Performance of sequential batching-based methods of output data analysis in distributed steady-state stochastic simulation

Wir haben die Anpassung von Sequentiellen Analysemethoden von Stochastik Simulationen an einem Szenario von mehreren Unabhängigen Replikationen in Parallel (MRIP) untersucht. Die Hauptidee ist, die statistische Kontrole bzw. die Beschleunigung eines Simulationexperiment zu automatisieren. Die vorgeschlagenen Methoden der Literatur sind auf einzelne Prozessorszenarien orientiert. Wenig ist bekannt hinsichtlich der Anwendungen von Verfahen, die auf Methoden unter MRIP basieren. Auf den ersten Blick sind beide Ziele entgegengesetzt, denn man braucht eine grosse Menge von Beobachtungen, um eine hohe Qualität der Resultate zu erreichen. Dafür benötig man viel Zeit. Man kann jedoch durch einen ausfürlichen Entwurf zusammen mit einem robusten Werkzeug, das auf unabhängige Replikationen basiert ist, ein effizientes Mittel bezüglich Analyse der Resultate produzieren. Diese Recherche wurde mit einer sequentiellen Version des klassischen Verfahren von Nonoverlaping Batch Means (NOBM) angefangen. Obwohl NOBM sehr intuitiv und populär ist, bietet es keine gute Lösung für das Problem starker Autokorrelation zwischen den Beobachtungen an, die normalerweise bei hohen Auslastungen entstehen. Es lohnt sich nicht, grösserer Rechnerleistung zu benutzen, um diese negative Merkmale zu vermindern. Das haben wir mittles einer vollständigen Untersuchung einer Gruppe von Warteschlangsystemen bestätig. Deswegen haben wir den Entwurf von sequentiellen Versionen von ein paar Varianten von Batch Means vorgeschlagen und sie genauso untersucht. Unter den implementierten Verfahren gibt es ein sehr attraktives: Overlapping Batch Means (OBM). OBM ermöglicht eine bessere Nutzung der Daten, da jede Beobachtungen ein neues Batch anfängt, d.h., die Anzahl von Batches ist viel grösser, und das ergibt eine kleinere Varianz. In diesem Fall ist die Anwendung von MRIP empfehlenswert, da diese Kombination weniger Beobachtungen benötigt und somit eine höhere Beschleunigung. Im Laufe der Recherche haben wir eine Klasse von Methoden (Standardized Time Series - STS) untersucht, die teoretisch bessere asymptotische Resultate als NOBM produziert. Die negative Auswirkung von STS ist, dass sie mehr Beobachtungen als die Batch-Means-Verfahren benoetigt. Aber das ist kein Hindernis, wenn wir STS zusammen mit MRIP anwenden. Die experimentelle Untersuchungen bestätigte, dass die Hypothese richtig ist. Die nächste Phase war es, OBM und STS einzustellen, um beide Verfahren unter den grösstmöglichen Anzahl von Prozessoren arbeiten lassen zu können. Fallstudien zeigten uns, dass sich beide sequentiellen Verfahren für die parallele Simulation sowie MRIP einigen.We investigated the feasibility of sequential methods of analysis of stochastic simulation under an environment of Multiple Replications in Parallel (MRIP). The main idea is twofold, the automation of the statistical control and speedup of simulation experiments. The methods of analysis found suggested in the literature were conceived for a single processor environment. Very few is known concerning the application of procedures based in such methods under MRIP. At first glance, sind both goals in opposition, since one needs a large amount of observations in order to achieve good quality of the results, i.e., the simulation takes frequently long time. However, by means of a careful design, together with a robust simulation tool based on independent replications, one can produce an efficient instrument of analysis of the simulation results. This research began with a sequential version of the classical method of Nonoverlapping Batch Means (NOBM). Although intuitiv and popular, under hight traffic intensity NOBM offers no good solution to the problem of strong correlation among the observations. It is not worthwhile to apply more computing power aiming to diminish this negative effect. We have confirmed this claim by means of a detailed and exhaustive analysis of four queuing systems. Therefore, we proposed the design of sequential versions of some Batch Means variants, and we investigated their statistical properties under MRIP. Among the implemented procedures there is one very attractive : Overlapping Batch Means (OBM). OBM makes a better use of collected data, since each observation initiates a new (overlapped) batch, that is, die number of batches is much larger, and this yields smaller variance. In this case, MRIP is highly recommended, since this combination requires less observations and, therefore, speedup. During the research, we investigated also a class of methods based on Standardized Time Series -- STS, that produces theoretically better asymptotical results than NOBM. The undesired negative effect of STS is the large number of observations it requires, when compared to NOBM. But that is no obstacle when we apply STS together with MRIP. The experimental investigation confirmed this hypothesis. The next phase was to tun OBM and STS, in order to put them working with the possible largest number of processors. A case study showed us that both procedures are suitable to the environment of MRIP

Epidgeons: Combining Drones and DTNs Technologies to Provide Connectivity in Remote Areas

© Owner/Author. 2015 This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM, In Proceedings of the First Workshop on Micro Aerial Vehicle Networks, Systems, and Applications for Civilian Use (pp. 57-58). http://dx.doi.org/10.1145/2750675.2750688.In certain geographical areas such as the rural areas or the developing regions, the lack of infrastructure, the temporary nature of the connections and the limited access to fixed public networks does not allow the use of all the advantages offered by the Internet. In this paper we present our project that aims to test a new communication system based on the combination of the use of unmanned aerial vehicles (called ePidgeons) and wireless networking technologies for Disruption Tolerant Networks (DTN). As a study case we will use the riparian communities along the rivers in the Amazon region.Manzoni, P.; Tavares De Araujo Cesariny Calafate, CM.; Cano Escribá, JC.; Mota, E. (2015). Epidgeons: Combining Drones and DTNs Technologies to Provide Connectivity in Remote Areas. ACM Digital Library. doi:10.1145/2750675.2750688SJ. Crowcroft, E. Yoneki, P. Hui, and T. Henderson, "Promoting tolerance for delay tolerant network research", ACM SIGCOMM Computer Communication Review, vol. 38, no. 5, Oct. 2008, pp. 63--68.Project "Networking for Communications Challenged Communities: Architecture, Test Beds and Innovative Alliances" (N4C), http://www.n4c.eu/.Project "DakNet", http://www.unitedvillages.com/.Phuong Tran Thi Ha, H. Yamamoto, K. Yamazaki, "Using Autonomous Air Vehicle in DTN Sensor Network for Environmental Observation", IEEE 37th Annual Computer Software and Applications Conference (COMPSAC) 2013, pp. 447--450.Uchida, N.; Kawamura, N.; Ishida, T.; Shibata, Y., "Proposal of Autonomous Flight Wireless Nodes with Delay Tolerant Networks for Disaster Use," 2014 Eighth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), 2-4 July 2014M. Coutinho, T. Moreira, E. Silva, A. Efrat, and T. Johnson. A new proposal of data mule network focused on Amazon riverine population. In Proceedings of the 3rd Extreme Conference on Communication: The Amazon Expedition (ExtremeCom '11). ACM, New York, NY, USAHervé Ntareme, Marco Zennaro, and Björn Pehrson. Delay tolerant network on smartphones: applications for communication challenged areas. In Proceedings of the 3rd Extreme Conference on Communication: The Amazon Expedition (ExtremeCom '11). ACM, New York, NY, USAMartinez-Vidal, Ruben, et al. "Mobile-agent based delay-tolerant network architecture for non-critical aeronautical data communications." Distributed Computing and Artificial Intelligence. Springer International Publishing, 2013. 513--520

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

Entropy based routing for mobile, low power and lossy wireless sensors networks

[EN] Routing protocol for low-power and lossy networks is a routing solution specifically developed for wireless sensor networks, which does not quickly rebuild topology of mobile networks. In this article, we propose a mechanism based on mobility entropy and integrate it into the corona RPL (CoRPL) mechanism, which is an extension of the IPv6 routing protocol for low-power and lossy networks (RPL). We extensively evaluated our proposal with a simulator for Internet of Things and wireless sensor networks. The mobility entropy-based mechanism, called CoRPL+E, considers the displacement of nodes as a deciding factor to define the links through which nodes communicate. Simulation results show that the proposed mechanism, when compared to CoRPL mechanism, is effective in reducing packet loss and latency in simulated mobile routing protocol for low-power and lossy networks. From the simulation results, one can see that the CoRPL+E proposal mechanism provides a packet loss reduction rate of up to 50% and delays reduction by up to 25% when compared to CoRPL mechanism.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by SIDIA Institute of Science and Technology, by Coordenacao de Aperfeicxoamento de Pessoal de Nivel Superior (CAPES), by Fundacao de Amparo a Pesquisa do Estado do Amazonas (FAPEAM)-support programs (Programa Primeiros Projetos (PPP) and Programa de Tecnologia da Informacao na Amazonia (PROTI)-Amazonia-Mobilidade), by Camara Tecnica de Reconstrucao e Recuperacao de Infraestrutura (CT-INFRA) of Ministerio da Ciencia, Tecnologia, Inovacoes e Comunicacoes(MCTI)/Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq), and by Secretaria de Estado de Ciencia, Tecnologia e Inovacao Amazonas (SECTI-AM) and Government of Amazon State, Brazil.Carvalho, C.; Mota, E.; Ferraz, E.; Seixas, P.; Souza, P.; Tavares, V.; Lucena Filho, W.... (2019). Entropy based routing for mobile, low power and lossy wireless sensors networks. International Journal of Distributed Sensor Networks (Online). 15(7):1-19. https://doi.org/10.1177/1550147719866134S119157Blanco-Novoa, O., Fernández-Caramés, T., Fraga-Lamas, P., & Castedo, L. (2018). A Cost-Effective IoT System for Monitoring Indoor Radon Gas Concentration. Sensors, 18(7), 2198. doi:10.3390/s18072198Ding, X., Tian, Y., & Yu, Y. (2016). A Real-Time Big Data Gathering Algorithm Based on Indoor Wireless Sensor Networks for Risk Analysis of Industrial Operations. IEEE Transactions on Industrial Informatics, 12(3), 1232-1242. doi:10.1109/tii.2015.2436337Rashid, B., & Rehmani, M. H. (2016). Applications of wireless sensor networks for urban areas: A survey. Journal of Network and Computer Applications, 60, 192-219. doi:10.1016/j.jnca.2015.09.008Laurindo, S., Moraes, R., Nassiffe, R., Montez, C., & Vasques, F. (2018). An Optimized Relay Selection Technique to Improve the Communication Reliability in Wireless Sensor Networks. Sensors, 18(10), 3263. doi:10.3390/s18103263Airehrour, D., Gutierrez, J., & Ray, S. K. (2016). Secure routing for internet of things: A survey. Journal of Network and Computer Applications, 66, 198-213. doi:10.1016/j.jnca.2016.03.006Mesodiakaki, A., Zola, E., Santos, R., & Kassler, A. (2018). Optimal user association, backhaul routing and switching off in 5G heterogeneous networks with mesh millimeter wave backhaul links. Ad Hoc Networks, 78, 99-114. doi:10.1016/j.adhoc.2018.05.008Marszałek, Z., Woźniak, M., & Połap, D. (2018). Fully Flexible Parallel Merge Sort for Multicore Architectures. Complexity, 2018, 1-19. doi:10.1155/2018/8679579Fotouhi, H., Moreira, D., & Alves, M. (2015). mRPL: Boosting mobility in the Internet of Things. Ad Hoc Networks, 26, 17-35. doi:10.1016/j.adhoc.2014.10.009Barcelo, M., Correa, A., Vicario, J. L., Morell, A., & Vilajosana, X. (2016). Addressing Mobility in RPL With Position Assisted Metrics. IEEE Sensors Journal, 16(7), 2151-2161. doi:10.1109/jsen.2015.2500916Bouaziz, M., Rachedi, A., & Belghith, A. (2019). EKF-MRPL: Advanced mobility support routing protocol for internet of mobile things: Movement prediction approach. Future Generation Computer Systems, 93, 822-832. doi:10.1016/j.future.2017.12.015Fotouhi, H., Moreira, D., Alves, M., & Yomsi, P. M. (2017). mRPL+: A mobility management framework in RPL/6LoWPAN. Computer Communications, 104, 34-54. doi:10.1016/j.comcom.2017.01.020Iova, O., Picco, P., Istomin, T., & Kiraly, C. (2016). RPL: The Routing Standard for the Internet of Things... Or Is It? IEEE Communications Magazine, 54(12), 16-22. doi:10.1109/mcom.2016.1600397cmFotouhi, H., Alves, M., Zamalloa, M. Z., & Koubaa, A. (2014). Reliable and Fast Hand-Offs in Low-Power Wireless Networks. IEEE Transactions on Mobile Computing, 13(11), 2620-2633. doi:10.1109/tmc.2014.2307867Kamgueu, P. O., Nataf, E., & Ndie, T. D. (2018). Survey on RPL enhancements: A focus on topology, security and mobility. Computer Communications, 120, 10-21. doi:10.1016/j.comcom.2018.02.011Park, J., Kim, K.-H., & Kim, K. (2017). An Algorithm for Timely Transmission of Solicitation Messages in RPL for Energy-Efficient Node Mobility. Sensors, 17(4), 899. doi:10.3390/s17040899Stanoev, A., Filiposka, S., In, V., & Kocarev, L. (2016). Cooperative method for wireless sensor network localization. Ad Hoc Networks, 40, 61-72. doi:10.1016/j.adhoc.2016.01.003Wallgren, L., Raza, S., & Voigt, T. (2013). Routing Attacks and Countermeasures in the RPL-Based Internet of Things. International Journal of Distributed Sensor Networks, 9(8), 794326. doi:10.1155/2013/794326Raza, S., Wallgren, L., & Voigt, T. (2013). SVELTE: Real-time intrusion detection in the Internet of Things. Ad Hoc Networks, 11(8), 2661-2674. doi:10.1016/j.adhoc.2013.04.014Zhang, K., Liang, X., Lu, R., & Shen, X. (2014). Sybil Attacks and Their Defenses in the Internet of Things. IEEE Internet of Things Journal, 1(5), 372-383. doi:10.1109/jiot.2014.2344013Mayzaud, A., Sehgal, A., Badonnel, R., Chrisment, I., & Schönwälder, J. (2015). Mitigation of topological inconsistency attacks in RPL-based low-power lossy networks. International Journal of Network Management, 25(5), 320-339. doi:10.1002/nem.1898Navidi, W., & Camp, T. (2004). Stationary distributions for the random waypoint mobility model. IEEE Transactions on Mobile Computing, 3(1), 99-108. doi:10.1109/tmc.2004.126182

[Photograph 2012.201.B1203.0708]

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