Modelling the Delay Distribution of Information Centric Delay Tolerant Networks in Disaster Area

Yang, Jinze; Sun, Yan; Carrión, Jesús Requena

doi:10.1145/3084035.3084040

Cited by 4 publications

(15 citation statements)

References 10 publications

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“…In our previous work [13], we exploited the relationship between end-to-end delay and encounter numbers regardless of storage consumed by contents. Concluded from [13], the endto-end delay is determined by mobility models, total number of nodes and initial number of contents in the network. However, in [13], the storage, which is another limited resource in disaster scenarios, is not taken into consideration.…”

Section: Information Centric Networkmentioning

confidence: 99%

“…Concluded from [13], the endto-end delay is determined by mobility models, total number of nodes and initial number of contents in the network. However, in [13], the storage, which is another limited resource in disaster scenarios, is not taken into consideration.…”

Section: Information Centric Networkmentioning

confidence: 99%

“…Thus, we develop CONI to study the delivery process of ICNs in opportunistic scenarios. The CONI-D is proposed by us in [13] while CONI-St is proposed in this paper.…”

Section: B Description Of Conimentioning

confidence: 99%

“…Aiming to study the quantitative relationship between delivery probability and delay in DTN-ICN, in conjunction with prefeteching, two proposed models in this work for analysis are named as Delay distribution of CONventional ICN (CONI-D) [13] and Delay distribution of ICN with All Agents Prefetching (IAAP-D) 1 . The Storage ratio models for CONcentional ICN (CONI-St) and ICN with All Agent Prefetching (IAAP-St) are defined as well to describe the percentage of nodes having content versus delay allowance.…”

Section: Introductionmentioning

confidence: 99%

“…Both CONI-St and IAAP-St can produce accurate estimation of storage along with time evolution for the given network configuration. The IAAP-D is also proposed to model the delay distribution of IAAP given elapsed time, comparing with CONI-D [13].…”

Section: Introductionmentioning

confidence: 99%

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An Analytical Model for Information Centric Internet of Things Networks in Opportunistic Scenarios

Yang

Sun

Requena‐Carrión

et al. 2020

IEEE Systems Journal

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The availability of environmental monitoring data collected by Internet of Things networks can be essential for many critical processes such as relief operations in disaster areas. The underlying communications infrastructure can be however severely compromised in these scenarios and therefore opportunistic approaches might be needed. Approaches based on Information Centric Networks (ICN), where moving devices forward collected data, have been proposed for opportunistic scenarios but to date, the dynamics of the delivery process in ICNs remain poorly understood. In this paper, we build a family of Markovian models for the delivery process of ICNs in opportunistic scenarios, that allows us to derive the end-to-end delay distribution and the storage ratio in terms of the encounter rate of the moving devices. Furthermore, we investigate how prefetching mechanisms affect the delivery process compared to conventional ICNs. The proposed models are fully validated in a computer simulation environment and demonstrate that the utility of delivery with prefetching reaches its peak in a short time and then decreases at a high rate. Our Markovian models can provide both the insight and quantitative estimations that are needed to design practical ICNs in opportunistic scenarios.

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Section: Information Centric Networkmentioning

confidence: 99%

Section: Information Centric Networkmentioning

confidence: 99%

“…Thus, we develop CONI to study the delivery process of ICNs in opportunistic scenarios. The CONI-D is proposed by us in [13] while CONI-St is proposed in this paper.…”

Section: B Description Of Conimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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