A framework of spreading disease monitoring system model

Ayubi, Soleh Udin Al

doi:10.1109/icici-bme.2009.5417277

Cited by 1 publication

(1 citation statement)

References 1 publication

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“…as an integrated solution. This allows IoT to be the subject for many research articles (Wei et al 2013;Al Ayubi 2209;Gao and Liu 2011;Shi and Rekeczky 2007;Freitas et al 2009;Hong et al 2010;Zorzi et al 2010;Yue et al 2014;Botta et al 2016;Khodkari and Maghrebi 2016;Tsai et al 2014;Li et al 2016;Raza et al 2013;Sheng et al 2013) analyzing IoT from different prospective. For example, the authors of (Yue et al 2014;Botta et al 2016;Khodkari and Maghrebi 2016) propose the use of cloud storage in IoT.…”

Section: Related Workmentioning

confidence: 99%

Towards internet of things modeling: a gateway approach

Altamimi

Ramadan

2016

Complex Adapt Syst Model

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Purpose Internet of things (IoT) is an interaction between more than one network to facilitate communication. These networks by themselves are complex networks. Therefore, IoT network is expected to grow at unprecedented scale involving other networks such as Mobile, VANET, and Wireless Sensor Networks (WSNs). In fact, modeling each network by itself is a complicated process. In addition, on a large scale, the communication among these networks increases the modeling complexity in which energy consumption could be critical due to large number of dropped messages. Therefore, this paper is a step forward towards modeling IoT complex network through gateway deployment. The paper answers the question of how to deploy these gateways in a way that guarantees an efficient and adaptive communication. Methods Two models/methods are proposed and examined which are geographical based and mobile ferry based models. Due to the complexity of the deployment problem in reality, the deployment problem is treated as a complex adaptive problem and simulated through different sets of experiments and settings. Results The two methods have been compared through set of experiments using ONE simulator with the same number of employed gateways in the two methods. The experiments shows that ferry based model outperforms geographical based model with 29% improvement in messages delivery probability. Additionally, when the number of mobile ferries are reduced by 34% compared to gateways that are distributed based on geographical area, the mobile ferries approach still outperform geographical area based approach when it comes to messages delivery probability. Conclusions The paper presents the two methods to model the complex internet of things environment and its sub networks interaction. The paper concludes that employing mobile ferries as gateways is better than deploying gateways based on geographical area when the sub networks interaction is facilitated in IoT network.

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