A DECENTRALIZED APPROACH FOR DETECTING DYNAMICALLY CHANGING DIFFUSE EVENT SOURCES IN NOISY WSN ENVIRONMENTS

Figures & data

FIGURE 1 Diffuse event example. (Figure is provided in color online.)

FIGURE 2 Low-power listening (from Na, Lim, and Kim 2008).

TABLE 1 Standard Settings for MPB

Params	Values	Params	Values
movrand	random	num. of peak	1–3
num. of dimensions	2	minheight	30
maxheight	100	stdheight	50
minwidth	0.1	maxwidth	5.0
stdwidth	0.0	mincoordinate	0
maxcoordinate	100	peak_function	cone

FIGURE 3 Scenario examples. (Figure is provided in color online.)

TABLE 2 Varying Sensor Number Without Noise

Sensor Number	Reads	Msgs	Failures	Adj. avg.
500	326.10	543.42	35.3%	9.2
1000	422.82	695.23	15.9%	18.76
2000	676.08	1106.77	4.9%	37.09
4000	1158.56	1887.11	2.9%	74.94
8000	2334.98	3805.85	1.9%	149.7175

TABLE 3 Varying Communication Range Without Noise

Comm. Rng	Reads	Msgs	Failures	Adj. avg.
20	4236.44	7081.07	32.8%	4.88
40	1078.08	1755.21	0.8%	19.42
60	1108.58	1804.63	0.7%	42.92
80	1158.56	1887.11	2.9%	75.38
100	1380.94	2263.52	6.3%	115.60

FIGURE 4 Performance results. (Figure is provided in color online.)

TABLE 4 Varying the Noise Factor γ

γ	Reads	Msgs	Failures
0%	422.82	695.23	15.9%
±2%	547.70	907.64	13.4%
±4%	698.31	1160.37	15.1%
±6%	776.21	1291.31	18.6%
±10%	878.73	1461.43	25.1%

TABLE 5 Varying the n _s Parameter without Noise

n s	Reads	Msgs	Failures
1	467.45	656.24	17.3%
2	425.96	663.72	16.7%
3	397.38	651.64	15.2%
4	435.79	740.80	12.6%
5	517.21	903.14	14.1%
6	525.74	934.45	14.4%
10	752.18	1391.42	13.1%

TABLE 6 Varying the n _s Parameter with Noise γ = ±4%

n s	Reads	Msgs	Failures
1	624.27	879.90	23.9%
2	655.37	1029.82	18.9%
3	698.31	1160.37	15.1%
4	728.97	1254.22	13.8%
5	756.40	1331.84	13.9%
6	788.16	1411.77	13.3%
10	954.97	1778.99	12.3%
20	1417.07	2725.35	12.0%

TABLE 7 Varying Agent Creation, P _a

P a	T w	Reads	Msgs	Failures
0.2%	20	322.30	538.29	37.7%
0.5%	20	422.82	695.23	15.9%
1%	20	484.01	783.83	4.5%
5%	20	654.21	1024.92	0.3%

TABLE 8 Varying Frequency, T _w

P a	T w	Reads	Msgs	Failures
0.5%	5	576.26	920.21	1.8%
0.5%	10	488.36	790.16	4.4%
0.5%	20	422.82	695.23	15.9%
0.5%	50	307.75	513.11	38.5%

TABLE 9 Varying Global Exploration with Noise γ = ±4%

P a	T w	Reads	Msgs	Failures
0.2%	20	525.94	881.25	39.8%
0.5%	20	622.29	1031.82	14.7%
1%	20	819.022	1351.79	5.0%
5%	20	1093.08	1787.67	1.0%

TABLE 10 The Exploration Cost

Noise	Reads	Msgs
0%	49.28	0±0
±2%	100.22	108.74
±5%	99.46	107.87
±10%	101.78	111.05

TABLE 11 Failure Tolerance

Failure Prob.	Reads	Msgs	Failures
0%	422.82	695.23	15.9%
5%	430.56	708.39	16.2%
10%	409.97	675.38	16.9%
20%	422.26	697.46	19.6%
40%	463.55	772.60	30.7%

TABLE 12 Wrong Measure Tolerance

Failure Prob.	Reads	Msgs	Failures
0%	422.82	695.23	15.9%
1%	551.01	919.66	17.8%
2%	589.79	990.09	18.0%
5%	870.43	1480.42	22.3%
10%	1292.06	2218.34	34.8%

Na , J. , Lim , S. , and C.-K. Kim 2008 . Dual wake-up low power listening for duty cycled wireless sensor networks. In EURASIP Journal on Wireless Communications and Networking. New York , NY : Hindawi Publishing .

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