發(fā)布時(shí)間：2018-06-15 20:49

  本文選題：WSN + 節(jié)點(diǎn)定位�。� 參考：《哈爾濱工程大學(xué)》2014年碩士論文

【摘要】：無線傳感器網(wǎng)絡(luò)(Wireless Sensor Network,WSN)作為一種全新的信息交互方式將物理世界與傳輸網(wǎng)絡(luò)聯(lián)系起來。它具有低成本、容錯(cuò)性好、可長期執(zhí)行監(jiān)測任務(wù)等特點(diǎn),被廣泛用于軍事、環(huán)境、醫(yī)療衛(wèi)生以及商業(yè)等領(lǐng)域。作為無線傳感器網(wǎng)絡(luò)重要支撐技術(shù)和理論基礎(chǔ)的節(jié)點(diǎn)定位和覆蓋控制技術(shù)也引起了人們的廣泛關(guān)注。大部分網(wǎng)絡(luò)應(yīng)用中起先都要求得位置信息,沒有位置信息的數(shù)據(jù)對(duì)于大多數(shù)傳感器網(wǎng)絡(luò)應(yīng)用來說是無意義的。WSN履行自身獲取信息的職責(zé),實(shí)現(xiàn)用戶服務(wù)質(zhì)量的前提,就是設(shè)法實(shí)現(xiàn)傳感器節(jié)點(diǎn)完全地覆蓋監(jiān)控區(qū)域或目標(biāo),只有達(dá)到了覆蓋控制要求,才能保證獲得的信息全面、可靠、準(zhǔn)確。本文圍繞節(jié)點(diǎn)定位和目標(biāo)覆蓋這兩方面進(jìn)行深入的研究。本文總結(jié)了前人的研究成果,針對(duì)分布式迭代定位算法中傳播誤差的問題,提出迭代平均思想,采用收斂速度快的種群分類和動(dòng)態(tài)學(xué)習(xí)因子的粒子群算法(Population Classification and Dynamic Learning Factor Particle Swarm Optimization, CDLPSO)對(duì)節(jié)點(diǎn)進(jìn)行定位,通過實(shí)驗(yàn)給出粒子群算法的分界值,能更好地為粒子速度選擇適合的進(jìn)化模型,通過實(shí)驗(yàn)仿真驗(yàn)證,本文算法在定位誤差和定位時(shí)間方面均優(yōu)于標(biāo)準(zhǔn)的粒子群定位算法。根據(jù)無線傳感器網(wǎng)絡(luò)的實(shí)際應(yīng)用環(huán)境,本文對(duì)三維空間異構(gòu)傳感器網(wǎng)絡(luò)的目標(biāo)覆蓋問題進(jìn)行研究,針對(duì)遺傳算法容易陷入局部最優(yōu)的缺點(diǎn),提出改進(jìn)二進(jìn)制差分算法(Chaotic Local Binary Differential Evolution,CLBDE)設(shè)計(jì)目標(biāo)覆蓋模型,引入混沌映射思想產(chǎn)生初始種群,使產(chǎn)生的初始種群均勻分布在搜索空間,并對(duì)后期優(yōu)化出的部分優(yōu)秀解進(jìn)行相似調(diào)度,通過多次實(shí)驗(yàn)仿真,在滿足所有目標(biāo)達(dá)到覆蓋要求的情況下,從普通節(jié)點(diǎn)最大無影響半徑,衰減因子,目標(biāo)個(gè)數(shù),覆蓋閾值,檢測區(qū)域大小等標(biāo)準(zhǔn)進(jìn)行評(píng)價(jià),本文算法較遺傳算法和二進(jìn)制差分算法尋優(yōu)得出的工作傳感器節(jié)點(diǎn)集最小。
[Abstract]:Wireless Sensor Network (WSN), as a new way of information interaction, connects the physical world with the transmission network. It has the characteristics of low cost, good fault tolerance, and can perform monitoring tasks for a long time. It is widely used in military, environment, medical health, business and other fields. It is important as a wireless sensor network. In most network applications, location information is required in most network applications. Data without location information is a meaningless.WSN for most sensor network applications to perform its own responsibility to obtain information, and to achieve the quality of user service. The premise is to realize the sensor nodes to fully cover the monitoring area or target, only to achieve the coverage control requirements, to ensure that the information obtained is comprehensive, reliable and accurate. This paper focuses on the two aspects of the node location and target coverage. This paper has always been the research results of the predecessors, aiming at the distributed iterative positioning. In the algorithm, the problem of propagation error is proposed, and the iterative mean idea is proposed. The particle swarm optimization (Population Classification and Dynamic Learning Factor Particle Swarm Optimization, CDLPSO) is used to locate the node, and the boundary value of the particle swarm optimization is given by experiment. According to the experimental simulation, the algorithm is superior to the standard particle swarm optimization algorithm in location error and location time. According to the actual application environment of wireless sensor network, this paper studies the target coverage problem of the three-dimensional spatial heterogeneous sensor network, aiming at genetic calculation. Chaotic Local Binary Differential Evolution (CLBDE) is proposed to design the target coverage model, and the initial population is generated by the idea of chaotic mapping, and the initial population is distributed uniformly in the search space, and a similar scheduling is carried out for some excellent solutions optimized in the later period. Through many experiments and simulation, in order to meet the requirements of all targets, the maximum unaffected radius, attenuation factor, target number, coverage threshold, detection area size and other standards are evaluated. The algorithm of this algorithm is minimal compared with genetic algorithm and binary difference algorithm.
【學(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN929.5;TP212.9

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本文編號(hào)：2023601