Internet of Things. Kim Jonatan Wessel Bjørneset LuLiu 26/02/2016

Transcription

1 Internet of Things Kim Jonatan Wessel Bjørneset LuLiu 26/02/2016 1

2 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 2

3 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 3

4 Introduction From home appliances and electronics to small battery powered devices Low powered Wi-Fi technology This article evaluates three typical sensor application scenarios: Power consumption Interference (and reliability) Range performance 4

5 IEEE with 6LoWPAN adaptation layer 6LoWPAN was developed for taking IP for wireless sensors Picture: wikipedia Traditionally considered for sensor network applications: ZigBee and other IEEE based protocols Low-power WiFi Decreasing power consumption on transceivers A Stronger candidate - power efficient Wi-Fi components Existing infrastructure Cost savings Years of battery time IT personell already familiar 5

6 System Model Network of WiFi enabled sensors Associated with an Access Point (AP) Basic operations: Initialization Keep-alive Messages Periodic Data Transmission Event-triggered Data Transmission Command Messages 6

7 Initialization Keep-alive Messages Device reads sensor data periodically Transmits data to a control unit Event-triggered Data Transmission Communicates with the AP periodically Periodic Data Transmission Sensor is powered up Authentication with an AP and acquires an IP Monitors the environment Transmits a message upon a certain event Command Messages Query, Configuration, Command or an action 7

8 Three application scenarios 1 Simple sensor device 2 Monitoring sensor device Ex. thermostat in a heating system Ex. smoke detector 3 Combination of 1 & 2 Configurable sensors and actuators Ex. fire alarm system 8

9 A ZigBee based solution would give years of battery life What about a low-power WiFi based solution? 9

10 Power Consumption Power Save Mode 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 bit CPU, real-time clock, HW encryption engine, sensor, b/g, PHY, MAC ecos & lwip TCP/IP stack Cheaper and more power efficient than n Also, the scenarios didn t require high data rates 10

11 Sleep Current and Wake-Up Energy Wake-Up Process 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 IEEE kb/s max data rate IEEE b/g 1Mb/s to 54Mb/s WiFi enabled sensors have higher data rate, spends less time....and therefore also spends less energy per bit 11

12 MAC Retransmissions 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 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 12

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14 Performance Evaluation Initialization: 250mJ, ~3s Keep-Alive messages Periodic Data Small packet size High data rate Event Triggered Messages Command Messages Infrequent PS with 10 sec 5 ½ years with AA 14

15 The bigger packet size the more power consumption Power consumption on different packet size 15

16 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 16

17 Interference and Reliability Measure impact of interference on reliability and real-time capability of Wi-Fi enabled sensors receiver sender T1 Benchmark phase (only background Wi-Fi traffic) 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 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. 17

18 Experimental Result Observations Sensor network perform better in out-of-network than in-network scenario. 18

19 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 19

20 The higher data rates of the sensors decrease the RTT slightly. 20

21 Packet size of the sensors have limited effect on RTT 21

22 Uplink channel to the AP is perfect in terms of PSR Downlink channel experiences significant losses Conclusion: AP becomes the bottleneck in this case. (AP fills up quickly and starts dropping packets) PSR decreases with bigger packets (AP send out smaller packets faster) 22

23 Communication Range 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 placed the AP in different location measure Wi-Fi signal lower data rates longer communication range more coverage area 23

24 Measurement Results With AP in basement High data rate coverage for ground floor low data rate for top floor(1 Mb/s) 24

25 With AP in the living area good coverage at high data rate at most locations data rate not so high in the basement ( 1-11 Mb/s ) 25

26 Conclusion & Summary Power consumption 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 26

27 Impact of interference Interference have little affect on reliability Except under heavy in-network traffic, the AP becomes the bottleneck Communication range AP even if not installed in an optimal location can provide full coverage for all potential sensor locations create a tradeoff between communication range and battery lifetime (data rate higher or lower) 27

28 The Internet of Things: A survey Authors: Note: All pictures used in this slide are from original article, and the internet 28

29 Introduction IoT - could be things or objects NIC predicts that by 2025, Internet nodes might reside in everyday things 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 29

30 IoT - Many visions IoT - Internet oriented IoT - Things oriented huge number of objects involved IoT - Semantic oriented unique addressing, representation and storing IoT semantically means WordWide network of interconnected objects uniquely addressable based on standard communication protocols 30

31 Things IPSO Alliance RFID tags, uid, NFC, WSAN, WISP, Spimes, smart items LoWPAN Internet Ø Internet over anything Web of Things Idea behind the semantic oriented IoT visions: Extremely large number of objects connected to the Internet Represent, store, search, interconnect etc 31

32 Enabling Technologies Reduced size, weight, energy consumption, and cost of radio RFID systems: reader(s), unique tag as identifier 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 receiving signals, and transmitting the tag ID induction, current signal power received divided by power transmitted = ID Passive, Semi-passive (battery) and active (battery) 32

33 Sensor Networks 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 Many nodes, few IP addresses largest phy layer 127 bytes, 102 octets at MAC layer sleep mode - cannot communicate 33

34 The green node in the figure: is a special node a sink, collecting data from the other nodes 34

35 WISPs Wireless Identification and Sensing Platforms RFID Small size, low costs, no battery WSN 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 sensing, computing and communication capabilites 35

36 Middleware Software layer between technological and application levels Simplifying development of new services Programmers doesn t need to know about the sets of technology in the lower layers Using a SOA approach 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 36

37 Applications Applications are on top of the architecture exporting all the system s functionalities to the end user exploits the features of the middleware layer Service Composition Provides functionalities to build the services for applications Only services visible, all currently connected service instances visible in a repository 37

38 Service Management 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 38

39 Object Abstraction 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 Privacy and Security RFID tags in clothes, groceries trigger ID and info without knowing, like a surveillance middleware must include functions to preserve security, trust and privacy 39

40 Applications Application domains and relevant major scenarios 40

41 Transportation and logistics domain Logistics Assisted driving Posters equipped with NFC tags or visual markers Monitoring environment parameters provide better navigation and safety find right path according to information about jam and incident Mobile ticketing Real-time monitoring supply chain(shorten supply time) improve the efficiency of the food supply chain Augmented maps Tourist maps equipped with tags 41

42 Healthcare domain Tracking Identification and authentication Reduce incidents harmful to patience Data collection Identification of a person or object in motion Reduce form processing time Sensing Diagnose patient condition provide real-time information on patient health indicator 42

43 Smart environment domain Comfortable homes and offices Industrial plants room heating adapted domestic incidents avoided energy saved quality control emergency event react Smart gym recognize trainee through RFID tag 43

44 Personal and social domain Social networking Historical queries record and display events extremly useful for applications support long-term activities Losses real-time updates in social networks control friend lists view the last recorded location leverages user-defined event to notify users Thefts objects are removed from a restricted area without authorization 44

45 Futuristic applications domain Robot taxi City information model 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 measure excitement and energy levels of players controllers recognize RFID tags on objects 45

46 Addressing issues IPv4 & IPv6 RFID tags use bit identifiers Proposed approach A integrate RFID identifiers and IPv6 addresses 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 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 46

47 Proposed approach B RFID message and headers are included into the IPv6 package payload 47

48 Networking issues Domain Name Service(DNS) Object Name Service(ONS) TCP is not appropriate 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 Traffic characteristics strongly depend on application scenario 48

49 Security issues Why IoT is vulnerable to attacks? Why authentication is difficult? 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 taking some sensor nodes role as gateway 49

50 Example of attack 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 50

51 Privacy issues Ensuring individuals can control the data collected Restrict network ability to gather data detail level 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 51

52 Conclusions 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 52

53 Thank you for your attention! 26/02/