A Research on Delay Time of the Message in Mobile Crowd Sensing

Öz

The ubiquity of smart devices such as smartphones and wearables along with human mobility enabled collecting useful information at a larger scale through mobile crowd-sensing (MCS). The availability of several onboard sensors and wireless communication capabilities on such devices not only provides a means to monitor environmental conditions but also offers intermittent connectivity to otherwise unconnected nodes by leveraging mobility and data caching. Despite the availability of unprecedented opportunities, MCS also poses several challenges that need to be tackled such as sparsity and scarcity of the collected data. If the collected data is sparse, some of the regions in the data collection area may not be covered. On the other hand, if the data is scarce, it will be more difficult to validate the collected data considering malicious participants which may alter the data intentionally or friendly participants which may send inaccurate data due to uncalibrated sensors. In this paper, we present the results which the delay time of the message from connected nodes to base node for three random mobility entity models. 

Anahtar Kelimeler

• Mobile Crowd Sensing,Mobile Random Entity Model,Random Walk,Random Direction,Random Waypoint,Message Delay,Mobile Data Collectors

Mobil Kitle Algılamada Mesaj Gecikme Zamanı Üzerine Bir Araştırma

Öz

Akıllı telefonlar ve giyilebilir cihazlar gibi akıllı cihazların yaygınlığı, insan hareketliliğinin yanı sıra, mobil kitle algılaması (MKA) aracılığıyla daha geniş bir ölçekte faydalı bilgilerin toplanmasını sağlamıştır. Yerleşik sensörlerin ve bu tür cihazlarda kablosuz iletişim yeteneklerinin mevcudiyeti, yalnızca çevresel koşulları izlemek için bir araç sağlamakla kalmaz, aynı zamanda mobilite ve veriyi önbelleğe almayı kaldırarak başka bir şekilde bağlı olmayan düğümlere aralıklı bağlantı sunar. Eşi görülmemiş fırsatların mevcut olmasına rağmen, MKA ayrıca toplanan verilerin azlığı gibi ele alınması gereken çeşitli zorluklar da ortaya koymaktadır. Toplanan veriler seyrekse, veri toplama alanındaki bazı bölgeler ele alınmayabilir. Öte yandan, eğer veri azsa, kasıtlı olarak verileri değiştirebilecek kötü niyetli katılımcıları veya kalibre edilmemiş sensörler nedeniyle yanlış veri gönderebilecek dostu katılımcıları dikkate alarak toplanan verileri doğrulamak daha zor olacaktır. Bu çalışmada, üç rastgele mobilite varlık modeli için mesajın bağlı düğümlerden baz düğüme geçme gecikmeleri araştırılmıştır.

Anahtar Kelimeler

• Mobil Kitle Algılama,Mobil Rastgele Varlık Modeli,Rastgele Yürüyüş,Rastgele Yol,Rastgele Yön

Kaynakça

1. [1] A. T. Campbell, S. B. Eisenman, N. D. Lane, E. Miluzzo, R. A. Peterson, H. Lu, X. Zheng, M. Musolesi, K. Fodor, and G.-S. Ahn, “The rise of people-centric sensing,” IEEE Internet Computing , vol. 12, no. 4, 2008.[2] M. F. Goodchild, “Citizens as sensors: the world of volunteered geography,” GeoJournal , vol. 69, no. 4, pp. 211–221, 2007. [3] M. N. K. Boulos, B. Resch, D. N. Crowley, J. G. Breslin, G. Sohn, R. Burtner, W. A. Pike, E. Jezierski, and K.-Y. S. Chuang, “Crowd-sourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: trends, ogc standards and application examples,” International journal of health Geographics , vol. 10, no. 1, p. 67, 2011.[4] J. Liu, H. Shen, H. S. Narman, W. Chung, and Z. Lin, “A survey of mobile crowdsensing techniques: A critical component for the internet of things,” ACM Transactions on Cyber-Physical Systems , vol. 2, no. 3, p. 18, 2018.[5] J. A. Burke, D. Estrin, M. Hansen, A. Parker, N. Ramanathan, S. Reddy, and M. B. Srivastava, “Participatory sensing,” 2006. [6] M.-R. Ra, B. Liu, T. F. L. Porta, and R. Govindan, “Medusa: a programming framework for crowd-sensing applications,” in MobiSys , 2012.[7] I. Carreras, D. Miorandi, A. Tamilin, E. Ssebaggala, and N. Conci, “Matador: Mobile task detector for context-aware crowd-sensing campaigns,” 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops) , pp. 212–217, 2013.[8] A. Zaslavsky, P. P. Jayaraman, and S. Krishnaswamy, “Sharelikescrowd: Mobile analytics for participatory sensing and crowd-sourcing applications,” in Data Engineering Workshops (ICDEW), 2013 IEEE 29th International Conference on . IEEE, 2013, pp. 128–135.[9] B. Guo, Z. Yu, X. Zhou, and D. Zhang, “From participatory sensing to mobile crowd sensing,” in Pervasive Computing and Communications Workshops (PERCOM Workshops), 2014 IEEE International Conference on. IEEE, 2014, pp. 593–598.[10] H. Ma, D. Zhao, and P. Yuan, “Opportunities in mobile crowd sensing,” IEEE Communications Magazine , vol. 52, no. 8, pp. 29–35, 2014. [11] D. Zhang, L. Wang, H. Xiong, and B. Guo, “4w1h in mobile crowd sensing,” IEEE Communications Magazine, vol. 52, no. 8, pp. 42–48, 2014.[12] L. Pournajaf, L. Xiong, V. Sunderam, and S. Goryczka, “Spatial task assignment for crowd sensing with cloaked locations,” in Mobile Data Management (MDM), 2014 IEEE 15th International Conference on, vol. 1. IEEE, 2014, pp. 73–82.[13] Y. Wen, J. Shi, Q. Zhang, X. Tian, Z. Huang, H. Yu, Y. Cheng, and X. Shen, “Quality-driven auction-based incentive mechanism for mobile crowd sensing,” IEEE Transactions on Vehicular Technology , vol. 64, no. 9, pp. 4203–4214, 2015.[14] H. Aly, A. Basalamah, and M. Youssef, “Map++: A crowd-sensing system for automatic map semantics identification,” in 2014 Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) . IEEE, 2014, pp. 546–554. [15] D. B. Johnson and D. A. Maltz, “Dynamic source routing in ad hoc wireless networks,” in Mobile computing. Springer, 1996, pp. 153– 181.[16] T. Camp, J. Boleng, and V. Davies, “A survey of mobility models for ad hoc network research,” Wireless communications and mobile computing, vol. 2, no. 5, pp. 483–502, 2002.[17] J. Broch, D. A. Maltz, D. B. Johnson, Y.-C. Hu, and J. Jetcheva, “A performance comparison of multi-hop wireless ad hoc network routing protocols,” in Proceedings of the 4th annual ACM/IEEE international conference on Mobile computing and networking. ACM, 1998, pp. 85–97.[18] C.-C. Chiang and M. Gerla, “On-demand multicast in mobile wireless networks,” in Network Protocols, 1998. Proceedings. Sixth International Conference on . IEEE, 1998, pp. 262–270. [19] J. J. Garcia-Luna-Aceves and M. Spohn, “Source-tree routing in wireless networks,” in Network Protocols, 1999.(ICNP’99) Proceedings. Seventh International Conference on . IEEE, 1999, pp. 273–282.[20] P. Johansson, “Routing protocols for mobile ad-adhoc networks-a comparative performance analysis,” Proc. IEEE/ACM MOBICOM’99, Aug., 1999.[21] M. Sanchez and P. Manzoni, “Anejos: a java based simulator for ad hoc networks,” Future generation computer systems , vol. 17, no. 5, pp. 573–583, 2001.[22] V. A. Davies et al. , “Evaluating mobility models within an ad hoc network,” Master’s thesis, Citeseer, 2000.[23] A. Bar-Noy, I. Kessler, and M. Sidi, “Mobile users: To update or not to update?” in Proceedings of INFOCOM . IEEE, 1994, pp. 570–576.[24] J. Garcia-Luna-Aceves and E. L. Madruga, “A multicast routing protocol for ad-hoc networks,” in INFOCOM’99. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE , vol. 2. IEEE, 1999, pp. 784–792.[25] I. Rubin and C. W. Choi, “Impact of the location area structure on the performance of signaling channels in wireless cellular networks,” IEEE Communications Magazine , vol. 35, no. 2, pp. 108–115, 1997.[26] M. M. Zonoozi and P. Dassanayake, “User mobility modeling and characterization of mobility patterns,” IEEE Journal on selected areas in communications , vol. 15, no. 7, pp. 1239–1252, 1997.