Context-aware Mobile Crowdsensing in Mobile Social Networks

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Introduction

Mobile crowdsensing aims to provide a mechanism to involve participants from the general public to efficiently and effectively contribute and utilize context-related sensing data from their mobile devices in solving specific problems in collaborations. The wide availability of sensing modules in mobile devices enables social networking services to be accessed and extended to incorporate location based services, media tag services, etc. Therefore, there is growing interest in fusing social networking services with real-world sensing, such as crowdsensing. Mobile social networks (MSNs) not only can provide an ideal and ubiquitous platform to enable mobile users to participate in crowdsensing, but can also help to improve the context-awareness of mobile applications and better assist users in mobile crowdsensing by analyzing and utilizing their social contexts.

A number of research works have identified that crowdsensing in MSN can be effectively used for many purposes and bring huge economic benefits, e.g., vehicular social networks (VSNs) in transportations liketransportation efficiency, green transportation, smart ridesharing, safe driving (SAfeDJ is available in Google Play now!); smart city; and crisis management. Our projects target to explore the cutting edge challenges of mobile crowdsensing in MSNs and provide systematic solutions, so as to facilitate the real-world deployment of different context-aware mobile crowdsensing applications.

 

 

 

Mobile Ecosystem of Context-aware Mobile Crowdsensing

In this work, we proposes a multi-dimensional context-aware social network architecture, which aims to provide a mobile ecosystem to enable context-awareness in the development and utilization of mobile crowdsensing applications. This mobile ecosystem is constructed to provide context-awareness capabilities for different roles (i.e., users or developers) in the system and facilitate the interactions between them.

This system can ease the development of context-aware mobile applications and enable context-aware mobile crowdsensing considering environmental, personal and social information. We present a flow of context-aware solution designed on this system, and highlight the orchestrations and the advantages of different context-aware schemes in the system for different types of users (requesters and participants) in mobile crowdsensing.

 

 

Vita: A Crowdsensing-oriented Mobile Cyber Physical System

As a prominent subcategory of cyber-physical systems, mobile cyber-physical systems could take advantage of widely used mobile devices such as smartphones as a convenient and economical platform that facilitates sophisticated and ubiquitous mobile sensing applications between humans and the surrounding physical world. This work presents Vita, a novel mobile cyber-physical system for crowdsensing applications, which enables mobile users to perform mobile crowdsensing tasks in an efficient manner through mobile devices.

Vita provides a flexible and universal architecture across mobile devices and cloud computing platforms by integrating the service-oriented architecture with resource optimization mechanism for crowdsensing, with extensive supports to application developers and end users. The customized platform of Vita enables intelligent deployments of tasks between human in the physical world, and dynamic collaborations of services between mobile devices and cloud computing platform during run-time of mobile devices with service failure handling support.

Our practical experiments show that Vita performs its tasks efficiently with a low computation and communication overhead on mobile devices, and eases the development of multiple mobile crowdsensing applications and services. Also, we present a context-aware mobile crowdsensing application Ė Smart City developed on Vita to demonstrate the functionalities and practical usage of Vita.

 

 

S-Aframe: Agent-based Multi-layer Framework with Context-aware Semantic Service for Vehicular Social Networks

This work presents S-Aframe, an agent based multi-layer framework with context-aware semantic service (CSS) to support the development and deployment of context-aware applications for vehicular social networks (VSNs) formed by in-vehicle or mobile devices used by drivers, passengers, and pedestrians.

The programming model of the framework incorporates features that support collaborations between mobile agents to provide communication services on behalf of owner applications, and service (or resident) agents to provide application services on mobile devices. Using this model, different self-adaptive applications and services for VSNs can be effectively developed and deployed.

Built on top of the mobile devicesí operating systems, the framework architecture consists of framework service layer, software agent layer and owner application layer. Integrated with the proposed novel CSS, applications developed on the framework can autonomously and intelligently self-adapt to rapidly changing network connectivity and dynamic contexts of VSN users.

 

 

A practical implementation and experimental evaluations of S-Aframe are presented to demonstrate its reliability and efficiency in terms of computation and communication performance on popular mobile devices. In addition, a VSN-based smart ride application is developed to demonstrate the functionality and practical usefulness of S-Aframe.

 

SAfeDJ: A Crowd-Cloud Co-design Approach to Situation-aware Music Delivery for Drivers (available in Google Play now!)

Driving is an integral part of our everyday lives, but it is also a time when people are uniquely vulnerable. Previous research has demonstrated that not only does listening to suitable music while driving not impair driving performance, but it could lead to an improved mood and a more relaxed body state, which could improve driving performance and promote safe driving significantly. In this work, we propose SAfeDJ, a smartphone-based situation-aware music recommendation system, which turns driving into a safe and enjoyable experience. SAfeDJ aims at helping drivers to diminish fatigue and negative emotion.

†† ††††††††††††††††††††††††Fig. 2. Overall work flow of SAfeDJ

Its design is based on novel interactive methods, which enable in-car smartphones to orchestrate multiple sources of sensing data and the driversí social context, in collaboration with cloud computing to form a seamless crowdsensing solution. This solution enables different smartphones to collaboratively recommend preferable music to drivers according to each driverís specific situations in an automated, precise and intelligent manner.

Practical experiments of SAfeDJ have proved its effectiveness in music-mood analysis, and mood-fatigue detections of drivers with reasonable computation and communication overheads on smartphones. Also, our user studies have demonstrated that SAfeDJ helps to decrease 49.09% fatigue degree and 36.35% negative mood degree of drivers compared to traditional smartphone-based music player under the same driving situations.

Social Drive: A Crowdsourcing-based Vehicular Social Networking System for Green Transportation

Social Drive is a novel crowdsourcing-based vehicular social networking (VSN) system for green transportation. Social Drive integrates the standard vehicular On-Board Diagnostics (OBD) module, leverages the advantages of cloud computing and popular social networks, and incorporates a novel rating mechanism about the fuel economy of drivers. Based on these, Social Drive provides a user-friendly mobile application on smartphones targeting drivers, which enables a seamless and economic solution that promote driversí awareness of their driving behaviors regarding fuel economy of specific trips. Our practical experiments have demonstrated that Social Drive works efficiently with low battery consumption and low networking overhead on popular mobile devices

 

 

People

The works were mainly done by Xiping Hu (email: xipingh@ece.ubc.ca) at The University of British of Colombia, Canada, during Sep. 2011 - Aug. 2015. These were international collaboration projects, which had been involving partners from global, such as The University of Hong Kong and The Hong Kong Polygenic University in Hong Kong, Uppsala University in Sweden, HEC Paris in France, University of Twente in Netherlands, East China Normal University, Shanghai Jiaotong University and Tsinghua University in China, Massachusetts Institute of Technology in USA, University of Cambridge and University of Sussex in UK, University of New Brunswick in Canada, Auckland University of Technology in New Zealand, TELUS and IBM Canada, Microsoft and IBM China etc.