DDCA-WSN: A Distributed Data Compression and Aggregation Approach for Low Resources Wireless Sensors Networks

Fute, Elie Tagne; Kamdjou, Hugues Marie; Nzeukou, Armand

doi:10.1007/s10776-021-00543-6

Cited by 11 publications

(11 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The RLE algorithm alone has a higher compression rate and outperforms than H-RLEAHE. In DDCA-WSN [20], using data collection, compression occurs in intermediate nodes through four algorithms: BWT [25], MTF [25], RLE [11][27], and Arithmetic. As a result, energy storage is specific to the intermediate nodes layer.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

LPRLC: Linear Predictive Run Length Coding to Improve Energy Consumption of WBANs

Vakil,

Shirmohammadi

2024

Preprint

View full text Add to dashboard Cite

The Wireless Body Area Networks (WBANs) are biosensors placed on the body, inside the body, and around it. Body Area Network (BAN) are designed in micro sizes and have limited resources. Biosensors sometimes have errors in data recording and faced with duplicate and noisy data in real time. Data redundancy causes a significant energy consumption in sending and receiving data in the sensor. One of the most effective ways to reduce data volume is to compress it to save more energy. To solve these problems, the Linear Predictive Run Length Coding method (LPRLC) is presented which is a combination of Linear Predictive Coding (LPC) for data prediction and Run Length Encoding (RLE) for data compression. The signals attained from biosensors include blood pressure systolic (BPsys), blood pressure diastolic (BPdias), Respiration, Oxygen, and Heart Rate, which are recorded as a time series. First, the received signal is predicted continuously, and then the error resulting from the actual signal and the expected signal is calculated. In the last step, the resulting error is compressed by the RLE algorithm and sent to the destination. To compare the criteria of Energy Conservation (EC) and Compression Rate (CR), Huffman, Arithmetic, and Lempel-Ziv-Welch (LZW) algorithms are placed instead of RLE. The results show that the RLE algorithm has an average of 98% energy saving and up to 70 times reduction of data volume compared to other algorithms, which has improved 6% in energy consumption and 9 times reduction of data volume.

show abstract

Section: Introductionmentioning

confidence: 99%

“…LPRLE is compared with different algorithms of lossless coding: Huffman [8], LZW [17], and Arithmetic [13]. Results show the LPRLE with LPC and RLE has the greatest energy saving on average of 98% by keeping the proper compression ratio in five biosensors than DDCA [20], SZ with Huffman [18] and H-RLEAHE [32].…”

Section: Introductionmentioning

confidence: 99%

LPRLC: Linear Predictive Run Length Coding to Improve Energy Consumption of WBANs

Vakil,

Shirmohammadi

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…It is also noted from the related works as in Table 1, the recent studies are limited as the nature of the nodes becomes different and distributed. The cost of the energy is still affected and ultimately affects the communication in Distributed data in WSN [19]. The motivation behind employing data compression in WSNs is to mitigate issues related to communication overload, energy consumption, and network lifetime [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…Data compression techniques, particularly Non-Negative Matrix Factorization (NMF), are used to transform unstructured data into a more interpretable format suitable for communication. As seen in [5][6][7]19], is chosen for its ability to handle the spectral representation of data and efficiently identify faulty nodes, reducing false measurements and improving overall network lifetime. Hence, considering this, the study is motivated to use the Matrix factorization-based technique to compress the data at the required position to improve the process of CH selection and reduce energy costs.…”

Section: Introductionmentioning

confidence: 99%

CCOA-DC: A Novel Optimization with NMF Data Compression in WSN Data Aggregation

2024

IJIES

View full text Add to dashboard Cite

Wireless Sensor Networks (WSNs) play a pivotal role in remote monitoring, surveillance, and Internet of Things (IoT) applications. The efficient utilization of battery-powered sensor nodes in WSNs, given their limited power capacity, is crucial for successful data transmission. Conventional clustering algorithms while efficient in clustering, often lacks the efficient management of data generated by sensor nodes, leading to redundant data in applications like IoT leading to reduced network lifetime. To overcome this issue, this paper introduces a novel approach, named CCOA-DC (Improved Coati Optimization with Cognitive Factor (CCOA) through Data Compression (DC)), in clustering heterogeneous aggregated WSN data. The research unfolds in two novel phases. Initially, a non-negative matrix factorization (NMF) model is introduced data compression for clustering, addressing the challenge of data transmission and energy efficiency. Subsequently, the performance is enhanced through load balancing, featuring dynamic cluster head selection via Improved Coati Optimization (COA) with cognitive factor (C), denoted as CCOA. A distinctive aspect is the incorporation of the NMF data compression technique in both clustering and cluster head selection processes, introducing an energy-efficient, load-balanced, and compressed data aggregation mechanism. The proposed CCOA-DC undergoes rigorous testing, comparing its performance against existing models to validate its superiority. Comparative analyses with renowned models such as TCBDGA, HEED, and FEEC-IIR underscore the distinct advantages of CCOA-DC. Notably, it achieves a reduction of 78.57% of packet loss ratio compared to FEEC-IIR model. The model achieves high packet delivery ratio which is 98.67%, and shows optimized energy consumption of 68.01% Joules. This novel compression-based metaheuristic data aggregation algorithm showcases its effectiveness in addressing the energy conservation challenge, affirming its prominence in the area of WSNs based IoT applications.

show abstract

“…Regarding the problem of unimportant data, [26] and [29] are introduced as hybrid methods. In [26] data aggregation is used to remove unimportant data.…”

Section: Introductionmentioning

confidence: 99%

SHDC: An Smart Hybrid Data Compression Method in WBANs

HajilooVakil,

Shirmohammadi

2023

Preprint

View full text Add to dashboard Cite

The Wireless Body Area Networks (WBANs) are a set of sensors on the human s body that are designed in very small sizes. These sensors are faced with the exchange of a huge amount of data, which causes them to run out of energy consumption. One of the most effective methods to save the sensors' energy is the use of data compression. However the problem of compression methods in WBANs is inadequate for compressing large amounts of duplicate data. In this regard, a new method called Smart Hybrid Data Compression (SHDC) is presented. SHDC method use Recurrent Neural Network (RNN) and Run Length Encoding (RLE) which involves four key steps: 1-create and fit the LSTM network, 2-prediction, 3-calculate error and 4-compressed by lossless procedure. The presented method has a very good performance in energy consumption, data volume reduction by removing unimportant and repetitive data in the exchange of information between sensors and sink, high accuracy in data prediction and achieving a high compression rate due to low error. The SHDC method is implemented on the data set received from body sensors including blood pressure systolic, blood pressure diastolic, heart rate, respiration and SPO2. In the last step the differences between predictions and original signals are coded by four lossless coding: RLE, Huffman, Limpel Ziv Welch (LZW) and Arithmetic. Three measurements of Compression Ratio (CR), Energy Remaining (ER) and Root Mean Square Error (RMSE) after data compression have been measured. Our results compared with state-of-art lossless coding methods. Results show the SHDC method with RNN and RLE has the highest energy saving on average of 98% by maintaining the appropriate compression ratio.

show abstract

DDCA-WSN: A Distributed Data Compression and Aggregation Approach for Low Resources Wireless Sensors Networks

Cited by 11 publications

References 32 publications

LPRLC: Linear Predictive Run Length Coding to Improve Energy Consumption of WBANs

LPRLC: Linear Predictive Run Length Coding to Improve Energy Consumption of WBANs

CCOA-DC: A Novel Optimization with NMF Data Compression in WSN Data Aggregation

SHDC: An Smart Hybrid Data Compression Method in WBANs

Contact Info

Product

Resources

About