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Presented by: Su Jin Kim. Collaboration of Mobile and Pervasive Devices for Embedded Networked Systems. Committee: Sandeep K. S. Gupta (Chair) Partha Dasgupta Hasan Davulcu Yann-Hang Lee. Outline. Introduction Mobile Edge Computing Devices (MECD) Mesh-Networked MECDs

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presented by su jin kim
Presented by: Su Jin Kim

Collaboration of Mobile and Pervasive Devices for Embedded Networked Systems

  • Committee:

Sandeep K. S. Gupta (Chair)

Partha Dasgupta

Hasan Davulcu

Yann-Hang Lee

outline
Outline
  • Introduction
  • Mobile Edge Computing Devices (MECD)
  • Mesh-Networked MECDs
  • Self-organizing Authentication for Embedded Networked Systems
  • Conclusion and Future Work
embedded networked system ens
Embedded Networked System (ENS)

Feature

Technology

Embedded

Networked

System (ENS)

  • ENS Architecture [1][2][3]
    • End nodes are embedded systems with wireless communication capability.
    • Gateways are capable of connecting end nodes to the external network.
    • Servers could access data remotely.
  • Collaboration of local gateways
    • Interaction is mostly local.
    • Local systems could detect same events (e.g. fire, earthquake).

Save bandwidth and reduce delay.

Collaboration

Embedded System

Context awareness

Wireless Network

Pervasive Computing

Smart Service in everyday life

Bandwidth and connectivity problem between gateways and servers

End nodes

research challenges
Research Challenges

Research Issues

ENS Characteristics

  • Large sized
  • Distributed and Autonomous
  • Mobile (high and unpredictable)
  • Resource limited
  • Heterogeneous
  • Scalability
  • End-to-end reliability
  • Local data processing
  • Efficiency
    • Energy, computation, and communication
  • Neighbor & Service discovery
  • Security
    • Authentication
    • Authorization (Access control)
    • Key management
  • Additional networks and works

Gateway-

level

collaboration

  • Dynamic characteristic of gateway level networks

Research Questions

  • Scalability
    • How to support additional networks of local gateways for a large sized network.
  • End-to-end reliability
    • How to ensure packet delivery between local and external networks within a certain delay.
  • Authentication
    • How to authenticate strangers without any knowledge.
outline1
Outline
  • Introduction
  • Mobile Edge Computing Devices (MECD)
  • Mesh-Networked MECDs
  • Self-organizing Authentication for Embedded Networked Systems
  • Conclusion and Future Work
scalability of gateway and related work
Scalability of Gateway and Related Work
  • Scalability Problem of Gateways in ENS
    • Interface for a large network
    • Collaboration between gateways
    • High and unpredictable mobility pattern
    • Hard to provide scalability to a huge number of mobile devices that have unpredictable mobility patterns.

 Use a mobile gateway per object not per area

Gateway

Mobile

Static

Improve Connectivity

Improve Lifetime

hierarchical network structure with mecds
Hierarchical Network Structure with MECDs
  • Mobile Edge Computing Device (MECD)
    • Mobile gateway
      • Manage the internal network of a moving object in a distributed manner
      • Perform local data processing
      • Support collaboration with neighboring gateways
  • 3-level network of ENS
    • Reduce the amount of data
    • Reduce the remote communication
    • Separate the internal network from outside
functional architecture of mecd
Functional Architecture of MECD
  • Communication with
    • remote servers
  • Communication with
    • end nodes
    • neighboring MECDs

Local Data Processing

case study intelligent container systems
Case Study: Intelligent Container Systems
  • Autonomous End-to-End monitoring system for cargo containers
    • Homeland Security
    • Global Supply Chain
test bed configuration
Test-bed Configuration

PDA:

Monitors data from MECD, Setup variables

802.11

MECD:

Data collection, Data reporting, Door Opening Detection, RFID reader Control, Datase Management

2.4 GHz

Container

Reader-mote module:

Reads tag IDs and reports fresh readings

MicaZ/TelosB mote(s):

Report sensed data periodically

deployment
Deployment

Supported by Mary Murphy-Hoye

lessons learned
Lessons Learned
  • Unreachability Problem
    • High temperature variance
    • High interference

between metalic containers

 Effect on connectivity between gateways

outline2
Outline
  • Introduction
  • Mobile Edge Computing Devices (MECD)
  • Mesh-Networked MECDs
  • Self-organizing Authentication for Embedded Networked Systems
  • Conclusion and Future Work
mesh network
Mesh Network
  • Multi-hop
  • Reliability
  • Self-healing
  • Self-organizing
  • More extensive range

A

B

  • Requirements of Mesh-networked MECDs
    • Server reachability
    • Low delay
    • Energy efficiency
simulation setup
Simulation Setup
  • International Standards Organization (ISO) Container Size
    • Common width for international commerce = 8 ft. (2.44 m)
    • Common height = 8.5 (2.6 m)
    • Length: 20 ft. – 53 ft.
      • Common lengths = 20 ft. (6.1 m), 40 ft. (12.2 m), 48 ft. (14.6 m), 53 ft. (16.2 m)

Forwarder

  • Temperature, T
  • 25C ≤ T ≤ 65C
  • Containers stack
    • – Up to 6 in height
    • – 1 ft distance
metrics
Metrics
  • Connectivity (Ci)
    • Ci= 1, If there is a path between MECD i and the forwarder.
    • Ci = 0, If not.
  • Sever Reachability (SR) (S is the set of MECDs in the network)
  • Network Latency
    • Average path length that is defined as the average number of hops along the shortest paths between the forwarder and all other MECDs in the network
  • Energy Efficiency
    • Total energy consumption when every node sends a packet to the forwarder via its shortest path.
simulation model communication model
Simulation Model: Communication Model
  • Received power (Log-distance Path Loss) [4]
  • Temperature Loss [5]
  • Maximum Transmission Range [5]

Distance

Zero-mean normally distributed random variable with standard deviation σ

Received Power at the refence distance d0

Path loss exponent

d0 = 1m

np = 3.3

Pr(0)= -45dBm

σ= 3dBm

Ps = -94 dBm

Temperature, 25C ≤ T ≤ 65C

Radio Sensitivity

simulation model energy consumption model
Simulation Model: Energy Consumption Model
  • First-order Radio Model [6]
    • Energy Consumption to transmit a k-bit packet to a distance d
    • Energy Consumption to receive a k-bit packet
  • Total Energy Consumption to transmit a k-bit packet over n hops

Energy loss at distance d

Energy consumption to run the transmitter circuitry for a k-bit packet

Energy consumption by transmitting amplifier

Energy consumption by transmitting amplifier to transmite k bits to distance d

Energy consumption to run the transmitter circuitry

Eelec = 50nJ/bit

amp = 100pJ/bit/m2

k = 20 bits, d = 50m

Energy consumption to run receiver circuitry for a k-bit packet

server reachability vs network density vs temperature
Server Reachability vs Network Density vs Temperature
  • MECD-level network can provide 100% server reachability for ISO standard containers within the range 25 C ≤ T ≤ 65 C.

ISO standard container lengths with 1 ft. space are less than 16.5 m.

path length vs network size vs temperature
Path length vs Network Size vs Temperature
  • The MECD-level network will produce a small amount of additional delays and is scalable to a large size of MECD networks

(a) T = 25 C

(b) T = 45 C

(c) T = 65 C

energy consumption vs network size vs temperature
Energy Consumption vs Network Size vs Temperature
  • The MECD-level network will consume a small amount of additional power and is scalable to a large size of MECD networks

(a) T = 25 C

(b) T = 45 C

(c) T = 65 C

outline3
Outline
  • Introduction
  • Mobile Edge Computing Devices (MECD)
  • Mesh-Networked MECDs
  • Self-organizing Authentication for Embedded Networked Systems
  • Conclusion and Future Work
authentication
Authentication
  • Collaboration among neighboring MECDs
    • Wireless communication
      • Sharing information and resources
  • Authentication
    • The process to prove a user’s claimed identity.
    • Required before granting access
related work
Related Work

Interact with the environment and use contextual information

  • Requirement of authentication process for MECD-level networks
    • Mutually unknown MECDs must verify each other’s claim without any knowledge.

Authentication

Context-aware

Traditional

Biometric

Pre-shared Secret

Trusted-Third Party

Location-based

Require securely pre-established information

location based authentication
Location-based authentication
  • The process to authenticate a user by detecting his presence at a distinct location
    • Trust relationship is based on a user’s current location
  • Localization Approach
    • Absolute location
    • Distance bounding
    • In-region
problem definition assumptions
Problem Definition & Assumptions
  • Self-organizing Region-based Authentication
    • A verifierv authenticates a requesterr when they are in a region R of interest.
    • Without human’s help, pre-shared information, and pre-established trust relationship.
    • R: the region (e.g., a room, a house, a ship, or a yard)
  • Trust-relationship
    • An entity’s presence of the region, R.
  • Assumptions
    • R must have some sort of physical control to restrict people into this area.
    • v and r are well-synchronized.
  • Threat model
        • Active adversaries – capture, replay, and insert
        • Passive adversaries - eavesdrop
        • Denial of Service attacks (DoS) not considered
          • an attempt to make a computer resource unavailable to its intended users
acoustic feature based approach
Acoustic Feature Based Approach
  • Challenges
    • Distinguish the region
    • Detecting the leave and closing the granted access
  • Approach
    • Acoustic feature based technique using environmental sound
      • Environmental sound is produced by random events in any physical location.
      • Devices within a particular region hear similar environmental sound.
      • A microphone is cheap.

Recording

Feature

Extraction

Feature

Exchange

Verification

acoustic feature extraction techniques
Acoustic Feature Extraction Techniques
  • Time-domain (temporal) feature extraction
    • Simple to implement. Requires highly well synchronized devices.
  • Frequency-domain (spectral) feature extraction
    • Relatively expensive. Relax synchronization requirement.
  • Hybrid feature extraction
    • Split into several windows and perform frequency-domain feature extraction.
  • Requirements
    • Distinctiveness of locating
    • Randomness and Timevariance
correlation
Correlation
  • Recorded sounds in 4 different environments
    • Café
    • Classroom
    • House
    • Office
  • Correlation coefficient
    • Measure the similarity of two frequency domain signals with different length of FFT functions.
  • Definition: Co-located devices
    • Devices within the same region
correlation with 256 point fft
Correlation with 256-point FFT

(a) Cafe

(b) Classroom

(d) Office

(c) House

distinctiveness vs cost of fft functions
Distinctiveness vs Cost of FFT functions
  • Percentage of overlapping between two case: correlation of co-located and not co-located devices
  • Specifications
  • Computation and Energy Cost

[7]

[8]

[9]

[10]

[11]

[9]

acoustic feature extraction
Acoustic Feature Extraction

Recording

  • A requester sends the request.
  • A verifier sends a random number, n.
  • Both devices start recording and feature extraction steps.
  • The requester sends the feature set to the verifier.
  • The verifier performs the verification step.

1

2

3

. . .

w

Windowing

FFT

Feature Extraction

Caculating the peak of each window

P1

P2

P3

…..

Pw

Hash

H(P1| n) mod l

H(P2| n) mod l

….

H(Pw | n) mod l

Bloom

Filter

0

1

0

0

1

0

0

0

0

…..

0

0

1

0

verification
Verification

Features received from the requester

0

1

0

0

1

0

0

0

0

…..

0

0

1

0

Not matched

Matched

Matched

Features extracted locally

0

1

0

0

0

0

0

1

0

…..

0

0

1

0

At least t % of

features match?

Yes

Authenticated

No

Reject

data collection
Data Collection
  • Implemented on Google Android Dev 1 phones.
  • Deployed at a room.
  • Distinctiveness Evaluation
    • False Positive Rate (FPR): the error rate of failing to reject authentication when it is in fact false
    • False Negative Rate (FNP): the error rate of rejecting authentication when it is actually true
  • To be completely distinguishable against attacks out of the region,
    • False positive rate must be 0.
    • False positive rate ≠ 0, a system is vulnerable.
    • False negative rate ≠ 0, re-trial can be used.

10 m

20 m

30 m

experimental results
Experimental Results
  • With t ≥40%, the complete dinstincitiveness can achieved within a small region.
  • Security Analysis
    • Replay attack: The random number, n, is generated by the verifier and used for a feature set. Therefore, an attacker can not reuse any valid feature set from the previous communications.
    • Guessing: To represent the 128-bit output of the MD-5 hash function in a filter, the length can be 128-bit. With the longer bits of the feature set, it is hard to guess a valid set.

(a) w=10

(b) w=6

outline4
Outline
  • Introduction
  • Mobile Edge Computing Devices (MECD)
  • Mesh-Networked MECDs
  • Self-organizing Authentication for Embedded Networked Systems
  • Conclusion and Future Work
conclusion and future work
Conclusion and Future Work
  • Conclusion
    • Collaboration among gateways is a key component to save bandwidth and reduce delay for the remote communication by sharing information locally.
    • A mesh networking approach of MECDs improves the connectivity with the remote server.
      • For an intelligent container scenairo, using mesh networked MECDs can solve reachability problem completely with small additional delay and energy consumption with the range of temperature, 25 – 65 °C.
    • The acoustic feature based technique is feasible for self-organizing region-based authentication within a small region.
      • With threshold of 40%, it provides 0.1 FNR for 10m and 0.4 FNR for 20m approximately for a smart home scenario.
  • Future Work
    • Power management for mobile gateways of ENS
    • Mesh-networked MECDs
      • Mesh network management for general USN
    • Self-organizing region-based authentication
      • Improve distinctiveness: multiple contextual information (e.g. temp, light, wifi)
      • Extend for heterogeneous devices in ENS
references
References

[1] S. Fukunaga, T. Tagawa, K. Fukui, K. Tanimoto, and H. Kanno., “Development of Ubiquitous Sensor Network, Oki Technical Review,” Vol. 71, No. 4, Oct. 2004.

[2] ITU-T Technology Watch Briefing Report Series, No. 4., “Ubiquitous Sensor Networks,” http://www.itu.int/dms\_pub/itu-t/oth/23/01/T23010000040001PDFE.pdf

[3] M. Kim, Y. Lee1, and J. Ryou, “What are Possible Security Threats in Ubiquitous Sensor Network Environment?,” In Proc. of Asia-Pacific Network Operations and Management Symposium (APNOMS 2007), LNCS4773, pp. 437-446, 2007.

[4] Randy H. Katz, “Radio propagation,” http://www.sss-mag.com/pdf/1propagation.pdf

[5] K. Bannister, G. Giorgetti, and S. K. S. Gupta, “Wireless Sensor Networking for Hot Applications: Effects of Temperature on Signal Strength, Data Collection and Localization,” In Proc. of the 5th Workshop on Embedded Networked Sensors (HotEmNets)}, Jun. 2008.

[6] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Eenergy-effieicnt communication protocol for wireless microsensor networks,” In Proc. of the 33rd Hawaii Int'l Conf, on System Science, Vol. 8, pp. 8020-8029, 2000.

[7] CrossBow, “Mica2 Datasheet,” https://www.eol.ucar.edu/rtf/facilities/isa/internal/CrossBow/DataSheets/mica2.pdf

[8] CrossBow, “CrossBow TelosB 2.4GHz datasheet,” http://www.willow.co.uk/TelosB\_Datasheet.pdf

[9] Shah Bhatti, James Carlson, Hui Dai, Jing Deng, Jeff Rose, Anmol Sheth, Brian Shucker, Charles Gruenwald, Adam Torgerson, and Richard Han, “MANTIS OS: An Embedded Multithreaded Operating System for Wireless Micro Sensor Platforms,” In ACM/Kluwer Mobile Networks \& Applications (MONET) Journal, Special Issue on Wireless Sensor Networks, August 2005.

[10] Robert M. Newman and Elena Gaura, “Size does matter - the case for big motes,” In Proc. of the 2006 NSTI Nanotechnology Conference and Trade Show (Nanotech 2006), May 2006.

[11] K. Venkatasubramanian, A. Banerjee, S. K. S. Gupta, “Green and Sustainable Cyber Physical Security Solutions for Body Area Networks,” In Proceedings of 6th Workshop on Body Sensor Networks (BSN'09), Berkeley, CA, June 2009.

thank you questions
Thank you!Questions?

Impact Lab (http://impact.asu.edu)

related publications
Related publications
  • Su Jin Kim, and Sandeep K. S. Gupta, “Design and Implementation of Monitoring Systems using Networked Mobile Edge Computing Devices for Ubiquitous Sensor Networks,” IEEE Trans. on Consumer Electronics, Under review.
  • Su Jin Kim, and Sandeep K. S. Gupta, “Audio-based Self-organizing Authentication for Pervasive Computing: a Cyber-Physical Approach,” The 2nd Int’l Workshop on Next Generation of Wireless and Mobile Networks (NGWMN’09), Vienna, Austria, 2009.
  • Su Jin Kim, Guofeng Deng, Sandeep K. S. Gupta and Mary Murphy-Hoye, “Enhancing Cargo Container Security during Transportation: A Mesh Networking Based Approach,” 2008 IEEE Int’l Conf. on Technologies for Homeland Security (HST'08), Waltham, MA, USA, April 2008.
  • Su Jin Kim, Guofeng Deng, Sandeep K. S. Gupta and Mary Murphy-Hoye, “Intelligent Networked Containers for Enhancing Global Supply Chain Security and Enabling New Commercial Value,” The 3rd Int'l Conf. on Communication System Software and Middleware (COMSWARE'08), Bangalore, India, 2008.
prototype implementation of mecd
Prototype Implementation of MECD

Stargate

USB Memory Card

MICAz mote 2.4 GHz

USB

51-pin

Stargate (Gateway)

Ethernet

  • Crossbow Stargate Gateway
    • Single-board embedded Linux computing designed for sensor networking applications
    • Low-power device
    • Various interfaces

RS232

PCMCIA

Compact Flash

802.11 Compact Flash card

rfid reader mote implementation
RFID Reader-Mote Implementation

Reader-Mote

MICAz mote 2.4 GHz

  • SkyeTek M9 UHF RFID eader
    • Small form factor, cost-efficient, energy-efficient and high-performance RFID reader
  • Converter
    • Two-way communications
    • Voltage conversion between the M9 reader (5V) and MicaZ mote (3V)

Converter

UART

RS232

M9 UHF RFID Reader

experimental study rfid read ranges
Experimental Study: RFID Read Ranges
  • According to the SkyeTek document, a M9 UHF RFID reader can approximately read 138 inches with the maximum output power (27 dBm). However, the average read ranges by our experiments are much smaller.
  • In Singapore and Taiwan, the government regulation of output power level is 0.5 watts ERP. In this case, the RFID read range is 10-18 inches.
experimental study lifetime of micaz motes
Experimental Study: Lifetime of MicaZ motes
  • MicaZ mote with MTS310 Sensor board
    • Broadcasts a packet every 10 sec with its voltage level
    • Uses the power saving mode (switching off radio and sensor board after readings)
    • 2 new AA batteries
    • The base station (4 meters away) collects packets
  • The mote lasts about 46 days

46 days

experimental study energy consumption
Experimental Study: Energy Consumption
  • ACSD requires 10 years and MATTS requires 1 year lifetime.
  • Unlike the gateway and motes, the RFID reader needs to operate only 760 hours per year for loading/unloading operations.
    • Using sleep mode, the energy consumption can be reduced.
  • For our 5-day test from Singapore to Taiwan, we used a large (car-size) battery.
government regulations for uhf rfid
Government Regulations for UHF RFID

ERP (Effective Isotropic Radiated Power)

EIRP (Effective Radiated Power)

ERP (dB) = EIRP (dB) – 2.15dB

interenal and external container networks
Interenal and External Container Networks

External Container Network

  • A container forms and participates in networks with their neighbors dynamically.

Internal Container Network

  • The network inside a container is isolated from the dynamic changes outside a container.
evaluation
Evaluation
  • Experimental Setip
    • 3 heterogeneous recorders
    • 2 different environments: Café (noisy) and classroom (moderate)
    • Sound recorded every minute (about 40 – 60 trials)
    • Simulation: Matlab
observation
Observation
  • Co-located devices have similar patterns on FFT
fft and coeff
FFT and Coeff
  • Fast Fourier Transform (FFT)
    • Transforms a signal to frequency-domain

Where x is a discrete audio signal and wN = e(-2i)/N

    • N-point FFT produces a N/2 length feature
  • Correlation coefficient
    • Measure the similarity of two signals, x and y

-1 ≤ corrcoef ≤ 1

    • Covariance between x and y

where E is mathematical expectation and x’ = E(x)

feature verification
Ph.D ProposalFeature verification
  • Correlation coefficient
    • Measure the similarity of two signals, x and y

-1 ≤ corrcoef ≤ 1

    • Covariance between x and y

where E is mathematical expectation and x’ = E(x)

security concepts
Security Concepts
  • Authentication
    • Proving one’s identity.
  • Confidentiality
    • Ensuring no one can read the message except the intended receiver.
  • Integrity
    • Assuring the receiver that the received message has not been altered in any way from the original.
  • Non-repudiation
    • Proving that the sender sent the message and the receiver received it.
localization
Localization
  • Approach
    • Absolute location
      • Determines the entity’s absolute location, (x, y)
    • Distance bounding
      • Determines whether the entity is closer to the verifier thatn some distance or not
    • In-region
      • Determines whether the entity is inside a certain region or not
  • Localization Category
    • Range-dependent
      • Measures physical properties of the exchanged signals
      • Time of Arrival (TOA), Angle of Arrival (AOA), Received Signal Strength (RSS)
      • Accurate, but expensive
    • Range-independent
      • Use other characteristic
      • Existence of beaon signals
      • Inexpensive, but less accurate
randomness test
Randomness test
  • Ent, a randomness test program [ent]
    • Entropy: the number of bits per character needed
      • Must be 8 for the perfect randomness
    • Chi-square Test: a way to evaluate differences between real and expected results due to normal random chance
      • The Interpreted percentage must be between 10% and 90%
    • Arithmetic Mean: the sum of all bytes in the file divided by the file length
      • Must be 127.5 for the perfect randomness
    • Monte Carlo Valud for Pi: the probability of that a trowing dart is inside a circle.
      • Must be pi for the perfect randomness
    • Serial correlation coefficient: the correlation of successive bytes
      • Must be zero for the perfect randomness

[ent] A Pseudorandom Number Sequence Test Program. http://www.fourmilab.ch/random/

results randomness
Results – Randomness
  • Using Ent, a randomness test program [ent]
time variance
Time-variance
  • Run Ent program on the sequence of all featrues (about 40 – 60) for each environment
security basics
Security Basics
  • Symmetric ciphers
    • The same secret key to encrypt and decrypt data.
    • Only sender and receiver know their symmetric ciphers.
    • Used for ensuring confidentiality of data.
  • Asymmetric ciphers (public key algorithm)
    • Two different keys are used.
      • Public key is shared with everyone and used for encryption.
      • Private key is only held by the receiver and used for decryption.
    • Typically used for identifying entities and exchanging symmetric keys.
  • Hashing algorithms
    • Provide ways of mapping messages with or without a key into a fixed-length value
    • Typically used for ensuring integrity of data
      • The sender calculates the hash value of a message
      • The receiver calculates the hash value of a message
      • Two hash values are compared to verify the integrity.
implementation
Implementation

1 sec window

(8 kHz)

1 sec window

(8 kHz)

  • FIR (Finite Impulse Response) low pass filter: Anti-aliasing filter

FIR filter

FIR filter

………

Downsample

Downsample

256-point FFT

256-point FFT

Generate a feature with a peak

Generate a feature with a peak

feature set (bloom filter)