A Bayesian Approach for an Efficient Data Reduction in IoT

Razafimandimby, Cristanel; Loscrì, Valeria; Vegni, Anna Maria; Aourir, Driss; Neri, Alessandro

doi:10.1007/978-3-319-93797-7_1

Cited by 9 publications

(24 citation statements)

References 7 publications

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“…Our simulations show a reduction of more than 70% of the overall data and 30 to 50 % while comparing with other methods. In the near future, several additional ideas can be added to our data reduction technique to help face the problems generated by the big data flow of wireless sensor nodes, to have a more complete work such as data correlation (e.g analysis of the correlation between temperature, humidity and wind speed) [13] in space and time, or adaptive Sampling Rate (in stable mode the sensors can capture 1 value each 30min or an hour). For example in all the cases where the trend is 0 for consecutive periods, a scheduling technique can be adopted to schedule the sensing process, or the sampling rate of the nodes is adapted for the same purpose.…”

Section: Discussionmentioning

confidence: 99%

“…Other papers worked on data correlation and data similarity between several features as in [16], [5], [13], [12], [1] and [4]. Authors in [12] present a Bayesian Inference Approach to detect data with high spatio-temporal correlated data, to avoid transmitting data that can be reconstructed from another data such as temperature and humidity in some cases.…”

Section: Related Workmentioning

confidence: 99%

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Machine Learning Based Data Reduction in WSN for Smart Agriculture

Salim

Mitton

2020

Advanced Information Networking and Applications

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Nowadays, the agriculture domain faces a lot of challenges for a better usage of its natural resources. For this purpose, and for the increasing danger of climate change, there is a need to locally monitor meteorological data and soil conditions to help make quicker and more adapted decision for the culture. Wireless Sensor Networks (WSN) can serve as a monitoring system for those types of features. However, WSN suffer from the limited energy resources of the motes which shorten the lifetime of the overall network. Every mote periodically captures the monitored feature and sends the data to the sink for further analysis depending on a certain sampling rate. This process of sending big amount of data causes a high energy consumption of the sensor node and an important bandwidth usage on the network. In this paper, a Machine Learning based Data Reduction Algorithm (MLDR) is introduced. MLDR focuses on environmental data for the benefits of agriculture. MLDR is a data reduction approach which reduces the amount of transmitted data to the sink by adding some machine learning techniques at the sensor node level by keeping data availability and accuracy at the sink. This data reduction helps reduce the energy consumption and the bandwidth usage and it enhances the use of the medium while maintaining the accuracy of the information. This approach is validated through simulations on MATLAB using real temperature data-sets from Weather-Underground sensor network. Results show that the amount of sent data is reduced by more than 70% while maintaining a very good accuracy with a variance that did not surpass 2 degrees.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Machine Learning Based Data Reduction in WSN for Smart Agriculture

Salim

Mitton

2020

Advanced Information Networking and Applications

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“…The DRUID-NET framework aims at extending them to multiple resources, while combining these approaches with predictive methods, which have only been slightly explored for IoT due to resource limitations. Thus far, methods such as ARIMA [20], deadreckonning [21], Kalman filters [22], Thompson sampling [23], or Bayesian approaches [24] have mainly been investigated for navigation and position prediction [20], data reduction [24], link prediction [25], or medium occupation [23]. Our aim is to provide a unique distributed and adaptive multi-resource estimation and prediction suitable for IoT devices.…”

Section: Iot Workload Profilementioning

confidence: 99%

Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective

Dechouniotis

Αθανασόπουλος

Leivadeas

et al. 2020

Sensors

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The potential offered by the abundance of sensors, actuators, and communications in the Internet of Things (IoT) era is hindered by the limited computational capacity of local nodes. Several key challenges should be addressed to optimally and jointly exploit the network, computing, and storage resources, guaranteeing at the same time feasibility for time-critical and mission-critical tasks. We propose the DRUID-NET framework to take upon these challenges by dynamically distributing resources when the demand is rapidly varying. It includes analytic dynamical modeling of the resources, offered workload, and networking environment, incorporating phenomena typically met in wireless communications and mobile edge computing, together with new estimators of time-varying profiles. Building on this framework, we aim to develop novel resource allocation mechanisms that explicitly include service differentiation and context-awareness, being capable of guaranteeing well-defined Quality of Service (QoS) metrics. DRUID-NET goes beyond the state of the art in the design of control algorithms by incorporating resource allocation mechanisms to the decision strategy itself. To achieve these breakthroughs, we combine tools from Automata and Graph theory, Machine Learning, Modern Control Theory, and Network Theory. DRUID-NET constitutes the first truly holistic, multidisciplinary approach that extends recent, albeit fragmented results from all aforementioned fields, thus bridging the gap between efforts of different communities.

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“…Some papers investigated data correlation and data similarity between several features as in [11], [12], [13], [14] and [15]. Authors in [12] present a Bayesian Inference Approach to detect data with high spatio-temporal correlated data, to avoid transmitting data that can be reconstructed from another data such as temperature and humidity in some cases. However this method presents one level of prediction by predicting one parameter from another one, in our approach one parameter value can help predicting more than two other parameters values.…”

Section: Background and Related Workmentioning

confidence: 99%

“…"Light data reduction algorithm" means that we are proposing an efficient algorithm with the least complexity and the maximum simplicity in equations while maintaining an acceptable accuracy. we compared our approach with two on-node processing techniques in [5] and [12]. The first part (trend variation) is compared to [5] where the authors present a technique similar but more complex than our approach to compute the trend and predict the next values of a predefined parameter.…”

Section: Background and Related Workmentioning

confidence: 99%

K-predictions based data reduction approach in WSN for smart agriculture

Salim

Mitton

2020

Computing

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Nowadays, climate change is one of the numerous factors affecting the agricultural sector. Optimising the usage of natural resources is one of the challenges this sector faces. For this reason, it could be necessary to locally monitor weather data and soil conditions to make faster and better decisions locally adapted to the crop. Wireless sensor networks (WSNs) can serve as a monitoring system for these types of parameters. However, in WSNs, sensor nodes suffer from limited energy resources. The process of sending a large amount of data from the nodes to the sink results in high energy consumption at the sensor node and significant use of network bandwidth, which reduces the lifetime of the overall network and increases the number of costly interference. Data reduction is one of the solutions for this kind of challenges. In this paper, data correlation is investigated and combined with a data prediction technique in order to avoid sending data that could be retrieved mathematically in the objective to reduce the energy consumed by sensor nodes and the bandwidth occupation. This data reduction technique relies on the observation of the variation of every monitored parameter as well as the degree of correlation between different parameters. This approach is validated through simulations on MATLAB using real meteorological data-sets from Weather-Underground sensor network. The results show the validity of our approach which reduces the amount of data by a percentage up to 88% while maintaining the accuracy of the information having a standard deviation of 2 degrees for the temperature and 7% for the humidity.

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A Bayesian Approach for an Efficient Data Reduction in IoT

Cited by 9 publications

References 7 publications

Machine Learning Based Data Reduction in WSN for Smart Agriculture

Machine Learning Based Data Reduction in WSN for Smart Agriculture

Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective

K-predictions based data reduction approach in WSN for smart agriculture

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