无线传感器网络的WPCS覆盖策略

延长无线传感器网络生存时间的有效方法是让冗余节点进入休眠状态。而现有研究多是基于传感器感知模型为圆形的假设前提。该文集中讨论传感器感知模型非圆时,覆盖与连通性之间的联系,并提出适用性更广的WPCS(Well-Proportioned Coverage Strategy)覆盖策略。WPCS覆盖策略以最小化重叠面积为准则,其目的是最大化网络生存时间。仿真实验表明,WPCS性能优于CCP(Coverage Configuration Protocol),且具有一般性,并能很好地减少工作传感器数目,延长网络寿命。     

无线传感器网络中基于效用模型的分布式功率控制机制

本文将效用模型引入无线传感器网络的功率控制设计中,提出了一种基于效用模型的分布式功率控制机制(简称UMDPC).该机制建立了网络中所有传感器节点的功率与效用模型的对应关系,将链路可靠性、网络能耗归纳到统一的网络效用优化框架中,并证明该效用优化问题是凸优化问题,构造基于对偶分解的分布式的优化算法,获得网络效用最大化条件下各节点的优化发射功率.最后,通过模拟实验对所提机制及其实现算法的性能进行比较和评价.实验结果表明,本文所提机制最大化了网络的效用,提高了网络的能量利用效率.     

传感器网络高阶模糊覆盖分析

覆盖是传感器网络的一个基本问题.在网络节点部署后,人们往往想知道监控区域是否被部署的节点所充分覆盖.本文建立了传感器网络的模糊覆盖模型,并在此基础上试图在监控区域内找到一组关键点使得仅仅判断这些点的覆盖情况即可回答该区域是否被完全模糊覆盖,分析了高阶Voronoi图并给出了区域被完全模糊覆盖的充分条件.所提出的相应的判别算法运行时间为O(K2N+NlogN).     

传感器网络最佳情况模糊覆盖问题研究

覆盖是传感器网络的一个基本问题.在网络节点部署后,人们往往想知道监控区域是否被部署的节点所充分覆盖.本文建立了传感器网络的模糊覆盖模型,并在此基础上试图在监控区域内找到一组关键点使得仅仅判断这些点的覆盖情况即可回答该区域是否被完全模糊覆盖,分析了高阶Voronoi图并给出了区域被完全模糊覆盖的充分条件.所提出的相应的判别算法运行时间为O(K2N+NlogN).     

基于效用最大化的无线可充电传感器网络有向充电调度方案

针对当前无线可充电传感器网络(WRSNs)一对一移动充电方式存在充电效率低、定向充电模型缺乏问题,该文提出了一种基于充电效用最大化(MUC)的一对多有向充电调度方案.方案首先筛选网络中充电增益最大的有向覆盖子集;然后根据有向覆盖子集确定充电锚点,并进而规划充电器的移动路径;最后在满足移动充电器能量和充电周期约束条件下优化移动充电器的充电时间.实验结果表明,该方案与平均能量充电(AEC)、固定能量充电(FEC)相比,充电效率分别提高了13.7%和32.7%;与最多节点覆盖(MNC)、最大平均增益覆盖(MAGC)子集筛选方案相比,充电效率分别提高了4.4%和35.9%;同时在网络饿死节点数目上与MNC, MAGC方案相比也显著降低.     

无线传感器网络基于参数可调增强型覆盖控制算法

覆盖问题是无线传感器网络领域的一个基本问题,也是无线传感器网络特性当中的一个重点问题.如何通过某种算法达到以最少传感器节点对监测区域的有效覆盖已成为目前研究的一项重要课题.因此,提出一种增强型覆盖控制算法(Enhanced Coverage Control Algorithm, ECCA).该算法通过概率理论知识可以有效地求解出对监测区域进行有效覆盖下的最少节点,给出了传感器节点概率的期望值计算方法以及目标节点首次被传感器节点覆盖和多次覆盖后的期望值求解过程,验证随机变量相互之间不独立时的比例函数关系.仿真结果表明,ECCA算法可以使用较少的传感器节点数量完成对监测区域的有效覆盖,提高了对监测区域的覆盖质量.     

一种基于超节点和能量优先的无线传感器网络的高效查询算法

文中提出一种基于超节点和能量优先的无线传感器网络的高效查询算法.该算法包括传感器节点的层次聚类算法及基于能量代价模型等支撑技术,主要解决了以下两个问题:(1)数据如何从传感器节点传送到汇聚节点;(2)通过对传感器节点进行聚类,形成超节点,使得在查询过程中减少对无关节点的访问.实验表明该算法在提高无线传感器网络查询效率的情况下,延长网络的使用寿命.     

无线传感器网络三维空间最佳覆盖路由协议

本文针对新兴无线传感器网络中的三维空间随机最佳覆盖NP难问题进行了研究.采用计算几何与图论着色方法建立了三维空间的随机最佳覆盖数学模型,给出了一种分布式启发算法,得到了完成最佳覆盖的低能量消耗路径.并在此基础上设计了一种可以实现无线传感器网络三维空间最佳覆盖的优化路由协议.最后进行了协议算法的性能评价以及最佳覆盖和网络生存时间的实验仿真,结果表明协议算法时间复杂度低,并具有可扩展性、有效性和鲁棒性.     

基于跨层网络编码感知的无线传感器网络节能路由算法研究

编码感知路由可以发现路由中的网络编码机会,减少数据传输次数,提高网络吞吐量,是近年来路由算法研究的一个热点.当前编码感知路由存在编码条件失效、未考虑节点能量的问题,不适合直接应用于无线传感器网络.本文提出基于跨层网络编码感知的无线传感器网络节能路由算法CAER (Cross layer coding Aware Energy efficient Routing).提出并证明了修正后的网络编码条件,以解决编码条件失效问题.基于跨层思想,将网络编码感知机制与拓扑控制、覆盖控制结合,挖掘潜在编码机会.提出综合考虑节点编码机会、节点能量的跨层综合路由度量CCRM (Cross layer Coding aware Routing Metric).仿真结果表明,相比现有编码感知路由,CAER能够提高网络编码感知准确性,增加网络编码机会数量5%~15%,延长网络生存时间8%~12%.     

基于成本及功耗联合优化的SDN虚拟网络映射算法

针对多个虚拟网络请求(Virtual Network Request,VNR)动态到达的网络场景,本文提出一种基于成本及功耗联合优化的软件定义网络(Software-Defined Networking,SDN)虚拟网络映射(Virtual Network Embedding,VNE)算法.在对虚拟节点及链路映射成本及功耗进行评估的基础上,建模VNE成本及功耗的代价函数,进而在满足资源需求等约束条件下,建模基于代价函数最小化的VNE模型.该优化问题为整数线性规划问题,难以直接求解;为解决此问题,提出基于时间窗的虚拟网络批处理映射策略动态处理在线请求.继而针对特定时间窗内的VNR,将其转换为虚拟节点映射子问题和虚拟链路映射子问题,并应用启发式算法对两个子问题分别进行求解,从而确定VNR映射策略.仿真结果表明,所提算法能显著减少VNE成本及功耗,提高VNR接受率.     

On coverage issues in directional sensor networks: A survey

The coverage optimization problem has been examined thoroughly for omni-directional sensor networks in the past decades. However, the coverage problem in directional sensor networks (DSN) has newly taken attraction, especially with the increasing number of wireless multimedia sensor network (WMSN) applications. Directional sensor nodes equipped with ultrasound, infrared, and video sensors differ from traditional omni-directional sensor nodes with their unique characteristics, such as angle of view, working direction, and line of sight (LoS) properties. Therefore, DSN applications require specific solutions and techniques for coverage enhancement. In this survey article, we mainly aim at categorizing available coverage optimization solutions and survey their problem definitions, assumptions, contributions, complexities and performance results. We categorize available studies about coverage enhancement into four categories. Target-based coverage enhancement, area-based coverage enhancement, coverage enhancement with guaranteed connectivity, and network lifetime prolonging. We define sensing models, design issues and challenges for directional sensor networks and describe their (dis)similarities to omni-directional sensor networks. We also give some information on the physical capabilities of directional sensors available on the market. Moreover, we specify the (dis)advantages of motility and mobility in terms of the coverage and network lifetime of DSNs.     

A Learning Automata-Based Solution to the Priority-Based Target Coverage Problem in Directional Sensor Networks

In recent years, directional sensor networks composed of directional sensors have attracted a great deal of attention due to their extensive applications. The main difficulties associated with directional sensors are their limited battery power and restricted sensing angle. Moreover, each target may have a different coverage quality requirement that can make the problem even more complicated. Therefore, satisfying the coverage quality requirement of all the targets in a specific area and maximizing the network lifetime, known as priority-based target coverage problem, has remained a challenge. As sensors are often densely deployed, organizing the sensor directions into several cover sets and then activating these cover sets successively is a promising solution to this problem. In this paper, we propose a learning automata-based algorithm to organize the directional sensors into several cover sets in such a way that each cover set can satisfy coverage quality requirement of all the targets. In order to verify the performance of the proposed algorithm, several simulations were conducted. The obtained results showed that the proposed algorithm was successful in extending the network lifetime.     

视频传感器网络中最坏情况覆盖检测与修补算法

 本文设计视频传感器网络中最坏情况覆盖检测与修补算法.首先,基于计算几何方法对待解决问题进行描述和定义;其次,采用质心替代节点扇形感知区域构造Voronoi图,寻找最大突破路径,实现最坏情况覆盖检测;再次,对找到的最大突破路径进行修补以提高整个视频传感器网络覆盖性能;最后,一系列仿真实验验证了该算法的有效性.     

Energy Balanced Model for Lifetime Maximization in Randomly Distributed Wireless Sensor Networks

In this paper, an energy balanced model (EBM) for lifetime maximization for a randomly distributed sensor network is proposed. The lifetime of a sensor network depends on the rate of energy depletion caused by multiple factors, such as load imbalance, sensor deployment distribution, scheduling, transmission power control, and routing. Therefore, in this work, we have developed a mathematical model for analysis of load imbalance under uniform and accumulated data flow. Based on this analysis, we developed a model to rationalize energy distribution among the sensors for enhancing the lifetime of the network. To realize the proposed EBM, three algorithms—annulus formation, connectivity ensured routing and coverage preserved scheduling have been proposed. The proposed model has been simulated in ns-2 and results are compared with Energy-Balanced Transmission Policy and Energy Balancing and unequal Clustering Algorithm. Lifetime has been measured in terms of the time duration for which the network provides satisfactory level of coverage and data delivery ratio. EBM outperform both the existing models. In our model the variance of residual energy distribution among the sensors is lower than other two models. This validated the essence of energy rationalization hypothesized by our model.     

Actor-oriented directional anycast routing in wireless sensor and actor networks with smart antennas

Current routing protocols in wireless sensor and actor networks (WSANs) shows a lack of unification for different traffic patterns because the communication for sensor to actor and that for actor to actor are designed separately. Such a design poses a challenge for interoperability between sensors and actors. With the presence of rich-resource actor nodes, we argue that to improve network lifetime, the problem transforms from reducing overall network energy consumption to reducing energy consumption of constrained sensor nodes. To reduce energy consumption of sensor nodes, especially in challenging environments with coverage holes/obstacles, we propose that actor nodes should share forwarding tasks with sensor nodes. To enable such a feature, efficient interoperability between sensors and actors is required, and thus a unified routing protocol for both sensors and actors is needed. This paper explores capabilities of directional transmission with smart antennas and rich-resource actors to design a novel unified actor-oriented directional anycast routing protocol (ADA) which supports arbitrary traffic in WSANs. The proposed routing protocol exploits actors as main routing anchors as much as possible because they have better energy and computing power compared to constraint sensor nodes. In addition, a directional anycast routing approach is also proposed to further reduce total delay and energy consumption of overall network. Through extensive experiments, we show that ADA outperforms state-of-the-art protocols in terms of packet delivery latency, network lifetime, and packet reliability. In addition, by offer fault tolerant features, ADA also performs well in challenging environments where coverage holes and obstacles are of concerns.     

Learning Automata-Based Algorithms for Solving the Target Coverage Problem in Directional Sensor Networks

Recently, directional sensor networks have received a great deal of attention due to their wide range of applications in different fields. A unique characteristic of directional sensors is their limitation in both sensing angle and battery power, which highlights the significance of covering all the targets and, at the same time, extending the network lifetime. It is known as the target coverage problem that has been proved as an NP-complete problem. In this paper, we propose four learning automata-based algorithms to solve this problem. Additionally, several pruning rules are designed to improve the performance of these algorithms. To evaluate the performance of the proposed algorithms, several experiments were carried out. The theoretical maximum was used as a baseline to which the results of all the proposed algorithms are compared. The obtained results showed that the proposed algorithms could solve efficiently the target coverage problem.     

Scheduling algorithms for extending directional sensor network lifetime

Recently, directional sensor networks that are composed of a large number of directional sensors have attracted a great deal of attention. The main issues associated with the directional sensors are limited battery power and restricted sensing angle. Therefore, monitoring all the targets in a given area and, at the same time, maximizing the network lifetime has remained a challenge. As sensors are often densely deployed, a promising approach to conserve the energy of directional sensors is developing efficient scheduling algorithms. These algorithms partition the sensor directions into multiple cover sets each of which is able to monitor all the targets. The problem of constructing the maximum number of cover sets has been modeled as the multiple directional cover sets (MDCS), which has been proved to be an NP-complete problem. In this study, we design two new scheduling algorithms, a greedy-based algorithm and a learning automata (LA)-based algorithm, in order to solve the MDCS problem. In order to evaluate the performance of the proposed algorithms, several experiments were conducted. The obtained results demonstrated the efficiency of both algorithms in terms of extending the network lifetime. Simulation results also revealed that the LA-based algorithm was more successful compared to the greedy-based one in terms of prolonging network lifetime.     

Lifetime optimization for reliable broadcast and multicast in wireless ad hoc networks

In this paper, we consider the reliable broadcast and multicast lifetime maximization problems in energy‐constrained wireless ad hoc networks, such as wireless sensor networks for environment monitoring and wireless ad hoc networks consisting of laptops or PDAs with limited battery capacities. In packet loss‐free networks, the optimal solution of lifetime maximization problem can be easily obtained by tree‐based algorithms. In unreliable networks, we formulate them as min–max tree problems and prove them NP‐complete by a reduction from a well‐known minimum degree spanning tree problem. A link quality‐aware heuristic algorithm called Maximum Lifetime Reliable Broadcast Tree (MLRBT) is proposed to build a broadcast tree that maximizes the network lifetime. The reliable multicast lifetime maximization problem can be solved as well by pruning the broadcast tree produced by the MLRBT algorithm. The time complexity analysis of both algorithms is also provided. Simulation results show that the proposed algorithms can significantly increase the network lifetime compared with the traditional algorithms under various distributions of error probability on lossy wireless links. Copyright © 2012 John Wiley & Sons, Ltd.     

Performance evaluation of sensor deployment using optimization techniques and scheduling approach for K-coverage in WSNs

In the wireless sensor networks, sensor deployment and coverage are the vital parameter that impacts the network lifetime. Network lifetime can be increased by optimal placement of sensor nodes and optimizing the coverage with the scheduling approach. For sensor deployment, heuristic algorithm is proposed which automatically adjusts the sensing range with overlapping sensing area without affecting the high degree of coverage. In order to demonstrate the network lifetime, we propose a new heuristic algorithm for scheduling which increases the network lifetime in the wireless sensor network. Further, the proposed heuristic algorithm is compared with the existing algorithms such as ant colony optimization, artificial bee colony algorithm and particle swarm optimization. The result reveals that the proposed heuristic algorithm with adjustable sensing range for sensor deployment and scheduling algorithm significantly increases the network lifetime.     

An optimal algorithm for solving partial target coverage problem in wireless sensor networks

This paper deals with the partial target coverage problem in wireless sensor networks under a novel coverage model. The most commonly used method in previous literature on the target coverage problem is to divide continuous time into discrete slots of different lengths, each of which is dominated by a subset of sensors while setting all the other sensors into the sleep state to save energy. This method, however, suffers from shortcomings such as high computational complexity and no performance bound. We showed that the partial target coverage problem can be optimally solved in polynomial time. First, we built a linear programming formulation, which considers the total time that a sensor spends on covering targets, in order to obtain a lifetime upper bound. Based on the information derived in previous formulation, we developed a sensor assignment algorithm to seek an optimal schedule meeting the lifetime upper bound. A formal proof of optimality was provided. We compared the proposed algorithm with the well‐known column generation algorithm and showed that the proposed algorithm significantly improves performance in terms of computational time. Experiments were conducted to study the impact of different network parameters on the network lifetime, and their results led us to several interesting insights. Copyright © 2011 John Wiley & Sons, Ltd.