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dc.contributor.author | Hernández-Orallo, Enrique | es_ES |
dc.contributor.author | Borrego, Carlos | es_ES |
dc.contributor.author | Manzoni, Pietro | es_ES |
dc.contributor.author | Marquez Barja, Johann M. | es_ES |
dc.contributor.author | Cano, Juan-Carlos | es_ES |
dc.contributor.author | Tavares De Araujo Cesariny Calafate, Carlos Miguel | es_ES |
dc.date.accessioned | 2021-03-09T04:32:13Z | |
dc.date.available | 2021-03-09T04:32:13Z | |
dc.date.issued | 2020-09 | es_ES |
dc.identifier.issn | 1574-1192 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/163480 | |
dc.description.abstract | [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. | es_ES |
dc.description.sponsorship | 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. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Pervasive and Mobile Computing | es_ES |
dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
dc.subject | Mobile crowdsensing | es_ES |
dc.subject | Opportunistic networking | es_ES |
dc.subject | Epidemic diffusion | es_ES |
dc.subject.classification | ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES | es_ES |
dc.title | Optimising data diffusion while reducing local resources consumption in Opportunistic Mobile Crowdsensing | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.pmcj.2020.101201 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/825012/EU/5G for Connected and Automated Road Mobility in the European UnioN/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-096384-B-I00/ES/SOLUCIONES PARA UNA GESTION EFICIENTE DEL TRAFICO VEHICULAR BASADAS EN SISTEMAS Y SERVICIOS EN RED/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors | es_ES |
dc.description.bibliographicCitation | 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.2020.101201 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.pmcj.2020.101201 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 18 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 67 | es_ES |
dc.relation.pasarela | S\414208 | es_ES |
dc.contributor.funder | European Commission | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
dc.description.references | Capponi, 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.2019.2914030 | es_ES |
dc.description.references | Trifunovic, 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.2017.1500527cm | es_ES |
dc.description.references | Dede, 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.2017.2782182 | es_ES |
dc.description.references | Udugama, 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/fi11020029 | es_ES |
dc.description.references | Groenevelt, 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.2005.07.018 | es_ES |
dc.description.references | Lindgren, A., Doria, A., & Schelén, O. (2003). Probabilistic routing in intermittently connected networks. ACM SIGMOBILE Mobile Computing and Communications Review, 7(3), 19-20. doi:10.1145/961268.961272 | es_ES |
dc.description.references | Borrego, 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.2019.01.001 | es_ES |
dc.description.references | Hernández-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.2016.07.006 | es_ES |
dc.description.references | Haas, 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.2005.863461 | es_ES |
dc.description.references | Zhang, X., Neglia, G., Kurose, J., & Towsley, D. (2007). Performance modeling of epidemic routing. Computer Networks, 51(10), 2867-2891. doi:10.1016/j.comnet.2006.11.028 | es_ES |
dc.description.references | Tsai, T.-C., & Chan, H.-H. (2015). NCCU Trace: social-network-aware mobility trace. IEEE Communications Magazine, 53(10), 144-149. doi:10.1109/mcom.2015.7295476 | es_ES |
dc.description.references | Yao, 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.2015.2410425 | es_ES |
dc.description.references | Wu, 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.2013.07.003 | es_ES |
dc.description.references | Amah, 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/info8030108 | es_ES |
dc.description.references | Pajevic, 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:10.1145/3232161 | es_ES |
dc.description.references | Hernandez-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.2014.2343627 | es_ES |
dc.description.references | Karaliopoulos, M. (2009). Assessing the vulnerability of DTN data relaying schemes to node selfishness. IEEE Communications Letters, 13(12), 923-925. doi:10.1109/lcomm.2009.12.091520 | es_ES |
dc.description.references | Whitbeck, 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.2011.020811.090163 | es_ES |
dc.description.references | Moutinho 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. Information Sciences, 475, 142-160. doi:10.1016/j.ins.2018.09.056 | es_ES |
dc.description.references | Herrera-Tapia, J., Hernández-Orallo, E., Tomás, A., Manzoni, P., Tavares Calafate, C., & Cano, J.-C. (2016). Friendly-Sharing: Improving the Performance of City Sensoring through Contact-Based Messaging Applications. Sensors, 16(9), 1523. doi:10.3390/s16091523 | es_ES |
dc.description.references | Hernandez-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.2015.2472407 | es_ES |
dc.description.references | Grossglauser, M., & Tse, D. N. C. (2002). Mobility increases the capacity of ad hoc wireless networks. IEEE/ACM Transactions on Networking, 10(4), 477-486. doi:10.1109/tnet.2002.801403 | es_ES |
dc.description.references | Borrego, C., Hernández-Orallo, E., & Magaia, N. (2019). General and mixed linear regressions to estimate inter-contact times and contact duration in opportunistic networks. Ad Hoc Networks, 93, 101927. doi:10.1016/j.adhoc.2019.101927 | es_ES |
dc.description.references | A. Keränen, J. Ott, T. Kärkkäinen, The ONE simulator for DTN protocol evaluation, in: Proceedings of SIMUTools’09, 2009, pp. 55:1–55:10. | es_ES |
dc.description.references | E. Hernández-Orallo, D. Fernández-Delegido, J. Herrera-Tapia, J. Cano, C. Calafate, P. Manzoni, GRChat: A contact-based messaging application for the evaluation of information diffusion, in: Proceedings of the 6th International Conference on Advanced Communications and Computation, INFOCOMP, 2016. | es_ES |