Understanding optimal data gathering in the energy and latency domains of a wireless sensor network

The problem of optimal data gathering in wireless sensor networks (WSNs) is addressed by means of optimization techniques. The goal of this work is to lay the foundations to develop algorithms and techniques that minimize the data gathering latency and at the same time balance the energy consumption among the nodes, so as to maximize the network lifetime. Following an incremental-complexity approach, several mathematical programming problems are proposed with focus on different network performance metrics. First, the static routing problem is formulated for large and dense WSNs. Optimal data-gathering trees are analyzed and the effects of several sensor capabilities and constraints are discussed, e.g., radio power constraints, energy consumption model, and data aggregation functionalities. Then, dynamic re-routing and scheduling are considered. An accurate network model is proposed that captures the tradeoff between the data gathering latency and the energy consumption, by modeling the interactions among the routing, medium access control and physical layers.For each problem, extensive simulation results are provided. The proposed models provide a deeper insight into the problem of timely and energy efficient data gathering. Useful guidelines for the design of efficient WSNs are derived and discussed.     

无线传感器网络中的最大生命期基因路由算法

无线传感器网络(wireless sensor networks,简称WSNs)由一组低功率且能量受限的传感器节点构成,设计此类网络的一个基本挑战便是最大化网络生命期的问题.在WSNs中,由于邻近传感器节点所收集的数据之间往往具有时空相关性,多采用数据聚合技术作为去除数据冗余、压缩数据大小的有效手段.合理地应用数据聚合技术,可以有效地减少数据传递量,降低网络能耗,从而延长网络生命期.研究了WSNs中结合数据聚合与节点功率控制的优化数据传递技术,提出了一种新的最大化网络生命期的路由算法.该算法采用遗传算法(genetic algorithm,简称GA)最优化数据聚合点的选择,并采用梯度算法进一步优化结果.该算法均衡节点能耗,并最大化网络生命期.仿真结果表明,该算法极大地提高了网络的生命期.     

无线传感器网络中的最大生命期基因路由算法

无线传感器网络(wireless sensor networks,简称WSNs)由一组低功率且能量受限的传感器节点构成,设计此类网络的一个基本挑战便是最大化网络生命期的问题.在WSNs中,由于邻近传感器节点所收集的数据之间往往具有时空相关性,多采用数据聚合技术作为去除数据冗余、压缩数据大小的有效手段.合理地应用数据聚合技术,可以有效地减少数据传递量,降低网络能耗,从而延长网络生命期.研究了WSNs中结合数据聚合与节点功率控制的优化数据传递技术,提出了一种新的最大化网络生命期的路由算法.该算法采用遗传算法(genetic algorithm,简称GA)最优化数据聚合点的选择,并采用梯度算法进一步优化结果.该算法均衡节点能耗,并最大化网络生命期.仿真结果表明,该算法极大地提高了网络的生命期.     

无线传感器网络中最大化网络寿命的数据聚合路由

针对数据聚合无线传感器网络寿命最大化问题,分析了网络流量和节点能耗,提出了数据聚合路由问题的网络流量模型,并将网络最大寿命与流量模型相结合设计了一组混合整数规划代价函数.采用对偶分解的方法,获得了近似最优的中继传输速率和路由.仿真实验表明,该算法能有效减少数据通信量,均衡各个节点的能量消耗,延长网络寿命.     

基于模拟退火的无线传感器网络最大生命期路由*

将节点功率控制与数据聚合有机结合,为进一步降低网络能耗提供了可能,但是也给路由算法的设计带来了新的挑战。为此,针对WSNs中结合数据聚合的节能数据传递方式进行了研究,提出了一种新的最大化网络生命期的路由算法。该算法采用模拟退火算法最优化数据聚合点的选择,均衡节点能耗,最大化网络生命期。仿真结果表明该算法性能明显优于现有算法,达到了提高网络生命期的目的。     

无线传感器网络最大生命期聚合树路由算法

提出了一种无线传感器网络最大生命期聚合树路由算法,根据能量等限制条件建立线性规划模型。考虑到网络最大生命期是NP难问题,在算法复杂度较低情况下,将网络最大生命期问题转化为网络最小归一化负载问题,在建立最大归一化负载聚合树过程中,不断调整负载较重节点的数据转发压力,最终建立一棵负载较轻的数据融合树,实现了网络生命期的最大化。通过仿真验证了算法的性能,并表明所提出算法可以有效延长网络生命期。     

Maximizing network lifetime in wireless sensor networks with regular topologies

Limited energy supply (battery-powered) is a crucial problem in wireless sensor networks (WSNs). Sensor node placement schemes and routing protocols are mostly proposed to address this problem. In this paper, we first present how to place sensor nodes by use of a minimal number of them to maximize the coverage area when the communication radius of the sensor node is different from the sensing radius, which results in the application of regular topology to WSNs deployment. With nodes placed at an equal distance and equipped with an equal power supply, the problem of unbalanced energy consumption in 2-D regular topologies becomes more severe and much more difficult to tackle than that in 1-D chains, though the latter is known as an already quite hard problem. We address this problem and propose an adaptive data collection scheme by employing different communication radii for nodes in different locations to balance the energy consumption in WSNs. In order to achieve the ultimate goal of maximizing network lifetime in grid-based WSNs, we give a mathematical formulation, which shows the problem of maximizing network lifetime is a nonlinear programming problem and NP-hard even in the 1-D case. We discuss several heuristic solutions and show that the halving shift data collection scheme is the best solution among them. We also generalize the maximizing network lifetime problem to the randomly-deployed WSNs, which shows the significance of our mathematical formulation for this crucial problem in WSNs.     

无线传感器网络权衡生存时间与数据分组跳数的分流路由算法*

无线传感器网络的节点大多采用电池供电.因而节能对无线传感器网络就显得至关重要.该文提出一种能耗感知的优化网络生存时间的路由算法,称之为分流路由算法(DTRA,Diffluent Traffic Routing Algorithm).DTRA算法采用一个优化模型以优化每个节点发出的数据比例,从而达到权衡网络生存时间和数据分组跳数.此外,采用一个简单的遗传算法求解该优化问题.仿真结果表明:DTRA算法能显著地提高网络的生存时间,同时将数据分组平均跳数保持在一个较低的水平;在网络生存时间上,DTRA算法比一些已有的知名算法更优.     

基于同构传感器网络的能量空洞避免策略

针对无线传感器网络中网络能量损耗不均匀的问题,提出了基于同构传感器网络的能量空洞避免策略.首先对原有的LEACH路由算法进行改进,得到均衡簇规模的BCS-L分簇算法;然后联合应用BCS-L算法与分环网络结构,以节点能耗均衡为目标,将能量空洞避免问题转化为求相邻环带的外半径的多项式问题,并通过最小化最内层环带节点的能量消耗得到最内层环带的半径,最后得到符合实际网络分布的局部最优解,即除最外层环带的其余环带节点能耗均衡.理论分析和实验结果表明,所提出的策略与传统分环网络相比,大幅地提高了网络寿命,较大地改善了网络的性能,是解决能量空洞问题的有效方案.     

无线传感器网络最大生命期与最大流路由算法

提出了一种无线传感器网络最大生命期和最大流路由算法,证明了网络最大生命期相当于获得网络最大流,根据最大流最小割定理,网络一定存在一个可行解满足网络最大流,在算法复杂度较低情况下,建立以最大生命期为最优目标的网络模型,依靠现有的启发式分布式算法解决该模型。通过仿真验证了算法的性能,表明所提出算法可以有效延长网络生命期。     

Optimizing data collection path in sensor networks with mobile elements

Exploiting mobile elements (MEs) to accomplish data collection in wireless sensor networks (WSNs) can improve the energy efficiency of sensor nodes, and prolong network lifetime. However, it will lead to large data collection latency for the network, which is unacceptable for data-critical applications. In this paper, we address this problem by minimizing the traveling length of MEs. Our methods mainly consist of two steps: we first construct a virtual grid network and select the minimal stop point set (SPS) from it; then, we make optimal scheduling for the MEs based on the SPS in order to minimize their traveling length. Different implementations of genetic algorithm (GA) are used to solve the problem. Our methods are evaluated by extensive simulations. The results show that these methods can greatly reduce the traveling length of MEs, and decrease the data collection latency.     

Lifetime extension for surveillance wireless sensor networks with intelligent redeployment

For wireless sensor networks (WSNs), uneven energy consumption is a major problem. A direct consequence of this is the energy hole problem, formation of sensing voids within the network field due to battery depleted sensors in the corresponding region. Hole formations are inherent in the network topology, yet it is possible to develop strategies to delay the hole formations to later stages of the network operation and essentially extend the network lifetime without sensing quality loss. In this work, we initially propose and analyze an approach that can be used to mitigate the hole problem. The approach is presented in detail and the effects on the sustained surveillance quality are presented. The results are based on simulations with different network configurations with realistic sensor models, MAC and routing protocols. By using the proposed approach, sustaining a sensing quality above a given threshold and more than doubling the network lifetime are possible. The results clearly indicate the suitability of the approach for especially demanding WSNs such as the ones used for border surveillance tasks.     

基于蚁群优化的无线传感器网络路由算法

如何在资源受限的无线传感器网络中进行高效的数据路由是无线传感器网络研究的热点之一。基于群智能优化技术的蚁群优化算法被广泛应用于网络路由算法。提出一种无线传感器网络蚁群优化路由算法,能够保持网络的生存时间最长,同时能找到从源节点到基站节点的最短路径;采用的多路数据传输也可提供高效可靠的数据传输,同时考虑节点的能量水平。仿真结果表明:提出的算法延长了无线传感器网络的寿命,实现无线传感器网络在通信过程中快速、节能的路由。     

Balancing energy consumption with mobile agents in wireless sensor networks

For Wireless Sensor Networks (WSNs), an unbalanced energy consumption will decrease the lifetime of network. In this paper, we leverage mobile agent technology to investigate the problem of how to balance the energy consumption during data collection in WSNs. We first demonstrate that for a sensor network with uniform node distribution and constant data reporting, balancing the energy of the whole network cannot be realized when the distribution of data among sensor nodes is unbalanced. We design a method to mitigate the uneven energy dissipation problem by controlling the mobility of agents, which is achieved by an energy prediction strategy to find their positions. Finally, we propose energy balancing cluster routing based on a mobile agent (EBMA) for WSNs. To obtain better performance, the cluster structure is formed based on cellular topology taking into consideration the energy balancing of inter-cluster and intra-cluster environments. Extensive simulation experiments are carried out to evaluate EBMA with several performance criteria. Our simulation results show that EBMA can effectively balance energy consumption and perform high efficiency in large-scale network deployment.     

基于LEACH和PEGASIS的簇头成链可靠路由协议研究

减少能耗、延长网络寿命是无线传感网络的关键技术.基于LEACH和PEGASIS算法,提出一种改进的有效路由算法.改进的算法规定LEACH中簇头数目为5个,利用PEGASIS算法使簇头成链,并选择剩余能量最多的簇头传送信息给基站.在选择簇头时,考虑节点的剩余能量,给节点设置一个能量阈值,小于该值则不能当选为簇头,因此提高了网络的健壮性.理论分析和仿真结果表明,改进后的算法比LEACH算法生命周期提高117%～351%,且能耗更加均匀,同时与PEGASIS相比,时延提高290%.     

Incremental Linear Discriminant Analysis Dimensionality Reduction and 3D Dynamic Hierarchical Clustering WSNs

Optimizing the sensor energy is one of the most important concern in Three-Dimensional (3D) Wireless Sensor Networks (WSNs). An improved dynamic hierarchical clustering has been used in previous works that computes optimum clusters count and thus, the total consumption of energy is optimal. However, the computational complexity will be increased due to data dimension, and this leads to increase in delay in network data transmission and reception. For solving the above-mentioned issues, an efficient dimensionality reduction model based on Incremental Linear Discriminant Analysis (ILDA) is proposed for 3D hierarchical clustering WSNs. The major objective of the proposed work is to design an efficient dimensionality reduction and energy efficient clustering algorithm in 3D hierarchical clustering WSNs. This ILDA approach consists of four major steps such as data dimension reduction, distance similarity index introduction, double cluster head technique and node dormancy approach. This protocol differs from normal hierarchical routing protocols in formulating the Cluster Head (CH) selection technique. According to node’s position and residual energy, optimal cluster-head function is generated, and every CH is elected by this formulation. For a 3D spherical structure, under the same network condition, the performance of the proposed ILDA with Improved Dynamic Hierarchical Clustering (IDHC) is compared with Distributed Energy-Efficient Clustering (DEEC), Hybrid Energy Efficient Distributed (HEED) and Stable Election Protocol (SEP) techniques. It is observed that the proposed ILDA based IDHC approach provides better results with respect to Throughput, network residual energy, network lifetime and first node death round.     

Satisfying the target network lifetime in wireless sensor networks

Generally, the lifetime of a wireless sensor network (WSN) is defined as the duration until any sensor node dies due to battery exhaustion. If the traffic load is not properly balanced, the batteries of some sensor nodes may be depleted quickly, and the lifetime of the WSN will be shortened. While many energy-efficient routing schemes have been proposed for WSNs, they focus on maximizing the WSN lifetime. In this paper, we propose a scheme that satisfies a given ‘target’ lifetime. Because energy consumption depends on traffic volume, the target lifetime cannot be guaranteed through energy-efficient routing alone. We take an approach that jointly optimizes the sensing rate (i.e., controlling the sensor-traffic generation or duty cycle) and route selection. Satisfying the target lifetime while maximizing the sensing rate is a NP-hard problem. Our scheme is based on a simple Linear Programming (LP) model and clever heuristics are applied to compute a near-optimal result from the LP solution. We prove that the proposed scheme guarantees a 1/2-approximation to the optimal solution in the worst case. The simulation results indicate that the proposed scheme achieves near-optimality in various network configurations.     

Balancing Energy Consumption to Maximize Network Lifetime in Data-Gathering Sensor Networks

Unbalanced energy consumption is an inherent problem in wireless sensor networks characterized by multihop routing and many-to-one traffic pattern, and this uneven energy dissipation can significantly reduce network lifetime. In this paper, we study the problem of maximizing network lifetime through balancing energy consumption for uniformly deployed data-gathering sensor networks. We formulate the energy consumption balancing problem as an optimal transmitting data distribution problem by combining the ideas of corona-based network division and mixed-routing strategy together with data aggregation. We first propose a localized zone-based routing scheme that guarantees balanced energy consumption among nodes within each corona. We then design an offline centralized algorithm with time complexity O(n) (n is the number of coronas) to solve the transmitting data distribution problem aimed at balancing energy consumption among nodes in different coronas. The approach for computing the optimal number of coronas in terms of maximizing network lifetime is also presented. Based on the mathematical model, an energy-balanced data gathering (EBDG) protocol is designed and the solution for extending EBDG to large-scale data-gathering sensor networks is also presented. Simulation results demonstrate that EBDG significantly outperforms conventional multihop transmission schemes, direct transmission schemes, and cluster-head rotation schemes in terms of network lifetime.     

无线传感器网络满足QoS带宽需求 的能量最优路由方案

有效使用片上受限资源以保持最长的生命周期是无线传感器网络的核心问题.QoS路由技术解决了无线传感器网络要求支持多种业务的差别服务和全网范围的资源有效利用的问题.本文提出了一种可以满足QoS带宽需求的能量最优路由发现方法.该方法构建了节点选择模型、能量评价模型和节点能耗权重模型,使用遗传算法利用可用节点集合建立满足QoS带宽需求的能量最优路由.利用Markov链证明该算法是可收敛并且是以指数速度概率强收敛的.另外,通过实验仿真与分析,给出了本文遗传算法的控制参数的选择区间,实验显示本文提出的满足QoS带宽需求的路由发现方法对无线传感器网络具有更好的适应性和更长的网络寿命.     

J-Sim下WSNs仿真框架的扩展设计与实现

随着无线传感器网络(WSNs)的应用,如何延长网络生命周期,对路由协议的研究提出了挑战。为了更好地研究能量高效利用的WSNs路由协议,建立对WSNs能耗支持较好的仿真平台具有十分重要的现实意义。针对J-Sim下WSNs框架对能耗仿真支持不好的问题,在现有的WSNs框架基础上提出了一种支持能耗仿真的WSNs仿真框架并在该框架上扩展实现LEACH协议。实验结果表明改进后的WSNs框架可以真实地仿真WSNs,获取有价值的仿真结果。