Cyber-Physical-Social-Mediated Communication

We introduce the concept of cyber-physical-social-mediated communication (CPS-C) and analyze why CPS-C is better than pure cyber-mediated communication for two popular applications: mobile social networks and mobile crowd sensing. CPS-C utilizes its unique characteristics (i.e., cyber-physical synchronization, human intelligence, and physical displacement) to bring benefits, including natural boundary of information exposure, tangible interaction, targeting receivers on the fly, decentralization, and piggybacking. As a result, energy efficiency and user experience are improved. We highlight the existence of human-machine intelligence in the communication process, which has rarely been addressed.

[1]  Marco Conti,et al.  Opportunistic networking: data forwarding in disconnected mobile ad hoc networks , 2006, IEEE Communications Magazine.

[2]  Alex Pentland,et al.  Reality mining: sensing complex social systems , 2006, Personal and Ubiquitous Computing.

[3]  Daqing Zhang,et al.  4W1H in mobile crowd sensing , 2014, IEEE Communications Magazine.

[4]  Zhiyong Yu,et al.  Multi-hop Mobility Prediction , 2016, Mob. Networks Appl..

[5]  Tao Mei,et al.  Supporting Serendipitous Social Interaction Using Human Mobility Prediction , 2015, IEEE Transactions on Human-Machine Systems.

[6]  Yang Zhang,et al.  CarTel: a distributed mobile sensor computing system , 2006, SenSys '06.

[7]  Xingshe Zhou,et al.  Hybrid SN: Interlinking Opportunistic and Online Communities to Augment Information Dissemination , 2012, 2012 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing.

[8]  Di Wu,et al.  From Intermittent to Ubiquitous , 2017, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[9]  Yong Wang,et al.  Energy-efficient computing for wildlife tracking: design tradeoffs and early experiences with ZebraNet , 2002, ASPLOS X.

[10]  Zhu Wang,et al.  Opportunistic IoT: Exploring the harmonious interaction between human and the internet of things , 2013, J. Netw. Comput. Appl..

[11]  Zhu Wang,et al.  Mobile Crowd Sensing and Computing , 2015, ACM Comput. Surv..

[12]  Fei-Yue Wang,et al.  The Emergence of Intelligent Enterprises: From CPS to CPSS , 2010, IEEE Intelligent Systems.

[13]  Xianghan Zheng,et al.  Buy4Me: A Delivery System via Mobility Prediction Based on Mobile Crowd Sensing , 2015, 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom).

[14]  James H. Aylor,et al.  Computer for the 21st Century , 1999, Computer.

[15]  Zhu Wang,et al.  Toward Context-Aware Mobile Social Networks , 2017, IEEE Communications Magazine.

[16]  Kim-Kwang Raymond Choo,et al.  Mobile crowd sensing of human-like intelligence using social sensors: A survey , 2017, Neurocomputing.

[17]  Benjamin B. Bederson,et al.  Human computation: a survey and taxonomy of a growing field , 2011, CHI.

[18]  Tao Zhang,et al.  Elevator-Assisted Sensor Data Collection for Structural Health Monitoring , 2012, IEEE Transactions on Mobile Computing.

[19]  Daqing Zhang,et al.  effSense: A Novel Mobile Crowd-Sensing Framework for Energy-Efficient and Cost-Effective Data Uploading , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.