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A Comprehensive Survey on Multi-hop Wireless Networks: Milestones, Changing Trends and Concomitant Challenges

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Abstract

With remarkable advancements in the fields of global satellite based navigation systems and wireless communication networks, there is a tremendous increase in the number of mobile device users throughout the globe. Each day, new arduous projects and applications utilizing mobile devices are evolving, with a prime motive to deploy wireless multi-hop networks into the real world. As these networks are, in general, deployed in extreme environmental conditions their performance evaluation is a matter of great concern and demands rigorous analysis. Several models, simulators, testbeds and visualization tools have evolved in the last two decades for analyzing the characteristics of these wireless multi-hop networks. In this paper, first we discuss a number of models and the changing trends of research along with the associated challenges. Then, we discuss several simulators, emulators, testbeds and real world projects implementing such networks. Besides, we also discuss an important aspect of wireless multi-hop networks, i.e., reliability and identify various imperative metrics from the literature for performance evaluation of such dynamic networks.

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References

  1. Conti, M., & Giordano, S. (2014). Mobile ad hoc networking: milestones, challenges, and new research directions. IEEE Communications Magazine, 52, 85–96.

    Article  Google Scholar 

  2. Bettstetter, C. (2001). Smooth is better than sharp: A random mobility model for simulation of wireless networks. In Proceedings of the 4th ACM international workshop on modeling, analysis and simulation of wireless and mobile systems, MSWIM ’01 (pp. 19–27). ACM.

  3. Camp, T., Boleng, J., & Davies, V. (2002). A survey of mobility models for ad hoc network research. Wireless Communications and Mobile Computing, 2(5), 483–502.

    Article  Google Scholar 

  4. Basagni, S., Conti, M., Giordano, S., & Stojmenovic, I. (2004). Mobile ad hoc networking. New York: Wiley.

    Book  Google Scholar 

  5. Agarwal, P. K., Guibas, L. J., Edelsbrunner, H., Erickson, J., Isard, M., Har-Peled, S., et al. (2002). Algorithmic issues in modeling motion. ACM Computing Surveys, 34, 550–572.

    Article  Google Scholar 

  6. Bai, F., & Helmy, A. (2004). A survey of mobility models. Wireless Adhoc Networks. University of Southern California, USA, 206, 147.

    Google Scholar 

  7. Ghouti, L., Sheltami, T. R., & Alutaibi, K. S. (2013). Mobility prediction in mobile ad hoc networks using extreme learning machines. Procedia Computer Science, 19, 305–312.

    Article  Google Scholar 

  8. Díaz, J., Mitsche, D., & Santi, P. (2011). Theoretical aspects of graph models for MANETs. Berlin: Springer.

    Book  MATH  Google Scholar 

  9. Padmavathy, N., & Chaturvedi, S. K. (2015). Reliability evaluation of mobile ad hoc network: With and without mobility considerations. Procedia Computer Science, 46, 1126–1139.

    Article  Google Scholar 

  10. Batabyal, S., & Bhaumik, P. (2015). Mobility models, traces and impact of mobility on opportunistic routing algorithms: A survey. IEEE Communications Surveys Tutorials, 17(3), 1679–1707.

    Article  Google Scholar 

  11. Aschenbruck, N., Ernst, R., Gerhards-Padilla, E., & Schwamborn, M. (2010) Bonnmotion: A mobility scenario generation and analysis tool. In Proceedings of the 3rd international ICST conference on simulation tools and techniques, SIMUTools ’10, (ICST, Brussels, Belgium, Belgium) (pp. 51:1–51:10). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering).

  12. Canu mobility simulation environment (canumobisim). http://canu.informatik.uni-stuttgart.de/mobisim/. Accessed June 07, 2017.

  13. Traffic analysis tools: Corridor simulation—FHWA operations. http://ops.fhwa.dot.gov/trafficanalysistools/corsim.htm/. Accessed June 07, 2017.

  14. Giurlanda, F., Perazzo, P., & Dini, G. (2015). HUMsim: A privacy-oriented human mobility simulator. Cham: Springer.

    Google Scholar 

  15. Bai, F., Sadagopan, N., & Helmy, A. (2003). Important: A framework to systematically analyze the impact of mobility on performance of routing protocols for adhoc networks. In IEEE INFOCOM 2003. Twenty-second annual joint conference of the IEEE computer and communications societies (Vol. 2, pp. 825–835).

  16. Mousavi, S. M., Rabiee, H. R., Moshref, M., & Dabirmoghaddam, A. (2007). Mobisim: A framework for simulation of mobility models in mobile ad-hoc networks. In Third IEEE international conference on wireless and mobile computing, networking and communications (WiMob 2007) (pp. 82–82).

  17. Mobility simulator (mobisim). http://masoudmoshref.com/old/myworks/documentpages/mobility_ simulator.htm. Accessed June 07, 2017.

  18. Ptv vissim. http://vision-traffic.ptvgroup.com/en-us/products/ptv-vissim/. Accessed June 07, 2017.

  19. Quadstone paramics \(\mid\) traffic and pedestrian simulation, analysis and design software. http://www.paramics-online.com/. Accessed June 07, 2017.

  20. DLR institute of transportation systems-sumosimulation of urban mobility. http://www.dlr.de/ts/en/ desktopdefault.aspx/tabid-9883/16931_ read-41000/. Accessed June 07, 2017.

  21. Toilers colorado school of mines code. http://toilers.mines.edu/Public/Code/. misc Accessed June 07, 2017.

  22. Piorkowski, M., Raya, M., Lugo, A. L., Papadimitratos, P., Grossglauser, M., & Hubaux, J.-P. (2008). TranNS: Realistic joint traffic and network simulator for VANETs. ACM SIGMOBILE Mobile Computing and Communications Review, 12(1), 31–33.

    Article  Google Scholar 

  23. Traffic and network simulation environment. http://lca.epfl.ch/projects/trans/. Accessed June 07, 2017.

  24. Transims-resources-transportation model improvement program (TMIP)-planning-FHWA. http://www.fhwa.dot.gov/ planning/tmip/resources/transims/. Accessed June 07, 2017.

  25. Vanetmobisim- newcom \(\mid\) institut eurecom \(\mid\) politecnico di torino. http://vanet.eurecom.fr/. Accessed June 07, 2017.

  26. Bajaj, R., Ranaweera, S. L., & Agrawal, D. P. (2002). GPS: Location-tracking technology. Computer, 35(4), 92–94.

    Article  Google Scholar 

  27. Beidou navigation satellite system system introduction. http://www.beidou.gov.cn/2012/12/14/201212142e8f29c30e0d464c9b34d6828706f81a.html. Accessed June 07, 2017.

  28. ESA, building galileo, european space agency. http://www.esa.int/Our_Activities/Navigation/Galileo/Launching_ Galileo/Building_Galileo. Accessed June 07, 2017.

  29. Constellation status. https://www.glonass-iac.ru/en/GLONASS/. Accessed June 07, 2017.

  30. Satellite navigation-isro. http://www.isro.gov.in/spacecraft/satellite-navigation. Accessed June 07, 2017.

  31. GPS constellation status. https://www.glonass-iac.ru/en/GPS/. Accessed June 07, 2017.

  32. Overview of the quasi-zenith satellite system (QZSS). http://qzss.go.jp/en/overview/services/sv01_what.html. Accessed June 07, 2017.

  33. Kaplan, E., & Hegarty, C. (2005). Understanding GPS: Principles and applications. Norwood: Artech House.

    Google Scholar 

  34. Rossi, L., Walker, J., & Musolesi, M. (2015). Spatio-temporal techniques for user identification by means of GPS mobility data. EPJ Data Science, 4(1), 11.

    Article  Google Scholar 

  35. Longley, P. (2005). Geographic information systems and science. Chichester: Wiley.

    Google Scholar 

  36. Kim, M., Kotz, D., & Kim, S. (2006). Extracting a mobility model from real user traces. INFOCOM, 6, 1–13.

    Google Scholar 

  37. Zignani, M., & Gaito, S. (2010). Extracting human mobility patterns from GPS-based traces. In Wireless Days (WD), 2010 IFIP (pp. 1–5). IEEE.

  38. Kang, J. H., Welbourne, W., Stewart, B., & Borriello, G. (2004). Extracting places from traces of locations. In Proceedings of the 2nd ACM international workshop on Wireless mobile applications and services on WLAN hotspots (pp. 110–118). ACM.

  39. Ashbrook, D., & Starner, T. (2003). Using GPS to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing, 7(5), 275–286.

    Article  Google Scholar 

  40. Azevedo, T. S., Bezerra, R. L., Campos, C. A., & de Moraes, L. F. (2009). An analysis of human mobility using real traces. In Wireless communications and networking conference, 2009. WCNC 2009 (pp. 1–6). IEEE.

  41. Whitbeck, J., de Amorim, M. D., Conan, V., Ammar, M., & Zegura, E. (2011). From encounters to plausible mobility. Pervasive and Mobile Computing, 7(2), 206–222.

    Article  Google Scholar 

  42. Raleigh, C., Linke, A., Hegre, H., & Karlsen, J. (2010). Introducing acled: An armed conflict location and event dataset special data feature. Journal of Peace Research, 47(5), 651–660.

    Article  Google Scholar 

  43. Pettersson, T., & Wallensteen, P. (2015). Armed conflicts, 1946–2014. Journal of Peace Research, 52(4), 536–550.

    Article  Google Scholar 

  44. Aschenbruck, N., Munjal, A., & Camp, T. (2011). Trace-based mobility modeling for multi-hop wireless networks. Computer Communications, 34(6), 704–714.

    Article  Google Scholar 

  45. Lambla, A. (2006). The exploratorium’s invisible dynamics project: Environmental research as artistic process. Leonardo, 39(4), 383–385.

    Article  Google Scholar 

  46. SocioPatterns. http://www.sociopatterns.org/. Accessed June 07, 2017.

  47. Crawdad. http://www.crawdad.org/about.html. Accessed June 07, 2017.

  48. Home page-umass trace repository. Accessed June 07, 2017.

  49. Network repository—the first interactive data repository with visual analytics for understanding data easily. http://networkrepository.com/. Accessed June 07, 2017.

  50. Data sets \(\mid\) foundations of data and visual analytics. http://fodava.gatech.edu/visual-data-analytics-data-sets. Accessed June 07, 2017.

  51. SNAP: Stanford network analysis platform. https://snap.stanford.edu/snap/. Accessed June 07, 2017.

  52. Uci machine learning repository. Accessed June 07, 2017.

  53. Helmy, A. (2015). Mobilib. http://www.cise.ufl.edu/helmy/MobiLib.htm#traces. Accessed June 07, 2017.

  54. Baudic, G., Pérennou, T., & Lochin, E. (2016). Following the right path: Using traces for the study of dtns. Computer Communications, 88, 25–33.

    Article  Google Scholar 

  55. Indoor user movement prediction from rss data data set. https://archive.ics.uci.edu/ml/datasets/Indoor+User+Movement+Prediction+from+RSS+data/. Accessed October 27, 2017.

  56. Bacciu, D., Barsocchi, P., Chessa, S., Gallicchio, C., & Micheli, A. (2014). An experimental characterization of reservoir computing in ambient assisted living applications. Neural Computing and Applications, 24(6), 1451–1464.

    Article  Google Scholar 

  57. Celltrace. http://www.celplan.com/products/celltrace.asp/. Accessed October 27, 2017.

  58. Winprop - indoor and campus. http://www.altairhyperworks.com/product/FEKO/WinProp---Indoor-and-Campus/. Accessed October 27, 2017.

  59. Silva, A. P., Hilário, M. R., Hirata, C. M., & Obraczka, K. (2015). A percolation-based approach to model DTN congestion control. In 2015 IEEE 12th international conference on mobile ad hoc and sensor systems (MASS) (pp. 100–108). IEEE.

  60. Chen, W. (2014). Explosive percolation in random networks. Berlin: Springer.

    Book  MATH  Google Scholar 

  61. Amor, S. B., Bui, M., & Lavallée, I. (2010). Optimizing mobile networks connectivity and routing using percolation theory and epidemic algorithms. In IICS (pp. 63–78), Citeseer.

  62. Shen, C.-C., Huang, Z., & Jaikaeo, C. (2006). Directional broadcast for mobile ad hoc networks with percolation theory. IEEE Transactions on Mobile Computing, 5(4), 317–332.

    Article  Google Scholar 

  63. Li, D., Zhang, Q., Zio, E., Havlin, S., & Kang, R. (2015). Network reliability analysis based on percolation theory. Reliability Engineering & System Safety, 142, 556–562.

    Article  Google Scholar 

  64. Avula, M., Yoo, S.-M., & Park, S. (2012). Constructing minimum connected dominating set in mobile ad hoc networks. International Journal on Applications of Graph Theory in Wireless Ad Hoc Networks and Sensor Networks, 4(2/3), 15.

    Article  Google Scholar 

  65. Erciyes, K., Dagdeviren, O., & Cokuslu, D. (2006). Modeling and simulation of wireless sensor and mobile ad hoc networks. In International conference on modeling and simulation.

  66. Raj, A., Saha, D., & Dasgupta, P. (2010). A cost-efficient algorithm for finding connected dominating sets in static wireless ad hoc networks with obstacles. In 2010 IEEE 4th international symposium on advanced networks and telecommunication systems (ANTS) (pp. 73–75). IEEE.

  67. Sharmila, C., & Amalanathan, G. (2016). Construction of pipelined strategic connected dominating set for mobile ad hoc networks. CIT. Journal of Computing and Information Technology, 24(2), 121–132.

    Article  Google Scholar 

  68. Watts, D. J. (1999). Small worlds: the dynamics of networks between order and randomness. Princeton: Princeton University Press.

    MATH  Google Scholar 

  69. Hekmat, R. (2006). Ad-hoc networks: Fundamental properties and network topologies. Berlin: Springer.

    MATH  Google Scholar 

  70. Penrose, M. (2003). Random geometric graphs, No. 5. Oxford: Oxford University Press.

    Book  MATH  Google Scholar 

  71. Chaturvedi, S. K., & Padmavathy, N. (2013). The influence of scenario metrics on network reliability of mobile ad hoc network. International Journal of Performability Engineering, 9(1), 61–74.

    Google Scholar 

  72. Bettstetter, C. (2002). On the minimum node degree and connectivity of a wireless multihop network. In Proceedings of the 3rd ACM international symposium on Mobile ad hoc networking & computing (pp. 80–91). ACM.

  73. Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.

    Article  Google Scholar 

  74. Ferreira, A. (2003). Building a reference combinatorial model for dynamic networks: Initial results in evolving graphs. Ph.D. thesis, INRIA.

  75. Casteigts, A., Flocchini, P., Quattrociocchi, W., & Santoro, N. (2012). Time-varying graphs and dynamic networks. International Journal of Parallel, Emergent and Distributed Systems, 27(5), 387–408.

    Article  Google Scholar 

  76. Ferreira, A. (2002). On models and algorithms for dynamic communication networks: The case for evolving graphs. In Proceedings of ALGOTEL

  77. Eiza, M. H., & Ni, Q. (2013). An evolving graph-based reliable routing scheme for vanets. IEEE Transactions on Vehicular Technology, 62(4), 1493–1504.

    Article  Google Scholar 

  78. Ferreira, A. (2004). Building a reference combinatorial model for manets. IEEE Network, 18(5), 24–29.

    Article  MathSciNet  Google Scholar 

  79. Holme, P. (2015). Modern temporal network theory: A colloquium. The European Physical Journal B, 88(9), 1–30.

    Article  Google Scholar 

  80. Kostakos, V. (2009). Temporal graphs. Physica A: Statistical Mechanics and its Applications, 388(6), 1007–1023.

    Article  MathSciNet  Google Scholar 

  81. Kolar, M., Song, L., Ahmed, A., & Xing, E. P. (2010). Estimating time-varying networks. The Annals of Applied Statistics, 4(1), 94–123.

    Article  MathSciNet  MATH  Google Scholar 

  82. Huang, M. (2012). Topology design for time-varying networks. Ph.D. thesis, The University of North Carolina at Charlotte.

  83. Dehmer, M., Emmert-Streib, F., & Pickl, S. (2015). Computational network theory: Theoretical foundations and applications (Vol. 5). Wiley.

  84. Holme, P., & Saramäki, E. J. (2013). Temporal networks. Berlin: Springer. https://doi.org/10.1007/978-3-642-36461-7.

    Book  Google Scholar 

  85. Zschaler, G. (2012). Adaptive-network models of collective dynamics. The European Physical Journal Special Topics, 211(1), 1–101.

    Article  Google Scholar 

  86. Afrasiabi Rad, A. (2016). Social network analysis and time varying graphs. Ph.D. thesis, Université d’Ottawa/University of Ottawa.

  87. Cai, X., Sha, D., & Wong, C. (2007). Time-varying network optimization (Vol. 103). Berlin: Springer.

    MATH  Google Scholar 

  88. Lentz, H. (2013). Paths for epidemics in static and temporal networks. Ph.D. thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I.

  89. El Alaoui, S. (2015). Routing optimization in interplanetary networks. Master's Thesis, University of Nebraska-Lincoln. http://digitalcommons.unl.edu/computerscidiss/94.

  90. Leskovec, J., Kleinberg, J., & Faloutsos, C. (2007). Graph evolution: Densification and shrinking diameters. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(1), 2.

    Article  Google Scholar 

  91. Acer, U. G., Drineas, P., & Abouzeid, A. A. (2011). Connectivity in time-graphs. Pervasive and Mobile Computing, 7(2), 160–171.

    Article  Google Scholar 

  92. Tang, J. K. (2012). Temporal network metrics and their application to real world networks. Ph.D. thesis, University of Cambridge.

  93. Hekmat, R., & Van Mieghem, P. (2006). Connectivity in wireless ad-hoc networks with a log-normal radio model. Mobile Networks and Applications, 11(3), 351–360.

    Article  Google Scholar 

  94. Hekmat, R., & Van Mieghem, P. (2008). Interference power statistics in ad-hoc and sensor networks. Wireless Networks, 14(5), 591–599.

    Article  Google Scholar 

  95. Peiravi, A., & Kheibari, H. T. (2008). Fast estimation of network reliability using modified Manhattan distance in mobile wireless networks. Journal of Applied Sciences, 8(23), 4303–4311.

    Article  MATH  Google Scholar 

  96. Padmavathy, N., & Chaturvedi, S. K. (2013). Evaluation of mobile ad hoc network reliability using propagation-based link reliability model. Reliability Engineering & System Safety, 115, 1–9.

    Article  Google Scholar 

  97. Coll-Perales, B., Gozalvez, J., Lazaro, O., & Sepulcre, M. (2015). Opportunistic multihopping for energy efficiency: Opportunistic multihop cellular networking for energy-efficient provision of mobile delay-tolerant services. IEEE Vehicular Technology Magazine, 10(2), 93–101.

    Article  Google Scholar 

  98. Coll-Perales, B., Gozálvez, J., & Sepulcre, M. (2015). Empirical models of the communications performance of multi-hop cellular networks using D2D. Journal of Network and Computer Applications, 58, 60–72.

    Article  Google Scholar 

  99. Frodigh, M., Johansson, P., & Larsson, P. (2000). Wireless ad hoc networking: The art of networking without a network. Ericsson Review, 4(4), 249.

    Google Scholar 

  100. Kawamoto, Y., Nishiyama, H., & Kato, N. (2013). Toward terminal-to-terminal communication networks: A hybrid MANET and DTN approach. In 2013 IEEE 18th international workshop on computer aided modeling and design of communication links and networks (CAMAD) (pp. 228–232). IEEE.

  101. Gottumukkala, R. N., Venna, S. R., & Raghavan, V. (2015). Visual analytics of time evolving large-scale graphs. IEEE Intelligence Information Bulletin, 16(1), 10–16.

    Google Scholar 

  102. Sazama, P. J. (2015). An overview of visualizing dynamic graphs. https://www.cs.umd.edu/sites/default/files/scholarly_papers/Sazama.pdf.

  103. Chapanond, A., Krishnamoorthy, M. S., Prabhu, G., & Punin, J. (2010). Evolving graph representation and visualization. arXiv preprint arXiv:1006.4608.

  104. Beck, F., Burch, M., Diehl, S., & Weiskopf, D. (2014). The state of the art in visualizing dynamic graphs. In EuroVis STAR (Vol. 2).

  105. Ma, K.-L., & Muelder, C. W. (2013). Large-scale graph visualization and analytics. Computer, 46(7), 39–46.

    Article  Google Scholar 

  106. Papanikos, N., Akestoridis, D. G., & Papapetrou, E. (2015). Adyton: A network simulator for opportunistic networks. https://github.com/npapanik/Adyton. Accessed June 07, 2017.

  107. Goldman, A., Floriano, P., & Ferreira, A. (2012). A tool for obtaining information on DTN traces. In 4th extreme conference on communication (ExtremeCom 2012) (p. 6).

  108. Bastian, M., Heymann, S., Jacomy, M., et al. (2009). Gephi: An open source software for exploring and manipulating networks. ICWSM, 8, 361–362.

    Google Scholar 

  109. Zeng, X., Bagrodia, R., & Gerla, M. (1998). Glomosim: A library for parallel simulation of large-scale wireless networks. In Twelfth workshop on parallel and distributed simulation, 1998. PADS 98. Proceedings (pp. 154–161). IEEE.

  110. Duarte, P., Macedo, J., Costa, A. D., Nicolau, M. J., & Santos, A. (2015). A probabilistic interest forwarding protocol for named data delay tolerant networks. In International conference on ad hoc networks (pp. 94–107). Springer.

  111. Welcome to icones documentation!icone 1.0 documentation. http://marco.uminho.pt/projects/ICONE/. Accessed June 07, 2017.

  112. Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5), 1–9.

    Google Scholar 

  113. Barr, R., Haas, Z. J., & Van Renesse, R. (2004). Jist: Embedding simulation time into a virtual machine. In EuroSim congress on modelling and simulation.

  114. Schoch, E., Feiri, M., Kargl, F., & Weber, M. (2008). Simulation of ad hoc networks: ns-2 compared to jist/swans. In Proceedings of the 1st international conference on Simulation tools and techniques for communications, networks and systems & workshops (p. 36). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering).

  115. Varga, A., & Hornig, R. (2008). An overview of the OMNeT++ simulation environment. In Proceedings of the 1st international conference on Simulation tools and techniques for communications, networks and systems & workshops (p. 60). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering).

  116. Sobeih, A., Chen, W.-P., Hou, J. C., Kung, L.-C., Li, N., Lim, H., Tyan, H.-Y., & Zhang, H. (2005). J-sim: A simulation environment for wireless sensor networks. In Proceedings of the 38th annual symposium on simulation (pp. 175–187). IEEE Computer Society.

  117. Kumonote. http://title-mode.sourceforge.net/kumonote/Kumonote.html. Accessed June 07, 2017.

  118. Frédéric guinand: Sarah project. http://litis.univ-lehavre.fr/guinand/Researches/Projects/sarah.html. Accessed June 07, 2017.

  119. Hogie, L., Guinand, F., & Bouvry, P. (2006). The madhoc metropolitan adhoc network simulator. Esch-sur-Alzette: Rapport technique, University of Luxembourg.

    Google Scholar 

  120. Matlab-mathworks-mathworks india. http://in.mathworks.com/products/matlab/. Accessed June 07, 2017.

  121. Netminer-social network analysis software. http://www.netminer.com/product/overview.do. Accessed June 07, 2017.

  122. Issariyakul, T., & Hossain, E. (2011). Introduction to network simulator NS2. Berlin: Springer.

    Google Scholar 

  123. Henderson, T. R., Lacage, M., Riley, G. F., Dowell, C., & Kopena, J. (2008). Network simulations with the ns-3 simulator. In SIGCOMM demonstration (Vol. 14).

  124. OMNeT++ Discrete Event Simulator-Home. https://omnetpp.org/. Accessed June 07, 2017.

  125. The one. https://www.netlab.tkk.fi/tutkimus/dtn/theone/. Accessed June 07, 2017.

  126. Keränen, A., Ott, J., & Kärkkäinen, T. (2009). The one simulator for DTN protocol evaluation. In Proceedings of the 2nd international conference on simulation tools and techniques (p. 55). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering).

  127. OPNET TechnologiesNetwork Simulator \(\mid\) Riverbed. http://www.riverbed.com/in/products/steelcentral/opnet.html? redirect=opnet. Accessed June 07, 2017.

  128. Batagelj, V., & Mrvar, A. (1998). Pajek-program for large network analysis. Connections, 21(2), 47–57.

    MATH  Google Scholar 

  129. De Nooy, W., Mrvar, A., & Batagelj, V. (2011). Exploratory social network analysis with Pajek (Vol. 27). Cambridge: Cambridge University Press.

    Book  Google Scholar 

  130. Program package pajek/pajekxxl. http://mrvar.fdv.uni-lj.si/pajek/. Accessed June 07, 2017.

  131. Mrvar, A., & Batagelj, V. (2016). Analysis and visualization of large networks with program package pajek. Complex Adaptive Systems Modeling, 4(1), 1–8.

    Article  MATH  Google Scholar 

  132. Ptolemy II home page. http://ptolemy.eecs.berkeley.edu/ptolemyII/. Accessed June 07, 2017.

  133. Ptolemaeus, C. (2014). System design, modeling, and simulation: using Ptolemy II. Ptolemy.org Berkeley.

  134. Brooks, C., Lee, E. A., Liu, X., Zhao, Y., Zheng, H., Bhattacharyya, S. S., et al. (2005). Ptolemy II-heterogeneous concurrent modeling and design in Java (Vol. 1: Introduction to ptolemy II), Memorandum UCB/ERL M05/21, EECS, University of California, Berkeley. https://ptolemy.eecs.berkeley.edu/papers/05/ptIIdesign1-intro/ptIIdesign1-intro.pdf.

  135. Simulator, Q. N. (2011). Scalable network technologies. Inc.[Online]. Available www.qualnet.com.

  136. Bender-deMoll, S., & McFarland, D. A. (2006). The art and science of dynamic network visualization. Journal of Social Structure, 7(2), 1–38.

    Google Scholar 

  137. SoNIA (social network image animator). https://sourceforge.net/projects/sonia/. Accessed June 07, 2017.

  138. Scalable simulation framework. http://www.ssfnet.org/homePage.html. Accessed June 07, 2017.

  139. Kropff, M., Krop, T., Hollick, M., Mogre, P. S., & Steinmetz, R. (2006). A survey on real world and emulation testbeds for mobile ad hoc networks. 2nd international conference on testbeds and research infrastructures for the development of networks and communities, 2006. TRIDENTCOM 2006 (pp. 448–453).

  140. Kiess, W., & Mauve, M. (2007). A survey on real-world implementations of mobile ad-hoc networks. Ad Hoc Networks, 5(3), 324–339.

    Article  Google Scholar 

  141. Ivanic, N., Rivera, B., & Adamson, B. (2009). Mobile ad hoc network emulation environment. In Military communications conference, 2009. MILCOM 2009 (pp. 1–6). IEEE.

  142. Patel, K. N., et al. (2015). A survey on emulation testbeds for mobile ad-hoc networks. Procedia Computer Science, 45, 581–591.

    Article  Google Scholar 

  143. Nordstrom, E., Gunningberg, P., & Lundgren, H. (2005). A testbed and methodology for experimental evaluation of wireless mobile ad hoc networks. In First international conference on testbeds and research infrastructures for the development of networks and communities, 2005. Tridentcom 2005 (pp. 100–109). IEEE.

  144. Ramanathan, R., & Hain, R. (2000). An ad hoc wireless testbed for scalable, adaptive QoS support. In Wireless communications and networking confernce, 2000. WCNC. 2000 IEEE (Vol. 3, pp. 998–1002). IEEE.

  145. Sanghani, S., Brown, T. X., Bhandare, S., & Doshi, S. (2003) Ewant: The emulated wireless ad hoc network testbed. In Wireless communications and networking, 2003. WCNC 2003. 2003 IEEE (Vol. 3, pp. 1844–1849). IEEE.

  146. Hui, P., & Crowcroft, J. (2007) How small labels create big improvements. In Fifth annual IEEE international conference on pervasive computing and communications workshops, 2007. PerCom Workshops’ 07 (pp. 65–70). IEEE.

  147. Hui, P., Crowcroft, J., & Yoneki, E. (2011). Bubble rap: Social-based forwarding in delay-tolerant networks. IEEE Transactions on Mobile Computing, 10(11), 1576–1589.

    Article  Google Scholar 

  148. Flynn, J., Tewari, H., & O’Mahony, D. (2001). Jemu: A real time emulation system for mobile ad hoc networks. In Proceedings of the first joint IEI/IEE symposium on telecommunications systems research (pp. 262–267).

  149. He, R., Yuan, M., Hu, J., Zhang, H., Ma, J., et al. (2003). A real-time scalable and dynamical test system for manet. In 14th IEEE proceedings on personal, indoor and mobile radio communications, 2003. PIMRC 2003 (Vol. 2, pp. 1644–1648). IEEE.

  150. Matthes, M., Biehl, H., Lauer, M., & Drobnik, O. (2005). Massive: An emulation environment for mobile ad-hoc networks. In Second annual conference on wireless on-demand network systems and services, 2005. WONS 2005 (pp. 54–59). IEEE.

  151. De, P., Raniwala, A., Sharma, S., & Chiueh, T.-C. (2005) Mint: A miniaturized network testbed for mobile wireless research. In INFOCOM 2005. 24th annual joint conference of the IEEE computer and communications societies. Proceedings IEEE (Vol. 4, pp. 2731–2742). IEEE.

  152. Raychaudhuri, D., Seskar, I., Ott, M., Ganu, S., Ramachandran, K., Kremo, H., et al. (2005). Overview of the orbit radio grid testbed for evaluation of next-generation wireless network protocols. In Wireless communications and networking conference, 2005 IEEE (Vol. 3, pp. 1664–1669). IEEE.

  153. Beyer, D. A. (1990). Accomplishments of the DARPA SURAN Program. In Military communications conference, 1990. MILCOM’90, Conference Record, A New Era. 1990 IEEE (pp. 855–862). IEEE.

  154. Little, M. (2005). Tealab: A testbed for ad hoc networking security research. In Military communications conference, 2005. MILCOM 2005. IEEE (pp. 936–942). IEEE.

  155. Johnson, D., Stack, T., Fish, R., Flickinger, D., Ricci, R., & Lepreau, J. (2006). Truemobile: A mobile robotic wireless and sensor network testbed. In The 25th annual joint conference of the IEEE computer and communications societies. IEEE Computer Society.

  156. BEAR: Berkeley aerobot research. http://robotics.eecs.berkeley.edu/bear/testbeds.html. Accessed June 07, 2017.

  157. Giordano, E., Tomatis, A., Ghosh, A., Pau, G., & Gerla, M. (2008). C-vet an open research platform for VANETs: Evaluation of peer to peer applications in vehicular networks. In IEEE 68th vehicular technology conference, 2008. VTC 2008-Fall (pp. 1–2) IEEE.

  158. Gerla, M., Weng, J.-T., Giordano, E., & Pau, G. (2012). Vehicular testbeds-model validation before large scale deployment. Journal of Communication, 7(6), 451–457.

    Google Scholar 

  159. Eriksson, J., Balakrishnan, H., & Madden, S. (2008). Cabernet: vehicular content delivery using WiFi. In Proceedings of the 14th ACM international conference on Mobile computing and networking (pp. 199–210). ACM.

  160. Ott, J., & Kutscher, D. (2005). A disconnection-tolerant transport for drive-thru internet environments. In INFOCOM 2005. 24th annual joint conference of the IEEE computer and communications societies. Proceedings IEEE (Vol. 3, pp. 1849–1862). IEEE.

  161. Drive-thru motivation. http://www.drive-thru-internet.org/motivation.html. Accessed June 07, 2017.

  162. El Alaoui, S., Palusa, S., & Ramamurthy, B. (2015). The interplanetary internet implemented on the geni testbed. In Global communications conference (GLOBECOM), 2015 IEEE (pp. 1–6). IEEE.

  163. Global environment for networking innovations (geni): Establishing the geni project office (gpo) (geni/gpo) nsf06601. https://www.nsf.gov/pubs/2006/nsf06601/nsf06601.htm. Accessed June 07, 2017.

  164. Ameixieira, C., Cardote, A., Neves, F., Meireles, R., Sargento, S., Coelho, L., et al. (2014). Harbornet: A real-world testbed for vehicular networks. IEEE Communications Magazine, 52(9), 108–114.

    Article  Google Scholar 

  165. Reich, J., Misra, V., & Rubenstein, D. (2008). Roomba madnet: A mobile ad-hoc delay tolerant network testbed. ACM SIGMOBILE Mobile Computing and Communications Review, 12(1), 68–70.

    Article  Google Scholar 

  166. Beuran, R., Miwa, S., & Shinoda, Y. (2013). Network emulation testbed for DTN applications and protocols. In 2013 IEEE conference on computer communications workshops (INFOCOM WKSHPS) (pp. 151–156). IEEE.

  167. How, J. P., BEHIHKE, B., Frank, A., Dale, D., & Vian, J. (2008). Real-time indoor autonomous vehicle test environment. IEEE Control Systems, 28(2), 51–64.

    Article  MathSciNet  MATH  Google Scholar 

  168. SUAAVE. http://web4.cs.ucl.ac.uk/research/suaave/. Accessed June 07, 2017.

  169. Patterson, T., McClean, S., Morrow, P., Parr, G., & Luo, C. (2014). Timely autonomous identification of uav safe landing zones. Image and Vision Computing, 32(9), 568–578.

    Article  Google Scholar 

  170. Georgia tech uav research facility. http://www.uavrf.gatech.edu/. Accessed June 07, 2017.

  171. Paula, M. C., Rodrigues, J. J., Dias, J. A., Isento, J. N., & Vinel, A. (2015). Performance evaluation of a real vehicular delay-tolerant network testbed. International Journal of Distributed Sensor Networks, 11(3), 219641.

    Article  Google Scholar 

  172. Paula, M. C., Rodrigues, J. J., Dias, J. A., Isento, J. N., & Vinel, A. (2012) Deployment of a real vehicular delay-tolerant network testbed. In 2012 12th international conference on ITS telecommunications (ITST) (pp. 103–107). IEEE.

  173. Mobile apps. http://www.redcross.org/get-help/prepare- for-emergencies/mobile-apps. Accessed June 07, 2017.

  174. Google crisis response. https://www.google.org/crisisresponse/about/. Accessed June 07, 2017.

  175. Hossmann, T., Carta, P., Schatzmann, D., Legendre, F., Gunningberg, P., & Rohner, C. (2011) Twitter in disaster mode: Security architecture. In Proceedings of the special workshop on internet and disasters (p. 7). ACM.

  176. CHIANTI. http://www.chianti-ict.org/chianti/public/chianti-D5.2.pdf. Accessed June 07, 2017.

  177. CHIANTI. http://www.chianti-ict.org/home/. Accessed June 07, 2017.

  178. Liu, M., Johnson, T., Agarwal, R., Efrat, A., Richa, A., & Coutinho, M. M. (2015). Robust data mule networks with remote healthcare applications in the amazon region: A fountain code approach. In 2015 17th international conference on E-health networking, application & services (HealthCom) (pp. 546–551). IEEE.

  179. Coutinho, M. M., Efrat, A., Johnson, T., Richa, A., & Liu, M. (2014). Healthcare supported by data mule networks in remote communities of the amazon region. International Scholarly Research Notices, 2014, 1–8.

    Article  Google Scholar 

  180. Coutinho, M. M., Moreira, T., Silva, E., Efrat, A., & Johnson, T. (2011). A new proposal of data mule network focused on amazon riverine population. In Proceedings of the 3rd extreme conference on communication: The amazon expedition (p. 10). ACM.

  181. What works: First mile solutions daknet takes rural communities online. http://www.firstmilesolutions.com/documents/FMS_Case_Study.pdf. Accessed June 07, 2017.

  182. Pentland, A., Fletcher, R., & Hasson, A. (2004). Daknet: Rethinking connectivity in developing nations. Computer, 37(1), 78–83.

    Article  Google Scholar 

  183. HAGGLE. http://cordis.europa.eu/pub/fp7/ict/docs/fire/projects-haggle_en.pdf. Accessed June 07, 2017.

  184. Exploratorium \(\mid\) invisible dynamics \(\mid\) cabspotting. http://www.exploratorium.edu/id/cab.html. Accessed June 07, 2017.

  185. Piorkowski, M., Sarafijanovic-Djukic, N., & Grossglauser, M. (2009). CRAWDAD dataset epfl/mobility (v. 2009-02-24).

  186. Eagle, N., & Pentland, A. S. (2006). Reality mining: Sensing complex social systems. Personal and Ubiquitous Computing, 10(4), 255–268.

    Article  Google Scholar 

  187. Lindgren, A., Doria, A., Lindblom, J., & Ek, M. (2008). Networking in the land of northern lights: Two years of experiences from DTN system deployments. In Proceedings of the 2008 ACM workshop on Wireless networks and systems for developing regions (pp. 1–8). ACM.

  188. Farrell, S., McMahon, A., Meehan, E., Weber, S., & Hartnett, K. (2011). Report on an arctic summer DTN trial. Wireless Networks, 17(5), 1127–1156.

    Article  Google Scholar 

  189. Project ANR SARAH. http://www-valoria.univ-ubs.fr/SARAH/presentation.shtml. Accessed June 07, 2017.

  190. McDonald, P., Geraghty, D., Humphreys, I., Farrell, S., & Cahill, V. (2007). Sensor network with delay tolerance (SeNDT). In Proceedings of 16th international conference on computer communications and networks, 2007. ICCCN 2007 (pp. 1333–1338). IEEE.

  191. SeNDT home. https://down.dsg.cs.tcd.ie/sendt/. Accessed June 07, 2017.

  192. Hubaux, J.-P., Gross, T., Le Boudec, J.-Y., & Vetterli, M. (2001). Toward self-organized mobile ad hoc networks: The terminodes project. IEEE Communications Magazine, 39(1), 118–124.

    Article  Google Scholar 

  193. Hubaux, J.-P., Le Boudec, J.-Y., Giordano, S., & Hamdi, M. (1999). The terminode project: Towards mobile ad-hoc wans. In 1999 IEEE international workshop on mobile multimedia communications, 1999 (MoMuC’99) (pp. 124–128). IEEE.

  194. About tier \(\mid\) technology and infrastructure for emerging regions. http://tier.cs.berkeley.edu/drupal/about. Accessed June 07, 2017.

  195. Burgess, J., Gallagher, B., Jensen, D., Levine, B. N. (2006). Maxprop: Routing for vehicle-based disruption-tolerant networks. In Proceedings IEEE INFOCOM 2006. 25TH IEEE international conference on computer communications (pp. 1–11).

  196. Balasubramanian, A., Zhou, Y., Croft, W. B., Levine, B. N., & Venkataramani, A. (2007). Web search from a bus. In Proceedings of the second ACM workshop on challenged networks (pp. 59–66). ACM.

  197. Burgess, J., et. al. (2008). CRAWDAD dataset umass/diesel (v. 2008-09-14).

  198. Caiti, A., Husoy, T., Jesus, S., Karasalo, I., Massimelli, R., Munafò, A., et al. (2012). Underwater acoustic networks: The Fp7 uan project. IFAC Proceedings Volumes, 45(27), 220–225.

    Article  Google Scholar 

  199. Juang, P., Oki, H., Wang, Y., Martonosi, M., Peh, L. S., & Rubenstein, D. (2002). Energy-efficient computing for wildlife tracking: Design tradeoffs and early experiences with ZebraNet. SIGARCH Computer Architecture News, 30, 96–107.

    Article  Google Scholar 

  200. Liu, T., Sadler, C. M., Zhang, P., & Martonosi, M. (2004). Implementing software on resource-constrained mobile sensors: Experiences with impala and ZebraNet. In Proceedings of the 2nd international conference on mobile systems, applications, and services (pp. 256–269). ACM.

  201. Bobbio, A., Ferraris, C., & Terruggia, R. (2006). New challenges in network reliability analysis. CNIP, 6, 554–564.

    Google Scholar 

  202. Fratta, L., & Montanari, U. (1973). A boolean algebra method for computing the terminal reliability in a communication network. IEEE Transactions on Circuit Theory, 20(3), 203–211.

    Article  MathSciNet  Google Scholar 

  203. Chaturvedi, S., & Misra, K. (2002). A hybrid method to evaluate reliability of complex networks. International Journal of Quality & Reliability Management, 19(8/9), 1098–1112.

    Article  Google Scholar 

  204. Torrieri, D. (1994). Calculation of node-pair reliability in large networks with unreliable nodes. IEEE Transactions on Reliability, 43(3), 375–377, 382.

  205. Meena, K., Vasanthi, T., Rajeswari, M., & UmamageswarI, P. (2016). Reliability analysis of MANET with RCFP: Reliable cluster forming protocol. International Journal of Applied Engineering Research, 11(1), 440–447.

    Google Scholar 

  206. Cook, J. L., Arsenal, P., & Ramirez-Marquez, J. E. (2007). Recent research on the reliability analysis methods for mobile ad-hoc networks. In Systems research forum (Vol. 2, No. 01, pp. 35–41). World Scientific Publishing Company.

  207. Cook, J. L., & Ramirez-Marquez, J. E. (2007). Reliability of capacitated mobile ad hoc networks. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 221(4), 307–318.

    Google Scholar 

  208. Padmavathy, N., & Chaturvedi, S. K. (2015). Reliability evaluation of capacitated mobile ad hoc network using log-normal shadowing propagation model. International Journal of Reliability and Safety, 9(1), 70–89.

    Article  Google Scholar 

  209. Cook, J. L., & Ramirez-Marquez, J. E. (2008). Mobility and reliability modeling for a mobile ad hoc network. IIE Transactions, 41(1), 23–31.

    Article  Google Scholar 

  210. Soh, S., Lau, W., Rai, S., & Brooks, R. R. (2007). On computing reliability and expected hop count of wireless communication networks. International Journal of Performability Engineering, 3(2), 267–279.

    Google Scholar 

  211. Cook, J. L., & Ramirez-Marquez, J. E. (2007). Two-terminal reliability analyses for a mobile ad hoc wireless network. Reliability Engineering & System Safety, 92(6), 821–829.

    Article  Google Scholar 

  212. Meena, K. S., & Vasanthi, T. (2016). Reliability design for a manet with cluster-head gateway routing protocol. Communications in Statistics-Theory and Methods, 45(13), 3904–3918.

    Article  MathSciNet  MATH  Google Scholar 

  213. Meena, K. S., & Vasanthi, T. (2016). Optimum reliability analysis of mobile adhoc networks using universal generating function under limited delivery time and cost. Proceedings of International Conference on Information Engineering, Management and Security, 1, 13–17.

    Google Scholar 

  214. Choudhary, A., Roy, O., & Tuithung, T. (2015). Reliability evaluation of mobile ad-hoc networks. International Journal of Future Generation Communication and Networking, 8(5), 207–220.

    Article  Google Scholar 

  215. Dimitar, T., Sonja, F., Bekim, C., & Aksenti, G. (2004). Link reliability analysis in ad hoc networks. In Proceedings of XII telekomunikacioni forum TELFOR.

  216. Chowdhury, C., & Neogy, S. (2011). Reliability estimate of mobile agent system for QoS MANET applications. In 2011 Proceedings—Annual reliability and maintainability symposium (pp. 1–6).

  217. Singh, M. M., Baruah, M., & Mandal, J. K. (2014). Reliability computation of mobile ad-hoc network using logistic regression. In 2014 Eleventh international conference on wireless and optical communications networks (WOCN) (pp. 1–5).

  218. Kharbash, S., & Wang, W. (2007). Computing two-terminal reliability in mobile ad hoc networks. In 2007 IEEE wireless communications and networking conference (pp. 2831–2836).

  219. Wang, T., Huang, C., Xiang, K., & Zhou, K. (2010) Survivability evaluation for MANET based on path reliability. In 2010 Second international conference on networks security, wireless communications and trusted computing (Vol. 1, pp. 378–381).

  220. Pouyan, A., & Tabari, M. Y. (2014). Estimating reliability in mobile ad-hoc networks based on monte carlo simulation (technical note). International Journal of Engineering-Transactions B: Applications, 27(5), 739.

    Google Scholar 

  221. Dana, A., Zadeh, A. K., & Noori, S. A. S. (2008). Backup path set selection in ad hoc wireless network using link expiration time. Computers & Electrical Engineering, 34(6), 503–519.

    Article  MATH  Google Scholar 

  222. Papadimitratos, P., Haas, Z. J., & Sirer, E. G. (2002). Path set selection in mobile ad hoc networks. In Proceedings of the 3rd ACM international symposium on mobile ad hoc networking & computing, MobiHoc ’02 (pp. 1–11).

  223. Migov, D. A., & Shakhov, V. (2014). Reliability of ad hoc networks with imperfect nodes. In International workshop on multiple access communications (pp. 49–58). Cham: Springer.

  224. Chaturvedi, S. K. (2016). Network reliability: Measures and evaluation. New York: Wiley.

    Book  MATH  Google Scholar 

  225. Andel, T. R., & Yasinsac, A. (2006). On the credibility of manet simulations. Computer, 39(7), 48–54.

    Article  Google Scholar 

  226. Manaseer, S. S. (2016). On the choice of parameter values for simulation based experiments on mobile ad hoc networks. International Journal of Communications, Network and System Sciences, 9(04), 90.

    Article  Google Scholar 

  227. Meena, K., & Vasanthi, T. (2016). Reliability analysis of mobile ad hoc networks using universal generating function. Quality and Reliability Engineering International, 32(1), 111–122.

    Article  Google Scholar 

  228. Rebaiaia, M.-L., & Ait-Kadi, D. (2015). Reliability evaluation of imperfect k-terminal stochastic networks using polygon-to chain and series-parallel reductions. In Proceedings of the 11th ACM symposium on QoS and security for wireless and mobile networks, Q2SWinet ’15 (pp. 115–122). ACM.

  229. Rai, S., Kumar, A., & Prasad, E. (1986). Computing terminal reliability of computer network. Reliability Engineering, 16(2), 109–119.

    Article  Google Scholar 

  230. Ahmad, M., & Mishra, D. K. (2012). A reliability calculations model for large-scale MANETs. International Journal of Computer Applications, 59(9), 17–21.

    Article  Google Scholar 

  231. Egeland, G., & Engelstad, P. E. (2009). The availability and reliability of wireless multi-hop networks with stochastic link failures. IEEE Journal on Selected Areas in Communications, 27(7), 1132–1146.

    Article  Google Scholar 

  232. Cook, J. L., & Ramirez-Marquez, J. E. (2009). Optimal design of cluster-based ad-hoc networks using probabilistic solution discovery. Reliability Engineering & System Safety, 94(2), 218–228.

    Article  Google Scholar 

  233. Cook, J. L., & Ramirez-Marquez, J. E. (2008). Reliability analysis of cluster-based ad-hoc networks. Reliability Engineering & System Safety, 93(10), 1512–1522.

    Article  Google Scholar 

  234. Soh, S., Rai, S., & Brooks, R. R. (2008). Performability Issues in Wireless Communication Networks. London: Springer.

    Book  Google Scholar 

  235. Pellegrini, F. D., Miorandi, D., Carreras, I., & Chlamtac, I. (2007). A graph-based model for disconnected ad hoc networks. In IEEE INFOCOM 2007—26th IEEE international conference on computer communications (pp. 373–381).

  236. Zhang, X., Liu, Q., Li, Z. (2014). A method to evaluate MANET connectivity based on communication demand and probability. In The proceedings of the second international conference on communications, signal processing, and systems (pp. 817–822). Springer.

  237. Dasgupta, S., Mao, G., & Anderson, B. (2015). A new measure of wireless network connectivity. IEEE Transactions on Mobile Computing, 14(9), 1765–1779.

    Article  Google Scholar 

  238. Brooks, R. R., Pillai, B., Racunas, S., & Rai, S. (2007). Mobile network analysis using probabilistic connectivity matrices. IEEE Transactions on Systems, Man, and Cybernetics Part C (Applications and Reviews), 37(4), 694–702.

    Article  Google Scholar 

  239. Boukerche, A., Turgut, B., Aydin, N., Ahmad, M. Z., Blni, L., & Turgut, D. (2011). Routing protocols in ad hoc networks: A survey. Computer Networks, 55(13), 3032–3080.

    Article  Google Scholar 

  240. Abolhasan, M., Wysocki, T., & Dutkiewicz, E. (2004). A review of routing protocols for mobile ad hoc networks. Ad Hoc Networks, 2(1), 1–22.

    Article  Google Scholar 

  241. Giordano, S., & Stojmenovic, I. (2004). Position Based routing algorithms for ad hoc networks: A taxonomy. Boston: Springer.

    Book  Google Scholar 

  242. Deng, J., Han, Y. S., Chen, P.-N., & Varshney, P. K. (2004). Optimum transmission range for wireless ad hoc networks. In 2004 IEEE wireless communications and networking conference (IEEE Cat. No. 04TH8733) (Vol. 2, pp. 1024–1029).

  243. Santi, P., & Blough, D. M. (2003). The critical transmitting range for connectivity in sparse wireless ad hoc networks. IEEE Transactions on Mobile Computing, 2(1), 25–39.

    Article  Google Scholar 

  244. Buchanan, M. (2003). Nexus: Small worlds and the groundbreaking theory of networks. New York, NY: W. W. Norton & Co., Inc.

    Google Scholar 

  245. Chaintreau, A., Mtibaa, A., Massoulie, L., & Diot, C. (2007). The diameter of opportunistic mobile networks. In Proceedings of the 2007 ACM CoNEXT conference (p. 12). ACM.

  246. Tang, J., Scellato, S., Musolesi, M., Mascolo, C., & Latora, V. (2010). Small-world behavior in time-varying graphs. Physical Review E, 81(5), 055101.

    Article  Google Scholar 

  247. Nishiyama, H., Ito, M., & Kato, N. (2014). Relay-by-smartphone: Realizing multihop device-to-device communications. IEEE Communications Magazine, 52(4), 56–65.

    Article  Google Scholar 

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Khanna, G., Chaturvedi, S.K. A Comprehensive Survey on Multi-hop Wireless Networks: Milestones, Changing Trends and Concomitant Challenges. Wireless Pers Commun 101, 677–722 (2018). https://doi.org/10.1007/s11277-018-5711-8

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