A dynamic skyline technique for a context-aware selection of the best sensors in an IoT architecture

Kertiou, Ismail; Benharzallah, Saber; Kahloul, Laïd; Beggas, Mounir; Euler, Reinhardt; Laouid, Abdelkader; Bounceur, Ahcène

doi:10.1016/j.adhoc.2018.08.011

Cited by 25 publications

(16 citation statements)

References 12 publications

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“…• Dynamic skyline algorithm: Firstly, this method [12] gets the user request and computes the local dynamic skyline to narrow the sensor dataset. Then, collecting a set of locally filtered sensor data to compute the dynamic skyline.…”

Section: ) Comparative Methodsmentioning

confidence: 99%

“…The ES model integrates the advantages of TOPSIS and the fast nondominated sorting algorithm. Kertiou proposed dynamic skyline algorithm [12] to improve the accuracy of multi-criteria decision analysis algorithm, which has been widely used in multi-criteria decision applications such as web service combination [13] and wireless AD hoc network routing. Compared with the fast nondominated algorithm mentioned above, the dynamic skyline algorithm has a lower time complexity, but it isn't safe to require users to input ideal values of sensor properties.…”

Section: Related Workmentioning

confidence: 99%

“…Before we conduct the experiments, we need determine which dataset we need to use. In order to simulate sensor resources selection problem in the real situation, in which has a large number of sensor resources, ES model proposed by Nunes [11] and dynamic skyline model proposed by Kertiou [12] adopt multiple datasets to measure the performance of the model. And These two models has the best performance when the size of dataset is 100,000.…”

Section: ) Impact Of Parameter Settingmentioning

confidence: 99%

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An Efficient Preference-Based Sensor Selection Method in Internet of Things

et al. 2019

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While it is well understood that the Internet of things (IoT) can facilitate numerous applications (e.g., environmental supervision, forest fire prevention and Intelligent farming), it also brings a significant challenge for efficiently selecting sensors that meet users' preference and specific requirement from millions of heterogeneous sensors. In this paper, we propose an improved fast nondominated sorting algorithm for efficiently preference-based sensor selection in IoT. Specifically, this proposed method mainly includes three parts: 1) Offline constructing R-tree to search sensor resources and narrowing the size of dataset according to user's preference; 2) Using an improved fast nondominated sorting approach to get nondominated front; 3) Employing TOPSIS to characterize every sensor option of the nondominated front. In order to illustrate the usability of the model, we conduct experiments on several simulation datasets. Experimental results show that this method outperforms several baselines in terms of both response time and accuracy.

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Section: ) Comparative Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: ) Impact Of Parameter Settingmentioning

confidence: 99%

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An Efficient Preference-Based Sensor Selection Method in Internet of Things

et al. 2019

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“…Kertiou et al [35] discuss how the data from the IoT layer could be filtered by autonomous sensor selection for a specific user request. A technique called Skyline selects the most suitable sensors and filters the most important information and trims the less important information.…”

Section: Automatic Data Filteringmentioning

confidence: 99%

Process Automation in an IoT–Fog–Cloud Ecosystem: A Survey and Taxonomy

Chegini

Naha

Mahanti

et al. 2021

IoT

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The number of IoT sensors and physical objects accommodated on the Internet is increasing day by day, and traditional Cloud Computing would not be able to host IoT data because of its high latency. Being challenged of processing all IoT big data on Cloud facilities, there is not enough study on automating components to deal with the big data and real-time tasks in the IoT–Fog–Cloud ecosystem. For instance, designing automatic data transfer from the fog layer to cloud layer, which contains enormous distributed devices is challenging. Considering fog as the supporting processing layer, dealing with decentralized devices in the IoT and fog layer leads us to think of other automatic mechanisms to manage the existing heterogeneity. The big data and heterogeneity challenges also motivated us to design other automatic components for Fog resiliency, which we address as the third challenge in the ecosystem. Fog resiliency makes the processing of IoT tasks independent to the Cloud layer. This survey aims to review, study, and analyze the automatic functions as a taxonomy to help researchers, who are implementing methods and algorithms for different IoT applications. We demonstrated the automatic functions through our research in accordance to each challenge. The study also discusses and suggests automating the tasks, methods, and processes of the ecosystem that still process the data manually.

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“…With the assistance of replica and redundancy of data in several domains [6][7][8][9][10][11], the distributed file system is able to reduce access time lag, network bandwidth consumption and system unreliability. However, this also leads to the problem of replica consistency management.…”

Section: Introductionmentioning

confidence: 99%

A comparative analysis of adaptive consistency approaches in cloud storage

Khelaifa

Benharzallah

Kahloul

et al. 2019

Journal of Parallel and Distributed Computing

Self Cite

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NoSQL storage systems are used extensively by web applications and provide an attractive alternative to conventional databases due to their high security and availability with a low cost. High data availability is achieved by replicating data in different servers in order to reduce access time lag, network bandwidth consumption and system unreliability. Hence, the data consistency is a major challenge in distributed systems. In this context, strong consistency guarantees data freshness but affects directly the performance and availability of the system. In contrast, weaker consistency enhances availability and performance but increases data staleness. Therefore, an adaptive consistency strategy is needed to tune, during runtime, the consistency level depending on the criticality of the requests or data items. Although there is a rich literature on adaptive consistency approaches in cloud storage, there is a need to classify as well as regroup the approaches based on their strategies. This paper will establish a set of comparative criteria and then make a comparative analysis of existing adaptive consistency approaches. A survey of this kind not only provides the user/researcher with a comparative performance analysis of the approaches but also clarifies the suitability of these for candidate cloud systems.

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A dynamic skyline technique for a context-aware selection of the best sensors in an IoT architecture

Cited by 25 publications

References 12 publications

An Efficient Preference-Based Sensor Selection Method in Internet of Things

An Efficient Preference-Based Sensor Selection Method in Internet of Things

Process Automation in an IoT–Fog–Cloud Ecosystem: A Survey and Taxonomy

A comparative analysis of adaptive consistency approaches in cloud storage

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