The advances in the areas of microelectronics and telecommunications are helping to materialize the vision of a pervasive computing model through the incorporation of sensors and communication interfaces into objects of everyday life. In recent years, there has been a growing interest in crowd assisted sensing applications, in which people serve as the building block that can be exploited to offer pervasive opportunistic sensing at scale. This paper describes a new crowd assisted pervasive opportunistic sensing framework able of exploiting people's mobility to overcome the coverage limitation of sensors and the diversity of devices, expanding the scale of sensing applications. This presentation was given in the 2nd IEEE PerCom Workshop on Crowd Assisted Sensing Pervasive Systems and Communications (CASPer 2015), on March 27th, 2015 in St. Louis, USA.

1. 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

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

3. Waldir Moreira, waldir.junior@ulusofona.pt http://copelabs.ulusofona.pt
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

4. Waldir Moreira, waldir.junior@ulusofona.pt http://copelabs.ulusofona.pt
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

5. Waldir Moreira, waldir.junior@ulusofona.pt http://copelabs.ulusofona.pt
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

6. Waldir Moreira, waldir.junior@ulusofona.pt http://copelabs.ulusofona.pt
Crowd Assisted Opp. Sensing Framework

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

7. Waldir Moreira, waldir.junior@ulusofona.pt http://copelabs.ulusofona.pt
Crowd Assisted Opp. Sensing Framework

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)

8. Waldir Moreira, waldir.junior@ulusofona.pt http://copelabs.ulusofona.pt
Crowd Assisted Opp. Sensing Framework

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)

9. Waldir Moreira, waldir.junior@ulusofona.pt http://copelabs.ulusofona.pt
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

10. Waldir Moreira, waldir.junior@ulusofona.pt http://copelabs.ulusofona.pt
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

11. Waldir Moreira, waldir.junior@ulusofona.pt http://copelabs.ulusofona.pt
Evaluation

Decentralized scenario
– 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

12. Waldir Moreira, waldir.junior@ulusofona.pt http://copelabs.ulusofona.pt
Evaluation

Decentralized scenario
– 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

13. Waldir Moreira, waldir.junior@ulusofona.pt http://copelabs.ulusofona.pt
Evaluation

Decentralized scenario
– Latency
• SCORP reaches up to
93.61% less latency
• Sharing interest on sensing
data aids in the dissemination
of such data

14. Waldir Moreira, waldir.junior@ulusofona.pt http://copelabs.ulusofona.pt
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