configuration, Command or an action
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Three application scenarios
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1 Simple sensor device
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2 Monitoring sensor device
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Ex. thermostat in a heating system
Ex. smoke detector
3 Combination of 1 & 2
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Configurable sensors and actuators
Ex. fire alarm system
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A ZigBee based solution would give years of battery life
What about a low-power WiFi based solution?
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Power Consumption
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Power Save Mode
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802.11 standard got mechanism for turning off transmitter and receiver to save power
AP buffers messages
Picture: tistory.com
■ mobile station wakes up periodically to receive
For broadcast or multicasting, AP sends message immediately
■ mobile station stays awake to receive
Unicast messages, mobile station sends a PS Poll message
■ receives the message accordingly
A Low-Power WiFi Module - G2M 5477
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32bit CPU, real-time clock, HW encryption engine, sensor, 802.11b/g, PHY, MAC
eCos & lwIP TCP/IP stack
Cheaper and more power efficient than 802.11n
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Also, the scenarios didn’t require high data rates
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Sleep Current and Wake-Up Energy
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Wake-Up Process
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Sleep state regular WiFi devices: 150 to 250µA
Sleep state G2 chip: 4µA
Time and energy depends on the application size
G2 chip allows multiple images to boot from
■ based on the reason
Transmit/receive energy
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IEEE 802.15.4
■ 250kb/s max data rate
IEEE 802.11b/g
■ 1Mb/s to 54Mb/s
WiFi enabled sensors have higher data rate, spends less time..
..and therefore also spends less energy per bit
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MAC Retransmissions
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802.11 uses acknowledgements to ensure reliability
Unacknowledged frames are retransmitted
Different MAC retransmission rates due to interference
Power consumption especially significant for low data rate operations
Security
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A tradeoff exists between security and energy
WEP
○ Security: bad
Time: fast, low power usage
WPA/TKIP-PSK
○ Security: good Time: authentication takes more time, more power usage
WPA2/AES-PSK
○ Security: good Time: authentication takes more time, more power usage
○ Best tradeoff! Since re-authentication should be avoided
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● Performance Evaluation
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Initialization: 250mJ, ~3s
Keep-Alive messages
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Periodic Data
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Small packet size
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High data rate
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Event Triggered Messages
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Command Messages
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Infrequent
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PS with 10 sec
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5 ½ years with AA
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● The bigger packet size the more power consumption
Power consumption on different packet size
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● The higher data rates the lower power consumption
Packet size at low data
rates has a noticeable
impact on power
consumption
Packet size at high data
rates has a minor impact
on power consumption
Power consumption on different data rates
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Interference and Reliability
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Measure impact of interference on reliability and real-time capability of Wi-Fi
enabled sensors
receiver
sender
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T1
Benchmark phase (only background Wi-Fi traffic)
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PSR - Packet Success Rate
RTT - Round-Trip-Time = T2-T1
100 percent PSR and 95 percent RTT was around 15 ms
T2
Add extra Wi-Fi interferers
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out-of-Network Interference
■ Wi-Fi enabled sensors and interferers are in the same channel but they are associated to
different APs.
In-Network Interference
■ Wi-Fi enabled sensors and interferers are associated to the same AP.
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Experimental Result
Observations
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Sensor network perform better in out-of-network than in-network scenario.
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RTT is significant higher here, almost 125 ms
PSR is almost 100 percent
Conclusion: MAC - layer retransmission packets
make RTT increase significantly, but packets
are not lost
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The higher data rates of the sensors decrease the RTT slightly.
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Packet size of the sensors have limited effect on RTT
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Uplink channel to the AP is perfect in terms of PSR
Downlink channel experiences significant losses
Conclusion:
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AP becomes the bottleneck in this case.
(AP fills up quickly and starts dropping packets)
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PSR decreases with bigger packets
(AP send out smaller packets faster)
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Communication Range
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AP →should placed in an optimal location to provide good coverage
Wi-Fi enabled sensors → possible deployed in all corners of the building
A measurement in a typical European house
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placed the AP in different location
measure Wi-Fi signal
lower data rates → longer communication range → more coverage area
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Measurement Results
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With AP in basement
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High data rate coverage for ground floor
low data rate for top floor(1 Mb/s)
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With AP in the living area
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good coverage at high data rate at most locations
data rate not so high in the basement ( 1-11 Mb/s )
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Conclusion & Summary
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Power consumption
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At a high data rate, packets size have small impact on power consumption
At a low data rate, packets size have noticeable impact on power consumption
Retransmission have an impact on energy consumption
WPA2 gives best tradeoff in terms of security and battery lifetime overhead
Timely command messages plays an important role in overall energy consumption
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Impact of interference
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Interference have little affect on reliability
Except under heavy in-network traffic, the AP becomes the bottleneck
Communication range
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AP even if not installed in an optimal location can provide full coverage for all potential sensor
locations
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create a tradeoff between communication range and battery lifetime (data rate higher or
lower)
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The Internet of Things: A survey
Authors:
Note:
All pictures used in this slide are from original article, and the internet
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Introduction
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IoT - could be things or objects
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NIC predicts that by 2025, Internet nodes might reside in everyday things
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such as RFID tags, sensors, actuators, mobile phones etc
food packages, furniture, paper documents and more
This article:
○ describes different visions of IoT
○ reviews enabling technology for IoT
○ description of the principal applications for IoT
○ analyzes major research issues to be faced
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IoT - Many visions
● IoT - Internet oriented
● IoT - Things oriented
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huge number of objects involved
● IoT - Semantic oriented
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unique addressing, representation and storing
IoT semantically means “WordWide network of interconnected objects
uniquely addressable based on standard communication protocols”
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Things
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IPSO Alliance
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RFID tags, uID, NFC, WSAN, WISP, Spimes, smart items
802.15.4
6LoWPAN
Internet Ø
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Internet over anything
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Web of Things
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Idea behind the semantic oriented IoT visions:
○ Extremely large number of objects connected to the Internet
○ Represent, store, search, interconnect etc
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Enabling Technologies
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Reduced size, weight, energy consumption, and cost of radio
RFID systems: reader(s), unique tag as identifier
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Picture: from the Internet
monitor objects in real time without the need to be in Line-Of-Sight
■ logistics, e-health, security
■ mapping real world -> virtual world
An RFID tag is a small chip with antenna
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receiving signals, and transmitting the tag ID
■ induction, current
■ signal power received divided by power transmitted = ID
Passive, Semi-passive (battery) and active (battery)
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● Sensor Networks
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Can cooperate with RFID
Used in e-health, environmental monitoring, intelligent transportation
systems, military etc
A number of sensing nodes communication in a wireless multi-hop
network
■ Can be many nodes
■ Nodes reporting to a special node, a sink
Many problems at all layers of the protocol stack
Mostly based on 802.15.4
■ Many nodes, few IP addresses
■ largest phy layer 127 bytes, 102 octets at MAC layer
■ sleep mode - cannot communicate
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The green node in the figure:
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is a special node
a “sink”, collecting data from the other nodes
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WISPs
● Wireless Identification and Sensing Platforms
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RFID
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Small size, low costs, no battery
WSN
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powered by regular RFID readers
integration of sensing technology into passive RFID tags leads to new applications to IoT
RFID sensor networks
■ RFID readers will be the “sinks”
Reader not required
high radio coverage
peer to peer
RSN
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sensing, computing and communication capabilites
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Middleware
● Software layer between technological and application levels
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Simplifying development of new services
Programmers doesn’t need to know about the sets of technology in the lower layers
Using a SOA approach
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SOA makes it easier for software components on computers connected over a network to
cooperate
Allows for software and hardware reusing
● not a specific technology for service implementation
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Applications
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Applications are on top of the architecture
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exporting all the system’s functionalities to the end user
exploits the features of the middleware layer
Service Composition
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Provides functionalities to build the services for applications
Only services visible, all currently connected service instances visible in a
repository
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Service Management
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Main functions available for each object in the IoT scenario
■ object dynamic discovery
■ status monitoring
■ service configuration
Might expand set of functionalities to QoS and lock management
Might enable remote deployment of new services during run-time for application needs
Services associated to each object in the network can be shown in a repository
Upper layer composes complex services by joining these services provided at this layer
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Object Abstraction
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Wrapping layer for devices with undiscoverable web service
■ main sub layers:
● interface: web interface, in/out msg operations communicate external world
● communication: logic behind web service methods
translates these into device-specific commands to communicate with real-world
objects
Often provided through a proxy
● opens a communication socket with the device’s console
● translated into a web service language, reducing complexity to end-device
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Privacy and Security
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RFID tags in clothes, groceries trigger ID and info without knowing, like a surveillance
■ middleware must include functions to preserve security, trust and privacy
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Applications
Application domains and relevant major scenarios
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Transportation and logistics domain
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Logistics
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Assisted driving
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Posters equipped with NFC tags or visual markers
Monitoring environment parameters
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provide better navigation and safety
find right path according to information about jam and incident
Mobile ticketing
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Real-time monitoring supply chain(shorten supply time)
improve the efficiency of the food supply chain
Augmented maps
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Tourist maps equipped with tags
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Healthcare domain
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Tracking
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Identification and authentication
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Reduce incidents harmful to patience
Data collection
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Identification of a person or object in motion
Reduce form processing time
Sensing
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Diagnose patient condition
provide real-time information on patient health indicator
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Smart environment domain
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Comfortable homes and offices
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Industrial plants
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room heating adapted
domestic incidents avoided
energy saved
quality control
emergency event react
Smart gym
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recognize trainee through RFID tag
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Personal and social domain
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Social networking
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Historical queries
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record and display events
extremly useful for applications support long-term activities
Losses
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real-time updates in social networks
control friend lists
view the last recorded location
leverages user-defined event to notify users
Thefts
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objects are removed from a restricted area without authorization
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Futuristic applications domain
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Robot taxi
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City information model
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automatically track user’s location via GPS
users can request taxi at certain location and time on a detailed map
sharing energy in the most cost-effective and resource-efficient fashion
Enhanced game room
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measure excitement and energy levels of players
controllers recognize RFID tags on objects
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Addressing issues
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IPv4 & IPv6
RFID tags use 64-96 bit identifiers
Proposed approach A
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integrate RFID identifiers and IPv6 addresses
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use 64 bits of the interface identifier of the IPv6 address to report the RFID tag
indentifier
other 64 bits of the network prefix to address the gateway between the RFID system and
the internet
if the RFID tag identifier is 96 bits long
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“agent”will be used, maps the RFID identifier into a 64 bits field used as interface ID of
the IPv6 address
“agent”must keep updated the mapping
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Proposed approach B
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RFID message and headers are included into the IPv6 package payload
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Networking issues
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Domain Name Service(DNS) → Object Name Service(ONS)
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TCP is not appropriate
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DNS provides IP address of a host from a certain input name
ONS associates specific object and the related RFID tag identifier
Connection setup is unnecessary
Congestion control is useless
Data buffer is too costly for battery-less devices
Traffic in IoT is unknown
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Traffic characteristics strongly depend on application scenario
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Security issues
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Why IoT is vulnerable to attacks?
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Why authentication is difficult?
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Physical attack easily (most time unattended)
Eavesdropping is simple(most communication are wireless)
Cannot implement complex security schemes(resource limited)
cannot exchange too many messages with the authentication servers
Limitation of existing solutions
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taking some sensor nodes role as gateway
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Example of attack
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A is the node to authenticate other system elements
an attacker wants to steal the identity of B
A’ and B’ are two transceivers
This attack can happen regardless the signal is encrypted or not
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Privacy issues
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Ensuring individuals can control the data collected
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Restrict network ability to gather data detail level
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example for comfortable homes and offices
■ information collected not linkable with identity
■ The scope and the way tracked should be informed
■ Data collected should be processed for basic purpose and then deleted
sensor network report approximate location
cameras for video surveillance blur people’s image
Periodically delete information after use for the purpose
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Conclusions
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IoT should be considered as part of the overall internet in the future
host-to-host communication is a limitation factor for now
Data-centric networks(self-addressable and self- routable)
Assigning an IPv6 address to reach IoT element
Internet evolution will require a change
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Thank you for your attention!
26/02/2016
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