Data estimation with predictive switching mechanism in wireless sensor networks

Karjee, Jyotirmoy; Kleinsteuber, Martin

doi:10.1504/ijsnet.2017.087709

Cited by 10 publications

(6 citation statements)

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“…They demonstrate a new predictive method that puts the sensor into hibernation to reduce data transmission based on a self‐organizing map. In Karjee and Kleinsteuber, 18 to track faulty data gathered at the sink, an online data tracking and estimation (ODTE) system is proposed. The data prediction system (DPS) and the distortion factor are the two basic systems used in ODTE (DF).…”

Section: Related Workmentioning

confidence: 99%

Distributed energy‐efficient data reduction approach based on prediction and compression to reduce data transmission in IoT networks

Hussein

Idrees

Couturier

2022

Int J Communication

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Summary In the modern world, it will be necessary to deploy a large number of sensor devices to sense everything around us in order to detect changes, risks, and hazards and to mitigate them. This increasing number of sensor devices represents an essential data provider in the Internet of Things (IoT). The devices generate and transmit a huge amounts of data which requires a large amount of storage and high processing power to come real‐time processing and speed up the network. It also leads to an increase in high energy consumption. Thus, it is important to remove redundant data to reduce the data transmission before sending it to the gateway while maintaining a good level of data quality. In this paper, a distributed energy‐efficient data reduction (DEDaR) approach based on prediction and compression to minimize the data transmission in IoT Networks is proposed. The DEDaR is used in periods to make decision. In each period, the autoregressive prediction (ARP) is used to predict the data of the next period and make a decision on whether to send the data of the current period to the gateway or not. In the case of data transmission, the redundant data are eliminated using an efficient compression approach based on adaptive piecewise constant approximation (APCA), symbolic aggregate approximation (SAX), and finally fixed code dictionary (FCD) based on Huffman encoding. The simulation results based on real‐sensed data show that the proposed DEDaR approach outperforms the other recent methods in terms of data reduction percentage, transmitted data size, energy consumption, and data accuracy.

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Section: Related Workmentioning

confidence: 99%

Distributed energy‐efficient data reduction approach based on prediction and compression to reduce data transmission in IoT networks

Hussein

Idrees

Couturier

2022

Int J Communication

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“…The authors in [18][19][20][21][22][23] dedicated their works to reducing the amount of data circulated in the network along the path to the sink, for example, at intermediates nodes. In [19], the authors propose a cluster-based data gathering algorithm for WSN called lifetime-enhancing cooperative data gathering and relaying (LCDGRA).…”

Section: Related Workmentioning

confidence: 99%

“…The simulations show that LCDGRA can ensure an efficient convergence of K‐means (average of 31 iterations), a reduced data latency (up to 18%) and less energy consumption (up to 37%) compared to other techniques. In [20], an online data tracking and estimation (ODTE) is proposed to track poor data collected at the sink. ODTE is mainly based on two systems: Data prediction system (DPS) and distortion factor (DF).…”

Section: Related Workmentioning

confidence: 99%

Orchestration‐based mechanism for sampling adaptation in sensing‐based applications

et al. 2021

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Currently, the world witnesses a boom in the sensing‐based applications where the number of connected devices is becoming higher than the people. Such small sensing devices are now deployed in billions around the world, collecting data about the surroundings and reporting them to the data analysis centres. This fact allows a better understanding of the world and helps to reduce the effects of potential risks. However, while the benefits of such devices are real and significant, sensing‐based applications face two major challenges: big data collection and restricted power of sensor battery. In order to overcome these challenges, data reduction and sampling sensor adaptation techniques have been proposed to reduce data collection and to save the sensor energy. The authors propose an orchestration‐based mechanism (OM) for adapting the sampling rate of the sensors in the network. OM is two‐fold: first, it proposes a data transmission model at the sensor level, based on the clustering and Spearman coefficient, in order to reduce the amount of data transmitted to the sink; second, it proposes a sampling rate mechanism at the cluster‐head level that allows searching the similarity between data collected by the neighbouring sensors, and then to adapt their sensing frequencies accordingly. A set of simulations on real sensor data have been conducted to evaluate the efficiency of OM, in terms of data reduction and energy conservation, compared to other exiting techniques.

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“…These models calculates a minimum set of sensor nodes which are sufficient to give the desired data accuracy level as achieved by the whole network. Similarly, in [14], [15] data accuracy model is developed under online data extraction to select optimal sensor nodes in the network. In [10], a trade off between data accuracy and energy consumption is developed to select an optimal number of sensor nodes, thereby reducing the communication overhead in the network.…”

Section: Related Workmentioning

confidence: 99%

“…Algorithm 2 Three Dimentional Node Placement Algorithm For each sensor node 10: Calculate the maximum information accuracy using (14) 11: Update the position of sensor nodes using (15) in 3D space 12: End Thus a set of sensor nodes can be selected with in a 3D network which maximizes information accuracy using 3D-NP algorithm.…”

Section: Node Placement In 3d Networkmentioning

confidence: 99%

Information Estimation with Node Placement Strategy in 3D Wireless Sensor Networks

Karjee¹,

Jamadagni

2017

IETE Journal of Research

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The cluster formation in Three Dimensional Wireless Sensor Networks (3D-WSN) give rise to overlapping of signals due to spherical sensing range which leads to information redundancy in the network. To address this problem, we develop a sensing algorithm for 3D-WSN based on dodecahedron topology which we call Three Dimensional Distributed Clustering (3D-DC) algorithm. Using 3D-DC algorithm in 3D-WSN, accurate information extraction appears to be a major challenge due to the environmental noise where a Cluster Head (CH) node gathers and estimates information in each dodecahedron cluster. Hence, to extract precise information in each dodecahedron cluster, we propose Three Dimensional Information Estimation (3D-IE) algorithm. Moreover, Node deployment strategy also plays an important factor to maximize information accuracy in 3D-WSN. In most cases, sensor nodes are deployed deterministically or randomly. But both the deployment scenario are not aware of where to exactly place the sensor nodes to extract more information in terms of accuracy. Therefore, placing nodes in its appropriate positions in 3D-WSN is a challenging task. We propose a Three Dimensional Node Placement (3D-NP) algorithm which can find the possible nodes and their deployment strategy to maximize information accuracy in the network. We perform simulations using MATLAB to validate the 3D-DC, 3D-IE and 3D-NP, algorithms respectively.

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Data estimation with predictive switching mechanism in wireless sensor networks

Cited by 10 publications

References 0 publications

Distributed energy‐efficient data reduction approach based on prediction and compression to reduce data transmission in IoT networks

Distributed energy‐efficient data reduction approach based on prediction and compression to reduce data transmission in IoT networks

Orchestration‐based mechanism for sampling adaptation in sensing‐based applications

Information Estimation with Node Placement Strategy in 3D Wireless Sensor Networks

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