Differential Run-Length Encryption in Sensor Networks
Abstract
:1. Introduction
2. Related Work
Algorithm 1 LEC Pseudocode |
Require:, Table of LEC codes Ensure: if () then else end if = Table() extract from LEC Table if () then return end if if () then is the low-order bits of v. else end if return |
Algorithm 2 LZW Pseudocode |
initialize Dictionary[0-255] = first 256 ASCII codes STRING ← get input symbol while there are still input symbols do SYMBOL ← get input symbol if (STRING+SYMBOL is in Dictonary) then STRING = STRING+SYMBOL else output the code for STRING add STRING+SYMBOL to Dictionary STRING = SYMBOL end if end while output the code for STRING |
Algorithm 3 RLE Pseudocode |
while there are still input symbols do repeat get input symbol until symbol unequal to next symbol output count and symbol end while |
Algorithm 4K-RLE Pseudocode |
read input value while there are still input values do read next input value if () then else output () end if end while output () |
3. Differential Run Length Encryption
3.1. Group Division by Chauvenet’s Criterion
Algorithm 5 Group Division |
Require: Ensure: initialize initialize arrays compute and add into for () do if () then add into update else add into end if end for |
3.2. Subgroups Division
Algorithm 6 Computing |
Require: Ensure: initialize whiledo ifthen else end if end while |
3.3. Adaptive Data Encoding
4. Performance Evaluation
4.1. Effect of K Value
4.2. Evaluation Results
Algorithm 7 Creating simulated temperature data |
Require: Ensure: initialize whiledo while () do if () then end if end while while () do if () then end if end while end while |
4.3. Performance Visualization
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Level ( | Bits | Prefix () | Suffix Range () | Value () |
---|---|---|---|---|
0 | 2 | 00 | - | 0 |
1 | 4 | 010 | 0…1 | −1, 1 |
2 | 5 | 011 | 00…11 | −3, −2, 2, 3 |
3 | 6 | 100 | 000…111 | −7, …, −4, 4, …, 7 |
4 | 7 | 101 | 0000…1111 | −15, …,−8, 8, …, 15 |
5 | 8 | 110 | 00000…11111 | −31, …, −16, 16, …, 31 |
6 | 10 | 1110 | 000000…111111 | −63, …, −32, 32, …, 63 |
7 | 12 | 11110 | 0000000…1111111 | −127, …, −64, 64, …, 127 |
String | Output | Dictionary | Total Bits |
---|---|---|---|
A | 65 | 256 = AA | 9 |
AA | 256 | 257 = AAA | 18 |
A | 65 | 258 = AB | 27 |
B | 66 | 259 = BA | 36 |
AAA | 257 | 260 = AAAB | 45 |
B | 66 | 261 = BC | 54 |
C | 67 | 262 = CC | 63 |
C | 67 | 72 |
Process | Format |
---|---|
raw data | |
group division | |
subgroup division | for each |
encoded data |
K | Uncompression (Bits) | Compression (Bits) | DRS (%) |
---|---|---|---|
0 | 256, 240 | 91, 81 | 64.45, 66.25, 65.32 |
1 | 256, 240 | 94, 88 | 63.28, 63.33, 63.31 |
2 | 256, 240 | 88, 88 | 65.63, 63.33, 64.52 |
3 | 256, 240 | 86, 88 | 66.41, 63.33, 64.92 |
4 | 256, 240 | 86, 76 | 66.41, 68.33, 67.34 |
5 | 256, 240 | 79, 79 | 69.14, 67.08, 68.15 |
Dataset | Algorithm | Compression Step | Transmission Step | |||||
---|---|---|---|---|---|---|---|---|
#Bits | Time (s) | DRS (%) | Energy (mJ) | Time (s) | #Packets | Energy (mJ) | ||
sine-like (352 bits) | RLE | 464 | 0.023 | −31.82 | 0.275 | 0.015 | 0.453 | 0.781 |
K-RLE | 320 | 0.023 | 19.09 | 0.303 | 0.011 | 0.313 | 0.473 | |
LEC | 149 | 0.037 | 57.67 | 0.313 | 0.005 | 0.146 | 0.222 | |
LZW | 207 | 0.110 | 41.19 | 0.396 | 0.008 | 0.202 | 0.309 | |
D-RLE | 132 | 0.075 | 62.50 | 0.327 | 0.004 | 0.129 | 0.203 | |
chaotic (1176 bits) | RLE | 1504 | 0.076 | −27.89 | 0.919 | 0.050 | 1.469 | 2.530 |
K-RLE | 1168 | 0.077 | 0.68 | 1.014 | 0.041 | 1.141 | 1.728 | |
LEC | 733 | 0.122 | 37.67 | 1.047 | 0.024 | 0.716 | 1.092 | |
LZW | 648 | 0.367 | 44.90 | 1.323 | 0.025 | 0.633 | 0.967 | |
D-RLE | 616 | 0.249 | 47.62 | 1.092 | 0.019 | 0.602 | 0.947 | |
simulated temperature (1600 bits) | RLE | 1600 | 0.103 | 10.00 | 1.250 | 0.053 | 1.563 | 2.692 |
K-RLE | 1280 | 0.104 | 20.00 | 1.379 | 0.045 | 1.250 | 1.893 | |
LEC | 651 | 0.166 | 59.31 | 1.424 | 0.021 | 0.636 | 0.970 | |
LZW | 1584 | 0.500 | 11.00 | 1.800 | 0.061 | 1.547 | 2.363 | |
D-RLE | 416 | 0.339 | 74.00 | 1.486 | 0.013 | 0.406 | 0.640 | |
temperatureHr (768 bits) | RLE | 752 | 0.049 | 12.08 | 0.600 | 0.025 | 0.734 | 1.265 |
K-RLE | 512 | 0.050 | 33.33 | 0.662 | 0.018 | 0.500 | 0.757 | |
LEC | 528 | 0.080 | 31.25 | 0.684 | 0.017 | 0.516 | 0.787 | |
LZW | 504 | 0.240 | 34.38 | 0.864 | 0.020 | 0.492 | 0.752 | |
D-RLE | 204 | 0.163 | 73.44 | 0.713 | 0.006 | 0.199 | 0.314 | |
temperatureMin (46,080 bits) | RLE | 984 | 2.963 | 97.87 | 36.005 | 0.033 | 0.961 | 1.655 |
K-RLE | 656 | 3.009 | 98.58 | 39.713 | 0.023 | 0.641 | 0.970 | |
LEC | 6240 | 4.780 | 86.46 | 41.018 | 0.202 | 6.094 | 9.299 | |
LZW | 4230 | 14.396 | 90.82 | 51.847 | 0.164 | 4.131 | 6.310 | |
D-RLE | 467 | 9.776 | 98.99 | 42.802 | 0.015 | 0.456 | 0.718 |
Dataset | Total Energy Use (mJ) | ||||
---|---|---|---|---|---|
RLE | K-RLE | LEC | LZW | D-RLE | |
sine-like | 1.056 | 0.777 | 0.535 | 0.705 | 0.530 |
chaotic | 3.449 | 2.741 | 2.139 | 2.290 | 2.040 |
simulated temperature | 3.942 | 3.272 | 2.394 | 4.163 | 2.126 |
temperatureHr | 1.865 | 1.419 | 1.470 | 1.616 | 1.027 |
temperatureMin | 37.660 | 40.683 | 50.317 | 58.157 | 43.520 |
Dataset | Algorithm | Compression Step | Transmission Step | |||||
---|---|---|---|---|---|---|---|---|
#Bits | Time (s) | DRS (%) | Energy (mJ) | Time (s) | #Packets | Energy (mJ) | ||
sine-like | RLE | 20,352 | 3.044 | 55.83 | 34.903 | 0.680 | 19.875 | 34.239 |
K-RLE | 16,056 | 3.060 | 65.16 | 37.493 | 0.570 | 15.680 | 23.749 | |
LEC | 6876 | 4.862 | 85.08 | 40.778 | 0.223 | 6.715 | 10.246 | |
LZW | 6588 | 14.156 | 85.70 | 50.407 | 0.256 | 6.434 | 9.828 | |
D-RLE | 4476 | 10.087 | 90.29 | 42.703 | 0.140 | 4.371 | 6.883 | |
chaotic | RLE | 21,888 | 2.991 | 52.50 | 34.651 | 0.731 | 21.375 | 36.823 |
K-RLE | 18,816 | 3.126 | 59.17 | 35.928 | 0.668 | 18.375 | 27.832 | |
LEC | 8796 | 4.762 | 80.91 | 40.538 | 0.285 | 8.590 | 13.107 | |
LZW | 7776 | 14.052 | 83.13 | 49.407 | 0.302 | 7.594 | 11.600 | |
D-RLE | 7332 | 9.367 | 84.09 | 41.047 | 0.230 | 7.160 | 11.275 | |
simulated temperature | RLE | 19,200 | 3.023 | 58.33 | 34.918 | 0.641 | 18.750 | 32.301 |
K-RLE | 16,560 | 3.005 | 64.06 | 36.986 | 0.588 | 16.172 | 24.495 | |
LEC | 7812 | 4.836 | 83.05 | 40.596 | 0.253 | 7.629 | 11.641 | |
LZW | 19,008 | 13.346 | 58.75 | 49.447 | 0.737 | 18.563 | 28.355 | |
D-RLE | 7332 | 9.967 | 84.09 | 41.630 | 0.230 | 7.160 | 11.275 | |
temperatureHr | RLE | 45,120 | 2.960 | 2.08 | 34.577 | 1.506 | 44.063 | 75.906 |
K-RLE | 30,720 | 3.006 | 33.33 | 35.976 | 1.091 | 30.000 | 45.439 | |
LEC | 31,680 | 4.755 | 31.25 | 39.098 | 1.028 | 30.938 | 47.208 | |
LZW | 30,240 | 13.368 | 34.38 | 47.657 | 1.173 | 29.531 | 45.111 | |
D-RLE | 12,240 | 9.420 | 73.44 | 41.023 | 0.384 | 11.953 | 18.823 | |
temperatureMin | RLE | 984 | 2.963 | 97.87 | 36.005 | 0.033 | 0.961 | 1.655 |
K-RLE | 656 | 3.009 | 98.58 | 39.713 | 0.023 | 0.641 | 0.970 | |
LEC | 6240 | 4.780 | 86.46 | 41.018 | 0.202 | 6.094 | 9.299 | |
LZW | 4230 | 14.396 | 90.82 | 51.847 | 0.164 | 4.131 | 6.310 | |
D-RLE | 467 | 9.776 | 98.99 | 42.802 | 0.015 | 0.456 | 0.718 |
Dataset | Total Energy Use (mJ) | ||||
---|---|---|---|---|---|
RLE | K-RLE | LEC | LZW | D-RLE | |
sine-like | 69.142 | 61.242 | 51.025 | 60.235 | 49.586 |
chaotic | 71.474 | 63.760 | 53.646 | 61.007 | 52.322 |
simulated temperature | 67.218 | 61.481 | 52.237 | 77.803 | 52.906 |
temperatureHr | 110.483 | 81.415 | 86.306 | 92.768 | 59.846 |
temperatureMin | 37.660 | 40.683 | 50.317 | 58.157 | 43.520 |
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Chianphatthanakit, C.; Boonsongsrikul, A.; Suppharangsan, S. Differential Run-Length Encryption in Sensor Networks. Sensors 2019, 19, 3190. https://doi.org/10.3390/s19143190
Chianphatthanakit C, Boonsongsrikul A, Suppharangsan S. Differential Run-Length Encryption in Sensor Networks. Sensors. 2019; 19(14):3190. https://doi.org/10.3390/s19143190
Chicago/Turabian StyleChianphatthanakit, Chiratheep, Anuparp Boonsongsrikul, and Somjet Suppharangsan. 2019. "Differential Run-Length Encryption in Sensor Networks" Sensors 19, no. 14: 3190. https://doi.org/10.3390/s19143190
APA StyleChianphatthanakit, C., Boonsongsrikul, A., & Suppharangsan, S. (2019). Differential Run-Length Encryption in Sensor Networks. Sensors, 19(14), 3190. https://doi.org/10.3390/s19143190