這篇論文基於一個結合手機與城市感測設施的資料蒐集平台，進一步地提出利用遊戲化的方式吸引更多玩家在智慧城市中進行感測資料的蒐集。
我們將這些蒐集感測資料的需求轉化為群眾外包任務，將這些任務發包給玩家去執行。為了讓整個系統可以快速地完成更多任務，並且不給玩家過多的負擔的情況下，
我們設計了一些演算法達成這些目的。我們透過模擬以及實作的方式去驗證我們提出的演算法和遊戲化的系統，模擬的結果證明我們的演算法在200個感測任務、100個玩家的情況下，比起現有的方法(1)有63%更高的任務完成率，(2)任務完成的速度將近3倍，(3)玩家花在感測的時間降低81%。另外，實作的系統經過玩家調查證明遊戲化的方式能確實達成我們的目的。

We present a gamified Smartphone Augmented Infrastructure Sensing (SAIS)
platform for leveraging mobile gamers for applications such as smart cities. We
develop a suite of algorithms to transparently guide the gamers to sensing task
locations, in order to complete more tasks at shorter response time without
incurring high workload on gamers. We evaluate our algorithms using extensive
simulations and a real prototype implementation. The simulation results confirm
that our algorithms achieve their design goals. For example, with 200 sensing
tasks and 100 gamers, our algorithms on average: (i) achieve 63% higher
completion ratio, (ii) cuts the response time by almost two-third, and (iii)
reduces the gamer working hour by 81%, compared to the existing solutions.
Furthermore, our prototype implementation demonstrates the practicality of our
algorithms, while our preliminary user study receives positive feedback.

1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.1 Incentive Mechanism . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.2 Gamer Assignment . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Background and Related Work 5
2.1 Smart City and Urban Computing . . . . . . . . . . . . . . . . . . . . . 5
2.2 Crowdsourcing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.3 Gamification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.4 Mobile Augmented Reality . . . . . . . . . . . . . . . . . . . . . . . . . 7
3 Problems and Solutions 9
3.1 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.2 Optimal Spot Locator . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.3 Nearest Gamer Assigner . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.4 Nature NPC Path Generator . . . . . . . . . . . . . . . . . . . . . . . . . 12
4 Simulation 14
4.1 Baseline Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.1.1 VSN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.1.2 Current Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.2 Mobility Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.3 Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
5 Implementation and User Study 26
5.1 System Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
5.2 Implementations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
5.3 User Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
6 Conclusion 32

[1] GO map - 3D map for AR gaming. https://goo.gl/Sf9Y9j, January 2017.
[2] Google map Android API. https://goo.gl/FbBsh3, January 2017.
[3] Google Tango. https://developers.google.com/tango/, January
2017.
[4] MySQL. https://www.mysql.com/, January 2017.
[5] NGINX. https://www.nginx.com/, January 2017.
[6] Openstreetmap. https://www.openstreetmap.org/, January 2017.
[7] Poke radar. https://www.pokemonradargo.com/, January 2017.
[8] Pokemongo. http://www.pokemongo.com/, January 2017.
[9] Smart city market will grow tremendously at a CAGR of close to 20% until 2020,
says Technavio. http://tinyurl.com/jcy6nqg, January 2017.
[10] Unity. https://unity3d.com/, January 2017.
[11] F. Alt, A. Shirazi, A. Schmidt, U. Kramer, and Z. Nawaz. Location-based
crowdsourcing: Extending crowdsourcing to the real world. In Proceedings of
the Nordic Conference on Human-Computer Interaction: Extending Boundaries
(NordiCHI’10), pages 13–22, Reykjavik, Iceland, October 2010.
[12] J. An, X. Gui, Z. Wang, J. Yang, and X. He. A crowdsourcing assignment model
based on mobile crowd sensing in the internet of things. IEEE Internet of Things
Journal, 2(5):358–369, 2015.
[13] F. Bai and A. Helmy. A survey of mobility models. Wireless Adhoc Networks.
University of Southern California, USA, 206:147, 2004.
[14] A. Chen. Don’t fret! losing 80% of your mobile app users is normal. https:
//goo.gl/b66zUz, January 2017.
[15] T. Chen, H. Tsai, C. Chen, and J. Peng. Object coverage with camera rotation in
visual sensor networks. In Proceedings of the International Wireless Communications
and Mobile Computing Conference (IWCMC’10), pages 79–83, Caen, France,
2010.
[16] E. Deci, H. Eghrari, B. Patrick, and D. Leone. Facilitating internalization: The
self-determination theory perspective. Journal of Personality, 62(1):119–142, 1994.
[17] S. Deterding, D. Dixon, R. Khaled, and L. Nacke. From game design elements to
gamefulness: Defining gamification. In Proceedings of the international academic
MindTrek conference: Envisioning future media environments, pages 9–15, 2011.
[18] J. Engel, T. Schops, and D. Cremers. Lsd-slam: Large-scale direct monocular slam. ¨
In Proceedings of the European Conference on Computer Vision (ECCV’14), pages
834–849, Zurich, Switzerland, 2014.
[19] Z. Feng, Y. Zhu, Q. Zhang, L. Ni, and A. Vasilakos. Trac: Truthful auction
for location-aware collaborative sensing in mobile crowdsourcing. In Proceedings
of IEEE Conference on Computer Communications (INFOCOM’14), pages 1231–
1239, Toronto, Canada, May 2014.
[20] J. Hamari, J. Koivisto, and H. Sarsa. Does gamification work?–a literature review of
empirical studies on gamification. In Proceedings of the 47th Hawaii International
Conference on System Sciences (HICSS’14), pages 3025–3034. IEEE, 2014.
[21] H. Hong, C. Fan, Y. Lin, and C. Hsu. Optimizing cloud-based video crowdsensing.
IEEE Internet of Things Journal, 3(3):299–313, 2016.
[22] S. Kanhere. Participatory sensing: Crowdsourcing data from mobile smartphones
in urban spaces. In Proceedings of IEEE International Conference on Mobile Data
Management (MDM’11), pages 3–6, Lulea, Sweden, June 2011. ˚
[23] L. Kazemi and C. Shahabi. Geocrowd: enabling query answering with spatial
crowdsourcing. In Proceedings of the 20th international conference on advances
in geographic information systems (SIGSPATIAL’ 12), pages 189–198, New York,
NY, USA, November 2012. ACM.
[24] N. Lane, E. Miluzzo, H. Lu, D. Peebles, T. Choudhury, and A. Campbell. A survey
of mobile phone sensing. IEEE Communications Magazine, 48(9):140–150,
September 2010.
[25] C. Liao. Detour planning problem on mobile crowdsensing systems. Master’s thesis,
Department of Computing Science, National Tsing Hua University, June 2015.
[26] C. Liao, T. Hou, T. Lin, Y. Cheng, A. Erbad, C. Hsu, and N. Venkatasubramania.
SAIS: Smartphone augmented infrastructure sensing for public safety and sustainability
in smart cities. In Proceedings of the International Workshop on Emerging
Multimedia Applications and Services for Smart Cities (EMASC’14), pages 3–8,
Orlando, FL, November 2014.
[27] C. Liao and C. Hsu. A detour planning algorithm in crowdsourcing systems for
multimedia content gathering. In Proceedings of the 5th Workshop on Mobile Video
(MoVid’13), pages 55–60, Oslo, Norway, 2013.
[28] V. D. Luca and R. Castri. The social power game: A smart application for sharing
energy-saving behaviours in the city. FSEA 2014, 27:4, 2014.
[29] A. McAfee, E. Brynjolfsson, T. Davenport, D. Patil, and D. Barton. Big data. The
management revolution. Harvard Bus Rev, 90(10):61–67, 2012.
[30] P. Milgram, H. Takemura, A. Utsumi, and F. Kishino. Augmented reality: A class
of displays on the reality-virtuality continuum. In Proceedings of the Photonics for
industrial applications, pages 282–292, October 1995.
[31] T. Nam and T. Pardo. Conceptualizing smart city with dimensions of technology,
people, and institutions. In Proceedings of the Annual International Digital Government
Research Conference: Digital Government Innovation in Challenging Times
(dg.o’11), pages 282–291, Maryland, USA, 2011.
[32] R. Newcombe, S. Lovegrove, and A. Davison. Dtam: Dense tracking and mapping
in real-time. In Proceedings of the IEEE International Conference on Computer
Vision (ICCV’11), pages 2320–2327. IEEE, 2011.
[33] G. Papagiannakis, G. Singh, and N. Magnenat-Thalmann. A survey of mobile and
wireless technologies for augmented reality systems. Computer Animation and Virtual
Worlds, 19(1):3–22, February 2008.
[34] K. Seaborn and D. Fels. Gamification in theory and action: A survey. International
Journal of Human-Computer Studies, 74:14–31, February 2015.
[35] K. Su, J. Li, and H. Fu. Smart city and the applications. In Electronics, Communications
and Control (ICECC), 2011 International Conference on, pages 1028–1031.
IEEE, 2011.
[36] M. Talasila, R. Curtmola, and C. Borcea. Alien vs. mobile user game: Fast and
efficient area coverage in crowdsensing. In Proceedings of International Conference
on Mobile Computing, Applications and Services (MobiCASE’14), pages 65–74,
Austin, TX, 2014.
[37] M. Talasila, R. Curtmola, and C. Borcea. Crowdsensing in the wild with aliens and
micropayments. IEEE Pervasive Computing, 15(1):68–77, Jan 2016.
[38] C. Tan and D. Soh. Augmented reality games: A review. Proceedings of GameonArabia,
Eurosis, 2010.
[39] D. Wagner, A. Mulloni, T. Langlotz, and D. Schmalstieg. Real-time panoramic
mapping and tracking on mobile phones. In Proceedings of the IEEE Virtual Reality
Conference (VR’10), pages 211–218, Boston, MA, March 2010.
[40] G. Welch and G. Bishop. Scaat: Incremental tracking with incomplete information.
In Proceedings of the annual conference on Computer graphics and interactive techniques,
pages 333–344, 1997.
[41] M. Yuen, I. King, and K. Leung. A survey of crowdsourcing systems. In Proceedings
of the Privacy, Security, Risk and Trust (PASSAT’11) and IEEE Inernational
Conference on Social Computing (SocialCom’11), pages 766–773, Boston, MA,
October 2011.
[42] Y. Zheng, L. Capra, O. Wolfson, and H. Yang. Urban computing: Concepts, methodologies,
and applications. ACM Transactions on Intelligent Systems and Technology,
5(3):38:1–38:55, September 2014.