多媒體信息檢索與環(huán)境監(jiān)測:共享數(shù)據(jù)融合的觀點(diǎn)
Alan F. Smeaton a,⁎， Edel O'Connor b，F(xiàn)iona Regan c

A 洞察數(shù)據(jù)分析中心和計(jì)算機(jī)學(xué)院，都柏林城市大學(xué)，格拉斯內(nèi)文，都柏林 9，愛爾蘭
b 清晰度：傳感器網(wǎng)絡(luò)技術(shù)中心，愛爾蘭都柏林城市大學(xué)，格拉斯內(nèi)文，都柏林9，愛爾蘭
C MESTECH：海洋環(huán)境遙感技術(shù)中心，都柏林城市大學(xué)，格拉斯內(nèi)文，都柏林9，愛爾蘭
文章信息
文章歷史：
2013年1月22日收到
2013年7月16日收到修改稿
2013年10月21日接受
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關(guān)鍵詞： 傳感器數(shù)據(jù)融合 多媒體信息檢索 信任和聲譽(yù)框架 環(huán)境監(jiān)測
摘要：基于計(jì)算機(jī)的遠(yuǎn)程監(jiān)控我們的環(huán)境正日益基礎(chǔ)上，結(jié)合從原位傳感器從遠(yuǎn)程來源的數(shù)據(jù)，如衛(wèi)星圖像或閉路電視得出的數(shù)據(jù)。在這樣的部署中，需要連續(xù)地監(jiān)視各傳感器的數(shù)據(jù)流的準(zhǔn)確性，以便我們可以考慮傳感器，或錯(cuò)誤的突發(fā)故障，由于校準(zhǔn)驅(qū)動(dòng)器或生物結(jié)垢。在多媒體信息檢索（MMIR），我們通過多媒體的文物，如視頻節(jié)目的檔案搜索，通過實(shí)現(xiàn)幾個(gè)獨(dú)立的檢索系統(tǒng)或代理，我們結(jié)合每一個(gè)檢索代理的輸出，以產(chǎn)生一個(gè)總排名。在本文中，我們借鑒這些看似完全不同的應(yīng)用程序之間的相似之處，并顯示他們?nèi)绾畏窒韼讉€(gè)相似之處。在環(huán)境監(jiān)測的情況下，我們還需要一些機(jī)制通過它我們可以建立信任，促進(jìn)各傳感器的聲譽(yù)，盡管這是我們不需要在MMIR 。在本文中，我們提出，我們已經(jīng)開發(fā)出一種信任和聲譽(yù)框架大綱和部署用于監(jiān)視在異構(gòu)傳感器網(wǎng)絡(luò)的傳感器的性能。



Multimedia information retrieva l and environmental monitoring: Shared perspectives on data fusion
Alan F. Smeatona,⁎, Edel O'Connorb, Fiona Reganc
A Insight Centre for Data Analytics and School of Computing, Dublin City University, Glasnevin, Dublin 9, Ireland
B CLARITY: Centre for Sensor Web Technologies, Dublin City University, Glasnevin, Dublin 9, Ireland
C MESTECH: Marine Environmental Sensing Technology Hub, Dublin City University, Glasnevin, Dublin 9, Ireland

a r t i c l e i n f o
Article history:
Received 22 January 2013
Received in revised form 16 July 2013
Accepted 21 October 2013
Available online xxxx
Keywords:
Sensor data fusion
Multimedia information retrieva l
Trust and reputation framework
Environmental monitoring

a b s t r a c t
Computer-based remote monitoring of our environment is increasingly based on combining data derived from in-situ-sensors with data derived from remote sources, such as satellite images or CCTV. In such deployments it is necessary to continuously monitor the accuracy of each of the sensor data streams so that we can account for sudden failures of sensors, or errors due to calibration drive or biofouling. In multimedia information retrieva l (MMIR), we search through archives of multimedia artefacts like video programs, by implementing several independent retrieva l systems or agents, and we combine the outputs of each retrieva l agent in order to generate an overall ranking. In this paper we draw parallels between these seemingly very different applications and show how they share several similarities. In the case of environmental monitoring we also need some mechanism by which we can establish the trust and reputation of each contributing sensor, though this is something we do not need in MMIR. In this paper we present an outline of a trust and reputation framework we have developed and deployed for monitoring the performance of sensors in a heterogeneous sensor network.