Minimizing Redundancy in Wireless Sensor Networks Using Sparse Vectors
Abstract
:1. Introduction
- At the sensor level, this paper presents an improved data-compression method. This method combines segmented regions with quartile screening to precisely locate the corresponding key values and then performs sparse vector compression. By deeply exploring the similarity between each segment of data and its corresponding key value, the amount of transmitted data can be significantly reduced.
- At the CH level, this paper optimizes the reverse sampling rate adjustment method. By fully leveraging the spatial correlation among the data of different sensor nodes, a sensor sleep decision is set. This decision can dynamically adjust the sampling frequency of active sensors within the cluster in the next cycle according to the actual network situation, effectively balancing the accuracy of data collection and energy consumption and providing a more efficient solution for energy-saving management at the CH level.
2. Methods
2.1. Network Model Overview
2.2. Minimizing Data Redundancy Algorithm Based on Segmented Sparse Vectors (SV-ZIZO Algorithm)
2.2.1. Indexed Bit Coding Compression Algorithm
2.2.2. Compression Method Based on Segmented Sparse Vector Representation
Algorithm 1: Data-Split function pseudocode |
01 Read vector Si_p, int V 02 Set vector si_p_v 03 len_v = floor(length(Si_p)/V) 04 for v = 0 to V 05 for i = 1 to len_v len_v + i) ~= NaN len_v + i) 08 Return si_p_v |
Algorithm 2: Key-value calculation and compression based on quartiles |
01 Read vector si_p_v, int ε 02 Set vector U 03 Q = [prctile(si_p_v, 25), prctile(si_p_v, 50), prctile(si_p_v, 75)] 04 m = mean(si_p_v) 05 index = min(abs(Q[1]-m), abs(Q[2]-m), abs(Q[3]-m))[2] 06 u_p_v = Q[index] 07 len_p_v = length(si_p_v) 08 Set code = zeros(len_p_v) 09 for i = 1 to len_p_v 10 if abs(si_p_v[i] − u_p_v) < ε 11 code[i] = 1 12 si_p_v[i] = NaN 13 else 14 code[i] = 0 15 c = HexToDec(code) 16 u(1) = (u_p_v, c) 17 append(U, u(1)) 18 while Si_p is all NaN 19 Set code = zeros(len_p_v) 20 u = min(si_p_v) 21 for k = 1 to len_p_v 22 if | u_i − Si_p(k)| <= ε 23 code(k) = 1 24 si_p_v[i] = NaN 25 else 26 code(k) = 0; 27 c = HexToDec(code) 28 u = (u_i, c) 29 append(U, u) 30 Return U |
2.2.3. Sensor Sampling Rate Adjustment Method at the CH Level
2.2.4. Complexity Analysis
3. Results
3.1. Parameter Settings and Evaluation Indicators
- The calculation formula of the evaluation metric Compressed Ratio (CR) is as follows:
- Calculate the average error between the reconstructed data of the i-th sensor within one cycle and the original collected data of the corresponding sensor. The calculation formula is as follows:
3.2. IBRL Dataset and Result Analysis
3.2.1. Comparison Results of Compression Ratios at the Sensor Level
3.2.2. Comparison Results of Reconstruction Accuracy at the CH Level
3.2.3. Adjustment Results of Dormant Sampling at the CH Level
3.2.4. Comparison Results of Total Network Energy Consumption
3.3. LUCE Dataset and Result Analysis
3.3.1. Comparison Results of Compression Ratios at the Sensor Level
3.3.2. Comparison Results of Reconstruction Accuracy at the CH Level
3.3.3. Adjustment Results of Dormant Sampling at the CH Level
3.3.4. Comparison Results of Total Network Energy Consumption
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Redundancy Rate (RR) | Large-Scale Adjustment of Sampling Frequency (LSA-SF) | Small-Scale Adjustment of Sampling Frequency (SSA-SF) |
---|---|---|
0 ≤ RR ≤ 0.4 | 60% | 100% |
0.4 < RR ≤ 0.7 | 40% | 60% |
0.7 < RR ≤ 1 | 20% | 40% |
Parameter | Values |
---|---|
N | IBRL: 47 LUCE: 34 |
32, 64 | |
50 nJ/bit | |
100 pJ/bit/m2 | |
T | 0.2 |
Temperature: 0.07, 0.1, 0.2 Humidity: 0.2, 0.5, 1 |
Data Type | Period | Threshold | Algorithm 1 | SV-ZIZO(LSA-SF) | SV-ZIZO(SSA-SF) | IBE | ZIZO(LSA-SF) | ZIZO(SSA-SF) |
---|---|---|---|---|---|---|---|---|
Temperature | 15.53% | 10.11% | 10.70% | 12.95% | 12.59% | 12.65% | ||
12.10% | 7.37% | 7.63% | 10.41% | 10.13% | 10.24% | |||
9.97% | 6.28% | 6.55% | 6.90% | 6.87% | 6.88% | |||
12.29% | 9.80% | 10.99% | 9.25% | 9.01% | 9.19% | |||
9.09% | 5.95% | 6.56% | 7.26% | 7.09% | 7.16% | |||
6.33% | 4.12% | 4.36% | 4.64% | 4.59% | 4.63% | |||
Humidity | 20.74% | 5.56% | 6.96% | 19.23% | 7.94% | 8.65% | ||
14.49% | 4.06% | 5.02% | 12.18% | 5.19% | 5.52% | |||
11.86% | 3.43% | 4.05% | 9.74% | 4.35% | 4.54% | |||
17.62% | 13.55% | 13.66% | 14.22% | 11.29% | 12.41% | |||
11.36% | 6.09% | 7.37% | 8.35% | 6.77% | 7.87% | |||
8.72% | 4.68% | 5.62% | 6.23% | 5.20% | 5.86% |
Data Type | Period | Threshold | Algorithm 1 | SV-ZIZO (LSA-SF) | SV-ZIZO (SSA-SF) | IBE | ZIZO (LSA-SF) | ZIZO (SSA-SF) |
---|---|---|---|---|---|---|---|---|
Temperature | 11.85% | 3.48% | 3.66% | 17.16% | 6.84% | 6.86% | ||
8.94% | 3.03% | 3.22% | 13.35% | 5.51% | 5.62% | |||
6.71% | 2.62% | 2.67% | 8.78% | 3.91% | 3.94% | |||
8.72% | 6.55% | 7.07% | 12.96% | 11.95% | 12.46% | |||
5.81% | 4.52% | 4.72% | 9.69% | 9.19% | 9.44% | |||
3.56% | 2.85% | 2.93% | 5.99% | 5.71% | 5.81% | |||
Humidity | 28.75% | 13.24% | 15.72% | 29.66% | 12.28% | 13.43% | ||
11.60% | 4.29% | 4.50% | 14.62% | 6.31% | 6.61% | |||
6.06% | 2.78% | 2.89% | 8.65% | 3.87% | 4.02% | |||
25.62% | 17.85% | 18.85% | 19.68% | 17.95% | 19.41% | |||
8.47% | 6.27% | 6.58% | 9.38% | 8.86% | 9.13% | |||
3.48% | 2.35% | 2.85% | 5.45% | 5.22% | 5.35% |
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Yuan, H.; Gao, C. Minimizing Redundancy in Wireless Sensor Networks Using Sparse Vectors. Sensors 2025, 25, 1557. https://doi.org/10.3390/s25051557
Yuan H, Gao C. Minimizing Redundancy in Wireless Sensor Networks Using Sparse Vectors. Sensors. 2025; 25(5):1557. https://doi.org/10.3390/s25051557
Chicago/Turabian StyleYuan, Huiying, and Cuifang Gao. 2025. "Minimizing Redundancy in Wireless Sensor Networks Using Sparse Vectors" Sensors 25, no. 5: 1557. https://doi.org/10.3390/s25051557
APA StyleYuan, H., & Gao, C. (2025). Minimizing Redundancy in Wireless Sensor Networks Using Sparse Vectors. Sensors, 25(5), 1557. https://doi.org/10.3390/s25051557