【正文】 I 無線傳感器網(wǎng)絡(luò)具有傳輸速率快、組網(wǎng)便捷等優(yōu)點(diǎn)。國家對傳感器網(wǎng)絡(luò)的研究也非常重視,國家自然科學(xué)基金委員會(huì)從2003年起開始設(shè)立了無線傳感器網(wǎng)絡(luò)相關(guān)研究課題,國家的“863”項(xiàng)目、國家自然科學(xué)基金項(xiàng)目、各省區(qū)的自然科學(xué)基金項(xiàng)目的課題中都有相當(dāng)?shù)谋壤沁M(jìn)行無線傳感器網(wǎng)絡(luò)研究的。麻省理工學(xué)院已經(jīng)著手研究超低功耗無線傳感器網(wǎng)絡(luò)的問題,試圖解決超低功耗無線傳感器系統(tǒng)的方法和技術(shù)問題。 3）目前技術(shù)存在的問題 ?無線傳感器網(wǎng)絡(luò)即便節(jié)點(diǎn)靈活，可減硬件成本，但依然受有限能量的制約，優(yōu)勢未能充分發(fā)揮。3. 采取的研究路線首先查閱大量關(guān)于無線傳感器的相關(guān)文獻(xiàn)，選用WIFI技術(shù)作為無線傳感器網(wǎng)絡(luò)的通信技術(shù)。 選用ARM芯片AT91SAM9G45作為處理器， 選用AD7492作為A/D轉(zhuǎn)換器，選用FIFO CY7C4261作為緩存器，F(xiàn)PGA芯片選用XC3S500E， WIFI 芯片選用 RT3070。第5周～第6周 擬定系統(tǒng)方案，進(jìn)行系統(tǒng)總體設(shè)計(jì)。第16周 準(zhǔn)備答辯。覆蓋環(huán)境感知對象是指節(jié)點(diǎn)判定為價(jià)值有效的監(jiān)測目標(biāo)，可以是監(jiān)測區(qū)域的聲音、光線、溫度、震動(dòng)等等，節(jié)點(diǎn)傳感器通過數(shù)據(jù)采集、轉(zhuǎn)化為系統(tǒng)可以識(shí)別的信息資源，并最終上傳給接收數(shù)據(jù)觀察者。傳感器模塊通過傳感器觸頭感知外界信息，獲取傳感數(shù)據(jù)；無線通信模塊通過天線與其他節(jié)點(diǎn)通信完成數(shù)據(jù)交換。WIFI技術(shù)傳輸速率快，采用直接序列擴(kuò)頻技術(shù)，提供很高的傳輸速率，具有高移動(dòng)性，在無線局域網(wǎng)覆蓋范圍內(nèi)，地理位置的限制進(jìn)行任意移動(dòng)，各個(gè)節(jié)點(diǎn)可以不受覆蓋范圍廣，WIFI 的覆蓋范圍半徑在 150m，但通過中繼能實(shí)現(xiàn)幾千米的通信距離。將 ARM 作為節(jié)點(diǎn)的主控制器可全面提高節(jié)點(diǎn)性能。兩路 USB 接口控制芯片，選用雙 USB 電源開關(guān)芯片 SP2526A2USB 和兩片 USB控制芯片 USBLC62P6 為系統(tǒng)提供兩路 USB 接口，一路用于與無線模塊進(jìn)行通信，一路用于測試數(shù)據(jù)的有限讀取。 嵌入式 Linux 是在 Linux 的基礎(chǔ)演變而成的，專門應(yīng)用于嵌入式設(shè)備中。Linux 是一個(gè)跨平臺(tái)的操作，它適應(yīng)于多種處理器，到目前為止，它可以支持幾十種處理器，所以它的移植性非常好。本文選用 Ateml 公司的工業(yè)級(jí) ARM 芯片 AT91SAM9G45，該處理器 BootLoader 和 Kernel 需要使用 Ateml 公司的 SAMBA 軟件通過 USB 口進(jìn)行燒寫，而 Rootfs 是通過網(wǎng)口進(jìn)行燒寫。參考文獻(xiàn)：[1]王亞超，寧濱，基于無線傳感器網(wǎng)絡(luò)的城軌列車運(yùn)行能耗數(shù)據(jù)采集系統(tǒng)設(shè)計(jì)[D]，.[2]林一多，高德云. 基于 ARM 的無線傳感器網(wǎng)絡(luò) MAC 協(xié)議設(shè)計(jì)與實(shí)現(xiàn) [J].計(jì)算機(jī)應(yīng)用，2010,30（5）:11451148.[3]林彬，基于 WIFI 的無線傳感器網(wǎng)絡(luò)檢測系統(tǒng)的設(shè)計(jì)[D]..[4黃茂芹，基于FPGA的實(shí)時(shí)無線傳感器網(wǎng)絡(luò)系統(tǒng)設(shè)計(jì)[D]，電子科技大學(xué) 電子與通信工程，.[5]曾強(qiáng)，張志杰，WIFI無線傳感器網(wǎng)絡(luò)的設(shè)計(jì)與實(shí)現(xiàn)[D]，中北大學(xué)，.[6]王賽博，劉素凱，毛先柏，無線傳感器網(wǎng)絡(luò)綜述[J]，信息通信，.[7]秦邵華，無線傳感器多信道通信技術(shù)的研究[D]，山東大學(xué)，.[8]孫宇,基于嵌入式 Linux 的無線傳感器網(wǎng)絡(luò)基站軟件設(shè)計(jì)與實(shí)現(xiàn) [D],吉林大學(xué),.[9][D].北京:北京交通大學(xué),2008[10] ARM 的無線測控系統(tǒng)[J].(4):156157.[11] ARM 的無線數(shù)據(jù)采集系統(tǒng) [J].廣東技術(shù)師范學(xué)院學(xué)報(bào)，:2528.[12] Camera calibration toolbox for matlab. [13] Free space optics:technology insight. .[14] Irda. ://[15]Mipav.[16] Stan moore astronomy. [17] , , N. B. Mandayam, J. Silva, K. Dana, and . Challenge: Mobile optical networks through visual mimo. In MobiCom ’10: Proceedings of the sixteenth annual international conference on Mobile puting and networking, pages 105–112, New York, NY, USA, 2010. ACM.外文文獻(xiàn)： Characterizing Multiplexing and Diversity in Visual MIMOAbstract Mobile optical wireless has so far been limited to very short ranges for high data rate systems. It may be feasible to overe the data rate limitations over large transmission range in optical wireless through camera receivers and light emitting transmitter arrays through a concept what we call ”visual MIMO”. In this concept multiple transmit elements of a light emitting array (LEA) are used as transmitters to municate to the individual pixel elements of the camera which act as multiple receive elements to create the visual MIMO channel. Multiplexing information over parallel data channels albeit be very similar to RF MIMO in concept, the visual MIMO approach dramatically differs in its characterization. In visual MIMO since the received signal is essentially the image of the transmitting element, the perspective distortions in the visual channel dominate over some of the important properties of a RF wireless channel such as distance based attenuation and multipath fading. Some of the prominent perspective distortions include the reduction in the size of the image with distance and skew/rotation in the image due to angular view. Further lens blur (typically due to focus imperfection or jerks while capturing the image) can also significantly depreciate the image quality. In this paper we will detail how MIMO techniques such as multiplexing and diversity are characterized based on the effect of perspective distortions. Based our visual MIMO channel model we will derive the analytical channel capacity of the visual MIMO channel and using the same we illustrate the significance of parameters such as distance, viewing angle and blur in characterizing multiplexing and diversity in visual MIMO.I. INTRODUCTIONHigh data rate mobile optical wireless munications, has so far been limited to very short transmission ranges of less than 10m [3]. To achieve transmission ranges greater than a few tens of meters in optical wireless requires highly directional light beams with very narrow angleofview [2]. Optical wireless channels are characterized by large path loss and high background noise typically from sunlight or other ambient light sources in vicinity [16]. Further the low transmit power levels in optical channels (due to output power regulations in optical sources such as LEDs and LASERs) limit the signaltonoise ratios in these channels and thus the transmission range. In our recent work in [6], we have argued that it is now being feasible to achieve high data rates over large transmission ranges in mobile optical wireless munications using camera receivers through a concept what we call ”visual MIMO”. In this concept, optical transmissions by an array of light emitting devices are received by an array of photodetector (pixels) elements of a camera. The pixels in a camera can essentially be viewed as an array of highly directional receive elements. Such a structure allows allows reducing interference and noise from other light sources in the channel. Such a system offers a degree of freedom in selecting and bining a subset of the receiver elements that receive a strong signal from the transmitter and thus achieve large SNRs. This may be very similar to the antenna selection in RFMIMO but will incur lesser overhead and nonplex processing at the camera receiver as the processing can be done in software using image processing and puter vision algorithms [6]. However, the tradeoffs in the visual MIMO system, are a limited receiver sampling frequency and strong lineofsight (LOS) requirements. We already showed in [6] that usingvisual MIMO it is possible to achieve considerable data rates over large transmission ranges with just a single transmitting element. Using MIMO techniques such as ”multiplexing” to send independent streams of bits using the multiple elements of the light transmitter array and recording over a group of camera pixels can further enhance the data rates. On the other hand the system