Programmable architecture based on Software Defined Network for Internet of Things: Connected Dominated Sets approach

Bendouda, Djamila; Rachedi, Abderrezak; Haffaf, Hafid

doi:10.1016/j.future.2017.09.070

Cited by 44 publications

(19 citation statements)

References 20 publications

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“…On the other hand, the weakness of our framework is the need to further enhance the models of each view and the transformations between these models (to be done in future work). In order to move toward IoT, we also seek to integrate concepts and works related to IoT in Modesene .…”

Section: Discussionmentioning

confidence: 99%

Model driven framework to enhance sensor network design cycle

Kifouche

Hamouche²,

Kocik³

et al. 2019

Trans Emerging Tel Tech

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Recent advances in sensor network provide more and more equipment with a large amount of sensors and actuators. However, their adoptions in modern life face several challenges. First, sensor network design is complex and requires several steps including network sizing, hardware and software development, deployment, and data exploitation. Often, each step is managed by a particular tool. These tools are rarely connected to each other. Therefore, descriptions are required in each used tool. These multiple descriptions are costly in terms of development time and can lead to design errors. In order to address these issues, we propose a new model-driven framework named Modesene to efficiently design a sensor network for different kind of application. Modesene is designed to ensure four main process including multifacets modeling, network simulations, deployment, and data exploitation. The proposed framework supports sensor network tools by providing bridges with these tools. Therefore, one description in Modesene can be automatically transformed in each used tool. The framework can perform simulations and code generation. It promotes a data-driven approach; in fact, data obtained by simulations is used to drive design choices. We validate our framework through use case of smart building with two possible solutions. Based on Omnet++ simulator, Modesene can assess node lifetime, costs, and packets loss. Thereafter, the obtained results drive the choice of the suitable solution. Then, the code generator provides source codes for sensor nodes, gateway nodes, and for data exploitation. Compared to existing frameworks, Modesene deals with all design steps. Abbreviations: IoT, internet of things; WSN, Wireless sensor network Trans Emerging Tel Tech. 2019;30:e3560. wileyonlinelibrary.com/journal/ett communication technologies), and sink node (to collect and process data). Nodes can use different communication media to transmit and receive data (eg, wired channel, wireless channel, optical channel). Wireless communications are the most used in modern sensor networks because, in a large scale network, it becomes difficult or impossible to link nodes by wired channels. In Wireless Sensor Network (wsn), nodes can have different communication technologies (eg, ZigBee, 4 Bluetooth, 5 rfid, 6,7 ). Each technology has specific characteristics such as throughput and range. Sensor network development involves different steps including network sizing, software and hardware development, analyses and simulations, physical deployment, and data exploitation. These steps can be grouped in sensor network life cycle terminology. The motivations of our work are related to the fact that sensor network design requires to deal with many domains, involving many actors such as hardware engineer, software developer, and network expert. Data collected from sensor network may be also used by nontechnical actor, such as doctors in health domain. Therefore, many domain specific tools are used through the life cycle to help actors involved in each step. T...

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Section: Discussionmentioning

confidence: 99%

Model driven framework to enhance sensor network design cycle

Kifouche

Hamouche²,

Kocik³

et al. 2019

Trans Emerging Tel Tech

Self Cite

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“…The basic concept is to select particular nodes to play a role as local controllers. () To this end, we have various types of controller such as the PC, the RSU, and the miners as presented in Figure . In Figure , we illustrated the interaction between different modules.…”

Section: Framework Based On Blockchain Architecturementioning

confidence: 99%

A blockchain‐based framework to secure vehicular social networks

Yahiatene

Rachedi

Riahla

et al. 2019

Trans Emerging Tel Tech

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Vehicular social network is emerging as a new promising concept, combining two types of network paradigms, namely, vehicular networks and social networks. In order to manage efficiently the security and the control of the network, this paper proposes a new framework based on the emerging concepts of software-defined vehicular network (SDVN) and blockchain. Using the SDVN makes the network more programmable, virtualized, and partitionable. However, on the other hand, it also creates a well-known vulnerability of a single point of failure. Hence, we propose to introduce the blockchain paradigm that will enable the certification of transactions and ensure data anonymity in a fully distributed manner. To this end, three levels of controllers are needed: a principal controller (PC), roadside units (RSUs), and a local controller. In order to dynamically select miners, a distributed miners connected dominating set algorithm (DM-CDS) has been proposed. The DM-CDS is a single-phase distributed algorithm that supports a dynamic topology based on a trust model and some other network parameters, such as the connectivity degree, the average link quality indicator, and the rank. The performance of the proposed DM-CDS is evaluated throughout multiple scenarios using different parameters, such as trust metric, node density, node mobility, and radio range. The obtained results highlight the importance of such proposed architecture, especially in terms of number of required miners. For instance, when the density of nodes increases, the number of selected miners increases similarly to when the network length increases. The node mobility impacts also on the stability of the selected miners, in terms of withdrawing and joining, showing a variation between 0% and 10%.The trust metric has also an important impact on the selection of miners, as only nodes with a higher trust level are selected to endorse the roles of miners.Trans Emerging Tel Tech. 2019;30:e3650.wileyonlinelibrary.com/journal/ett

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“…Nowadays, Internet of Things (IoT) technology begins to take an important place in economic systems and in society daily life [1]. It has got a large success in several application areas [2,3,4], ranging from smart city [5,6], Industry 4.0 [7], to smart grid applications [8]. However, most of actuated devices constituting IoT paradigm are only passive so far.…”

Section: Introductionmentioning

confidence: 99%