移动无线传感网的移动感知路径选择算法
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摘要
为解决稀疏网络环境下移动传感节点的区域全覆盖和数据传输问题,提出一种移动无线传感网的移动感知路径选择算法(MSPS)。在MSPS算法中,用数学公式表示邻居网格集合、区域覆盖率、数据传输时延、节点平均能耗等参数。采用机会路由算法进行数据传输,并建立能保证全覆盖监测区域且权衡数据传输时延、数据传输率和节点平均能耗的移动路径选择优化模型。提出到目标网格的路径寻找方法、初始染色体的确定方法和染色体适应度值计算方法。最终提出修正的多种群遗传算法求解优化模型,获得移动传感节点的最优移动方案。仿真结果表明:不管监测区域内是否存在障碍物,MSPS算法都能提高数据传输率,降低数据传输时延和节点丢弃的总数据量。在一定的条件下,MSPS算法比SGA、TCM_M、RAND_D和RAND算法更优。
In order to solve the area full coverage problem and data transmission problem of mobile sensor nodes in sparse network environment,a mobile sensing path selection algorithm for mobile wireless sensor networks( MSPS) is proposed. In MSPS algorithm,mathematical formulae are used to represent parameters such as neighbor grid set,area coverage rate,data transmission delay and average energy consumption of nodes. The opportunistic routing algorithm is adopted for data transmission and a movement path selection optimization model which can guarantee full coverage of monitoring area and is trade-off of data transmission delay,data transmission rate and average energy consumption is established. The path finding method of target grid,initial chromosome determination method and chromosome fitness value calculation method are proposed. Finally,modified multi-population genetic algorithm is proposed to solve the optimization model,and optimal movement scheme of mobile sensor nodes is obtained. The simulation results show that regardless of whether there are obstacles in the monitoring area,MSPS algorithm improves data transmission rate and reduces data transmission delay and total discarded data amount of nodes. Under certain conditions,MSPS algorithm is better than SGA,TCM_M,RAND_D and RAND algorithms.
In order to solve the area full coverage problem and data transmission problem of mobile sensor nodes in sparse network environment,a mobile sensing path selection algorithm for mobile wireless sensor networks( MSPS) is proposed. In MSPS algorithm,mathematical formulae are used to represent parameters such as neighbor grid set,area coverage rate,data transmission delay and average energy consumption of nodes. The opportunistic routing algorithm is adopted for data transmission and a movement path selection optimization model which can guarantee full coverage of monitoring area and is trade-off of data transmission delay,data transmission rate and average energy consumption is established. The path finding method of target grid,initial chromosome determination method and chromosome fitness value calculation method are proposed. Finally,modified multi-population genetic algorithm is proposed to solve the optimization model,and optimal movement scheme of mobile sensor nodes is obtained. The simulation results show that regardless of whether there are obstacles in the monitoring area,MSPS algorithm improves data transmission rate and reduces data transmission delay and total discarded data amount of nodes. Under certain conditions,MSPS algorithm is better than SGA,TCM_M,RAND_D and RAND algorithms.
引文
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[2] Vafaeinezhad A R,Alesheikh A A,Nouri J. Developing a SpatioTemporal Model of Risk Management for Earthquake Life Detection Rescue Team[J]. International Journal of Environmental Science and Technology,2010,7(2):243-250.
[3]童孟军,李光辉,徐小良.基于分簇的能量有效多路径路由协议的研究[J].传感技术学报,2013,26(8):1126-1134.
[4] Zhu C,Shu L,Hara T,et al. A Survey on Communication and Data Management Issues in Mobile Sensor Networks[J]. Wireless Communications and Mobile Comuting,2014,14(1):19-36.
[5] Abdul W K,Abdul H A,Mohammad H A,et al. A Comprehensive Study of Data Collection Schemes Using Mobile Sinks in Wireless Sensor Networks[J]. Sensors,2014,14(2):2510-2548.
[6]王荔豪,王爱华,柯楠,等.基于Dubins曲线的高超声速飞行器路径规划[J].弹箭与制导学报,2015,35(3):41-44.
[7]陆亮,王佳琪,宗成星,等.基于A*算法的四轴飞行器三维路径规划仿真[J].合肥工业大学学报(自然科学版),2017,40(3):304-309.
[8] Albert A,Leira F S,Imsland L. UAV Path Planning Using MILP with Experiments[J]. Modeling,Identification and Control,2017,38(1):1890-1328.
[9] Guillermo F,David J M,Juan A B,et al. Approximate 3D Euclidean Shortest Paths for Unmanned Aircraft in Urban Environments[J].Journal of Intelligent&Robotic Systems,2017,85(2):353-368.
[10] Chen Y,Yu J,Mei Y,et al. Modified Central Force Optimization(MCFO)Algorithm for 3D UAV Path Planning[J]. Neurocomputing,2016,171(C):878-888.
[11] Hung L L,Huang Y W,Lin C C. Temporal Coverage Mechanism for Distinct Quality of Monitoring in Wireless Mobile Sensor Networks[J]. Ad Hoc Networks,2014,21(5):97-108.
[12] Nimisha G,Banerjee I. An Energy-Efficient Path Determination Strategy for Mobile Data Collectors in Wireless Sensor Network[J].Computers and Electrical Engineering,2015,48:417-435.
[13]郁磊,史峰,王辉,等. MATLAB智能算法30个案例分析[M].第2版.北京:北京航空航天大学出版社,2015.
[14] Chen Y,Wang Z,Ren T,et al. Lifetime Optimization Algorithm with Mobile Sink Nodes for Wireless Sensor Networks Based on Location Information[J]. International Journal of Distributed Sensor Networks,2015:1-15.