A Cost Efficient Design of a Multi-sink Multi-controller WSN in a Smart Factory

Faragardi, Hamid Reza; Fotouhi, Hossein; Nolte, Thomas; Rahmani, Rahim

doi:10.1109/hpcc-smartcity-dss.2017.77

Cited by 20 publications

(19 citation statements)

References 16 publications

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“…In this paper, the proposed placement algorithm is based on a powerful metaheuristic evolutionary algorithm originally proposed in the work of Faragardi et al It is based on a parallel version of a Max‐Min Ant System being leveraged by a memory‐based SA algorithm, which is called PACSA (Parallel Ant Colony leveraged by Simulated Annealing). The experimental results for two other combinatorial optimization problems, including task allocation on multicores and sink placement problem in IoT systems, demonstrated its strength to find high‐quality solutions (ie, optimal or very close to optimal) in a reasonable computation time. Moreover, as is shown by Faragardi et al, PACSA surpasses multiple well‐known heuristic‐based algorithms such as some variations of bin‐packing algorithms.…”

Section: Solution Approachmentioning

confidence: 99%

“…The experimental results for two other combinatorial optimization problems, including task allocation on multicores and sink placement problem in IoT systems, demonstrated its strength to find high‐quality solutions (ie, optimal or very close to optimal) in a reasonable computation time. Moreover, as is shown by Faragardi et al, PACSA surpasses multiple well‐known heuristic‐based algorithms such as some variations of bin‐packing algorithms.…”

Section: Solution Approachmentioning

confidence: 99%

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An energy‐aware resource provisioning scheme for real‐time applications in a cloud data center

et al. 2018

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Based on a pay-as-you-go model, cloud computing provides the possibility of hosting pervasive applications from both academic and business domains.However, data centers hosting cloud applications consume huge amounts of electrical energy, contributing to high operational costs and large carbon footprints to the environment. Energy-aware resource provisioning is an effective solution to diminish the energy consumption of cloud data centers. Recently, a growing trend has emerged, where cloud technology is used to run periodic real-time applications such as multimedia, telecommunication, video gaming, and industrial applications. In order for a real-time application to be able to use cloud services, cloud providers have to be able to provide timing guarantees. In this paper, we introduce an energy-aware resource provisioning mechanism for cloud data centers, which are capable of serving real-time periodic tasks following the Software as a Service model. The proposed method is compared against an energy-aware version of the RT-OpenStack. RT-OpenStack is a recently proposed approach to provide a time-predictable version of OpenStack. The experimental results manifest that our proposed resource provisioning method outperforms energy-aware version of the RT-OpenStack by 16.01%, 25.45%, and 25.45% in terms of energy consumption, number of used servers, and average utilization of used servers, respectively. Moreover, from the scalability perspective, the preference of the proposed method for large-scale data centers is more considerable.

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Section: Solution Approachmentioning

confidence: 99%

Section: Solution Approachmentioning

confidence: 99%

An energy‐aware resource provisioning scheme for real‐time applications in a cloud data center

et al. 2018

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“…For example, as shown in Figure A, the set of candidate sinks A S is equal to {1,2,3,4,5,6}, and as shown in Figure B, the set of selected sinks S is equal to {2,4,5}. In this paper, we assume that the sets A S and A C are known in advance, similar to the works of Faragardi et al and Poe and Schmitt; however, there are multiple methods in the literature discussing the number of candidate locations to place sink nodes, such as those by Safa et al In Section 5, we further investigate the effect of the size of A S and A C on the chance of finding an optimal sink/controller placement. Figure A illustrates an example of our network assumption with six candidate sinks and three candidate controllers.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…The proposed method is a powerful metaheuristic evolutionary algorithm, originally proposed in the work of Faragardi et al, which was then extended in another preliminary work . It is based on a parallel version of a max‐min ant system being leveraged by an SA algorithm using limited memory.…”

Section: Solution Frameworkmentioning

confidence: 99%

“…Indeed, this paper is envisioning a network that satisfies network reliability and timeliness by wisely placing sinks and controllers; in other words, we deal with the problem of finding the right configuration of fog elements through specifying the number and capacity of controller and sink nodes. We enhance our preliminary work with the following new contributions. Specifying the placement of nodes (ie, sinks and controllers) and, at the same time, determining the capacity of nodes with respect to both the size and frequency of data generated by sensors. Applying a new setting on the evolutionary algorithm proposed in our preliminary work to make it more likely to escape from local optima and converge to the optimal solution. Comparing the proposed method with (1) three well‐known metaheuristic algorithms, namely, Genetic Algorithm (GA), Simulated Annealing (SA), and Parallel Max‐min Ant Systems (PMAS), (2) the extended version of GRASP‐MSP, and (3) the results generated by the CPLEX optimizer …”

Section: Introductionmentioning

confidence: 99%

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An efficient placement of sinks and SDN controller nodes for optimizing the design cost of industrial IoT systems

et al. 2018

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Recently, a growing trend has emerged toward using Internet of Things (IoT) in the context of industrial systems, which is referred to as industrial IoT. To deal with the time-critical requirements of industrial applications, it is necessary to consider reliability and timeliness during the design of an industrial IoT system. Through the separation of the control plane and the data plane, software-defined networking provides control units (controllers) coexisting with sink nodes, efficiently coping with network dynamics during run-time. It is of paramount importance to select a proper number of these devices (i.e., software-defined networking controllers and sink nodes) and locate them wisely in a network to reduce deployment cost. In this paper, we optimize the type and location of sinks and controllers in the network, subject to reliability and timeliness as the prominent performance requirements in time-critical IoT systems through ensuring that each sensor node is covered by a certain number of sinks and controllers. We propose PACSA-MSCP, an algorithm hybridizing a parallel version of the max-min ant system with simulated annealing for multiple-sink/controller placement. We evaluate the proposed algorithm through extensive experiments. The performance is compared against several well-known methods, and it is shown that our approach outperforms those methods by lowering the total deployment cost by up to 19%. Moreover, the deviation from the optimal solution achieved by CPLEX is shown to be less than 2.7%.

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Efficient controller placement and reelection mechanism in distributed control system for software defined wireless sensor networks

Kobo

Abu‐Mahfouz

Hancke

2019

Trans Emerging Tel Tech

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Software‐defined wireless sensor networking (SDWSN) is a new wireless networking paradigm formed by applying software‐defined networking to wireless sensor networks. Fragmentation‐based distributed control system offers an efficient way of distributing the SDWSN control services across the network. Fragmentation aims to bring the control services closer to the infrastructure network in order to reduce the propagation latency. It also aims to improve the scalability, reliability, and performance of the network. The fragmentation model is earmarked to play a huge role in stimulating participation of SDWSN in Internet of Things. To realize this, we optimize the model for deployment and integration as well as for operational efficiency. We consider two aspects: controller placement and controller reelection after failure. This paper discusses the controller placement techniques suitable for SDWSN and the controller replacement in a case of failure for SDWSN. The controller placement and the controller replacement mechanism were both evaluated and the results proved to be effective and efficient.

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A Cost Efficient Design of a Multi-sink Multi-controller WSN in a Smart Factory

Cited by 20 publications

References 16 publications

An energy‐aware resource provisioning scheme for real‐time applications in a cloud data center

An energy‐aware resource provisioning scheme for real‐time applications in a cloud data center

An efficient placement of sinks and SDN controller nodes for optimizing the design cost of industrial IoT systems

Efficient controller placement and reelection mechanism in distributed control system for software defined wireless sensor networks

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