On the Dynamics of Human Proximity for Data Diffusion in Ad-Hoc Networks

We report on a data-driven investigation aimed at understanding the dynamics of message spreading in a real-world dynamical network of human proximity. We use data collected by means of a proximity-sensing network of wearable sensors that we deployed at three different social gatherings, simultaneously involving several hundred individuals. We simulate a message spreading process over the recorded proximity network, focusing on both the topological and the temporal properties. We show that by using an appropriate technique to deal with the temporal heterogeneity of proximity events, a universal statistical pattern emerges for the delivery times of messages, robust across all the data sets. Our results are useful to set constraints for generic processes of data dissemination, as well as to validate established models of human mobility and proximity that are frequently used to simulate realistic behaviors.Comment: A. Panisson et al., On the dynamics of human proximity for data diffusion in ad-hoc networks, Ad Hoc Netw. (2011

Swarm-based Intelligent Routing (SIR) - a new approach for efficient routing in content centric delay tolerant networks

This paper introduces Swarm-based Intelligent Routing (SIR), a swarm intelligence based approach used for routing content in content centric Pocket Switched Networks. We first formalize the notion of optimal path in DTN, then introduce a swarm intelligence based routing protocol adapted to content centric DTN that use a publish/subscribe communication paradigm. The protocol works in a fully decentralized way in which nodes do not have any knowledge about the global topology. Nodes, via opportunistic contacts, update utility functions which synthesizes their spatio-temporal proximity from the content subscribers. This individual behavior applied by each node leads to the collective formation of gradient fields between content subscribers and content providers. Therefore, content routing simply sums up to follow the steepest slope along these gradient fields to reach subscribers who are located at the minima of the field. Via real traces analysis and simulation, we demonstrate the existence and relevance of such gradient field and show routing performance improvements when compared to classical routing protocols previously defined for information routing in DTN

Social-Aware Stateless Forwarding in Pocket Switched Networks

Several social-aware routing protocols for pocket switched networks have been recently introduced in the literature. The main idea underlying these protocols is to exploit state information (e.g., history of past encounters) to deduce information on the social structure of the network, and to optimize routing based on this information. While social-aware routing protocols have been shown to have superior performance to social-oblivious, stateless routing protocols such as, e.g., BinarySW, the improvement comes at the cost of considerable storage overhead required on the nodes, which is instead not required for stateless approaches. So, whether the benefits of social-aware routing protocols would still be present when storage capacity at the nodes is constrained is not clear. In this paper we present SANE, the first forwarding mechanism that combines the advantages of both social-aware and stateless approaches. SANE is based on the observation-that we validate on real-world traces-that individuals with similar interests tend to meet more often. In our approach, individuals (network members) are characterized by their interest profile, a compact representation of their interests. By implementing a simple interest profile similarity based forwarding rule, SANE is free of network state information, thus overcoming the storage capacity problem with existing social-aware approaches. Through extensive experiments, we show the superiority of social-aware, stateless forwarding over existing stateful, social-aware and stateless, social-oblivious routing approaches. An important byproduct of our interest-based approach is that it easily enables innovative routing primitives, such as interest-casting. An interest-casting protocol is also introduced in this paper, and extensively evaluated through experiments based on both real-world and synthetic mobility traces

Fast community structure local uncovering by independent vertex-centred process

This paper addresses the task of community detection and proposes a local approach based on a distributed list building, where each vertex broadcasts basic information that only depends on its degree and that of its neighbours. A decentralised external process then unveils the community structure. The relevance of the proposed method is experimentally shown on both artificial and real data.Comment: 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Aug 2015, Paris, France. Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Minin

LSSTCS- A Social-Based DTN Routing in Cooperative Vehicular Sensor Networks

As a cooperative information system, vehicles in Vehicular Sensor Networks delivery messages based on collaboration. Due to the high speed of vehicles, the topology of the network is highly dynamic, and the network may be disconnected frequently. So how to transfer large files in such network is worth considering. In case that the encountering nodes which never meet before flood messages blindly to cause tremendous network overhead. We address this challenge by introducing the Encounter Utility Rank Router(EURR) based on social metrics. EURR includes three cases: Utility Replication Strategy, Lifetime Replication Strategy and SocialRank Replication Strategy. The Lifetime Replication is promising complement to Utility Replication. It enhances the delivery ratio by relaying the copy via the remaining lifetime. Considering network overhead, the SocialRank Replication replicates a copy according to the SocialRank when two communicating nodes do not meet before. The routing mechanism explores the utility of history encounter information and social opportunistic forwarding. The results under the scenario show an advantage of the proposed Encounter Utility Rank Router (EURR) over the compared algorithms in terms of delivery ratio, average delivery latency and overhead ratio

SPoT: Representing the Social, Spatial, and Temporal Dimensions of Human Mobility with a Unifying Framework

Modeling human mobility is crucial in the analysis and simulation of opportunistic networks, where contacts are exploited as opportunities for peer-topeer message forwarding. The current approach with human mobility modeling has been based on continuously modifying models, trying to embed in them the mobility properties (e.g., visiting patterns to locations or specific distributions of inter-contact times) as they came up from trace analysis. As a consequence, with these models it is difficult, if not impossible, to modify the features of mobility or to control the exact shape of mobility metrics (e.g., modifying the distribution of inter-contact times). For these reasons, in this paper we propose a mobility framework rather than a mobility model, with the explicit goal of providing a exible and controllable tool for modeling mathematically and generating simulatively different possible features of human mobility. Our framework, named SPoT, is able to incorporate the three dimensions - spatial, social, and temporal - of human mobility. The way SPoT does it is by mapping the different social communities of the network into different locations, whose members visit with a configurable temporal pattern. In order to characterize the temporal patterns of user visits to locations and the relative positioning of locations based on their shared users, we analyze the traces of real user movements extracted from three location-based online social networks (Gowalla, Foursquare, and Altergeo). We observe that a Bernoulli process effectively approximates user visits to locations in the majority of cases and that locations that share many common users visiting them frequently tend to be located close to each other. In addition, we use these traces to test the exibility of the framework, and we show that SPoT is able to accurately reproduce the mobility behavior observed in traces. Finally, relying on the Bernoulli assumption for arrival processes, we provide a throughout mathematical analysis of the controllability of the framework, deriving the conditions under which heavy-tailed and exponentially-tailed aggregate inter-contact times (often observed in real traces) emerge

FSF: Applying machine learning techniques to data forwarding in socially selfish Opportunistic Networks

[EN] Opportunistic networks are becoming a solution to provide communication support in areas with overloaded cellular networks, and in scenarios where a fixed infrastructure is not available, as in remote and developing regions. A critical issue, which still requires a satisfactory solution, is the design of an efficient data delivery solution trading off delivery efficiency, delay, and cost. To tackle this problem, most researchers have used either the network state or node mobility as a forwarding criterion. Solutions based on social behaviour have recently been considered as a promising alternative. Following the philosophy from this new category of protocols, in this work, we present our ¿FriendShip and Acquaintanceship Forwarding¿ (FSF) protocol, a routing protocol that makes its routing decisions considering the social ties between the nodes and both the selfishness and the device resources levels of the candidate node for message relaying. When a contact opportunity arises, FSF first classifies the social ties between the message destination and the candidate to relay. Then, by using logistic functions, FSF assesses the relay node selfishness to consider those cases in which the relay node is socially selfish. To consider those cases in which the relay node does not accept receipt of the message because its device has resource constraints at that moment, FSF looks at the resource levels of the relay node. By using the ONE simulator to carry out trace-driven simulation experiments, we find that, when accounting for selfishness on routing decisions, our FSF algorithm outperforms previously proposed schemes, by increasing the delivery ratio up to 20%, with the additional advantage of introducing a lower number of forwarding events. We also find that the chosen buffer management algorithm can become a critical element to improve network performance in scenarios with selfish nodes.This work was partially supported by the "Camilo Batista de Souza/Programa Doutorado-sanduiche no Exterior (PDSE)/Processo 88881.133931/2016-01" and by the Ministerio de Ciencia, Innovacion y Universidades, Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad, Proyectos I+D+I 2018, Spain, under Grant RTI2018-096384-B-I00".Souza, C.; Mota, E.; Soares, D.; Manzoni, P.; Cano, J.; Tavares De Araujo Cesariny Calafate, CM.; Hernández-Orallo, E. (2019). FSF: Applying machine learning techniques to data forwarding in socially selfish Opportunistic Networks. Sensors. 19(10):1-26. https://doi.org/10.3390/s19102374S1261910Trifunovic, S., Kouyoumdjieva, S. T., Distl, B., Pajevic, L., Karlsson, G., & Plattner, B. (2017). A Decade of Research in Opportunistic Networks: Challenges, Relevance, and Future Directions. IEEE Communications Magazine, 55(1), 168-173. doi:10.1109/mcom.2017.1500527cmLu, X., Lio, P., & Hui, P. (2016). Distance-Based Opportunistic Mobile Data Offloading. Sensors, 16(6), 878. doi:10.3390/s16060878Zeng, F., Zhao, N., & Li, W. (2017). Effective Social Relationship Measurement and Cluster Based Routing in Mobile Opportunistic Networks. Sensors, 17(5), 1109. doi:10.3390/s17051109Khabbaz, M. J., Assi, C. M., & Fawaz, W. F. (2012). Disruption-Tolerant Networking: A Comprehensive Survey on Recent Developments and Persisting Challenges. IEEE Communications Surveys & Tutorials, 14(2), 607-640. doi:10.1109/surv.2011.041911.00093Miao, J., Hasan, O., Mokhtar, S. B., Brunie, L., & Yim, K. (2013). An investigation on the unwillingness of nodes to participate in mobile delay tolerant network routing. International Journal of Information Management, 33(2), 252-262. doi:10.1016/j.ijinfomgt.2012.11.001CRAWDAD Dataset Uoi/Haggle (v. 2016-08-28): Derived from Cambridge/Haggle (v. 2009-05-29)https://crawdad.org/uoi/haggle/20160828Eagle, N., Pentland, A., & Lazer, D. (2009). Inferring friendship network structure by using mobile phone data. Proceedings of the National Academy of Sciences, 106(36), 15274-15278. doi:10.1073/pnas.0900282106Tsai, T.-C., & Chan, H.-H. (2015). NCCU Trace: social-network-aware mobility trace. IEEE Communications Magazine, 53(10), 144-149. doi:10.1109/mcom.2015.7295476Hui, P., Crowcroft, J., & Yoneki, E. (2011). BUBBLE Rap: Social-Based Forwarding in Delay-Tolerant Networks. IEEE Transactions on Mobile Computing, 10(11), 1576-1589. doi:10.1109/tmc.2010.246Lindgren, A., Doria, A., & Schelén, O. (2003). Probabilistic routing in intermittently connected networks. ACM SIGMOBILE Mobile Computing and Communications Review, 7(3), 19-20. doi:10.1145/961268.961272Cao, Y., & Sun, Z. (2013). Routing in Delay/Disruption Tolerant Networks: A Taxonomy, Survey and Challenges. IEEE Communications Surveys & Tutorials, 15(2), 654-677. doi:10.1109/surv.2012.042512.00053Zhu, Y., Xu, B., Shi, X., & Wang, Y. (2013). A Survey of Social-Based Routing in Delay Tolerant Networks: Positive and Negative Social Effects. IEEE Communications Surveys & Tutorials, 15(1), 387-401. doi:10.1109/surv.2012.032612.00004Shah, R. C., Roy, S., Jain, S., & Brunette, W. (2003). Data MULEs: modeling and analysis of a three-tier architecture for sparse sensor networks. Ad Hoc Networks, 1(2-3), 215-233. doi:10.1016/s1570-8705(03)00003-9Burns, B., Brock, O., & Levine, B. N. (2008). MORA routing and capacity building in disruption-tolerant networks. Ad Hoc Networks, 6(4), 600-620. doi:10.1016/j.adhoc.2007.05.002Shaghaghian, S., & Coates, M. (2015). Optimal Forwarding in Opportunistic Delay Tolerant Networks With Meeting Rate Estimations. IEEE Transactions on Signal and Information Processing over Networks, 1(2), 104-116. doi:10.1109/tsipn.2015.2452811Li, L., Qin, Y., & Zhong, X. (2016). A Novel Routing Scheme for Resource-Constraint Opportunistic Networks: A Cooperative Multiplayer Bargaining Game Approach. IEEE Transactions on Vehicular Technology, 65(8), 6547-6561. doi:10.1109/tvt.2015.2476703Juang, P., Oki, H., Wang, Y., Martonosi, M., Peh, L. S., & Rubenstein, D. (2002). Energy-efficient computing for wildlife tracking. ACM SIGPLAN Notices, 37(10), 96-107. doi:10.1145/605432.605408Spyropoulos, T., Psounis, K., & Raghavendra, C. S. (2008). Efficient Routing in Intermittently Connected Mobile Networks: The Single-Copy Case. IEEE/ACM Transactions on Networking, 16(1), 63-76. doi:10.1109/tnet.2007.897962Zhang, L., Wang, X., Lu, J., Ren, M., Duan, Z., & Cai, Z. (2014). A novel contact prediction-based routing scheme for DTNs. Transactions on Emerging Telecommunications Technologies, 28(1), e2889. doi:10.1002/ett.2889Okasha, S. (2005). Altruism, Group Selection and Correlated Interaction. The British Journal for the Philosophy of Science, 56(4), 703-725. doi:10.1093/bjps/axi143Hernandez-Orallo, E., Olmos, M. D. S., Cano, J.-C., Calafate, C. T., & Manzoni, P. (2015). CoCoWa: A Collaborative Contact-Based Watchdog for Detecting Selfish Nodes. IEEE Transactions on Mobile Computing, 14(6), 1162-1175. doi:10.1109/tmc.2014.234362