Abstract
In the convergence of the Cyber-Physical World, user devices will act as proxies of the humans in the cyber world. They will be required to act in a vast information landscape, asserting the relevance of data spread in the cyber world, in order to let their human users become aware of the content they really need. This is a remarkably similar situation to what the human brain has to do all the time when deciding what information coming from the surrounding environment is interesting and what can simply be ignored. The brain performs this task using so called cognitive heuristics, i.e. simple, rapid, yet very effective schemes. In this article, we propose a new approach that exploits one of these heuristics, the recognition heuristic, for developing a self-adaptive system that deals with effective data dissemination in opportunistic networks. We show how to implement it and provide an extensive analysis via simulation. Specifically, results show that the proposed solution is as effective as state-of-the-art solutions for data dissemination in opportunistic networks, while requiring far less resources. Finally, our sensitiveness analysis shows how various parameters depend on the context where nodes are situated, and suggest corresponding optimal configurations for the algorithm.
- Alba, J. and Chattopadhyay, A. 1985. Effects of context and part-category cues on recall of competing brands. J. Market. Res. 340--349.Google Scholar
Cross Ref
- Balamash, A. and Krunz, M. 2004. An overview of web caching replacement algorithms. IEEE Commun. Surv. Tutorials 6, 1--4, 44--56. Google Scholar
Digital Library
- Boldrini, C. and Passarella, A. 2010. HCMM: Modelling spatial and temporal properties of human mobility driven by users’ social relationships. Comput. Comm. 33, 1056--1074. Google Scholar
Digital Library
- Boldrini, C. and Passarella, A. 2013. Data dissemination in opportunistic networks. In Mobile Ad Hoc Networking: Cutting Edge Directions 2nd Ed., S. Basagni, M. Conti, S. Giordano, and I. Stojmenovic Eds., John Wiley & Sons, Inc., 453--490.Google Scholar
- Boldrini, C., Conti, M., and Passarella, A. 2010. Design and performance evaluation of ContentPlace, a social-aware data dissemination system for opportunistic networks. Comput. Netw. 54, 589--604. Google Scholar
Digital Library
- Bröder, A. and Eichler, A. 2006. The use of recognition information and additional cues in inferences from memory. Acta Psychologica 121, 275--284.Google Scholar
Cross Ref
- Carreras, I., De Pellegrini, F., Miorandi, D., Tacconi, D., and Chlamtac, I. 2008. Why neighbourhood matters: Interests-driven opportunistic data diffusion schemes. In Proceedings of the 3rd ACM Workshop on Challenged Networks (CHANTS). 81--88. Google Scholar
Digital Library
- Conti, M. 2011. Special section on mobile opportunistic networking. Perv. Mobile Comput. 7, 2, 159. Google Scholar
Digital Library
- Conti, M., Chong, S., Fdida, S., Jia, W., Karl, H., Lin, Y.-D., Mähönen, P., Maier, M., Molva, R., Uhlig, S., and Zukerman, M. 2011a. Research challenges towards the future internet. Comput. Comm. 34, 18, 2115--2134. Google Scholar
Digital Library
- Conti, M., Mordacchini, M., and Passarella, A. 2011b. Data dissemination in opportunistic networks using cognitive heuristics. In Proceedings of the 5th IEEE WOWMOM Workshop on Autonomic and Opportunistic Computing (AOC). 1--6. Google Scholar
Digital Library
- Conti, M., Das, S. K., Bisdikian, C., Kumar, M., Ni, L. M., Passarella, A., Roussos, G., Troester, G., Tsudik, G., and Zambonelli, F. 2012. Looking ahead in pervasive computing: Challenges and opportunities in the era of cyberphysical convergence. Perv. Mobile Comput. 8, 1, 2--21. Google Scholar
Digital Library
- Conti, M., Mordacchini, M., Passarella, A., and Rozanova, L. 2013. A semantic-based algorithm for data dissemination in opportunistic networks. In Proceedings of the 7th International Workshop on Self-Organizing Systems (IWSOS). Springer.Google Scholar
- Czerlinski, J., Gigerenzer, G., and Goldstein, D. 1999. How Good Are Simple Heuristics? Oxford University Press, 97--118.Google Scholar
- Dawes, R. 1979. The robust beauty of improper linear models in decision making. Amer. Psychol. 34, 7, 571.Google Scholar
Cross Ref
- DeMiguel, V., Garlappi, L., and Uppal, R. 2009. Optimal versus naive diversification: How inefficient is the 1/n portfolio strategy? Rev. Financ. Studies 22, 5, 1915--1953.Google Scholar
Cross Ref
- Erdfelder, E., Küpper-Tetzel, C., and Mattern, S. 2011. Threshold models of recognition and the recognition heuristic. Judg. Decis. Making 6, 1, 7--22.Google Scholar
- Evans, J. and Over, D. 2010. Heuristic thinking and human intelligence: A commentary on Marewski, Gaissmaier and Gigerenzer. Cog. Process. 11, 171--175.Google Scholar
Cross Ref
- Gaissmaier, W. and Marewski, J. 2011. Forecasting elections with mere recognition from small, lousy samples: A comparison of collective recognition, wisdom of crowds, and representative polls. Judg. Decis. Making 6, 1, 73--88.Google Scholar
- Gao, W. and Cao, G. 2011. User-centric data dissemination in disruption tolerant networks. In Proceedings of IEEE INFOCOM. 3119--3127.Google Scholar
- Gigerenzer, G. 2004. Fast and frugal heuristics: The tools of bounded rationality. In Blackwell Handbook of Judgment and Decision Making, 62--88.Google Scholar
- Gigerenzer, G. 2008. Why heuristics work. Persp. Psychol. Sci. 3, 1, 20--29.Google Scholar
Cross Ref
- Gigerenzer, G. and Goldstein, D. G. 2002. Models of ecological rationality: The recognition heuristic. Psychol. Rev. 109, 1, 75--90.Google Scholar
Cross Ref
- Gigerenzer, G. and Todd, P. 1999. Fast and frugal heuristics: The adaptive toolbox. In Simple Heuristics that Make Us Smart. Evolution and Cognition, 3--34. Oxford University Press, NY.Google Scholar
- Glöckner, A. and Bröder, A. 2011. Processing of recognition information and additional cues: A model-based analysis of choice, confidence, and response time. Judg. Decis. Making 6, 1, 23--42.Google Scholar
- Goldstein, D. G. and Gigerenzer, G. 1996. Reasoning the fast and frugal way: Models of bounded rationality. Psychol. Rev. 103, 4, 650--669.Google Scholar
Cross Ref
- Goldstein, D. G. and Gigerenzer, G. 1999. The recognition heuristic: How ignorance makes us smart. In Simple Heuristics That Make Us Smart, G. Gigerenzer and P. M. Todd Eds., Oxford University Press, 37--58.Google Scholar
- Gros, C., Kaczor, G., and Marković, D. 2012. Neuropsychological constraints to human data production on a global scale. Euro. Phys. J. B-Condensed Matter and Complex Syst. 85, 1, 1--5.Google Scholar
- Hauser, J. and Wernerfelt, B. 1990. An evaluation cost model of consideration sets. J. Consum. Res. 393--408.Google Scholar
Cross Ref
- Hilbig, B. E. and Pohl, R. F. 2009. Ignorance- versus evidence-based decision making: A decision time analysis of the recognition heuristic. J. Exper. Psychol. Learn. Memory Cog. 35, 1296--1305.Google Scholar
Cross Ref
- Ioannidis, S., Chaintreau, A., and Massoulié, L. 2009. Optimal and scalable distribution of content updates over a mobile social network. In Proceedings of IEEE INFOCOM. 1422--1430.Google Scholar
- Jacoby, L. and Brooks, L. 1984. Nonanalytic cognition: Memory, perception, and concept learning. In The Psychology of Learning and Motivation: Advances in Research and Theory 18, 1--47.Google Scholar
Cross Ref
- Jaho, E., Karaliopoulos, M., and Stavrakakis, I. 2010. Social similarity as a driver for selfish, cooperative and altruistic behavior. In Proceedings of the IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks (WoWMoM). 1--6. Google Scholar
Digital Library
- Jaho, E., Koukoutsidis, I., Stavrakakis, I., and Jaho, I. 2012. Cooperative content replication in networks with autonomous nodes. Comput. Comm. 35, 5, 637--647. Google Scholar
Digital Library
- Johnson, E. and Goldstein, D. 2003. Do defaults save lives? Science 302, 5649, 1338.Google Scholar
- Krifa, A., Barakat, C., and Spyropoulos, T. 2011. Mobitrade: Trading content in disruption tolerant networks. In Proceedings of the 6th ACM Workshop on Challenged Networks. 31--36. Google Scholar
Digital Library
- Laroche, M., Kim, C., and Matsui, T. 2003. Which decision heuristics are used in consideration set formation? J. Consum. Market. 20, 3, 192--209.Google Scholar
Cross Ref
- Law, A. and Kelton, W. 1991. Simulation Modeling and Analysis. Vol. 2, McGraw-Hill New York.Google Scholar
- Lenders, V., May, M., Karlsson, G., and Wacha, C. 2008. Wireless ad hoc podcasting. SIGMOBILE Mob. Comput. Comm. Rev. 12, 65--67. Google Scholar
Digital Library
- Marewski, J. N. and Mehlhorn, K. 2011. Using the ACT-R architecture to specify 39 quantitative process models of decision making. Judg. Decis. Making 6, 6, 439--519.Google Scholar
- Marewski, J. N. and Schooler, L. J. 2011. Cognitive niches: An ecological model of strategy selection. Psychol. Rev. 118, 393--437.Google Scholar
Cross Ref
- Marewski, J., Gaissmaier, W., and Gigerenzer, G. 2010a. We favor formal models of heuristics rather than lists of loose dichotomies: A reply to Evans and Over. Cog. Process. 11, 177--179.Google Scholar
Cross Ref
- Marewski, J. N., Gaissmaier, W., and Gigerenzer, G. 2010b. Good judgments do not require complex cognition. Cog. Process. 11, 103--121.Google Scholar
Cross Ref
- Marewski, J. N., Gaissmaier, W., Schooler, L. J., Goldstein, D. G., and Gigerenzer, G. 2010c. From recognition to decisions: Extending and testing recognition-based models for multialternative inference. Psychonom. Bull. Rev. 17, 3, 287--309.Google Scholar
Cross Ref
- Marewski, J. N., Pohl, R. F., and Vitouch, O. 2010d. Recognition-based judgments and decisions: Introduction to the special issue (vol. 1). Judg. Decis. Making 5, 207--215.Google Scholar
- Marewski, J. N., Pohl, R. F., and Vitouch, O. 2011a. Recognition-based judgments and decisions: Introduction to the special issue (II). Judg. Decis. Making 6, 1--6.Google Scholar
- Marewski, J. N., Pohl, R. F., and Vitouch, O. 2011b. Recognition-based judgments and decisions: What we have learned (so far). Judg. Decis. Making 6, 359--380.Google Scholar
- Monti, M., Martignon, L., Gigerenzer, G., and Berg, N. 2009. The impact of simplicity on financial decision-making. In Proceedings of CogSci. The Cognitive Science Society, Inc., 1846--1851.Google Scholar
- Oppenheimer, D. 2003. Not so fast! (and not so frugal!): Rethinking the recognition heuristic. Cognition 90, 1, B1--B9.Google Scholar
Cross Ref
- Ortmann, A., Gigerenzer, G., Borges, B., and Goldstein, D. 2008. The recognition heuristic: A fast and frugal way to investment choice? In Handbook of Experimental Economics Results 1, 993--1003.Google Scholar
Cross Ref
- Pachur, T., Bröder, A., and Marewski, J. N. 2008. The recognition heuristic in memory-based inference: Is recognition a non-compensatory cue? J. Behav. Decis. Making 21, 183--210.Google Scholar
Cross Ref
- Pantazopoulos, P., Stavrakakis, I., Passarella, A., and Conti, M. 2010. Efficient social-aware content placement in opportunistic networks. In Proceedings of the 7th International Conference on Wireless On-demand Network Systems and Services (WONS). 17--24. Google Scholar
Digital Library
- Passarella, A. 2012. A survey on content-centric technologies for the current Internet: CDN and p2p solutions. Comput. Comm. 35, 1, 1--32. Google Scholar
Digital Library
- Paul, S., Pan, J., and Jain, R. 2011. Architectures for the future networks and the next generation internet: A survey. Comput. Comm. 34, 1, 2--42. Google Scholar
Digital Library
- Pelusi, L., Passarella, A., and Conti, M. 2006. Opportunistic networking: Data forwarding in disconnected mobile ad hoc networks. IEEE Comm. Mag. 44, 11, 134--141. Google Scholar
Digital Library
- Reich, J. and Chaintreau, A. 2009. The age of impatience: Optimal replication schemes for opportunistic networks. In Proceedings of the 5th International Conference on Emerging Networking Experiments and Technologies (CoNEXT). ACM, New York, NY, 85--96. Google Scholar
Digital Library
- Schooler, L. and Hertwig, R. 2005. How forgetting aids heuristic inference. Psychol. Rev. 112, 3, 610.Google Scholar
Cross Ref
- Serwe, S. and Frings, C. 2006. Who will win wimbledon? The recognition heuristic in predicting sports events. J. Behav. Dec. Making 19, 4, 321--332.Google Scholar
- Shocker, A., Ben-Akiva, M., Boccara, B., and Nedungadi, P. 1991. Consideration set influences on consumer decision-making and choice: Issues, models, and suggestions. Market. Lett. 2, 3, 181--197.Google Scholar
Cross Ref
- Simon, H. 1955. A behavioral model of rational choice. Quart. J. Econ. 69, 1, 99.Google Scholar
Cross Ref
- Simon, H. 1990. Invariants of human behavior. Annual Rev. Psychol. 41, 1, 1--20.Google Scholar
Cross Ref
- Tang, S., Jaho, E., Stavrakakis, I., Koukoutsidis, I., and Mieghem, P. V. 2011. Modeling gossip-based content dissemination and search in distributed networking. Comput. Comm. 34, 6, 765--779. Google Scholar
Digital Library
- Whitbeck, J., Amorim, M., Lopez, Y., Leguay, J., and Conan, V. 2011. Relieving the wireless infrastructure: When opportunistic networks meet guaranteed delays. In Proceedings of the IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM). 1--10. Google Scholar
Digital Library
- Whittlesea, B. 1993. Illusions of familiarity. J. Exper. Psychol. Learn. Mem. Cog. 19, 6, 1235.Google Scholar
Cross Ref
- Yoneki, E., Hui, P., Chan, S., and Crowcroft, J. 2007. A socio-aware overlay for publish/subscribe communication in delay tolerant networks. In Proceedings of MSWiM. Google Scholar
Digital Library
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Design and Performance Evaluation of Data Dissemination Systems for Opportunistic Networks Based on Cognitive Heuristics
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