Smart world: a better world

Abstract

With the advancement of technologies, our world is becoming a smart world. In this paper, we share our vision of a smart world, demonstrate different application scenarios and introduce the emerging techniques. We envision that in a smart world, we will become more connected, safe, productive and efficient. To enable a smart world, many advanced techniques such as advanced network, ubiquitous sensing and collaborative computation have been developed. More specifically, they include heterogeneous advanced wireless networks, intelligent transportation, accurate indoor localisation, wireless sensor network, unobtrusive human behaviour sensing and mobile cloud computing. Compared with the previous work, the proposed techniques are faster, more accurate and non-invasive. We firmly believe that by exploiting those techniques, the smart world will be a better world.

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References

  1. 1

    Cao J, Xie K, Wu W, et al. HAWK: real-world implementation of high-performance heterogeneous wireless network for internet access. In: 29th IEEE International Conference on Distributed Computing Systems Workshops, Montreal, 2009. 214–220

    Google Scholar 

  2. 2

    Zhou B, Cao J, Wu H. Adaptive traffic light control of multiple intersections in wsn-based ITS. In: Proceedings of the 73rd IEEE Vehicular Technology Conference, Budapest, 2011. 1–5

    Google Scholar 

  3. 3

    Liu X, Cao J, Tang S, et al. A generalized coveragepreserving scheduling in wsns: a case study in structural health monitoring. In: IEEE Conference on Computer Communications, Toronto, 2014. 718–726

    Google Scholar 

  4. 4

    Yang L, Cao J, Yuan Y, et al. A framework for partitioning and execution of data stream applications in mobile cloud computing. ACM SIGMETRICS Perform Eval Rev, 2013, 40: 23–32

    Article  Google Scholar 

  5. 5

    Liang G, Cao J, Liu X, et al. Cushionware: a practical sitting posture-based interaction system. In: CHI Conference on Human Factors in Computing Systems, CHI’14, Toronto, 2014. 591–594

    Google Scholar 

  6. 6

    Liu X, Cao J, Tang S, et al. Wi-sleep: contactless sleep monitoring via wifi signals. In: Proceedings of the IEEE 35th IEEE Real-Time Systems Symposium, Rome, 2014. 346–355

    Google Scholar 

  7. 7

    Klingler F, Tang S, Liu X, et al. Faster distributed localization of large numbers of nodes using clustering. In: 38th Annual IEEE Conference on Local Computer Networks, Sydney, 2013. 711–714

    Chapter  Google Scholar 

  8. 8

    Sano A, Picard R W. Stress recognition using wearable sensors and mobile phones. In: Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII), Geneva, 2013. 671–676

    Google Scholar 

  9. 9

    Treviño RP. Camera program to monitor schoolchildrens’ eating habits. http://www.foxnews.com/us/2011/05/11/texasschools-pictures-worth-1000-calories/, 2011

    Google Scholar 

  10. 10

    Xu W Y, Li Z N, Huang M C, et al. Ecushion: an etextile device for sitting posture monitoring. In: International Conference on Body Sensor Networks (BSN), Dallas, 2011. 194–199

    Google Scholar 

  11. 11

    Tan H Z, Slivovsky L A, Pentland A. A sensing chair using pressure distribution sensors. IEEE/ASME Trans Mech, 2001, 6: 261–268

    Article  Google Scholar 

Download references

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Correspondence to Guanqing Liang.

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Liang, G., Ca, J., Liu, X. et al. Smart world: a better world. Sci. China Inf. Sci. 59, 043401 (2016). https://doi.org/10.1007/s11432-016-5518-8

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Keywords

  • smart world
  • emerging techniques
  • sensing