无线传感器网络干扰分类识别机制的研究

doi:10.3969/j.issn.1000-436x.2013.10.004

Abstract

Abstract:

The interference identification and classification of wireless sensor networks are important problems to im-prove network performance.To solve such problems,methods for interference identification and classification were de-signed and implemented.The experimental transmission parameters of the sensor nodes were obtained in differ-ent interference state,and then the Logistic model was used to identify the state of interference and classify the type of the interference based on the parameters given.The actual network data tests show that the classification model in the identification accuracy can be achieved more than 97%,which can effectively address the problem of recognition of net-work interference.

Key words: wireless sensor network, interference identification, Logistic model, correlation analysis, classification

Ze ZHAO,Peng-fei SHANG,Hai-ming CHEN,Qiang LIU,Dong LI,Zhao-liang ZHANG,Li CUI. Interference identification and classification mechanism for wireless sensor network[J]. Journal on Communications, 2013, 34(10): 28-36.

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References 14

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	Length	TxPower	RSSI	LQI	CCAtimes	Throughput	PRR
Length	1.000	0.000	0.006	0.021	0.025	0.802	-0.267
TxPower	0.000	1.000	0.937	0.775	-0.017	0.409	0.784
RSSI	0.006	0.937	1.000	0.833	0.003	0.427	0.814
LQI	0.021	0.775	0.833	1.000	0.019	0.395	0.706
CCAtimes	0.025	-0.017	0.003	0.019	1.000	-0.004	0.023
Throughput	0.802	0.409	0.427	0.395	-0.004	1.000	0.227
PRR	-0.267	0.784	0.814	0.706	0.023	0.227	1.000

	Length	TxPower	RSSI	LQI	CCAtimes	Throughput	PRR	Int_Length	Int_Power
Length	1.000	0.005	-0.006	0.185	0.011	0.675	-0.251	-0.003	0.000
TxPower	0.005	1.000	0.742	0.188	-0.039	0.265	0.456	-0.005	-0.033
RSSI	-0.006	0.742	1.000	0.508	0.158	0.246	0.588	0.005	0.192
LQI	0.185	0.188	0.508	1.000	0.198	0.234	0.290	0.095	0.162
CCAtimes	0.011	-0.039	0.158	0.198	1.000	-0.385	0.032	0.536	0.539
Throughput	0.675	0.265	0.246	0.234	-0.385	1.000	0.199	-0.305	-0.216
PRR	-0.251	0.456	0.588	0.290	0.032	0.199	1.000	-0.009	-0.023
Int_Length	-0.003	-0.005	0.005	0.095	0.536	-0.305	-0.009	1.000	0.004
Int_Power	0.000	-0.033	0.192	0.162	0.539	-0.216	-0.023	0.004	1.000

	Length	TxPower	RSSI	LQI	CCAtimes	Throughput	PRR	Int_Length	Int_Power
Length	1.000	0.074	0.012	0.165	-0.286	-0.237	-0.626	-0.097	-0.080
TxPower	0.074	1.000	0.652	0.451	-0.043	0.179	0.133	-0.086	0.093
RSSI	0.012	0.652	1.000	0.749	0.188	-0.136	-0.048	0.410	-0.198
LQI	0.165	0.451	0.749	1.000	0.142	-0.255	-0.214	0.377	-0.109
CCAtimes	-0.286	-0.043	0.188	0.142	1.000	-0.285	-0.021	0.553	0.157
Throughput	-0.237	0.179	-0.136	-0.255	-0.285	1.000	0.877	-0.690	-0.035
PRR	-0.626	0.133	-0.048	-0.214	-0.021	0.877	1.000	-0.410	0.055
Int_Length	-0.097	-0.086	0.410	0.377	0.553	-0.690	-0.410	1.000	0.013
Int_Power	-0.080	0.093	-0.198	-0.109	0.157	-0.035	0.055	0.013	1.000

参数	b
RSSI	0.021 3
LQI	0.017 5
CCATimes	-0.005 6
Throughput	-2.094 6×10^{- 4}
PRR	7.562 4
e₁	-0.429 5

参数	b′
RSSI	-0.030 3
LQI	0.138 6
CCA Times	-0.010 1
Throughput	5.104 8×10^{- 5}
PRR	0.578 1
e₂	-16.954 7

Interference identification and classification mechanism for wireless sensor network

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Figures/Tables 12

References 14

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分类情况	实际值	判别值
不受干扰	3 183	3 198
网内干扰	841	722
网间干扰	976	1 080
一次判别正确率	—	99.70%
二次判别正确率	—	94.23%
综合判别正确率	—	97.62%

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