Crowd Assisted Approach for Pervasive Opportunistic Sensing

http://copelabs.ulusofona.pt
Human-centered Computing Lab
Crowd Assisted Approach
for Pervasive Opportunistic Sensing
Paulo Mendes and Waldir Moreira
waldir.junior@ulusofona.pt
March 27th, 2015
2nd IEEE PerCom Workshop on Crowd Assisted Sensing Pervasive Systems and Communications (CASPer 2015)
St. Louis, USA

Waldir Moreira, waldir.junior@ulusofona.pt http://copelabs.ulusofona.pt
Agenda

Introduction

Crowd Assisted Opportunistic Sensing Framework

Evaluation

Conclusions and Future Work

Introduction

New paradigms emerged, impacting on how people access information
– Proliferation of mobile, and very powerful, personal devices
• Support more intensive computation, provide data storage, and
offer long-range communication channels
• Useful to extract information about people daily habits
– Pervasive, opportunistic computing
• Allows devices to share content, resources, and services according
to how people interact
– Crowd assisted sensing
• Users actively or passively participate in sensing data collection

Introduction

Despite of these options, mobile sensing applications are programmed
using models, which still rely on static configurations

There is the need for a cooperative middleware
– Seamlessly consider individual sensors from different devices

Maestroo, a crowd assisted pervasive opportunistic sensing framework
– Exploits user mobility and sensor diversity on devices
– Extracts and shares sensing data according to user needs
– Expanding sensing applicability
– Overcoming coverage limitation and sensor availability

Crowd Assisted Opp. Sensing Framework

Challenges to address
– Sensor availability, processing cost, limited coverage,
communication intermittency, device heterogeneity

How to address
– Sensing abstraction: allowing sampling control of available
sensors
– Virtual sensing: using sensing data obtained from sensors on
other devices
– Robust processing: well-known servers or cloud systems
– Opportunism: data collection and exchange done as users interact


Node design
– Modular to be cross-platform, flexible, and easy to maintain

Kernel, instantiates devices/sensors
and controls message flow

Device, support to different devices
and their specific capabilities

Sensor, provides connectors to
both real and virtual sensors

Network, manages communication
interfaces and protocols

Data, manages storage of sensing data


The user can control
– Sensors and virtual sensors
– General settings (identifier and type), memory size and export types
– Data by managing the SQLite internal database, and the server dumps
– Network by defining messaging and state operations, as well as by
defining the broadcast interval used to share sensing data

Design choices
– C#/.NET Framework to allow cross platform development
– Dependency injection/TinyIoC library for less dependencies (runtime)
– SQLite for data handling
– Protobuffers for fast (de)serialization of message objects (readings)


Sensing abstraction
– Communication and integration, useful for crowd assisted sensing
– Creates device, sensing and comm profiles, as well as virtual sensors

Data sharing
– Centralized (server + Internet access)
– Decentralized (disruptive scenario + interest on sensing data)

Evaluation

Centralized scenario
– Goal: sensing framework stability (sensor operation and broadcast int.)
– Devices:
• Samsung phone: accelerometer, GPS and Wi-Fi
• Android emulator: temperature, gyroscope and Wi-Fi
• Workstation: backend server for data storage and inference
– Process: devices dump data to server, virtual sensor used
– Results:
• Broadcast interval is not below 7 milliseconds
• Otherwise, network flooding occurs
• Boot loading times are less than 5 seconds on real devices

Evaluation

Decentralized scenario
– Goal: capability to share sensing data in an opportunistic scenario
– Proposals:
• SCORP, data-centric opportunistic forwarding
• dLife, based on the levels of social interaction between users
• Bubble Rap, a community-based proposal
• Spray and Wait, a social-oblivious proposal
– Process: sensing data is exchanged among devices based on user
interest

Evaluation

– Delivery probability
• The more interests
a node has, the better
it is to deliver sensing data
• As the ability of nodes
becoming good message
carriers increases, so does
the protocols’ delivery capability

Evaluation

– Cost
• Sensing data only shared with
those strictly interested in it,
or with those who are socially
well connected to nodes with
such interest
• Low resource consumption:
Buffer utilization ranging
from ~0.03 MB to 0.15 MB

Evaluation

– Latency
• SCORP reaches up to
93.61% less latency
• Sharing interest on sensing
data aids in the dissemination
of such data

Conclusions and Future Work

Maestroo exploits user mobility and the diversity of sensing devices
– Overcome the coverage and sensor availability limitations

Stable in centralized scenario (broadcast interval)

In decentralized scenario, Maestroo delivers 97% of sensing data in an
average of 46.9 minutes, creating up to 13.9 times less replicas

Future steps (to increase the data accuracy)
– Allocation of sensing activities (same and different devices)
– Incentives for sensing
– Continuous sensing
– Context privacy
– Reliability of data readings

Crowd Assisted Approach for Pervasive Opportunistic Sensing

Crowd Assisted Approach for Pervasive Opportunistic Sensing

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