南京理工大学学报（自然科学版）
南京理工大學學報（自然科學版）
남경리공대학학보（자연과학판）
JOURNAL OF NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY
2014年
4期
537-543
,共7页
无线传感器网络%任务协同%动态联盟%粒子群算法%蚁群算法%计算能力%感知子任务%计算子任务%节点冗余%能量消耗
無線傳感器網絡%任務協同%動態聯盟%粒子群算法%蟻群算法%計算能力%感知子任務%計算子任務%節點冗餘%能量消耗
무선전감기망락%임무협동%동태련맹%입자군산법%의군산법%계산능력%감지자임무%계산자임무%절점용여%능량소모
wireless sensor networks%task collaboration%dynamic alliance%particle swarm optimization%ant colony algorithm%computing power%perception subtasks%computation subtasks%nodes redundancy%energy consumption
针对无线传感器网络( Wireless sensor networks,WSNs)中单个节点的计算能力有限、完成数据发送任务比较困难的问题,提出一种协同处理的方式传送数据,可以将协同任务分为感知子任务和计算子任务。在传感节点任务协同的动态联盟中,引入基于粒子群算法优化蚁群算法(Particle swarm optimization ant colony algorithm,PSO-ACO)构建传感网的数据汇集路由树。利用传感器网络在采集数据之间的相关性,运用群智能算法来优化节点发送数据的传输路径,以保证动态联盟执行任务时的连续性,在一定程度上保证传感网的性能,从而降低了通信能耗。仿真实验表明：当传感器网络的感知节点与网络节点总数的比值小于28%时,网络监测性能最优,该文方案可以消除同一任务检测传感器节点冗余、降低系统能量消耗。
針對無線傳感器網絡( Wireless sensor networks,WSNs)中單箇節點的計算能力有限、完成數據髮送任務比較睏難的問題,提齣一種協同處理的方式傳送數據,可以將協同任務分為感知子任務和計算子任務。在傳感節點任務協同的動態聯盟中,引入基于粒子群算法優化蟻群算法(Particle swarm optimization ant colony algorithm,PSO-ACO)構建傳感網的數據彙集路由樹。利用傳感器網絡在採集數據之間的相關性,運用群智能算法來優化節點髮送數據的傳輸路徑,以保證動態聯盟執行任務時的連續性,在一定程度上保證傳感網的性能,從而降低瞭通信能耗。倣真實驗錶明：噹傳感器網絡的感知節點與網絡節點總數的比值小于28%時,網絡鑑測性能最優,該文方案可以消除同一任務檢測傳感器節點冗餘、降低繫統能量消耗。
침대무선전감기망락( Wireless sensor networks,WSNs)중단개절점적계산능력유한、완성수거발송임무비교곤난적문제,제출일충협동처리적방식전송수거,가이장협동임무분위감지자임무화계산자임무。재전감절점임무협동적동태련맹중,인입기우입자군산법우화의군산법(Particle swarm optimization ant colony algorithm,PSO-ACO)구건전감망적수거회집로유수。이용전감기망락재채집수거지간적상관성,운용군지능산법래우화절점발송수거적전수로경,이보증동태련맹집행임무시적련속성,재일정정도상보증전감망적성능,종이강저료통신능모。방진실험표명：당전감기망락적감지절점여망락절점총수적비치소우28%시,망락감측성능최우,해문방안가이소제동일임무검측전감기절점용여、강저계통능량소모。
In response to the limited computing power and difficulties in completing data transmission of a single node in wireless sensor networks ( WSNs ) , this paper proposes a co-processing data-transmitting method that can divide the cooperative task into perception and computation. By introducing the particle swarm optimization ant colony algorithm ( PSO-ACO ) into the dynamic alliance of task collaboration of sensing nodes,the data collection routing tree of the sensor network is built. Based on the correlation of data collection in the sensor network, the swarm intelligence algorithm is used to optimize the transmission path of the data sent by the nodes to ensure the continuity of the dynamic alliance in executing the tasks. In this way,the performance of the sensor network is ensured to some extent and the communication energy consumption is reduced accordingly. Simulation experiments indicate that,the sensor network achieves optimal monitoring performance when the percentage of sensing nodes in the total number of nodes in the network is less than 28%. The scheme proposed here can eliminate the sensor node redundancy detected in the same task and reduce energy consumption of the system.
