Resource Allocation Model for Sensor Clouds under the Sensing as a Service Paradigm

Guerreiro, Joel; Rodrı́gues, L.; Correia, N.

doi:10.3390/computers8010018

Cited by 12 publications

(24 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our application can use the cloud functions in response to each customer's events so that it is aware of recent data relevant to changing local variable states on its system. The cloud function gives total access with considerable speed and scalable power to select the best duty cycle values in the corresponding time possible at the slightest fluctuation of the input or output values of the system [25,26].…”

Section: The Positive Effects Of the Cloud Functions Of Firebase On Tmentioning

confidence: 99%

A New Approach for Optimizing Management of a Real Time Solar Charger Using the Firebase Platform Under Android

Rezoug

Chenni

Taibi

2019

JLPEA

View full text Add to dashboard Cite

With the continuous growth of energy consumption, the rationalization of energy has become a priority. The photovoltaic energy sector remains a major occupation for researchers in the field of production optimization or storage methods. The concept developed in this work is a mixed optimization approach for energy management during battery charging with a duty cycle. A selective collaborative algorithm intervenes to choose and use the appropriate results of the few techniques to optimize the charging time of a battery and estimate its state of charge by using the minimum possible tools. This is done using a collective database that is accessible in real time. It also effectively allows the synchronization of information between several customers. This approach is performed on a mobile application on android, through a Google Firebase platform that allows us to secure collaborative access between multiple customers and use the results of the calculations of some algorithms. It gives us the values obtained by the various sensors in real time to accelerate the charging speed of the battery. The validation of this approach led us to practice a few scenarios using an Arduino board to show that this approach has a better performance.

show abstract

Section: The Positive Effects Of the Cloud Functions Of Firebase On Tmentioning

confidence: 99%

A New Approach for Optimizing Management of a Real Time Solar Charger Using the Firebase Platform Under Android

Rezoug

Chenni

Taibi

2019

JLPEA

View full text Add to dashboard Cite

show abstract

“…Managing Thing mashups at the cloud, instead of leaving this to the client, has significant advantages: i) multiple data travels to the client side are avoided, improving delay and Quality of Service (QoS); ii) mashups involve workflow between mashup elements, and the assignment of mashup elements to virtual Things (just mentioned many-to-one assignments) should take such flow dependencies into account, for optimization of flows between workspaces of virtual Things at the cloud. This requires the development of new resource allocation approaches, which has been addressed recently in [7]. In such work, a theoretical model is proposed and used as a basis for the development of a heuristic.…”

Section: Introductionmentioning

confidence: 99%

“…• The proposed optimization model and heuristic algorithm are compared against the results presented in [7]. The optimization model is also used to assess the benefits of managing Thing mashups in the cloud, when compared with their management at the client side.…”

Section: Introductionmentioning

confidence: 99%

Allocation of Resources in SAaaS Clouds Managing Thing Mashups

Guerreiro

Rodrı́gues

Correia

2020

IEEE Trans. Netw. Serv. Manage.

Self Cite

View full text Add to dashboard Cite

The sensing and actuation as-a-service is an emerging business model to make sensors, actuators and data from the Internet of Things more attainable to everyday consumer. With the increase in the number of accessible Things, mashups can be created to combine services/data from one or multiple Things with services/data from virtual Web resources. These may involve complex tasks, with high computation requirements, and for this reason cloud infrastructures are envisaged as the most appropriate solution for storage and processing. This means that cloudbased services should be prepared to manage Thing mashups. Mashup management within the cloud allows not only the optimization of resources but also the reduction of the delay associated with data travel between client applications and the cloud. In this article, an optimization model is developed for the optimal allocation of resources in clouds under the sensing and actuation as-a-service paradigm. A heuristic algorithm is also proposed to solve the problem more quickly.

show abstract

“…With the rapid development of Internet of Things (IoT) technologies, various types of IoT devices (e.g., sensor nodes) can be deployed in large dam engineering to measure the different physical quantities and sense their changes in various regions of the structure, such as deformation, stress, pressure, etc. [1][2][3][4]. In the dam safety monitoring systems, the massive monitoring data are generated from the deployed sensor nodes.…”

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Features Based Sensor Network Partition in Dam Safety Monitoring System

Chen

Mao

Wang

et al. 2020

Sensors

View full text Add to dashboard Cite

Many various types of sensors have been installed to monitor the deformation and stress in the dam structure. It is difficult to directly evaluate the operation status of the dam structure based on the massive monitoring data. The sensor network is divided into multiple regions according to the design specifications, simulation data, and engineering experiences. The local results from sub-regions are integrated to achieve overall evaluation. However, it ignores the spatial distribution of sensors and the variation of time series, which cannot meet the real-time evaluation for the dam safety monitoring. If the network partitions can provide the preliminary foundation for analyzing the dynamic change laws of the dam’s working conditions in a real-way, we should consider the similarity of structure and stresses in the local region of the dam and the correlation among the monitoring data. A time-series denoising autoencoder (TSDA) is proposed to represent the spatial and temporal features of the nodes by compressing high-dimensional monitoring data. Then, a network partitioning algorithm (NPA) based on spatial-temporal features based on the TSDA is presented. The NPA ensures that the partition results can support the analysis of the physical change laws by introducing the auxiliary objective variable to optimize the network partition objective function. Experimental results on the public datasets and a real dataset from an arch dam demonstrate that the proposed network partition algorithm NPA can achieve better partition performance than TSDA+K-Means and TSDA+GMM. The NPA can improve the silhouette coefficient by 45.1% and 58.4% higher than the TSDA+K-Means and TSDA+GMM, respectively. The NPA can increase the Calinski-Harabaz Index by 30.8% and 61.6%, respectively.

show abstract

Resource Allocation Model for Sensor Clouds under the Sensing as a Service Paradigm

Cited by 12 publications

References 27 publications

A New Approach for Optimizing Management of a Real Time Solar Charger Using the Firebase Platform Under Android

A New Approach for Optimizing Management of a Real Time Solar Charger Using the Firebase Platform Under Android

Allocation of Resources in SAaaS Clouds Managing Thing Mashups

Spatial-Temporal Features Based Sensor Network Partition in Dam Safety Monitoring System

Contact Info

Product

Resources

About