基于農(nóng)田無線傳感網(wǎng)絡(luò)的分簇路由算法

江 冰，毛 天，唐大衛(wèi)，鄔智俊，韓光潔（. 河海大學(xué)物聯(lián)網(wǎng)工程學(xué)院，常州 30；. 常州市傳感網(wǎng)與環(huán)境感知重點(diǎn)實(shí)驗(yàn)室，常州 30）

基于農(nóng)田無線傳感網(wǎng)絡(luò)的分簇路由算法

江 冰1，毛 天1，唐大衛(wèi)1，鄔智俊1，韓光潔2
（1. 河海大學(xué)物聯(lián)網(wǎng)工程學(xué)院，常州 213022；2. 常州市傳感網(wǎng)與環(huán)境感知重點(diǎn)實(shí)驗(yàn)室，常州 213022）

由于無線傳感網(wǎng)絡(luò)節(jié)點(diǎn)的能量有限，如何有效地利用有限資源以及實(shí)現(xiàn)數(shù)據(jù)的有效傳輸，成為研究熱點(diǎn)問題。針對農(nóng)田區(qū)域廣以及種植作物雜等環(huán)境特征，為延長農(nóng)田無線傳感器網(wǎng)絡(luò)的生命周期，提高傳感網(wǎng)的數(shù)據(jù)包投遞率，構(gòu)建了適用于農(nóng)田信息采集的無線傳感器網(wǎng)絡(luò)架構(gòu)，提出了一種混合式的分簇路由算法HCRA（hybrid clustering routing algorithm），研究了簇的形成、簇頭競選以及簇間路由過程，并對HCRA算法與低功耗自適應(yīng)集簇分層型算法LEACH（low-energy adaptive clustering hierarchy），以及使用固定簇半徑的混合節(jié)能分簇算法HEED（hybrid energy-efficient distributed clustering）進(jìn)行了仿真試驗(yàn)。結(jié)果表明：在1 000次迭代周期下，采用HCRA算法的網(wǎng)絡(luò)生存時間要比LEACH算法長約28%，比HEED算法長約12%；采用HCRA算法的數(shù)據(jù)包投遞率要比LEACH算法高約34個百分點(diǎn)，比HEED算法高約16個百分點(diǎn)。該研究可為農(nóng)田環(huán)境信息采集自動化監(jiān)測系統(tǒng)提供參考。

無線傳感網(wǎng)絡(luò)；數(shù)據(jù)傳輸；算法；分簇路由算法；網(wǎng)絡(luò)生命周期；數(shù)據(jù)包投遞率；HRCA算法

0 引 言

中國傳統(tǒng)農(nóng)業(yè)中，農(nóng)戶往往是通過實(shí)地考察農(nóng)作物生長環(huán)境，憑借經(jīng)驗(yàn)栽種農(nóng)作物。這種傳統(tǒng)的人工監(jiān)測辦法，容易造成判斷錯誤，導(dǎo)致農(nóng)戶采取不當(dāng)措施，造成農(nóng)作物減產(chǎn)[1]。近些年來，農(nóng)業(yè)領(lǐng)域出現(xiàn)了一些監(jiān)測系統(tǒng)，然而大多為有線實(shí)時監(jiān)測系統(tǒng)，其存在不足之處[2]：1）有線監(jiān)測節(jié)點(diǎn)的布置需要大量地布線，在實(shí)際環(huán)境下操作不靈活；2）有線方式在實(shí)際的農(nóng)業(yè)生產(chǎn)應(yīng)用中，線纜容易損壞導(dǎo)致系統(tǒng)可靠性降低。而無線傳感網(wǎng)技術(shù)可以有效地解決有線監(jiān)測系統(tǒng)中存在的問題[3-5]。因此，需要有效地利用無線傳感器網(wǎng)絡(luò)技術(shù)來改造傳統(tǒng)農(nóng)業(yè)。

雖然無線傳感器網(wǎng)絡(luò)技術(shù)在農(nóng)業(yè)上得到了一定的應(yīng)用。如張波等[6]基于無線傳感器網(wǎng)絡(luò)的無人機(jī)農(nóng)田信息監(jiān)測系統(tǒng)；杜克明等[7]WebGIS在農(nóng)業(yè)環(huán)境物聯(lián)網(wǎng)監(jiān)測系統(tǒng)中的設(shè)計(jì)與實(shí)現(xiàn)；高峰等[8]基于無線傳感器網(wǎng)絡(luò)的作物水分狀況監(jiān)測系統(tǒng)研究與設(shè)計(jì)。然而，無線傳感器網(wǎng)絡(luò)節(jié)點(diǎn)在使用過程中能量通常不能補(bǔ)給，因而如何高效利用能量以及延長網(wǎng)絡(luò)生命周期，成為無線傳感器網(wǎng)絡(luò)的首要設(shè)計(jì)目標(biāo)。研究表明，采用合理的無線傳感器網(wǎng)絡(luò)分簇路由算法可以有效地延長網(wǎng)絡(luò)生命周期，提高網(wǎng)絡(luò)數(shù)據(jù)包傳遞率[9-11]。

為延長網(wǎng)絡(luò)生存周期，分簇是比較常用的一種辦法。LEACH (low-energy adaptive clustering hierarchy)是為WSN (wireless sensor networks)設(shè)計(jì)的低功耗自適應(yīng)路由協(xié)議，其基本思想是網(wǎng)絡(luò)周期性地隨機(jī)選擇簇頭節(jié)點(diǎn)，其它的非簇頭節(jié)點(diǎn)就近地加入相應(yīng)的簇頭，形成虛擬簇[12-14]。

PEGASIS（power efficient gathering in sensor information system）協(xié)議基本思想是從網(wǎng)絡(luò)中離匯聚節(jié)點(diǎn)最遠(yuǎn)的節(jié)點(diǎn)開始，采用貪婪算法，將網(wǎng)絡(luò)中的所有傳感器節(jié)點(diǎn)形成一條鏈，使得節(jié)點(diǎn)只需要與離它距離最近的相鄰節(jié)點(diǎn)進(jìn)行通信即可[15-17]。HEED（hybrid energy-efficient distributed clustering）協(xié)議是由Ossama等提出的。簇頭的選擇主要依據(jù)主、次2個參數(shù)。主參數(shù)依賴于剩余能量，用于隨機(jī)選取初始簇頭集合。具有較多剩余能量的節(jié)點(diǎn)將有較大的概率暫時成為簇頭，而最終該節(jié)點(diǎn)是否一定是簇頭取決于剩余能量是否比周圍節(jié)點(diǎn)多得多?？紤]到分簇后簇內(nèi)的通信開銷，HEED以簇內(nèi)平均可達(dá)能量（AMRP，average minimum reachability power）作為衡量簇內(nèi)通信代價(jià)的標(biāo)準(zhǔn)[18-20]。

近些年，國內(nèi)外相關(guān)學(xué)者對路由分簇算法也有一定的研究。陳炳才等[21]提出了一種基于LEACH協(xié)議改進(jìn)的簇間多跳路由協(xié)議，劉偉強(qiáng)等[22]對無線傳感器網(wǎng)絡(luò)中的PEGASIS算法進(jìn)行了研究與改進(jìn)，姚新兵等[23]提出了一種基于HEED的簇首多跳融合路由算法， Amirthalingam[24]、Sapna Gambhir[25]、Bennani Mohamed Taj[26]等對LEACH 算法進(jìn)行了研究與改進(jìn)，Mishra[27]、Ghosh[28]等對PEGASIS算法進(jìn)行了研究與改進(jìn)，Taheri[29]、Jain[30]等對HEED協(xié)議進(jìn)行了研究與改進(jìn)。

LEACH算法中，所有簇頭節(jié)點(diǎn)都直接與匯聚節(jié)點(diǎn)進(jìn)行通信，這會使得離匯聚節(jié)點(diǎn)較遠(yuǎn)的簇頭節(jié)點(diǎn)能量消耗嚴(yán)重；PEGASIS算法雖然減少了與匯聚節(jié)點(diǎn)直接通信的節(jié)點(diǎn)數(shù)，然而容易使網(wǎng)絡(luò)形成較長的鏈路，從而縮短網(wǎng)絡(luò)的壽命；HEED算法采用簇頭直接與匯聚節(jié)點(diǎn)通信也要消耗很大的能量[31]?；谵r(nóng)田監(jiān)測范圍大，針對無線傳感網(wǎng)的能量消耗問題，參考LEACH算法和HEED算法的原理，本文提出了一種適用于農(nóng)田信息采集的混合式分簇路由算法HCRA(hybrid clustering routing algorithm)。

1 網(wǎng)絡(luò)模型與能耗模型

1.1 網(wǎng)絡(luò)模型

本文研究的被監(jiān)測對象是農(nóng)田土壤，傳感器節(jié)點(diǎn)安置在農(nóng)田里，為固定節(jié)點(diǎn)，研究所選的農(nóng)田抽象成為圓形區(qū)域，節(jié)點(diǎn)信息可以在這片區(qū)域內(nèi)按照一定的規(guī)則來傳遞。由于農(nóng)田的監(jiān)測范圍大，節(jié)點(diǎn)之間進(jìn)行通信所需的能量消耗較大。此外，隨著農(nóng)作物的生長，節(jié)點(diǎn)間傳輸消息所需的能耗也會越來越大，遠(yuǎn)離匯聚節(jié)點(diǎn)的節(jié)點(diǎn)信息可能無法有效地傳輸給匯聚節(jié)點(diǎn)，因此網(wǎng)絡(luò)中需要多個匯聚節(jié)點(diǎn)。根據(jù)農(nóng)田環(huán)境的特征，提出了具有以下特點(diǎn)的無線傳感器網(wǎng)絡(luò)模型：

1）傳感網(wǎng)所監(jiān)測的區(qū)域?yàn)閳A形，半徑為R（m）；

2）傳感網(wǎng)由N個普通節(jié)點(diǎn)組成，均能正常工作，且具有完全相同的功能和能量，節(jié)點(diǎn)部署完成后其位置固定不變，節(jié)點(diǎn)均勻分布在區(qū)域內(nèi)；

3）節(jié)點(diǎn)能感知自身的剩余能量，當(dāng)節(jié)點(diǎn)能量低于節(jié)點(diǎn)收發(fā)數(shù)據(jù)實(shí)際可用的最大能量時，節(jié)點(diǎn)不能進(jìn)行收發(fā)工作；

4）每個節(jié)點(diǎn)都有唯一的ID，可以通過接收到的信號強(qiáng)度估算出發(fā)送節(jié)點(diǎn)與自身的距離，并可以根據(jù)通信距離的長短調(diào)整發(fā)射功率的大小。

1.2 能耗模型

無線傳感器網(wǎng)絡(luò)節(jié)點(diǎn)的能量消耗模型[32]如圖1所示。

圖1 無線傳感網(wǎng)節(jié)點(diǎn)能量消耗模型Fig.1 Wireless sensor network node energy consumption model

根據(jù)此模型，假設(shè)節(jié)點(diǎn)X向與之距離為d的節(jié)點(diǎn)Y傳輸k比特?cái)?shù)據(jù)，則發(fā)射節(jié)點(diǎn)和接收節(jié)點(diǎn)能量消耗分別如式（1）和式（2）所示。

式中a1和a2分別表示放大器在自由空間模型和多路徑衰減模型中將1 bit數(shù)據(jù)傳輸1 m2時所需的能量消耗。d0=為臨界距離。當(dāng)傳輸距離d小于d0時，功率放大損耗采用自由空間模型；當(dāng)傳輸距離d大于等于d0時，采用多路徑衰減模型。此外，從式（1）可以看出，無線傳感器網(wǎng)絡(luò)節(jié)點(diǎn)發(fā)送數(shù)據(jù)的能量消耗隨著其與接收節(jié)點(diǎn)之間的距離的增大而增大。

2 HCRA算法

基于農(nóng)田無線傳感器網(wǎng)絡(luò)的分簇路由算法HCRA由簇的形成、簇頭競選、和簇間路由3個階段組成。

2.1 簇的形成

基于節(jié)點(diǎn)能量的利用率考慮，HCRA算法將整片農(nóng)田區(qū)域劃分成若干小型區(qū)域。假定整個農(nóng)田灌溉區(qū)域的基本形狀是一個圓形區(qū)域，共部署了N個傳感器節(jié)點(diǎn)。首先將中心節(jié)點(diǎn)sink置于圓心位置，然后從sink節(jié)點(diǎn)出發(fā)，以sink為圓心分別以r（m），2r，3r…為半徑將整個圓形區(qū)域劃分為m層，再將整個圓區(qū)域平均分為6部分（扇區(qū)）。每一個部分中的每個層分別再細(xì)分，例如，第一層分為1份，第二層分為2份…記Aijk，i（1～m）為節(jié)點(diǎn)所在層數(shù)編號，j（1～6）為扇區(qū)編號，k（1～m）為第i層第j個扇區(qū)中劃分的區(qū)域編號（按順時針進(jìn)行編號），以此類推。每一層中的一小份區(qū)域即組成了無線傳感器網(wǎng)絡(luò)中的一個簇。算法中簇的形成如圖2所示。

由于每一層各簇面積相等，進(jìn)而使得同層中的簇處于一種同等的水平。層與層之間具有相等的面積差，因而不同層中的簇也處于同一水平。為保證簇頭之間能夠進(jìn)行通信，即扇形的相鄰環(huán)之間的簇能夠進(jìn)行通信，需滿足條件2r〈d2（r(m)為層與層之間的距離，d2(m)為兩節(jié)點(diǎn)在農(nóng)田環(huán)境下的通信距離閾值）。在某一區(qū)域出現(xiàn)異常的情況下，基于編號的機(jī)制，一方面可以通過編號對節(jié)點(diǎn)進(jìn)行定位，能及時發(fā)現(xiàn)問題區(qū)域；另一個方面，可以減小因?yàn)楣?jié)點(diǎn)定位而產(chǎn)生的能耗。

圖2 傳感器網(wǎng)絡(luò)分簇示意圖Fig.2 Sensor network clustering diagram

HCRA算法中，簇形成后不再改變，與LEACH這種每輪循環(huán)都要重新構(gòu)造簇的算法相比，大大減少了構(gòu)造簇的能量消耗。同時，此算法中，簇頭之間的通信僅局限于同一個扇形下的相鄰層之間，因此通信的距離較之HEED算法也相應(yīng)地減小了，一定程度上減小了簇頭數(shù)據(jù)傳輸所需的能量消耗。

2.2 簇頭競選

在農(nóng)田的無線傳感器網(wǎng)絡(luò)區(qū)域內(nèi)，首先按照HEED算法選出每個簇的簇頭節(jié)點(diǎn)。HEED算法具有健壯性，能夠不依賴于網(wǎng)絡(luò)規(guī)模的大小，通過O(1)次迭代過程實(shí)現(xiàn)分簇過程[33]。初始階段，每個節(jié)點(diǎn)都需要確定同在一個簇范圍內(nèi)的所有相鄰節(jié)點(diǎn)的集合，加以計(jì)算并進(jìn)行廣播。初始化階段，節(jié)點(diǎn)之間競爭簇頭，并發(fā)送競爭的消息，簇頭的產(chǎn)生依據(jù)于各節(jié)點(diǎn)的初始化概率。初始化概率CHpmb的計(jì)算公式由式（3）確定。

式中Cpmb和Pmin是整個網(wǎng)絡(luò)統(tǒng)一的參量，Eresident/Emax代表節(jié)點(diǎn)剩余能量與初始化能量的百分比。當(dāng)簇頭競選成功后，其他非簇頭節(jié)點(diǎn)依據(jù)在競爭階段中收集來的信息有選擇的加入簇。在每一次的迭代過程中，如果已有的臨時節(jié)點(diǎn)已經(jīng)被挑選出，則其他的節(jié)點(diǎn)則會將選擇所有的臨時節(jié)點(diǎn)中代價(jià)最小的一個作為它的臨時簇頭。如果沒有臨時簇頭被挑選出，初始化概率將會加倍，繼而能夠以新的概率推薦自己成為臨時簇頭。如果成功，則將把自己已成為簇頭節(jié)點(diǎn)的消息進(jìn)行廣播。

2.3 簇間路由

簇間路由是位于同一扇區(qū)不同層的簇頭節(jié)點(diǎn)之間進(jìn)行通信的路徑，由此可以減小簇頭之間的通信距離，減少能耗。第i層第j扇區(qū)的第l（1≤l≤I）個簇頭節(jié)點(diǎn)需要將數(shù)據(jù)傳給下一跳即第i-1層第j扇區(qū)的第n（1≤n≤l）個簇頭節(jié)點(diǎn)，由簇形成的方式可知，下一跳有多個選擇，必須綜合考慮下一跳簇頭節(jié)點(diǎn)的剩余能量以及通信代價(jià)。根據(jù)式（1）可以得出，兩節(jié)點(diǎn)間的通信能耗與節(jié)點(diǎn)之間的距離平方成正比，因此兩簇頭之間的通信能耗同樣與簇頭之間的距離平方成正比。本文將簇頭節(jié)點(diǎn)的剩余能量與單位數(shù)據(jù)通信代價(jià)的比值定義為簇頭能耗比，并且選擇能耗比大的簇頭節(jié)點(diǎn)作為簇間路由的下一跳簇頭，簇頭能耗比的計(jì)算公式如式（4）所示。

式中Erem(ijl)是第i層第j扇區(qū)的第l個簇頭節(jié)點(diǎn)的剩余能量（J），D(cijl,c(i-1)jn)表示第i層第j扇區(qū)的第l個簇頭節(jié)點(diǎn)到第i-1層第j扇區(qū)的第n個簇頭節(jié)點(diǎn)的距離（m），由于節(jié)點(diǎn)間通信能耗與距離的平方成正比，所以D2(c,c)即代表通信代價(jià)。

ijl(i-1)jn

3 算法仿真試驗(yàn)

本文使用MATLAB作為平臺，對提出的HCRA算法進(jìn)行了仿真試驗(yàn)，并將其與LEACH、HEED 2種算法在無線傳感網(wǎng)絡(luò)生存時間和能量消耗方面進(jìn)行了對比，網(wǎng)絡(luò)仿真參數(shù)如表1所示。

3.1 網(wǎng)絡(luò)生命周期

從網(wǎng)絡(luò)生命周期上分析，仿真結(jié)果如圖3a所示。LEACH算法，在0～150次的網(wǎng)絡(luò)運(yùn)行輪數(shù)中，節(jié)點(diǎn)死亡速率較快，100～700次，節(jié)點(diǎn)近乎全部死亡。HEED算法，在0～50次網(wǎng)絡(luò)運(yùn)行輪數(shù)時，沒有節(jié)點(diǎn)死亡，在50～300次中，節(jié)點(diǎn)死亡速率較快，300～800次，節(jié)點(diǎn)死亡速率緩慢，近800次之后，節(jié)點(diǎn)幾乎全部死亡。而HCRA算法經(jīng)過1 000次的迭代，在0～75次的網(wǎng)絡(luò)運(yùn)行輪數(shù)中，幾乎沒有節(jié)點(diǎn)死亡，75～400次，節(jié)點(diǎn)死亡幅度較快，400～500次，節(jié)點(diǎn)死亡速率較75～400有所下降，500～900次，死亡幅度較慢，近900次之后，節(jié)點(diǎn)近乎全部死亡。據(jù)仿真結(jié)果分析可知，在迭代1 000次的情況下，采用HCRA算法的網(wǎng)絡(luò)生存時間在相同的條件下要比LEACH算法長約28%，比HEED算法長約12%。

3.2 數(shù)據(jù)包傳遞率

從數(shù)據(jù)包傳遞率上分析，所有節(jié)點(diǎn)發(fā)送4 000 bit數(shù)據(jù)，對比3種算法下基站收到的數(shù)據(jù)包數(shù)目，仿真結(jié)果如圖3b所示。基于LEACH算法時，在0～150次的網(wǎng)絡(luò)運(yùn)行輪數(shù)中，基站收到的數(shù)據(jù)包數(shù)目在不斷增加，在200次時，投遞率達(dá)到33.7%左右，并且隨著節(jié)點(diǎn)全部死亡之后，基站收到的數(shù)據(jù)包數(shù)目不再增加。HEED算法在0～300次的網(wǎng)絡(luò)運(yùn)行輪數(shù)中，基站收到的數(shù)據(jù)包數(shù)目在不斷增加，在500次時，投遞率達(dá)到51.2%左右，500次之后數(shù)據(jù)包數(shù)目不再增加。而HCRA算法在0～500次的網(wǎng)絡(luò)運(yùn)行輪數(shù)中，基站收到的數(shù)據(jù)包數(shù)目在不斷增加，在600次時，投遞率達(dá)到67%左右，700次之后數(shù)據(jù)包數(shù)目不再增加。從仿真結(jié)果可以看出，在迭代1 000次的情況下，基于HCRA算法的數(shù)據(jù)包投遞率在相同條件下要比LEACH算法高約34個百分點(diǎn)，比HEED算法高約16個百分點(diǎn)。

圖3 網(wǎng)絡(luò)運(yùn)行輪數(shù)與各參數(shù)關(guān)系曲線Fig.3 Graph between number of running cycles of network and parameters

4 結(jié) 論

依據(jù)農(nóng)田環(huán)境，構(gòu)建了適用于農(nóng)田信息采集的無線傳感器網(wǎng)絡(luò)架構(gòu)，將農(nóng)田劃分為無數(shù)個小的扇區(qū)，便于節(jié)點(diǎn)的定位，采用層次型路由形式，實(shí)現(xiàn)了傳感網(wǎng)簇頭節(jié)點(diǎn)的均勻分布以及能量消耗的減少?；贖CRA（hybrid clustering routing algorithm）算法的分析，詳細(xì)描述了傳感網(wǎng)分簇過程、簇頭的競選過程以及簇頭之間的路由，給出了仿真結(jié)果。根據(jù)仿真結(jié)果分析，在1 000次迭代周期下，采用HCRA算法的網(wǎng)絡(luò)生存時間要比LEACH(low-energy adaptive clustering hierarchy)算法長約28%，比HEED(hybrid clustering routing algorithm)算法長約12%；采用HCRA(hybrid clustering routing algorithm)算法的數(shù)據(jù)包投遞率要比LEACH算法高約34個百分點(diǎn)，比HEED算法高約16個百分點(diǎn)。

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Clustering routing algorithm based on farmland wireless sensor network

Jiang Bing1, Mao Tian1, Tang Dawei1, Wu Zhijun1, Han Guangjie2
(1. Department of Internet of Things Engineering, Hohai University, Changzhou 213022, China; 2. Changzhou Key Laboratory of Sensing Network and Environmental Perception, Changzhou 213022, China)

As the wireless sensor network node's energy is limited, how to effectively use the limited resources and the effective transmission of data becomes a hot topic. In order to extend the life cycle of farmland wireless sensor network and improve the packet delivery rate of sensor network, a wireless sensor network suitable for farmland information collection was constructed, and a hybrid clustering routing algorithm (HCRA) was proposed in this paper. Two classical algorithms, LEACH (low-energy adaptive clustering hierarchy) and HEED (hybrid energy-efficient distributed clustering), were evaluated for their merit to extend life cycle of wireless sensors. In the LEACH algorithm, all cluster head nodes communicate directly with the sink nodes, so that energy consumption of the cluster head node far from the sink node was serious, and cannot guarantee the cluster head evenly distributed, which could make some cluster head nodes’ energy consumption too large, affecting the network life cycle. The HEED algorithm, on the other hand, used the cluster head to communicate directly with the sink node, which consumed a lot of energy. Because the farmland information collection range was large, those two algorithms were not fully applicable to the wireless sensor network information gathering in farmland environment. Based on this evaluation and the principle of the HEED algorithm, we proposed a hybrid clustering routing algorithm (HCRA) for farmland information collection. In the proposed algorithm, the network model and the energy consumption models were described separately. In the network model, the monitoring area of the sensor network was abstracted as a circle area. Each node in the network had its own unique ID, and the location was not changed after the deployment was completed. All nodes were distributed evenly in the area. In the energy consumption model, the energy consumption of the wireless sensor network increased with the increase of the distance between the transmitting node and the receiving node, regardless of the power dissipation loss using the free space model or the multi-channel attenuation model. Besides, we also descried the HCRA algorithm in detail in this paper, including formation of cluster, cluster bidding and inter-cluster routing. The formation of the cluster was as follows: the center node sink was placed in the center position, and then from the sink node, taking r (the distance between layers and layers), 2r, 3r…as the radius, the entire circular area was divided into m flows. The entire area was divided into six parts, each of which was subdivided into small areas with equal area according to the number of layers. Each small area was a cluster in the wireless sensor network. The cluster head bidding process was as follows: the cluster head node was selected according to the heed algorithm, i.e., the cluster head was generated according to the initial probability of each node. When the cluster head was selected, other non-cluster head nodes joined the cluster selectively according to the collected information from the competition stage. The inter-cluster routing was the information transfer process between cluster heads, cluster head selected the next cluster head based on energy consumption. Finally, the proposed HCRA algorithm along with LEACH and HEED algorithm were simulated experimentally under the same conditions. The experimental results showed that the HCRA algorithm had 28% longer network lifetime than the LEACH algorithm, about 12% faster than the HEED algorithm, under the iteration period of 1000 times. The HCRA algorithm had a packet delivery rate, which was about 34 percentage points higher than the LEACH, and about 16 percentage points higher than HEED.

wireless sensor networks; data transfer; algorithms; clustered routing protocol; life cycle of network; packet delivery rate; HCRA(hybrid clustering routing algorithm) algorithm

10.11975/j.issn.1002-6819.2017.16.024

TP393; S24

A

1002-6819(2017)-16-0182-06

江 冰，毛 天，唐大衛(wèi)，鄔智俊，韓光潔. 基于農(nóng)田無線傳感網(wǎng)絡(luò)的分簇路由算法[J]. 農(nóng)業(yè)工程學(xué)報(bào)，2017，33(16)：182-187.

10.11975/j.issn.1002-6819.2017.16.024 http://www.tcsae.org

Jiang Bing, Mao Tian, Tang Dawei, Wu Zhijun, Han Guangjie. Clustering routing algorithm based on farmland wireless sensor network[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2017, 33(16): 182-187. (in Chinese with English abstract) doi：10.11975/j.issn.1002-6819.2017.16.024 http://www.tcsae.org

2017-03-12

2017-08-05

國家自然科學(xué)基金（61573128）

江 冰，女（漢族），江蘇常州人，教授，研究方向：現(xiàn)代通信技術(shù)、智能信息處理。Email：jiangb@hhuc.edu.cn