Modeling homogeneous contact distribution of nodes and its application in routing in Mobile Social Networks

نوع مقاله : علمی-پژوهشی

نویسندگان

1 Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Department of Computer Engineering, Urmia University, Urmia, Iran

چکیده

Different types of contact, including contact between node pairs, any-contact of nodes, and contacts of the entire network, are used to characterize social relations in mobile social networks. Different modes of routing, from the point of view of message delivery semantics, encompass unicasting, multicasting, any-casting, and broadcasting. Studies have shown that using probability distribution functions of contact data, which is mainly assumed to be homogeneous for nodes, improves the performance of these networks. However, there exists an important challenge in studies on distributions. A lot of works apply the distribution of one type of contact to other types. Hence in routing applications, it causes to use of the distribution of one type of contact for any mode of routing. This study provides a complete solution to model each type of homogeneous contact data distribution and to use them in different modes of routing. We propose a routing algorithm that uses this new model. Results show that our solution improves the average latency of comparing methods Epidemic, TCCB, and DR about 3.5-times, 30%, and 45%, respectively. It achieves a delivery rate of about 5% and 6%, and average latency about 6% and 8% better than that of DR and TCCB, respectively.

کلیدواژه‌ها

عنوان مقاله [English]

Modeling homogeneous contact distribution of nodes and its application in routing in Mobile Social Networks

نویسندگان [English]

  • S. Moradi 1
  • J. Bagherzadeh Mohasefi 2
  • E. Mahdipour 1
1 Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 Department of Computer Engineering, Urmia University, Urmia, Iran
چکیده [English]

Different types of contact, including contact between node pairs, any-contact of nodes, and contacts of the entire network, are used to characterize social relations in mobile social networks. Different modes of routing, from the point of view of message delivery semantics, encompass unicasting, multicasting, any-casting, and broadcasting. Studies have shown that using probability distribution functions of contact data, which is mainly assumed to be homogeneous for nodes, improves the performance of these networks. However, there exists an important challenge in studies on distributions. A lot of works apply the distribution of one type of contact to other types. Hence in routing applications, it causes to use of the distribution of one type of contact for any mode of routing. This study provides a complete solution to model each type of homogeneous contact data distribution and to use them in different modes of routing. We propose a routing algorithm that uses this new model. Results show that our solution improves the average latency of comparing methods Epidemic, TCCB, and DR about 3.5-times, 30%, and 45%, respectively. It achieves a delivery rate of about 5% and 6%, and average latency about 6% and 8% better than that of DR and TCCB, respectively.
 

کلیدواژه‌ها [English]

  • Mobile social networks
  • Types of contact
  • Homogeneous contact
  • Contact data distribution
  • Modeling of contacts

مراجع

[1] C. Boldrini, M. Conti, A. Passarella, “Performance modeling of opportunistic forwarding under heterogeneous mobility”, Computer Communications, vol. 48, no. 5, pp. 56-70, 2014.
[2] Vinícius F.S. Mota, Felipe D. Cunha, Daniel F. Macedo, José M.S. Nogueira, Antonio A.F. Loureiro, “Protocols, mobility models and tools in opportunistic networks: A survey“, Computer Communications, vol.  48,  no. 1, pp.  5-19, 2014.
[3] Kumaram, Shubham & Srivastava, Shefali & Sharma, Deepak, “Neural network-based routing protocol for opportunistic networks with intelligent water drop optimization: IWDNN”, International Journal of Communication Systems, vol. 33, issue 8, e4368,  2020.
[4] Ciobanu RI, Dobre C, Reina DG, Toral SL, “ A dynamic data routing solution for opportunistic networks”, 14th International Conference on Telecommunications (ConTEL), June  2017, Zagreb, Croatia, pp. 83-90.
[5] Aloizio P. Silva, Scott Burleigh, Celso M. Hirata, Katia Obraczka, “A survey on congestion control for delay and disruption tolerant networks”, Ad Hoc Networks, vol. 25, Part B, no. 15, pp. 480-494, 2015.
[6] فاطمه مشاک، علیرضا کشاورز حداد، شاپور گلبهار حقیقی، « ارتقاء عملکرد شبکه‌های سیار متحمل تأخیر با اولویت‌بندی بسته‌های بافرشده»، مجله مهندسی برق دانشگاه تبریز، جلد 50,  شماره 4, صفحات 1833-1845، 1399.
[7] راضیه نظری، صالح یوسفی، بابک قالب‌ساز جدّی، « ارائه مکانیسمی برای کمینه کردن هزینه‌های اپراتور سلولی در فرآیند برون‌سپاری ترافیک»، مجله مهندسی برق دانشگاه تبریز, جلد 47، شماره 4، صفحات 1745-1757، 1396.
[8] Xia F, Liu L, Li J, Ma J, Vasilakos AV, “Socially aware networking: A survey”, IEEE Systems Journal, vol. 9, issue 3, pp. 904-921, 2015.
[9] S. CC, V. Raychoudhury, G. Marfia, A. Singla, ”A survey of routing and data dissemination in Delay Tolerant Networks", Journal of Network and Computer Applications, vol. 67, no. 9, pp. 128-146, 2016.
[10] P. Hui and J. Crowcroft, “How Small Labels Create Big Improvements”, Fifth Annual IEEE International Conference on Pervasive Computing and Communications Workshops (PerComW'07), March  2007, White Plains NY, USA,  pp. 65-70.
[11] A. Roy, S. Bose, A. Tamaghna, S. Dasbit, “ Social-based energy-aware multicasting in delay tolerant networks”, Journal of Network and Computer Applications, vol. 87, no. 12, pp. 169-184, 2017.
[12] A. Passarella, M. Conti, “Analysis of individual pair and aggregate inter-contact times in heterogeneous opportunistic networks”, IEEE Transactions on Mobile Computing, vol. 12, issue 12, pp.  2483–2495, 2013
[13] W. Gao, Q. Li, B. Zhao, G. Cao, “Multicasting in delay tolerant networks: a social network perspective”, Proceedings of the tenth ACM international symposium on Mobile ad hoc networking and computing ACM (MobiHoc’09), May 2009, LA New Orleans, USA, pp. 299–308.
[14] D. Zhang, H. Ma, and D. Zhao,” Social-Aware Backbone-Based Multicast Routing in Mobile Opportunistic Networks”,  3rd International Conference on Big Data Computing and Communications (BIGCOM), Aug. 2017, Chengdu, China,  pp. 31-38.
[15] N. Gondaliya, D. Kathiriya, “Community Detection Using Inter Contact Time and Social Characteristics Based Single Copy Routing in Delay Tolerant Networks”, International Journal of Ad hoc, Sensor & Ubiquitous Computing, vol. 7, no. 1,pp. 21-35, 2016.
[16] M. M.Tulu, M. E. Mkiramweni, R. Hou, S. Feisso, T. Younas,” Influential nodes selection to enhance data dissemination in mobile social networks: A survey”, Journal of Network and Computer Applications, vol. 169, no. 2, Article 102768,2020.
[17] Q. Xu, Z. Xiong, N. Xiao, Y. Xie, D. Yong, M. Deng, H. Min, “Intelligent distributed routing scheme based on social similarity for mobile social networks”, Future Generation Computer Systems, vol. 69, no. 19, pp. 472-480,2019.
[18] X. Wang, X. Zhong, L. Li, S. Zhang, R. Lu, T. Yang, “TOT: Trust aware opportunistic transmission in cognitive radio Social Internet of Things”, Computer Communications, vol. 162, no. 1,pp. 1-11, 2020.
[19] W. Gao, G. Cao, T. La Porta, and J. Han, “On Exploiting Transient Social Contact Patterns for Data Forwarding in Delay-Tolerant Networks”, IEEE Transactions on Mobile Computing, vol. 12, no. 1, pp. 151-165, 2013.
[20] L. Waltman, NJ. Van Eck, “A smart local moving algorithm for large-scale modularity-based community detection”, The European Physical Journal B, Vol. 86, no. 11, pp. 1-4, 2013.
[21] A. Keränen, J. Ott, T. Kärkkäinen, “The one simulator for dtn protocol evaluation”, in Proceedings of the Second International Conference on Simulation Tools and Techniques (Simutools’9), vol. 55, Brussels, Belgium,  March 2009, pp. 1–10.
[22] A. Vahdat, D. Becker, “Epidemic routing for partially connected ad hoc networks”, Handbook of Systemic Autoimmune Diseases, Elsevier, 2000.
[23] Haggle, Haggle project, 2013. <http://www.haggleproject.org>.
[24] N. Eagle, A. Pentland, and D. Lazer, “Inferring Social Network Structure using Mobile Phone Data”, Proceedings of the National Academy of Sciences, vol. 106, no. 36, pp. 15274-15278, 2009.
[25] R. Akhtar, Y. Shengua, Z. Zhiyu, Z. Khan, I. Memon, S.Rehman, S. Awan, “Content distribution and protocol design issue for mobile social networks: a survey”, EURASIP Journal on Wireless Communications and Networking, 2019, no. 128, 2019.
[26] H. Zhou, V. C. M. Leung, C. Zhu, S. Xu, and J. Fan, “Predicting Temporal Social Contact Patterns for Data Forwarding in Opportunistic Mobile Networks”, in IEEE Transactions on Vehicular Technology, vol. 66, no. 11, pp. 10372-10383, 2017.
[27] A. Passarella, M. Conti, “Analysis of individual pair and aggregate intercontact times in heterogeneous opportunistic networks”, IEEE Transactions on Mobile Computing, vol.  12, no. 12, pp.  2483–2495, 2013.
[28] M. Xiao, J. Wu and L. Huang, “Home-Based Zero-Knowledge Multi-Copy Routing in Mobile Social Networks”, in IEEE Transactions on Parallel and Distributed Systems, vol. 26, no. 5, pp. 1238-1250, 2015.
[29] Moradi. S, Mohasefi. JB, Mahdipour. E, “MSN-CDF: New community detection framework to improve routing in mobile social networks”, International Journal of Communication Systems, 2021;  vol. 34, no. 18 , e4989, 2021.

فایل ها

هم رسانی

ارجاع به این مقاله

آمار