无线传感网生存时间优化算法的研究
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摘要
无线传感网最早起源于军事领域,主要应用在军事监视系统。现在低成本的无线传感网已经应用于环境和气象监测、洪灾预警、农田管理、智能家居、智能交通等众多领域,越来越受到学术界和产业界的关注。
在无线传感网中,网络生存时间是衡量一个网络性能好坏的重要指标之一,也是无线传感网的一个主要研究方向。然而无线传感节点大多采用电池供电,其能量有限。一旦节点能量耗尽,该节点就会失效,这将影响到网络的数据路由,甚至导致网络出现分裂而缩短网络生存时间。为了避免网络中部分节点能量消耗过快而过早失效,无线传感网的相关算法都需要考虑节点能耗问题,最大限度延长网络生存时间。因此,有必要研究无线传感网生存时间的优化问题。
本文围绕网络生存时间的优化问题,采用图论的最短路径法、最优化方法、功率控制方法等多个方法解决不同场景下的无线传感网生存时间优化问题,提高网络生存时间。本文的主要工作和成果如下：
1.研究静态无线传感网的生存时间优化问题,提出基于最短路径树的优化生存时间路由算法(LORA_SPT)。该算法构造了基于能耗因子、自身节点剩余能量因子、邻居节点剩余能量因子和类型权值因子等多个因子的权值函数。将节点划分成标准节点和警告节点,不同类型节点的链路权值采用不同的类型权值因子。最后利用Dijkstra算法完成最短路径树,所有节点沿着最短路径树将数据发送给Sink节点。
2.研究静态无线传感网生存时间的最优方案,提出基于牛顿法的最大化生存时间分布式算法(LMDA_NM)。 LMDA_NM算法建立和分解网络优化模型,引入非负数的松弛变量和对数函数,建立依靠局部信息的节点优化模型。采用牛顿法求解节点优化模型,获得网络最大生存时间和链路发送数据总量的最优值。
3.研究Sink节点的移动问题,提出移动Sink节点的优化生存时间分布式算法(DNLM_MSN)。该算法将Sink节点的移动认为是离散运动,其优化问题可分解成若干个Sink节点静止的无线传感网最大化生存时间问题。采用LMDA NM算法求解静态无线传感网最大化生存时间问题,最终获得Sink节点移动多次的网络最大生存时间最优值。
4.结合近邻算法和功率控制算法,研究节点分布均匀网络的较优发送功率和分布不均匀网络的簇较优发送功率,提出单,簇无线传感网的优化生存时间近邻功率控制算法(NPCAOL_SC)和多簇无线传感网的优化生存时间近邻功率控制算法(NPCAOL_MC)。针对均匀分布的无线传感网,Sink节点保存整个网络拓扑结构的信息,利用近邻算法评估节点密度,确定较优通信距离。结合Friss自由空间模型计算当前网络较优发送功率,Sink节点广播通知其它节点采用较优发送功率发送数据。若节点采用较优发送功率通信,但是找不到邻居节点,则采用最大发送功率发送数据。针对非均匀分布的无线传感网,采用k-means算法确定网络的簇个数和相应每个簇的节点。节点采用簇较优发送功率与同一簇内节点通信,采用最大发送功率与不同簇间节点通信。
5.当节点不能测量到邻居节点距离时,研究发送功率随剩余能量的变化方案,提出基于最短路径树的优化生存时间分布式功率控制(DPCAOL_SPT)。该算法综合考虑网络中节点间数据传输的能耗和邻居节点的剩余能量,引入新的权值函数和阶梯衰减模型、γn阶衰减模型和线性衰减模型三种功率衰减模型。最终运用分布式非同步Bellmam-Ford算法构建最短路径树,所有节点沿着最短路径树将数据发送给Sink节点。
6.当节点发射功率固定时,研究发送功率约束下的网络生存时间优化方案,提出基于次梯度法的优化生存时间分布式功率控制(DPCAOL_SA)。该算法分析节点发送功率固定的能耗约束等约束条件,建立网络生存时间的优化模型。采用分布式功率迭代和次梯度算法求解该模型。节点获知与各邻居节点通信所需要的最低发送功率集,随机选择发送功率集中的功率作为当前发送功率,采用次梯度算法分布式计算节点最大生存时间。通过多次计算获得网络最大生存时间的局部最优值,各个节点局部最优发送功率和当前的数据转发概率
论文通过仿真研究验证了所提出的算法有效性。最后,对全文进行总结,对进一步的研究提出一些展望。
Wireless sensor networks were originated in the military field and mainly used in military surveillance system. Now, low-cost wireless sensor networks have been used in environmental and meteorological monitoring, flood warning, farm management, intelligent home, intelligent transportation and other fields. Therefore, it gets more and more attention from academia and industry.
In wireless sensor networks, network lifetime is one of the most important indicators to judge the network performance good or bad. It is a major research. But wireless sensor nodes are mostly battery-powered and have limited power. Once one node exhausts energy and is disabled, it may affect network data routing, even break up the network to shorten network lifetime. Therefore, to avoid excessive energy consumption and premature failure of some nodes, the algorithms of wireless sensor networks must take power-saving into account to prolong the network lifetime. Therefore, it is necessary to research on the lifetime optimization problem of wireless sensor networks.
This thesis focuses on network lifetime optimization problem. It uses some methods such as shortest path methods in graph theory, optimization methods and power control methods to solve lifetime optimization problem of wireless sensor networks under different scenarios and prolong the network lifetime. The main work and achievements of the thesis are as follows:
1. Lifetime optimization problem of static wireless sensor networks is researched and lifetime optimized routing algorithm based on shortest path tree (LORA_SPT) is proposed. The weight function with energy for transmitting data between network nodes, residual energy of own nodes and residual energy of neighbor nodes are established. The nodes are divided into standard nodes and warning nodes. The link weights with different types of nodes use different weighting factors. Finally, dijkstra algorithm is used to construct the shortest path tree. All nodes transmit data along the shortest path tree to sink node.
2. The scheme of optimal lifetime in static wireless sensor networks is researched and lifetime maximization distributed algorithm based on Newton method (LMDA_NM) is proposed. The algorithm establishes and decomposes the network optimization model, introduces nonnegative slack variables and logarithmic barrier functions, and establishes node optimization model with local information. Newton method is used to solve the model and obtain optimal values of network maximum lifetime and link transmission data amount.
3. The problem of sink node mobility is researched and lifetime optimized distributed algorithm for mobile sink node (LODA_MSN) is proposed. The algorithm considers the mobility of sink node as discrete movement. Then the optimization problem is divided into lifetime maximization problems of several networks when sink node is static. Each lifetime maximization problem of static wireless sensor network is solved by LMDA_NM algorithm and finally the optimal value of network maximum lifetime is obtained when sink node moves several times.
4. With nearest-neighbor algorithm and power control algorithm, the preferable transmission power in node uniform distribution network and cluster optimal transmission power in non-uniform distribution network are researched. Nearest-neighbor power control algorithm for optimizing lifetime in single cluster (NPCAOL_SC) and nearest-neighbor power control algorithm for optimizing network lifetime in multi-clusters (NPCAOL_MC) are proposed. For uniformly distributed wireless sensor networks, sink node saves the entire network topology information, uses various nearest-neighbor distances algorithm to measure node density, and determines preferable communication distance. Then preferable transmission power is calculated with Friss free space model. Finally sink node broadcasts to inform nodes that they transmit data with the transmission power. If nodes can not find neighbor node with preferable transmission power, they use maximum transmission power. For non-uniformly distributed wireless sensor networks, k-means algorithm is used to determine number of clusters and the corresponding network nodes in each cluster. In the same cluster, nodes use preferable transmission power of cluster to communication. Nodes between different clusters use maximum transmission power to communication.
5. When the node can not measure the distance to its neighbor nodes, the transmission power change scheme with residual energy is researched, distributed power control algorithm for optimizing lifetime based on shortest path tree (DPCAOL_SPT) is proposed. Considering energy for transmitting data and neighbor nodes'residual energy, the new weight function and three power attenuation models such as stepwise attenuation model. γn order attenuation model and linear attenuation model are introduced. Finally distributed asynchronous Bellmam-Ford algorithm is used to construct the shortest path tree. All nodes transmit data along the shortest path tree to sink node.
6. When node transmission power is fixed, lifetime optimization scheme under transmission power constraint is researched and distributed power control algorithm for optimizing lifetime based on subgradient algorithm (DPCAOL_SA) is proposed. The algorithm analyzes the conditions such as node energy constraint when transmission power is fixed. It establishes the maximum network lifetime model. To solve the model, distributed transmission power iteration and subgradient algorithm are used. Nodes obtain the minimum transmission power set needed to communicate with neighbors, randomly select the current transmission power from the set. receive the parameter information of neighbor nodes, and distributed compute node maximum lifetime with subgradient algorithm. After several iteration calculations, DPCAOL_SA can obtain the local optimal value of network maximum lifetime, local optimal transmission power of each node and current data forwarding probability.
A number of illustrative simulations are given to show the effectiveness of proposed algorithms. Finally, the conclusion and future work are presented.
在无线传感网中,网络生存时间是衡量一个网络性能好坏的重要指标之一,也是无线传感网的一个主要研究方向。然而无线传感节点大多采用电池供电,其能量有限。一旦节点能量耗尽,该节点就会失效,这将影响到网络的数据路由,甚至导致网络出现分裂而缩短网络生存时间。为了避免网络中部分节点能量消耗过快而过早失效,无线传感网的相关算法都需要考虑节点能耗问题,最大限度延长网络生存时间。因此,有必要研究无线传感网生存时间的优化问题。
本文围绕网络生存时间的优化问题,采用图论的最短路径法、最优化方法、功率控制方法等多个方法解决不同场景下的无线传感网生存时间优化问题,提高网络生存时间。本文的主要工作和成果如下：
1.研究静态无线传感网的生存时间优化问题,提出基于最短路径树的优化生存时间路由算法(LORA_SPT)。该算法构造了基于能耗因子、自身节点剩余能量因子、邻居节点剩余能量因子和类型权值因子等多个因子的权值函数。将节点划分成标准节点和警告节点,不同类型节点的链路权值采用不同的类型权值因子。最后利用Dijkstra算法完成最短路径树,所有节点沿着最短路径树将数据发送给Sink节点。
2.研究静态无线传感网生存时间的最优方案,提出基于牛顿法的最大化生存时间分布式算法(LMDA_NM)。 LMDA_NM算法建立和分解网络优化模型,引入非负数的松弛变量和对数函数,建立依靠局部信息的节点优化模型。采用牛顿法求解节点优化模型,获得网络最大生存时间和链路发送数据总量的最优值。
3.研究Sink节点的移动问题,提出移动Sink节点的优化生存时间分布式算法(DNLM_MSN)。该算法将Sink节点的移动认为是离散运动,其优化问题可分解成若干个Sink节点静止的无线传感网最大化生存时间问题。采用LMDA NM算法求解静态无线传感网最大化生存时间问题,最终获得Sink节点移动多次的网络最大生存时间最优值。
4.结合近邻算法和功率控制算法,研究节点分布均匀网络的较优发送功率和分布不均匀网络的簇较优发送功率,提出单,簇无线传感网的优化生存时间近邻功率控制算法(NPCAOL_SC)和多簇无线传感网的优化生存时间近邻功率控制算法(NPCAOL_MC)。针对均匀分布的无线传感网,Sink节点保存整个网络拓扑结构的信息,利用近邻算法评估节点密度,确定较优通信距离。结合Friss自由空间模型计算当前网络较优发送功率,Sink节点广播通知其它节点采用较优发送功率发送数据。若节点采用较优发送功率通信,但是找不到邻居节点,则采用最大发送功率发送数据。针对非均匀分布的无线传感网,采用k-means算法确定网络的簇个数和相应每个簇的节点。节点采用簇较优发送功率与同一簇内节点通信,采用最大发送功率与不同簇间节点通信。
5.当节点不能测量到邻居节点距离时,研究发送功率随剩余能量的变化方案,提出基于最短路径树的优化生存时间分布式功率控制(DPCAOL_SPT)。该算法综合考虑网络中节点间数据传输的能耗和邻居节点的剩余能量,引入新的权值函数和阶梯衰减模型、γn阶衰减模型和线性衰减模型三种功率衰减模型。最终运用分布式非同步Bellmam-Ford算法构建最短路径树,所有节点沿着最短路径树将数据发送给Sink节点。
6.当节点发射功率固定时,研究发送功率约束下的网络生存时间优化方案,提出基于次梯度法的优化生存时间分布式功率控制(DPCAOL_SA)。该算法分析节点发送功率固定的能耗约束等约束条件,建立网络生存时间的优化模型。采用分布式功率迭代和次梯度算法求解该模型。节点获知与各邻居节点通信所需要的最低发送功率集,随机选择发送功率集中的功率作为当前发送功率,采用次梯度算法分布式计算节点最大生存时间。通过多次计算获得网络最大生存时间的局部最优值,各个节点局部最优发送功率和当前的数据转发概率
论文通过仿真研究验证了所提出的算法有效性。最后,对全文进行总结,对进一步的研究提出一些展望。
Wireless sensor networks were originated in the military field and mainly used in military surveillance system. Now, low-cost wireless sensor networks have been used in environmental and meteorological monitoring, flood warning, farm management, intelligent home, intelligent transportation and other fields. Therefore, it gets more and more attention from academia and industry.
In wireless sensor networks, network lifetime is one of the most important indicators to judge the network performance good or bad. It is a major research. But wireless sensor nodes are mostly battery-powered and have limited power. Once one node exhausts energy and is disabled, it may affect network data routing, even break up the network to shorten network lifetime. Therefore, to avoid excessive energy consumption and premature failure of some nodes, the algorithms of wireless sensor networks must take power-saving into account to prolong the network lifetime. Therefore, it is necessary to research on the lifetime optimization problem of wireless sensor networks.
This thesis focuses on network lifetime optimization problem. It uses some methods such as shortest path methods in graph theory, optimization methods and power control methods to solve lifetime optimization problem of wireless sensor networks under different scenarios and prolong the network lifetime. The main work and achievements of the thesis are as follows:
1. Lifetime optimization problem of static wireless sensor networks is researched and lifetime optimized routing algorithm based on shortest path tree (LORA_SPT) is proposed. The weight function with energy for transmitting data between network nodes, residual energy of own nodes and residual energy of neighbor nodes are established. The nodes are divided into standard nodes and warning nodes. The link weights with different types of nodes use different weighting factors. Finally, dijkstra algorithm is used to construct the shortest path tree. All nodes transmit data along the shortest path tree to sink node.
2. The scheme of optimal lifetime in static wireless sensor networks is researched and lifetime maximization distributed algorithm based on Newton method (LMDA_NM) is proposed. The algorithm establishes and decomposes the network optimization model, introduces nonnegative slack variables and logarithmic barrier functions, and establishes node optimization model with local information. Newton method is used to solve the model and obtain optimal values of network maximum lifetime and link transmission data amount.
3. The problem of sink node mobility is researched and lifetime optimized distributed algorithm for mobile sink node (LODA_MSN) is proposed. The algorithm considers the mobility of sink node as discrete movement. Then the optimization problem is divided into lifetime maximization problems of several networks when sink node is static. Each lifetime maximization problem of static wireless sensor network is solved by LMDA_NM algorithm and finally the optimal value of network maximum lifetime is obtained when sink node moves several times.
4. With nearest-neighbor algorithm and power control algorithm, the preferable transmission power in node uniform distribution network and cluster optimal transmission power in non-uniform distribution network are researched. Nearest-neighbor power control algorithm for optimizing lifetime in single cluster (NPCAOL_SC) and nearest-neighbor power control algorithm for optimizing network lifetime in multi-clusters (NPCAOL_MC) are proposed. For uniformly distributed wireless sensor networks, sink node saves the entire network topology information, uses various nearest-neighbor distances algorithm to measure node density, and determines preferable communication distance. Then preferable transmission power is calculated with Friss free space model. Finally sink node broadcasts to inform nodes that they transmit data with the transmission power. If nodes can not find neighbor node with preferable transmission power, they use maximum transmission power. For non-uniformly distributed wireless sensor networks, k-means algorithm is used to determine number of clusters and the corresponding network nodes in each cluster. In the same cluster, nodes use preferable transmission power of cluster to communication. Nodes between different clusters use maximum transmission power to communication.
5. When the node can not measure the distance to its neighbor nodes, the transmission power change scheme with residual energy is researched, distributed power control algorithm for optimizing lifetime based on shortest path tree (DPCAOL_SPT) is proposed. Considering energy for transmitting data and neighbor nodes'residual energy, the new weight function and three power attenuation models such as stepwise attenuation model. γn order attenuation model and linear attenuation model are introduced. Finally distributed asynchronous Bellmam-Ford algorithm is used to construct the shortest path tree. All nodes transmit data along the shortest path tree to sink node.
6. When node transmission power is fixed, lifetime optimization scheme under transmission power constraint is researched and distributed power control algorithm for optimizing lifetime based on subgradient algorithm (DPCAOL_SA) is proposed. The algorithm analyzes the conditions such as node energy constraint when transmission power is fixed. It establishes the maximum network lifetime model. To solve the model, distributed transmission power iteration and subgradient algorithm are used. Nodes obtain the minimum transmission power set needed to communicate with neighbors, randomly select the current transmission power from the set. receive the parameter information of neighbor nodes, and distributed compute node maximum lifetime with subgradient algorithm. After several iteration calculations, DPCAOL_SA can obtain the local optimal value of network maximum lifetime, local optimal transmission power of each node and current data forwarding probability.
A number of illustrative simulations are given to show the effectiveness of proposed algorithms. Finally, the conclusion and future work are presented.
引文
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