Performance of Opportunistic Epidemic Routing on Edge-Markovian Dynamic Graphs

Connectivity patterns in intermittently-connected mobile networks (ICMN) can be modeled as edge-Markovian dynamic graphs. We propose a new model for epidemic propagation on such graphs and calculate a closed-form expression that links the best achievable delivery ratio to common ICMN parameters such as message size, maximum tolerated delay, and link lifetime. These theoretical results are compared to those obtained by replaying a real-life contact trace.Comment: 5 pages, 4 figures. Accepted for publication in IEEE Transactions on Communication

Diffusion probabiliste dans les réseaux dynamiques

National audienceLa diffusion probabiliste est une des techniques les plus populaires pour diffuser de l'information dans les réseaux à grande échelle. Cette technique est appréciée pour sa simplicité, sa robustesse et son efficacité. Dans le cas du protocole \push, chaque nœud informé choisit à chaque étape un de ses voisins aléatoirement de manière uniforme, et lui transmet l'information. Ce protocole est connu pour permettre la diffusion en $O(\log n)$ étapes, avec forte probabilité, dans plusieurs familles de réseaux \emph{statiques} de $n$ nœuds. De plus, il a été montré empiriquement que le protocole \push\/ offre de très bonnes performances en pratique. En particulier, il se montre robuste aux évolutions dynamiques de la structure réseau. Dans cet article, nous analysons le protocole \push\/ dans le cas de réseaux \emph{dynamiques}. Nous considérons le modèle des graphes à évolution arête-markovienne, qui permet de capturer une forme de dépendance temporelle entre la structure du réseau au temps $t$ et celle au temps $t+1$. Plus précisément, une arête inexistante apparaît avec probabilité $p$, tandis qu'une arête existante disparaît avec probabilité $q$. Ayant pour objectif de coller avec des traces réelles, nous concentrons principalement notre étude sur le cas $p=\Omega(\frac{1}{n})$ et $q$ constant. Nous prouvons que, dans ce cas réaliste, le protocole \push\/ permet de diffuser l'information en $O(\log n)$ étapes, avec forte probabilité. Cette borne reste valide même lorsque, avec forte probabilité, le réseau est déconnecté à chaque étape (typiquement, lorsque $p\ll \frac{\log n}{n}$). Ce résultat démontre ainsi formellement la robustesse du protocole \push\/ dans le cadre d'évolution temporelle de la structure du réseau. La version complète de cet article, en cours de soumission, est disponible sur arXiv (voir~\cite{CCDFPS13} qui contient un sur-ensemble des résultats présentés ici)

Distributed Community Detection in Dynamic Graphs

Inspired by the increasing interest in self-organizing social opportunistic networks, we investigate the problem of distributed detection of unknown communities in dynamic random graphs. As a formal framework, we consider the dynamic version of the well-studied \emph{Planted Bisection Model} \sdG(n,p,q) where the node set $[n]$ of the network is partitioned into two unknown communities and, at every time step, each possible edge $(u,v)$ is active with probability $p$ if both nodes belong to the same community, while it is active with probability $q$ (with $q<<p$) otherwise. We also consider a time-Markovian generalization of this model. We propose a distributed protocol based on the popular \emph{Label Propagation Algorithm} and prove that, when the ratio $p/q$ is larger than $n^{b}$ (for an arbitrarily small constant $b>0$), the protocol finds the right "planted" partition in $O(\log n)$ time even when the snapshots of the dynamic graph are sparse and disconnected (i.e. in the case $p=\Theta(1/n)$).Comment: Version I

On performance modeling of 3D mobile ad hoc networks

指導教員：姜 暁

Optimising data diffusion while reducing local resources consumption in Opportunistic Mobile Crowdsensing

[EN] The combination of Mobile Crowdsensing (MCS) with Opportunistic Networking (Opp-Net) allows mobile users to share sensed data easily and conveniently without the use of fixed infrastructure. OppNet is based on intermittent connectivity among wireless mobile devices, in which mobile nodes may store, carry and forward messages (sensing information) by taking advantage of wireless ad hoc communication opportunities. A common approach for the diffusion of this sensing data in OppNet is the epidemic protocol, which carries out a fast data diffusion at the expense of increasing the usage of local buffers on mobile nodes and also the number of transmissions, thereby limiting scalability. A way to reduce this consumption of local resources is to set a message expiration time that forces the removal of old messages from local buffers. Since dropping messages too early may reduce the speed of information diffusion, we propose a dynamic expiration time setting to limit this effect. Moreover, we introduce an epidemic diffusion model for evaluating the impact of the expiration time. This model allows us to obtain optimal expiration times that achieve performances similar to those other approaches where no expiration is considered, with a significant reduction of local buffer and network usage. Furthermore, in our proposed model, the buffer utilisation remains steady with the number of nodes, whereas in other approaches it increases sharply. Finally, our approach is evaluated and validated in a mobile crowdsensing scenario, where students collect and broadcast information regarding a university campus, showing a significant reduction on buffer usage and nodes message transmissions, and therefore, decreasing battery consumption.This work was partially supported by the Ministerio de Ciencia, Innovación y Universidades, Spain, under Grant RTI2018- 096384-B-I00. Also, this work has been partially performed in the framework of the European Union¿s Horizon 2020 project 5G-CARMEN co-funded by the EU under grant agreement No. 825012. The views expressed are those of the authors and do not necessarily represent the project. The Commission is not liable for any use that may be made of any of the information contained therein.Hernández-Orallo, E.; Borrego, C.; Manzoni, P.; Marquez Barja, JM.; Cano, J.; Tavares De Araujo Cesariny Calafate, CM. (2020). Optimising data diffusion while reducing local resources consumption in Opportunistic Mobile Crowdsensing. Pervasive and Mobile Computing. 67:1-18. https://doi.org/10.1016/j.pmcj.2020.101201S11867Capponi, A., Fiandrino, C., Kantarci, B., Foschini, L., Kliazovich, D., & Bouvry, P. (2019). A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities. IEEE Communications Surveys & Tutorials, 21(3), 2419-2465. doi:10.1109/comst.2019.2914030Trifunovic, 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.2017.1500527cmDede, J., Forster, A., Hernandez-Orallo, E., Herrera-Tapia, J., Kuladinithi, K., Kuppusamy, V., … Vatandas, Z. (2018). Simulating Opportunistic Networks: Survey and Future Directions. IEEE Communications Surveys & Tutorials, 20(2), 1547-1573. doi:10.1109/comst.2017.2782182Udugama, A., Dede, J., Förster, A., Kuppusamy, V., Kuladinithi, K., Timm-Giel, A., & Vatandas, Z. (2019). My Smartphone tattles: Considering Popularity of Messages in Opportunistic Data Dissemination. Future Internet, 11(2), 29. doi:10.3390/fi11020029Groenevelt, R., Nain, P., & Koole, G. (2005). The message delay in mobile ad hoc networks. Performance Evaluation, 62(1-4), 210-228. doi:10.1016/j.peva.2005.07.018Lindgren, A., Doria, A., & Schelén, O. (2003). Probabilistic routing in intermittently connected networks. ACM SIGMOBILE Mobile Computing and Communications Review, 7(3), 19-20. doi:10.1145/961268.961272Borrego, C., Borrell, J., & Robles, S. (2019). Efficient broadcast in opportunistic networks using optimal stopping theory. Ad Hoc Networks, 88, 5-17. doi:10.1016/j.adhoc.2019.01.001Hernández-Orallo, E., Murillo-Arcila, M., Calafate, C. T., Cano, J. C., Conejero, J. A., & Manzoni, P. (2016). Analytical evaluation of the performance of contact-Based messaging applications. Computer Networks, 111, 45-54. doi:10.1016/j.comnet.2016.07.006Haas, Z. J., & Small, T. (2006). A new networking model for biological applications of ad hoc sensor networks. IEEE/ACM Transactions on Networking, 14(1), 27-40. doi:10.1109/tnet.2005.863461Zhang, X., Neglia, G., Kurose, J., & Towsley, D. (2007). Performance modeling of epidemic routing. Computer Networks, 51(10), 2867-2891. doi:10.1016/j.comnet.2006.11.028Tsai, T.-C., & Chan, H.-H. (2015). NCCU Trace: social-network-aware mobility trace. IEEE Communications Magazine, 53(10), 144-149. doi:10.1109/mcom.2015.7295476Yao, Y., Yang, L. T., & Xiong, N. N. (2015). Anonymity-Based Privacy-Preserving Data Reporting for Participatory Sensing. IEEE Internet of Things Journal, 2(5), 381-390. doi:10.1109/jiot.2015.2410425Wu, X., Brown, K. N., & Sreenan, C. J. (2013). Analysis of smartphone user mobility traces for opportunistic data collection in wireless sensor networks. Pervasive and Mobile Computing, 9(6), 881-891. doi:10.1016/j.pmcj.2013.07.003Amah, T., Kamat, M., Bakar, K., Rahman, S., Mohammed, M., Abali, A., … Oliveira, A. (2017). Collecting Sensed Data with Opportunistic Networks: The Case of Contact Information Overhead. Information, 8(3), 108. doi:10.3390/info8030108Pajevic, L., Fodor, V., & Karlsson, G. (Eds.). (2018). Ensuring Persistent Content in Opportunistic Networks via Stochastic Stability Analysis. ACM Transactions on Modeling and Performance Evaluation of Computing Systems, 3(4), 1-23. doi:10.1145/3232161Hernandez-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.2014.2343627Karaliopoulos, M. (2009). Assessing the vulnerability of DTN data relaying schemes to node selfishness. IEEE Communications Letters, 13(12), 923-925. doi:10.1109/lcomm.2009.12.091520Whitbeck, J., Conan, V., & de Amorim, M. D. (2011). Performance of Opportunistic Epidemic Routing on Edge-Markovian Dynamic Graphs. IEEE Transactions on Communications, 59(5), 1259-1263. doi:10.1109/tcomm.2011.020811.090163Moutinho de Souza Dias, G., Ferreira de Rezende, J., & Moreira Salles, R. (2019). Mathematical modeling of delivery delay for multi-copy opportunistic networks with heterogeneous pairwise encounter rates. 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Optimising message broadcasting in opportunistic networks

[EN] Message Broadcasting in Opportunistic Networks is based on the opportunity of establishing contacts among mobiles nodes for message exchange. Nevertheless, as the amount of information transmitted in a contact is limited by the transmission speed and the contact duration, large messages are less likely to be exchanged, and thus their diffusion is severely limited. Furthermore, these failed transmissions can also lead to an important waste of network resources, since the message transmission is aborted when the contact ends and the message needs to be transmitted again in the next contact. Therefore, in this paper we study the impact that contact duration has on the broadcast of messages, showing that splitting a large message into smaller parts can improve its diffusion. Based on this idea, we propose an extension of the epidemic protocol called Xpread. The efficiency of this protocol mainly depends on how the original message is partitioned. Thus, in order to evaluate the impact and the efficiency of the partition scheme, we have developed an analytical model based on Population Processes, showing that a fixed size partition is the best option, while also providing a simple expression to obtain the optimal size. The Xpread has been evaluated exhaustively using four different mobiles traces, comprising both pedestrian and vehicular scenarios. The results show that the diffusion of large messages is improved up to four times with a slight reduction in the delivery time and overhead, minimising also message forwarding failures.This work was partially supported by the Ministerio de Ciencia, Innovacion y Universidades, Spain, under Grant RTI2018-096384-B-I00; and the Secretaria Nacional de Educacion Superior, Ciencia, Tecnologia e Innovacion del Ecuador (SENESCYT), Ecuador.Chancay-García, L.; Hernández-Orallo, E.; Manzoni, P.; Vegni, AM.; Loscrí, V.; Cano, J.; Tavares De Araujo Cesariny Calafate, CM. (2020). 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Analysis of Individual Pair and Aggregate Intercontact Times in Heterogeneous Opportunistic Networks. IEEE Transactions on Mobile Computing, 12(12), 2483-2495. doi:10.1109/tmc.2012.213Hernández-Orallo, E., Cano, J. C., Calafate, C. T., & Manzoni, P. (2016). New approaches for characterizing inter-contact times in opportunistic networks. Ad Hoc Networks, 52, 160-172. doi:10.1016/j.adhoc.2016.04.003Hernandez-Orallo, E., Herrera-Tapia, J., Cano, J.-C., Calafate, C. T., & Manzoni, P. (2015). Evaluating the Impact of Data Transfer Time in Contact-Based Messaging Applications. IEEE Communications Letters, 19(10), 1814-1817. doi:10.1109/lcomm.2015.2472407Hernández-Orallo, E., Murillo-Arcila, M., Calafate, C. T., Cano, J. C., Conejero, J. A., & Manzoni, P. (2016). Analytical evaluation of the performance of contact-Based messaging applications. 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