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基于鄰域搜索粒子群算法的節(jié)點(diǎn)定位算法研究
2022年電子技術(shù)應(yīng)用第9期
劉芷珺1,,張玲華2
1.南京郵電大學(xué) 通信與信息工程學(xué)院,,江蘇 南京 210003; 2.南京郵電大學(xué) 江蘇省通信與網(wǎng)絡(luò)技術(shù)工程研究中心,,江蘇 南京 210023
摘要: 針對(duì)DV-Hop定位算法誤差大的缺點(diǎn),深入分析定位誤差來(lái)源后,,在改進(jìn)的PSO(Particle Swarm Optimization)算法的基礎(chǔ)上提出了IDVHop-NSPSO(Improved DVHop-Neighborhood Search Particle Swarm Optimization)節(jié)點(diǎn)定位算法,。該算法通過(guò)對(duì)三部分的改進(jìn)達(dá)到DV-Hop定位精度提高的要求:(1)增設(shè)半跳細(xì)化最小跳數(shù);(2)在計(jì)算平均跳距時(shí)引入權(quán)重系數(shù)使求得的跳距更加精確,;(3)利用鄰域搜索粒子群優(yōu)化算法替代最小二乘法來(lái)計(jì)算未知節(jié)點(diǎn)的位置,。仿真實(shí)驗(yàn)的結(jié)果表明:相較于DV-Hop算法、DV-Hop+PSO算法,、模擬退火加權(quán)DV-Hop算法,,IDVHop-NSPSO算法可在不顯著增加計(jì)算資源的同時(shí),明顯地提高定位精度,。
中圖分類(lèi)號(hào): TP393
文獻(xiàn)標(biāo)識(shí)碼: A
DOI:10.16157/j.issn.0258-7998.222633
中文引用格式: 劉芷珺,,張玲華. 基于鄰域搜索粒子群算法的節(jié)點(diǎn)定位算法研究[J].電子技術(shù)應(yīng)用,2022,,48(9):97-102.
英文引用格式: Liu Zhijun, Zhang Linghua. Research on node location algorithm based on neighborhood search particle swarm optimization algorithm[J]. Application of Electronic Technique,,2022,48(9):97-102.
Research on node location algorithm based on neighborhood search particle swarm optimization algorithm
Liu Zhijun1,,Zhang Linghua2
1.College of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China; 2.Jiangsu Engineering Research Center of Communication and Network Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Abstract: In view of the large error of DV- Hop positioning algorithm, after in-depth analysis of the source of positioning error, IDVHop-NSPSO algorithm based on Particle Swarm Optimization was presented on the basis of in-depth analysis of the source of positioning error of DV-Hop algorithm. The algorithm improves the precision of DV-Hop through the improvements on three parts. Firstly, half jump is added refining the minimum hop.Secondly, the weight coefficient is introduced to calculate the average jump distance to make the calculated jump distance more accurate. Thirdly, neighborhood search particle swarm optimization algorithm is used instead of least square method to calculate the location of unknown nodes.The simulation results show that compared with DV-Hop, DV-Hop+PSO and simulated annealing weighted DV-Hop algorithm, IDVHop-NSPSO algorithm can significantly improve the positioning accuracy without significantly increasing the computing resources.
Key words : wireless sensor network; DV-Hop algorithm; particle swarm optimization algorithm; neighboring search strategy; location accuracy

0 引言

    無(wú)線(xiàn)傳感器網(wǎng)絡(luò)(Wireless Sensor Networks, WSNs)中有大量的傳感器節(jié)點(diǎn)[1],,對(duì)于大多數(shù)WSNs應(yīng)用,如果節(jié)點(diǎn)收集到的數(shù)據(jù)信息沒(méi)有結(jié)合位置信息,,那么這個(gè)信息的可用度將大大降低,。因此,準(zhǔn)確知曉節(jié)點(diǎn)的物理位置是WSNs應(yīng)用的關(guān)鍵,,傳感器節(jié)點(diǎn)的定位技術(shù)是WSNs的一個(gè)重要技術(shù)[2],。目前無(wú)線(xiàn)傳感器網(wǎng)絡(luò)的定位算法通常分為兩大類(lèi):基于測(cè)距的定位算法(Range-based Algorithm)[3-6]和無(wú)需測(cè)距的定位算法(Range-free Algorithm)[7-10]?;跍y(cè)距的定位算法與無(wú)需測(cè)距的定位算法相比,,前者的性能通常要優(yōu)于后者,但前者需要投入大量的成本且對(duì)硬件要求很高;后者能耗低,、實(shí)現(xiàn)成本低,、不需要硬件支持,同時(shí)又可以滿(mǎn)足許多應(yīng)用需求[11],。因此,,無(wú)需測(cè)距的定位算法有著更加重要的研究意義。

    DV-Hop定位算法之所以可以得到非常廣泛的應(yīng)用,,離不開(kāi)其步驟簡(jiǎn)單,、容易實(shí)現(xiàn)并且定位覆蓋面積大等優(yōu)點(diǎn),但是作為經(jīng)典的無(wú)需測(cè)距定位算法,,它的缺點(diǎn)也非常顯著,,即受網(wǎng)絡(luò)拓?fù)溆绊懘螅ㄎ徽`差較大,。針對(duì)DV-Hop定位誤差大這個(gè)問(wèn)題,,已經(jīng)有很多學(xué)者提出了自己的改進(jìn)措施:文獻(xiàn)[12]利用節(jié)點(diǎn)接收的信號(hào)強(qiáng)度值對(duì)跳數(shù)進(jìn)行修正從而獲得更加精確的節(jié)點(diǎn)坐標(biāo);文獻(xiàn)[13]構(gòu)建測(cè)距誤差代價(jià)函數(shù),,并利用無(wú)偏估計(jì)對(duì)跳距進(jìn)行校正,;文獻(xiàn)[14]將DV-Hop算法的定位結(jié)果作為斯蒂芬森迭代模型的初始值,最后通過(guò)斯蒂芬森不斷迭代得到最優(yōu)的節(jié)點(diǎn)位置,;還有文獻(xiàn)引入智能優(yōu)化算法 (如狼群優(yōu)化算法[15],、粒子群優(yōu)化算法[16]、模擬退火算法[17],、遺傳算法[18])來(lái)實(shí)現(xiàn)對(duì)DV-Hop算法的改進(jìn),。然而,這些算法雖然針對(duì)經(jīng)典的DV-Hop算法進(jìn)行改進(jìn),,但是改進(jìn)后的定位精度仍然不是很高,。尤其像遺傳算法、狼群優(yōu)化算法這種計(jì)算量較大的優(yōu)化算法會(huì)大大增加節(jié)點(diǎn)的通信負(fù)擔(dān),。




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作者信息:

劉芷珺1,張玲華2

(1.南京郵電大學(xué) 通信與信息工程學(xué)院,,江蘇 南京 210003,;

2.南京郵電大學(xué) 江蘇省通信與網(wǎng)絡(luò)技術(shù)工程研究中心,江蘇 南京 210023)




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