Similarity based image selection with frame rate adaptation and local event detection in wireless video sensor networks

2020

Computing

Self Cite

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.

Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

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

2020

Computing

Self Cite

“…Adapting the sampling rate of the sensor nodes can be a solution to reduce the amount of sent data from the sensor nodes to the sink using different approaches: machine learning and the history of the data as in [15], [16], [14] and [6], the channel selection using a Thompson sampling based approach as in [17], and the cubic adaptive sampling as the authors proposed in [7]. Our approach focuses on the amount of transmitted data and not on the amount of captured data.…”

Section: Related Workmentioning

confidence: 99%

“…Machine learning for data prediction is widely used for data reduction as in [15], [7], [18], [10], [2], [14] and [6]. In the dual prediction model [15] [7], the sensor node and the sink both predict the next values of the monitored feature simultaneously.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Based Data Reduction in WSN for Smart Agriculture

Advanced Information Networking and Applications

2020

Self Cite

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.

Image Similarity Based Data Reduction Technique in Wireless Video Sensor Networks for Smart Agriculture

Lecture Notes in Networks and Systems

2021

Self Cite

Nowadays, to improve animal well being in livestock farming or beekeeping application, a wireless video sensor network (WVSN) can be deployed to early detect injury or Asiatic hornets attacks. WVSN represents a low-cost monitoring solution compared to other technologies such as the closed circuit television technology (CCTV). WVSNs are composed of low-power resource-constrained video sensor nodes (motes). These nodes capture frames from videos at a given frequency (frame rate) and wirelessly send them to the sink. The big amount of data transferred from the nodes to the sink consumes a lot of energy on the sensor node, which represents a major challenge for energy-limited nodes. In this paper, we introduce two complementary mechanisms to reduce the overall number of frames sent to the sink. First, the Transmission Data Reduction algorithm (TDR) run on the sensor node leverages the similarity degree of consecutive images. Second, the Inter-Nodes Similarity algorithm (INS) exploits the spatio-temporal correlation between neighbouring nodes in order reduce the number of captured frames. The results show a 95% data reduction, surpassing other techniques in the literature by 30% at least.