修士論文最終発表User Driven Code Propagation Mechanism

for Urban Sensor Networks

政策・メディア研究科修士 2 年金澤 貴俊

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Overview

• Presentation of master’s dissertation for academic year 2010• Proposal of an efficient application code propagation mechanism for edge networks in a people centric sensor network• Implementation and evaluation

of our mechanism on a real life environment

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Outline

• Background information on People Centric Sensor Networking (PCSN)

• Research issues in a PCSN environment

• Proposal of an efficient application code propagation mechanism

• Implementation• Evaluation• Conclusion

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People Centric Sensor Networks (PCSN)

• Traditional Sensor Networking Technology– Specific purpose deployment under specific

environments– Pre-defined tasks for execution– Deployment examples

• Agricultural management, vehicle location, environmental monitoring

• People Centric Sensor Networks– Sense information for people, by people– Integration of pre-deployed static sensing

infrastructure with mobile nodes (i.e., people, vehicles)

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Use-Case Scenario• Scenario: An elderly woman

walks through a sensor network with several requirements

• Core assumptions– User is equipped with a

handheld device capable of interaction with the deployed sensors

– Applications are pre-installed onto the user’s handheld device

User Requirement Enabling Application

UV Ray Avoidance UV Ray Sensing and Guidance

Bird Watching Movement Sensing

Life Logging Environmental Sensing

User Requirements and Enabling Applications

User Handheld Device

Sensor Network

Sensing Query

Localization Query

Localization Response

Application Code

Application Data

Transition Diagram

Use-Case Environment

Environmental Assumptions

• Private Sensor Network Deployment– Authentication of user-to-PCSN handled by individual authorities

(ex. theme parks, national park)

• Heterogeneous environmental plentitude– Power lines, wired-networks– Accessibility to the area

• Non-uniform sensor network deployment– Dense and precise deployment in urban areas– Sparse and random deployment in edge areas

• Application Reprogrammable Infrastructure– Users are able to dynamically interact and install application

code onto the sensing infrastructure via handheld device

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Comparison with Existing PCSN Architectures

Characteristics

MetroSense • Tired sensing architecture (Sensing Tier, Sensor Access Point Tier, Server Tier)

• Network symbiosis with existing infrastructures

• Oppourtunistic Sensing Paradigm

SenseWeb • Tiered architecture (Sensor Gateway, Mobile Proxy)

• API Interconnected components

CitySense • Test-bed infrastructure for urban sensor network deployment

PCSN Management Infrastructure

Existing PCSN Architectures

Use-case of Existing PCSN Architectures

Application propagation requires several complex operations

Slow and Ineffective when user wishes to acquire nearby

sensor data

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Proposal Overview• Problem Definition• In an existing PCSN, user requirement must pass the process of

opportunistic sensing in order to achieve the desired data• Application propagation, sensor node localization, and data

acquisition is slow under edge regions of the network

• Our Architectural Proposal• User-Driven Application Code Propagation Mechanism• Directly localizes and propagates application code to the user’s

nearby nodes• Rapid application code propagation and data acquisition due to

direct interaction of the user and sensor nodes

User Driven Code Propagation Mechanism

Node LocalizationRSSI value based node localization

1. User broadcasts localization query2. Each sensing nodes sends an

acknowledgement message to the following packet

3. Propagation node selects the largest RSSI value and its corresponding node ID

4. The selected node(s) are sent to the application code propagation mechanism for application propagation

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・・・

Application Code Propagation Module

Sensing Nodes

Internal Buffer for RSSI Value Storage

Selection of Node ID with Largest RSSI value

Propagation Node

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User Driven Code Propagation Mechanism

Application Code Propagation• Deluge T2 Architecture• Application propagation mechanism for an entire sensor

network• Divides application image into pages• Data transmission is performed in unit of pages• Transmission of each page requires authentication via

CRC hash of page

Maintain

Transmit Receive

Application Image Data Format State Transition of Nodes Propagation Scheme

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System Architecture

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Implementation

• Software– Tinyos 2.1.1– nesC 1.3.2

• Hardware– Sensing Node

• IRIS Mote

– Propagation Node• IRIS Mote +

MacBookPro 15in

Line Count +1629 lines

Application Image Size (ROM) + 6826 bytes

Application Image Size (RAM) + 67 bytes

IRIS Mote Hardware Specification

IRIS Mote Hardware

Software Comparison with Deluge T2 Framework

Propagation Node

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EvaluationEnvironment and Method

• Environment– 18 IRIS Mote deployment in an 16m x 64m

environment– 12/29/10 11:44 – 12/30/10 0:28– Minor obstacles, no human interaction

• Metrics– Time required of application code propagation to

data acquisition

• Method– User would traverse the environment in a pre-

defined order– Starting from Pa, at each point the user would;

1. Localize two of the nearest nodes2. Send an application propagation query (application size =

80KB)3. Wait until the first application packet is received 4. Move to the next point

– Until the user reaches the edge of the network at Pr

Sensor Node Deployment

Evaluation Environment

User Traversal Path

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EvaluationPre-Experiments

• RSSI Value and application code propagation time between two nodes, to varying distance (0m-20m) under our environment

• Application code propagation of an simple sense-and send application (application image size = 80KB , deluge page size = 1KB)

Results• Distance from propagation to sensing node does not affect application code

propagation time• RSSI values shows proportion to distance

Application Code Propagation Time to Distance RSSI Value to Distance

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 200

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Distance Between Nodes (m)

Prop

agai

on C

ompl

etion

Tim

e (s

ec)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 200

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Distance Between Nodes (m)RS

SI V

alue

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EvaluationLocalization Time Requirement

• Time required for node localization (sending of localization query to reception of nearest nodes on the propagation node)

Time delay of node localization is deniable considering application propagation (90ms maximum)

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EvaluationNode Localization Precision

• Measurement of the mean of distance between each point and corresponding node

• 1 theoretical unit (1U) represents 8m (minimal node interval)

Half of the localization scheme successfully localized nodes with a 1.5U (12m) precision

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EvaluationApplication Code Propagation Time

• Measurement of application code propagation time (propagation request to reception of application’s first data packet)

Variation of query response

Rapid CompletionSensing nodes responding to already received (cached in the past) application query

Slow CompletionSensing nodes receiving the full application code image on response to query

Rapid Completion Cluster

Slow Completion Cluster

Application caching proves great efficiency in an environment where the user would traverse pre-visited path

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Summarization of Results and Future Works

• Summarization– Node localization could be done

rapidly compared to one-on—one application code propagation in our environment

– architecture proved to be applicable in an best case scenario

EvaluationParameters

Mean (Median) Time (sec)

Node Localization 0.06 0.05

Application Propagation 41.29 16.72

Total 41.35• Future Considerations

– Architectural• Reduction of application code propagation time

– Reduction of number of pages corresponding to {signal strength | size of application image | checksum calculation | number of users in the environment}

• Localization precision among complex environments

– Evaluation• Deployment of static sensors and user movement patterns could

call for a different localization/code propagation mechanism

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Conclusion

• Our architectural proposal enables efficient user application code propagation and data acquisition in edge networks of an urban sensing environment

• We have implemented and evaluated our work under a real life environment

• Evaluation results show our localization mechanism to be sufficient and rapid enough in our target deployment environment

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References• A. T. Campbell, S. B. Eisenman, N. D. Lane, E. Miluzzo, and

R. a.Peterson. People-centric urban sensing. Proceedings of the 2nd annualinternational workshop on Wireless internet - WICON '06, pages 18–es,2006.

• A. Kansal, S. Nath, J. Liu, and F. Zhao. Senseweb: An infrastructurefor shared sensing. IEEE MultiMedia, 14(4):8–13, 2007.

• R. Murty, A. Gosain, M. Tierney, A. Brody, A. Fahad, J. Bers, andM. Welsh. CitySense: A vision for an urban-scale wireless networkingtestbed. In Proceedings of the 2008 IEEE International Conference onTechnologies for Homeland Security, Waltham, MA. Citeseer, 2008.

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Why Network Reprogramming?

• Future Sensor Networking Technology–More computing power, storage More capability on computerized

components within the sensor networkex)• FFT and conversion of dynamic streaming audio

data to user required context• Long term storage and analysis of video data

Simple query based approaches are insufficient to meet the various user requests issued towards the network