Mobile Sensing: A new IoT paradigm. Shin-Ming Cheng Assistant Professor,

Transcription

1 Mobile Sensing: A new IoT paradigm Shin-Ming Cheng Assistant Professor, Department of Computer Science and Information Engineering National Taiwan University of Science and Technology

2 Outline Wireless Sensor Networks Participatory/Mobile Sensing Applications Architecture Issues Solutions

3 Wireless Sensor Networks (1/6) Composed of a large number of sensor nodes Compared with ad hoc networks The nodes in a sensor network can be several orders of magnitude higher than those in an ad hoc network. Sensor nodes are densely deployed. Sensor nodes are limited in power, computational capability and memory. Sensor nodes are prone to failures. The topology of a sensor network could change frequently.

4 Wireless Sensor Networks (2/6) Architecture User sink node Event Monitored Area

5 Wireless Sensor Networks (3/6) Military applications Monitoring forces, equipment and ammunition Reconnaissance of opposing forces and terrain Battlefield surveillance Battle damage assessment Nuclear, biological and chemical attack detection Environmental applications Forest fire detection Biocomplexity mapping of the environment Flood detection Precision agriculture

6 Wireless Sensor Networks (4/6) Health applications Tele-monitoring of human physiological data Tracking and monitoring patients and doctors inside a hospital Drug administration in hospitals Home and other commercial applications Home automation and smart environment Interactive museums Managing inventory control Vehicle tracking and detection

7 Wireless Sensor Networks (5/6) Unstructured WSN Dense collection of nodes Ad hoc deployment Difficulty in network maintenance Structured WSN Few and scarcely distributed nodes Pre-planned deployment Lower network maintenance

8 Wireless Sensor Networks (5/6) Sensor nodes autonomously form a group called clusters sink sink Layer Architecture Clustered Architecture

9 The Evolution of WSN (a) static sink WSN static sink with stable sensor static sink with mobile agent follow a trajectory spanning all the nodes piloted vehicle during disaster Mobile sensing static sink with mobile sensor (b) (c) mobile agent static sink static sink mobile phone with sensors

10 Participatory/Mobile Sensing (1/2) Sensor side Smartphone carried by participants Various sensing capabilities camera Gyroscope GPS Accelerator Light sensor Digital compass Communication platform Leverage the existing cellular infrastructure

11 Participatory/Mobile Sensing (2/2) Advantages Human mobility Human ubiquity Rechargeable battery on smartphone low deployment and equipment costs Sensing activity information Features People share and distribute sensed information via Physical proximity Social relations

12 Applications: BikeNet (SenSys 07) System Overview

13 Applications: BikeNet Physical implementation

14 Applications: BikeNet

15 Applications: UbiFit Garden (CHI 08) Mobile Sensing Platform (MSP) 3-d accelerometer barometer Bluetooth networking automatically infer physical activities in real time

16 Applications: UbiFit Garden Interactive Application Details about inferred activities journal to add, edit, or delete information about activities

17 Applications: UbiFit Garden Use garden s flowers to metaphor Flower represents individual event (physical activity) Butterfly represents goal attainment Healthy garden represents healthy behavior

18 Applications: UBIGreen (CHI 09) Mobile phone application Semi-automatically senses Reveals information about transportation behavior Visual Designs Tree Virtual polar bear

19 Applications: UBIGreen

20 Applications: PEIR (MobiSys 09) Personal Environmental Impact Report Carbon Impact a measure of transportation-related carbon footprint Sensitive Site Impact a user s transportation related airborne particulate matter emissions near sites with populations sensitive to it Smog Exposure a user s transportation-related exposure to particulate matter emissions Fast Food Exposure the time integral of proximity to fast-food eating establishment

21 Applications: PEIR using existing infrastructure without user intervention emphasizes how individual transportation choices simultaneously influence both environmental impact and exposure

22 Applications: PEIR

23 Applications: Ikarus (HotMobile 11) Large-scale Participatory Sensing at High Altitudes Use GPS flight records from paraglider pilots to generate maps of thermal active areas (hotspots) Data provided by 2,331 unique users in Switzerland ( ) 30,000 flight tracks analyzed, several GByte of data processed

24 Applications: Ikarus System Architecture Flight navigation devices Records time, GPS position Accurate height measured by barometric pressure Variometer reports climb rate

25 Applications: Ikarus Problem: Flight Distribution Flight tracks are distributed very unequally

26 Architecture (1/2) Internet Application Server Sensing information Cellular Core Network Cellular Infrastructure

27 Architecture (2/2) Mobile User (MU): plays the roles of Querier requests information data collector Provides information Base Station (BS) provide wireless communication for information transmission to/from MU. Application Server stores collected data extracts features of interest from the collected data and distributes information to queriers via the transmission paths.

28 Sensing Contents (1/2) Environmental information beneficial for all the general public is exploited to understand and improve the current environment. quality, noise, weather, road information, traffic Public information daily information for general public in order to improve quality of life price of product, promotions

29 Sensing Contents (2/2) Group information shared among friends or strangers within social group let relationship between friends more close pictures, messages, videos Personal information Personal monitoring and benefits to the person Daily life patterns Activities: sport Health: heart rate, blood pressure Interests: discover the potential friends with same interest

30 Mobile Sensing Paradigms collector 2 collector 1 w 1 Direct mobile sensing Indirect mobile sensing collector m w 2 w m querier What is the best restaurant/scenary neaby? How is the spectrum vacancy? What is the air pollution index around this area? How is the video streaming quality? collector m Server Relay w m w 1 w 2 w k w k+1 w k+2 collector 1 collector 2 collector k collector k+1 collector k+2 D2D communication technology: Wi-Fi Direct, ZigBee, BLE, NFC Communication technology with infrastructure: Wi-Fi, WiMAX, GSM/GPRS/UMTS/LTE,

31 Direct Mobile Sensing (1/3) Direct communication between a querier and the collectors D2D communication technologies Wi-Fi Direct, ZigBee, BLE or NFC Store-carry-forward sensed information could be stored in a sensor node in the absence of immediate connectivity to any other node, and could be relayed to other sensor nodes at encounters.

32 Direct Mobile Sensing (2/3) Proximity sensing in mobile social networks (MSN) Sensing potential friends with similar interests nearby one can simply scan the environment for discoverable Bluetooth devices to analyze crowd density and crowd flow direction one can further make new social interactions with nearby devices

33 Direct Mobile Sensing (3/3) Cooperative spectrum sensing in cognitive radio networks (CRN) Unlicensed secondary users (SUs) sense the surrounding environment and exploit spectrum holes unoccupied by licensed primary users (PUs) for secondary transmission with minimal interference to PUs a querier could exploit observations on local spectrum vacancy from surrounding SUs (i.e., crowds).

34 Indirect Mobile Sensing (1/2) a querier and crowds are indirectly connected through a communication system in a centralized or a distributed fashion. Environmental measurements Personal activity sharing Online recommendation Crowds provide recommendations to a usercentric query, such as the best seafood restaurant within 2 miles, or the next video to watch for multimedia applications.

35 Indirect Mobile Sensing (2/2) Annotation Crowds annotate labels, such as scenery labels for a picture or comments and interactions for multimedia contents

36 Applications: LifeTie (IEEE CommMag 2015)

37 Issues (1/3) Unbiased feedback The number or participants Incentive The wisdom of crowds Example more collectors provide independent and unbiased feedback the wisdom of crowds become more effective quality of feedback to querier is improved encourages more people to participate mobile sensing.

38 Issues (2/3) Privacy considerations the collection and sharing of the personal information related to human activity the participants don t what to reveal any sensitive personal information The connection between sensitive information and identity time, location, pictures, sound, acceleration, biometric data

39 Issues (3/3) Trustworthiness Allows anyone to contribute data exposes the applications to erroneous and malicious contributions users may inadvertently position their devices such that incorrect measurements are recorded Noise/storing the phone in a bag Malicious users may deliberately pollute sensor data for their own benefits a leasing agent contribute fabricated low noise readings to promote the properties in a particular suburb

40 Solutions (1/4) Achieve ambiguity/indistinguishable between personal information and identity Domain for ambiguity Content-based The identities who query or collect the same type of content are obscured Attribute-based The identities with the same attributed are indistinguishable Region-based Location privacy: the identities who locates in the same region are obscured

41 Solutions (2/4) Technique for ambiguity K-anonymity Blind Signature Ring Signature Group Signature any member of its group can sign a document on behalf of the entire group

42 Solutions (3/4) The Original Trace Adding Noise Spatial Rounding Selective Hiding

43 Solutions (4/4) Trust/Reputation evaluate the trustworthiness of contributing devices identify the corrupted/malicious contributions lower the weights of corrupted/malicious data Reputation score with each contributing device react the level of trust perceived by the application server about the data uploaded by that device over a period of time.