Design and Performance Evaluation of Data Dissemination Systems for Opportunistic Networks Based on Cognitive Heuristics

Abstract

Co on nt ti i, , M M. . M Mo or rd da ac cc ch hi in ni i, , A A. . P Pa as ss sa ar re el ll la a IIT ABSTRACTIt is often argued that the Future Internet will be a very large scale content-centric network. Scalability issues will stem even more from the amount of content nodes will generate, share and consume. In order to let users become aware and retrieve the content they really need, these nodes will be required to swiftly react to stimuli and assert the relevance of discovered data under uncertainty and only partial information. The human brain performs the task of information filtering and selection using the so-called cognitive heuristics, i.e. simple, rapid, low-resource demanding, yet very effective schemes that can be modeled using a functional approach. In this paper we propose a solution based on one such heuristics, namely the recognition heuristic, for dealing with data dissemination in opportunistic networks. We show how to implement an algorithm that exploits the environmental information in order to implement an effective dissemination of data based on the recognition heuristic, and provide a performance evaluation of such a solution via simulation.

Targeted content dissemination in mobile social networks taking account of resource limitation

“…The movement of a node from a community to the next community consists of selection of the speed uniformly at random over [ v min , v max ] . Various flavors of HCMM (such as CMM, HCMM, and HCMM‐SO) are widely used to evaluate forwarding and dissemination methods in the literatures …”

“…43 Various flavors of HCMM (such as CMM, 64 HCMM, 43 and HCMM-SO 61 ) are widely used to evaluate forwarding and dissemination methods in the literatures. 7,34,62,63,[65][66][67][68][69][70][71][72] The number of communities in HCMM-SO simulation scenarios is randomly selected. Distribution of the number of communities for a scenario with 100 nodes is expressed by the following formula and depicted in Figure 4, which follows the normal distribution with mean 39.641 and variance 6.95532:…”

Design and evaluation of a cognitive approach for disseminating semantic knowledge and content in opportunistic networks

“…219 Although such global optimisation approach can find the optimal 220 policy and lead to an optimal content allocation, it requires global 221 knowledge of the network and a-priori information on how users 222 behave, that in practice, might be unlikely to be available in an 223 opportunistic scenario. 224 The use of cognitive heuristics to drive the dissemination pro-225 cess has been first proposed in [23]. The idea is to drive the 226 dissemination process using procedures that mimic, in functional This work proves the suitability and effectiveness of these heuris-235 tics in problems, like data dissemination in opportunistic networks, 236 where every node has only a partial knowledge about its environ-237 ment.…”

“…They are most of the time together 19 with their users in the physical world, and can thus, for example, 20 gather context information about their behaviour and the physical 21 places they visit. On the other hand, they are probably the most 22 typical way through which users access information in the cyber 23 world. Therefore, they can be usefully instructed to autonomously 24 act in the cyber world (e.g., by proactively filtering or fetching 25 information) on behalf of their users, by exploiting context infor-26 mation about their behaviour in the physical world.…”

“…These functional 93 models, called cognitive heuristics, differently from other biologi-94 cal models as artificial neural networks, do not aim at reproduc- 95 ing the physiology of the brain's processes, but model their func-96 tionality. By taking advantage of these descriptions, previous works 97 [23][24][25] have shown how rules and procedures used by the human 98 brain, when assessing the relevance of information (in face of time 99 and resource restrictions), can be exploited to design adaptive, low 100 resource-demanding, yet very effective, algorithms for data dis-101 semination in opportunistic networks but none -to the best of our 102 knowledge -has explored how to apply these models to optimise 103 1 In the paper we use the terms content and data item interchangeably.…”