一种三角形网格空洞修复算法

无线传感器网络由大量传感器节点组成,在网络初始化时节点随机部署在目标区域中,导致某一区域未被覆盖而形成覆盖空洞.针对目标区域中存在覆盖空洞问题,设计了一种基于三角形网格的无需地理信息的空洞探测算法ATN和空洞修复算法TNR.利用ATN算法检测节点与其邻居形成的三角形网格是否被完全覆盖,TNR算法以ATN算法理论为基础,向三角形网格中添加节点使目标区域完全覆盖.理论与仿真实验分析表明,ANR算法能够探测出目标区域中所有空洞,TNR算法在部署密集的传感网络中能够快速完成空洞修复.     

异构传感器网络虚拟网格移动算法

通过把复杂问题逐步简化,分析了同构传感器网络的菱形网格和正方形网格划分方法,从拓补学角度提出了异构正六边形网格和异构正方形网格划分方法,并且计算采用两种网格方法实现无缝完全覆盖所需的最少节点数量,得到正方形网格更有优势.最后利用虚拟力算法,提出异构虚拟正方形网格移动算法,仿真实验证明了算法的有效性.     

无线网格资源调度优化策略

文中提出了一种基于改进GEAR算法的无线网格资源调度算法.它通过引入Q-learning机制,使传感节点具有网络负载平衡的功能,降低了节点的能量损耗,延长了无线传感器网络的生命周期.实验结果表明,该算法在具有较优调度性能的前提下,有效地提高调度的工作效率.     

无线传感器网络的覆盖优化机制研究

如何实现最优覆盖是无线传感器组网的一个基本问题.文章分析了传感器覆盖问题的背景,给出了节点调度方案的主要方法和技术原理,探讨了基于网络能量高效的覆盖优化与网络连通性之间的关系,重点阐述了实现区域覆盖和点覆盖的机制.对于覆盖薄弱地区,文章提出了采用分簇方式将覆盖地区划分成许多子区域或簇,用动态移动修复机制提供细粒度的网络监测与覆盖控制.文章认为调度传感器节点在休眠和活动模式之间进行切换,是一种重要节能方法;对于资源受限且拓扑动态变化的无线传感器网络,宜采用分布式和局部化的覆盖控制协议和算法.     

基于改进虚拟力算法的水质传感器部署

文中设计了基于网格划分的改进虚拟力算法。将水底表面划分成三维网格,定义的虚拟力包括邻居节点的作用力、网格对节点的吸引力、障碍物的斥力和目标覆盖区域边界斥力,以及补偿虚拟力,综合这些虚拟力使传感器节点移动,求得最佳位置。通过理论论证和仿真实验,该算法能够有效提高水质传感器三维覆盖率和均匀性,节约节点数量和部署成本。     

一种基于有向感知模型的传感器节点覆盖算法

覆盖控制作为无线传感器网络中的一个基本问题,反映了传感器网络所能提供的“感知”服务质量.优化传感器网络覆盖对于合理分配网络的空间资源,更好地完成环境感知、信息获取任务以及提高网络生存能力都具有重要的意义.针对无线传感器网络方向个数固定的有向感知模型提出一种覆盖增强算法,采用复杂网络社团结构算法划分对网络进行节点子集划分,重新调整节点的感知方向,增强网络的覆盖率,同时有效降低了算法的时间复杂度.     

一种视频传感器网络的栅栏覆盖方法

针对视频传感器网络的全视域覆盖要求,文中设计了一种视频传感器网络的栅栏覆盖方法。该方法选择尽可能少的视频传感器并确定传感器工作方向,实现全视域的视频栅栏覆盖。采用离散化处理方法,将给定区域划分网格单元,判断每个网格单元是否可能被全视域覆盖,并使用迪杰斯特拉算法找到全视域覆盖的最短路径,提出不冲突选择算法挑选不冲突的最小覆盖集合构成视频栅栏。实验结果表明,该方法应用于多工作方向视频传感器网络可以获得良好的性能。     

基于全局贪心的有向传感器网络覆盖算法

针对分布式贪心算法（DGreedy）以传感器节点的剩余能量为优先级,节点处理顺序没有考虑相邻节点间的关系对网络覆盖率的影响,从而影响覆盖率的不足,在此提出了一种新的有向传感器网络覆盖算法。基于全局贪心的原则,以节点一重覆盖区域面积的大小为优先级,优先确定一重覆盖区域面积最大的传感器节点方向,从而保证传感器网络的一重覆盖区域面积更大,重叠覆盖区域较少。对比实验结果表明,该算法能有效提高覆盖率。     

基于改进微粒群算法的WSN节点调度

为了降低无线传感器网络监测区域节点能耗和延长网络生命周期,设计了一种基于改进微粒群算法的节点调度方法.首先,以网络覆盖率和休眠工作节点数为目标建立了系统的数学模型,然后设计了粒子的编码方式、适应度函数以及自适应动态惯性权重,并定义了使用改进的微粒群算法对传感器网络节点调度的具体算法.仿真实验表明,该方法能正确地实现无线传感器网络监测区域的节点调度,在迭代次数较少时,就能以较少的节点获得较高的网络覆盖率,且与其他方法相比,具有收敛速度快和全局寻优能力强的优点.     

一种面向三维感知的无线多媒体传感器网络覆盖增强算法

 覆盖作为无线传感器网络中的基础问题直接反映了网络感知服务质量.本文在分析现有无线多媒体传感器网络覆盖增强算法的基础上,构建节点三维感知模型,提出面向三维感知的多媒体传感器网络覆盖增强算法(Three-Dimensional Perception Based Coverage-Enhancing Algorithm,TDPCA).该算法将节点主感知方向划分为仰俯角和偏向角,并根据节点自身位置及监测区域计算并调整各节点最佳仰俯角,在此基础上基于粒子群优化调整节点偏向角,从而有效减少节点感知重叠区及感知盲区,最终实现监测场景的区域覆盖增强.仿真实验表明:对比已有的覆盖增强算法,TDPCA可有效降低除节点感知重叠区和盲区,最终实现网络的高效覆盖.     

A distributed reinforcement learning based sensor node scheduling algorithm for coverage and connectivity maintenance in wireless sensor network

The fundamental challenge for randomly deployed resource-constrained wireless sensor network is to enhance the network lifetime without compromising its performance metrics such as coverage rate and network connectivity. One way is to schedule the activities of sensor nodes and form scheduling rounds autonomously in such a way that each spatial point is covered by at least one sensor node and there must be at least one communication path from the sensor nodes to base station. This autonomous activity scheduling of the sensor nodes can be efficiently done with Reinforcement Learning (RL), a technique of machine learning because it does not require prior environment modeling. In this paper, a Nash Q-Learning based node scheduling algorithm for coverage and connectivity maintenance (CCM-RL) is proposed where each node autonomously learns its optimal action (active/hibernate/sleep/customize the sensing range) to maximize the coverage rate and maintain network connectivity. The learning algorithm resides inside each sensor node. The main objective of this algorithm is to enable the sensor nodes to learn their optimal action so that the total number of activated nodes in each scheduling round becomes minimum and preserves the criteria of coverage rate and network connectivity. The comparison of CCM-RL protocol with other protocols proves its accuracy and reliability. The simulative comparison shows that CCM-RL performs better in terms of an average number of active sensor nodes in one scheduling round, coverage rate, and energy consumption.

     

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 Energy Efficient Barrier Coverage Algorithm for Wireless Sensor Networks

Intrusion detection is one of the most important applications of wireless sensor networks. When mobile objects are entering into the boundary of a sensor field or are moving cross the sensor field, they should be detected by the scattered sensor nodes before they pierce through the field of sensor (barrier coverage). In this paper, we propose an energy efficient scheduling method based on learning automata, in which each node is equipped with a learning automaton, which helps the node to select best node to guarantee barrier coverage, at any given time. To apply our method, we used coverage graph of deployed networks and learning automata of each node operates based on nodes that located in adjacency of current node. Our algorithm tries to select minimum number of required nodes to monitor barriers in deployed network. To investigate the efficiency of the proposed barrier coverage algorithm several computer simulation experiments are conducted. Numerical results show the superiority of the proposed method over the existing methods in term of the network lifetime and our proposed algorithm can operate very close to optimal method.     

A Coverage-Based Scheduling Algorithm for WSNs

Node scheduling in wireless sensor networks (WSNs) plays a vital role in conserving energy and lengthening the lifetime of networks, which are considered as prime design challenges. In large-scaled WSNs, especially where sensor nodes are deployed randomly, 100 % coverage is not possible all the times. Additionally, several types of applications of WSNs do not require 100 % coverage. Following these facts, in this paper, we propose a coverage based node scheduling algorithm. The algorithm shows that by sacrificing a little amount of coverage, a huge amount of energy can be saved. This, in turns, helps to increase the lifetime of the network. We provide mathematical analysis, which verifies the correctness of the proposed algorithm. The proposed algorithm ensures balanced energy consumption over the sensor networks. Moreover, simulation results demonstrate that the proposed algorithm almost doubles the lifetime of a wireless sensor network by sacrificing only 5–8 % of coverage.     

Square partition‐based node scheduling algorithm for wireless passive sensor networks

Wireless passive sensor networks play an important role in solving the energy limitation of nodes in the Internet of Things, and node scheduling is a significant method used to improve the energy utilization of nodes. In this work, an unused energy model based on analyzing the energy consumption characteristics of passive nodes is proposed because no unified model of passive sensor nodes is reported in previous studies. A rapid square partition clustering method is proposed according to the analysis of the relation between the sensing and communication radii of nodes, and the secondary grouping and node scheduling in each cluster are implemented to ensure the coverage rate of networks. Experimental results show that the state distribution of nodes in the proposed algorithm is favorable. The performance of the proposed algorithm is significantly affected by the P ratio between the working and charging powers of nodes. When the value of P is less than 100, the network coverage and connectivity rate are maintained at more than 95% and 90%, respectively, and are both higher than the existing algorithm.     

Improved approximation algorithms for connected sensor cover

Wireless sensor networks have recently posed many new system building challenges. One of the main problems is energy conservation since most of the sensors are devices with limited battery life and it is infeasible to replenish energy via replacing batteries. An effective approach for energy conservation is scheduling sleep intervals for some sensors, while the remaining sensors stay active providing continuous service. In this paper we consider the problem of selecting a set of active sensors of minimum cardinality so that sensing coverage and network connectivity are maintained. We show that the greedy algorithm that provides complete coverage has an approximation factor no better than Ω(log n), where n is the number of sensor nodes. Then we present algorithms that provide approximate coverage while the number of nodes selected is a constant factor far from the optimal solution. Finally, we show how to connect a set of sensors that already provides coverage.     

Method for optimal allocation of network resources based on discrete probability model

The method for optimal allocation of network resources based on discrete probability model is proposed. In order to take into account multiple coverage of the monitored points, the method constructs the discrete probability perception model of the network nodes. The model is introduced into the solution of the node coverage area, and the optimized parameters of the sensor optimization arrangement are used to optimize the layout of the multimedia sensor nodes. After setting the node scheduling standard, the interaction force between the sensor nodes and the points on the curve path is analyzed by the virtual force analysis method based on the discrete probability model At the same time On this basis, the path coverage algorithm based on the moving target is used to optimize the coverage of the wireless sensor network node in order to achieve optimal configuration of network resources. The experimental results show that the proposed method has good convergence and can complete the node coverage process in a short time. The introduction of the node selection criteria and the adoption of the dormant scheduling mechanism greatly improve the energy saving effect and enhance the network resource optimization effect.

     

A distributed activity scheduling algorithm for wireless sensor networks with partial coverage

One of the most important design objectives in wireless sensor networks (WSN) is minimizing the energy consumption since these networks are expected to operate in harsh conditions where the recharging of batteries is impractical, if not impossible. The sleep scheduling mechanism allows sensors to sleep intermittently in order to reduce energy consumption and extend network lifetime. In applications where 100% coverage of the network field is not crucial, allowing the coverage to drop below full coverage while keeping above a predetermined threshold, i.e., partial coverage, can further increase the network lifetime. In this paper, we develop the distributed adaptive sleep scheduling algorithm (DASSA) for WSNs with partial coverage. DASSA does not require location information of sensors while maintaining connectivity and satisfying a user defined coverage target. In DASSA, nodes use the residual energy levels and feedback from the sink for scheduling the activity of their neighbors. This feedback mechanism reduces the randomness in scheduling that would otherwise occur due to the absence of location information. The performance of DASSA is compared with an integer linear programming (ILP) based centralized sleep scheduling algorithm (CSSA), which is devised to find the maximum number of rounds the network can survive assuming that the location information of all sensors is available. DASSA is also compared with the decentralized DGT algorithm. DASSA attains network lifetimes up to 92% of the centralized solution and it achieves significantly longer lifetimes compared with the DGT algorithm.     

Effective sensor deployment based on field information coverage in precision agriculture

Coverage is an importance issue in wireless sensor networks. In this work, we first propose a novel notion of information coverage, which refers to the coverage efficiency of field information covered by deployed sensor nodes. On the basis of information coverage, we consider an optimization problem of how to partition the given field into multiple parcels and to deploy sensor nodes in some selected parcels such that the field information covered by the deployed sensor nodes meets the requirement. First, we develop two effective polynomial‐time algorithms to determine the deployed locations of source nodes for information 1‐coverage and q‐coverage of the field, respectively, without consideration of communication, where information q‐coverage implies that the field information in terms of information point is covered by at least q source nodes. Also, we prove the upper bound in the theoretical for the approximate solution derived by our proposed method. Second, another polynomial‐time algorithm is presented for deriving the deployed locations of relay nodes. In the theoretical, this proposed algorithm can achieve the minimized number of relay nodes. Further, the related information 1‐coverage algorithms are applied in our wireless sensor network‐based automatic irrigation project in precision agriculture. Experimental results show the major trade‐offs of impact factors in sensor deployment and significant performance improvements achieved by our proposed method. Copyright © 2013 John Wiley & Sons, Ltd.     

Traffic-Aware Density-Based Sleep Scheduling and Energy Modeling for Two Dimensional Gaussian Distributed Wireless Sensor Network

Sleep scheduling of sensors in network domain is considered to be the most fundamental way of achieving higher life expectancy of wireless sensor networks. In this paper we have proposed density-based sleep scheduling strategy with traffic awareness in Gaussian distributed sensor network for minimizing energy consumption. In uniform distributed sensor network, it has been found that nodes in the nearest belt around the sink consume more energy. The reason behind is that the nodes near the sink involve more packet relaying load than the distant nodes. Consequently, the energy of these sensors get exhausted rapidly, thereby creating connectivity breaks known as energy hole. For this purpose, Gaussian distribution is used by densely deploying nodes around the sink which well-balances the relaying load. In addition, we have developed the analytical model for computing the energy consumption and coverage analysis in the sensor network. The performance of our sleep scheduling method is evaluated with respect to the Randomized Scheduling and Linear Distance-based Scheduling protocols. The simulation results of our proposed work show commendable improvement in network lifetime.