A social-aware routing protocol for opportunistic networks |
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| Affiliation: | 1. Graduate Program in Applied Computing (PPGCA);2. Graduate Program in Electrical and Computer Engineering (CPGEI), Federal University of Technology - Parana (UTFPR). Av. Sete de Setembro, 3165. CEP 80230-901, Curitiba, Brazil;3. Institut National de Recherche en Informatique et en Automatique (INRIA) Saclay - Ile de France. 4, rue Jacques Monod, 91893 Orsay Cedex, France;1. School of Electrical & Electronic Engineering, Biometrics Engineering Research Center (BERC) Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, Republic of Korea;2. Mobile Communications Business, Samsung Electronics Co., Ltd, Maetan 3-dong, Yeongtong-gu, Suwon-si, Gyeonggi-do, 443-742, Republic of Korea;1. Faculty of Computers and Information, Beni-Suef University, Egypt;2. Faculty of Computers and Information, Cairo University, Cairo, Egypt;3. Scientific Research Group in Egypt (SRGE);1. Facultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Mons. Álvaro del Portillo 12455, Las Condes, Santiago, Chile;2. Facultad de Ingeniería, Universidad Diego Portales, Ejército 441, Santiago, Chile;1. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong\n;2. Information Technology Services Centre, The Chinese University of Hong Kong, Shatin, Hong Kong\n;3. American Express, 18850 N. 56th Street, Phoenix, AZ 85054, USA |
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| Abstract: | Understanding nodes mobility is of fundamental importance for data delivery in opportunistic and intermittently connected networks referred to as Delay Tolerant Networks (DTNs). The analysis of such mobility patterns and the understanding of how mobile nodes interact play a critical role when designing new routing protocols for DTNs. The Cultural Greedy Ant (CGrAnt) protocol is a hybrid Swarm Intelligence-based approach designed to address the routing problem in such dynamic and complex environment. CGrAnt is based on: (1) Cultural Algorithms (CA) and Ant Colony Optimization (ACO) and (2) operational metrics that characterize the opportunistic social connectivity between wireless users. The most promising message forwarders are selected via a greedy transition rule based mainly on local information captured from the DTN environment. Whenever global information is available, it can also be used to support decisions. We compare the performance of CGrAnt with Epidemic, PROPHET, and dLife protocols in two different mobility scenarios under varying networking parameters. Results obtained by the ONE simulator show that CGrAnt achieves a higher message delivery and lower message redundancy than the three protocols in both scenarios. The only exception is in one of the scenarios, when messages have a time to live lower than 900 min, where CGrAnt delivers a bit less messages than dLife, although with a lower message redundancy. |
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