基于聚類模型預(yù)測的無線傳感網(wǎng)自適應(yīng)采樣技術(shù)研究
doi: 10.11999/JEIT140175 cstr: 32379.14.JEIT140175
基金項目:
國家自然科學(xué)基金(61102067)和浙江省自然科學(xué)基金(Y15F030066)資助課題
Clustered Predictive Model Based Adaptive Sampling Techniques in Wireless Sensor Networks
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摘要: 該文利用無線傳感網(wǎng)(WSNs)的數(shù)據(jù)空間相關(guān)性，提出一種基于數(shù)據(jù)梯度的聚類機制，聚類內(nèi)簇頭節(jié)點維護(hù)簇成員節(jié)點的數(shù)據(jù)時間域自回歸(AR)預(yù)測模型，在聚類內(nèi)范圍實施基于預(yù)測模型的采樣頻率自適應(yīng)算法。通過自適應(yīng)優(yōu)化調(diào)整采樣頻率，在保證數(shù)據(jù)采樣精度的前提下減少了冗余數(shù)據(jù)傳輸，提高無線傳感網(wǎng)的能效水平。該文提出的時間域采樣頻率調(diào)整算法綜合考慮了感知數(shù)據(jù)的時空聯(lián)合相關(guān)性特點，仿真結(jié)果驗證了該文算法的性能優(yōu)勢。
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關(guān)鍵詞:
- 無線傳感網(wǎng) /
- 自適應(yīng)采樣 /
- 模型匹配 /
- 模型預(yù)測
Abstract: According to the data spatial correlation of Wireless Sensor Networks (WSNs), this study proposes a clustering mechanism based on the data gradient. In the proposed clustering mechanism, the cluster head nodes maintain Auto Regressive (AR) prediction model of the sensory data within each cluster in the time domain. Moreover, the cluster head nodes adjust the temporal sampling frequency based on the implementation of above predicted adaptive algorithm model. By adjusting the temporal sampling frequency, the redundant data transmission is reduced as well as ensuring desired sampling accuracy, so as energy efficiency is improved. The temporal sampling frequency adjustment algorithm takes into account spatial and temporal combined correlation characteristics of sensory data. As a result, the simulation results demonstrate the performance benefits of the proposed algorithm.-
Key words:
- Wireless Sensor Networks (WSNs) /
- Adaptive sampling /
- Model matching /
- Model forecasting
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