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.

     

Wireless Sensor Network Path Optimization Using Sensor Node Coverage Area Calculation Approach

The proposed work is based on the path optimization approach for wireless sensor network (WSN). Path optimization is achieved by using the NSG 2.1 Tool, TCL Script file and NS2 simulator to improve the quality of service (QoS). Path optimization approach finds best suitable path between sensor nodes of WSN. The routing approach is not only the solution to improve the quality but also improves the WSN performance. The node cardinally is taken under consideration using the ad-hoc on demand distance vector routing protocol mechanism. Ad hoc approach emphasize on sensor nodes coverage area performance along with simulation time. NSG 2.1 Tool calculates the sensor node packet data delivery speed which can facilitate inter-node communication successfully. An experimental result verified that the proposed design is the best possible method which can escape from slow network response while covering maximum sensor nodes. It achieves coverage support in sensor node deployment. The result outcomes show best path for transferring packet from one sensor node to another node. The coverage area of sensor node gives the percentage of average coverage ratio of each node with respect to the simulation time.

     

无线传感网络安全防护路径激光追踪方法

为了提高无线传感网络安全防护能力,需要进行网络安全防护路径设计,提出基于联合节点行为覆盖的无线传感网络安全防护路径激光追踪方法.构建无线传感网络安全防护路径的覆盖关系模型,根据传感器节点与目标节点从属关系进行无线传感网络安全防护的路径空间规划设计,采用最短路径寻优方法进行无线传感网络安全防护路径的激光控制,采用激光扫描...     

基于拨备满足模型的无线传感网节点覆盖算法

针对无线传感网预覆盖过程中存在覆盖盲区以及数据频繁重传而导致的节点覆盖抑制现象,提出了一种基于拨备满足模型的无线传感网网络覆盖算法。设计一种新的无线传感网节点覆盖模型,并构建覆盖指数、覆盖强度、覆盖均衡评估系数等评估维度,快速评估节点覆盖质量。再计算覆盖均衡评估系数,并采用拨备模型优化覆盖质量,确定覆盖性能优越的备用工作节点。随后,基于覆盖相似性原则评估工作节点覆盖性能,设计了节点首次覆盖评估方法,按节点移动路径依次评估覆盖指数统计均值,并根据目标节点进入覆盖区域的先后,逐次激活性能最佳的工作节点进行监测。仿真实验表明：与当前无线传感网常数节点覆盖方案相比,所提方案具有更高的网络覆盖率、更短的覆盖启动时间和更少的工作节点数目。     

Coverage optimization algorithm based on VF-CS in mobile sensor network

A random placement of large-scale sensor network in the outdoor environment often causes low coverage.An area coverage optimization algorithm of mobile sensor network (MSN) based on virtual force perturbation and Cuckoo search (VF-CS) was proposed.Firstly,the virtual force of the sensor nodes within the Thiessen polygon was analyzed based on the partitioning of Voronoi diagram of the monitoring area.Secondly,the force of polygon vertices and neighbor nodes was taken as the perturbation factor for updating the node’s location of the Cuckoo search (CS).Finally,the VF-CS guided the node to move so as to achieve the optimal coverage.The simulation results demonstrate that the proposed algorithm has higher coverage and shorter average moving distance of nodes than the Voronoi diagram based algorithms in literatures.     

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.     

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.     

Adaptive directed evolved NSGA2 based node placement optimization for wireless sensor networks

Wireless sensor network (WSN) is a wireless network composed of a large number of static or mobile sensors in a self-organizing and multi-hop manner. In WSN research, node placement is one of the basic problems. In view of the coverage, energy consumption and the distance of node movement, an improved multi-objective optimization algorithm based on NSGA2 is proposed in this paper. The proposed algorithm is used to optimize the node placement of WSN. The proposed algorithm can optimize both the node coverage and lifetime of WSN while also considering the moving distance of nodes, so as to optimize the node placement of WSN. The experiments show that the improved NSGA2 has improvements in both searching performance and convergence speed when solving the node placement problem.

     

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.     

Shortest path planning of a data mule in wireless sensor networks

Data mules are extensively used for data collection in wireless sensor networks (WSNs), which significantly reduces energy consumption at sensor nodes but increases the data delivery latency. In this paper, we focus on minimizing the length of the traveling path to reduce the data delivery latency. We first model the shortest path planning of a data mule as an optimization problem, and propose an optimal model and corresponding solving algorithm. The optimal model solution has high time complexity, mainly due to the parallel optimization of node visit arrangements and data access point (DAP) settings during the solution process, which is to obtain the shortest path result. In order to improve the computational efficiency, we next give the approximate model and its solving algorithm, which is mainly to decompose the path planning problem into the Traveling Salesman Problem (TSP) and nonlinear optimization problem, and optimize the two parts separately. The proposed approach is capable of expressing the influence of the communication range of each sensor node, which is suitable for more general application scenarios than the existing methods. Theoretical analysis and simulation results show that the solution has good performances in terms of path length and computational efforts.

     

能量收集认知无线电网络下的多接入用户信息年龄研究

针对信道资源有限的多接入信道无线传感器网络场景,实时信息的传送需要考虑信道环境和信息新鲜度问题。该文基于认知无线电物联网（Cognitive Radio-Internet of Things, CR-IoT）系统,构建了一个具有频谱访问权限的主用户（Primary User, PU）和两个可共享PU频谱次用户（Secondary User, SU）的网络模型。在考虑PU工作状态和SU数据队列稳定的条件下,提出了一个以最小化节点平均AoI为目标的优化问题。其次使用两种策略进行优化,包括概率随机接入策略（Probabilistic Random Access Policy, PRA）,该策略下两个SU节点根据相应的概率分布做出独立的传输决策;以及基于李雅普诺夫优化框架优化时隙内调度决策的漂移加罚策略（Drift Plus Penalty Policy, DPP）。仿真结果可知,DPP策略下得到的平均AoI的值要明显低于PRA策略,表明使用DPP策略对平均AoI的优化更加显著,可以有效提升数据包的时效性和新鲜度。      

无线传感器网络中基于区域通信覆盖的节点定位投放算法

针对无线传感器网络中传感器节点投放分布对投放区域有效通信信号覆盖的影响,该文提出了一种基于通信覆盖的分布式投放概率覆盖(DDCP)算法。在保证投放精度的前提下,该算法根据传感器节点在投放区域中位置的不确定性以及信号衰减特性,建立信号覆盖模型,并通过概率优化获取传感器节点的最佳投放位置和投放数目。这样改善了区域通信覆盖,同时提高了投放效率和节省网络资源。通过仿真比较了在不同定位投放方法下的各相关性数据,验证了该算法实现高效投放的优越性和正确性。     

Sleep scheduling with expected common coverage in wireless sensor networks

Sleep scheduling, which is putting some sensor nodes into sleep mode without harming network functionality, is a common method to reduce energy consumption in dense wireless sensor networks. This paper proposes a distributed and energy efficient sleep scheduling and routing scheme that can be used to extend the lifetime of a sensor network while maintaining a user defined coverage and connectivity. The scheme can activate and deactivate the three basic units of a sensor node (sensing, processing, and communication units) independently. The paper also provides a probabilistic method to estimate how much the sensing area of a node is covered by other active nodes in its neighborhood. The method is utilized by the proposed scheduling and routing scheme to reduce the control message overhead while deciding the next modes (full-active, semi-active, inactive/sleeping) of sensor nodes. We evaluated our estimation method and scheduling scheme via simulation experiments and compared our scheme also with another scheme. The results validate our probabilistic method for coverage estimation and show that our sleep scheduling and routing scheme can significantly increase the network lifetime while keeping the message complexity low and preserving both connectivity and coverage.     

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.     

一种基于网格划分的有向传感网时空覆盖调度算法

针对有向传感器网络中的时空覆盖调度问题进行研究,从有向传感器节点感知模型出发,设计了基于网格划分的网络基本区域生成方法,在此基础上提出了节点最大覆盖调度迭代选择MaxGreedy算法.通过仿真实验验证了网格划分方法的有效性,设计了一系列的时空覆盖算法对比实验,深入评估MaxGreedy算法的性能.对比试验结果表明,MaxGreedy算法可以高效地生成网络的节点调度模式,并在一定程度上提高网络的时空覆盖率.     

Multi mobile agent itinerary planning based on network coverage and multi-objective discrete social spider optimization algorithm

The multi mobile agent collaboration planning model was constructed based on the mobile agent load balancing and total network energy consumption index.In order to prolong the network lifetime,the network node dormancy mechanism based on WSN network coverage was put forward,using fewer worked nodes to meet the requirements of network coverage.According to the multi mobile agent collaborative planning technical features,the multi-objective discrete social spider optimization algorithm (MDSSO) with Pareto optimal solutions was designed.The interpolation learning and exchange variations particle updating strategy was redefined,and the optimal set size was adjusted dynamically,which helps to improve the accuracy of MDSSO.Simulation results show that the proposed algorithm can quickly give the WSN multi mobile agent path planning scheme,and compared with other schemes,the network total energy consumption has reduced by 15%,and the network lifetime has increased by 23%.     

Design and Analysis of Circular Microstrip Patch Antenna for White Space TV Band Application

The wide range of wireless sensor network applications has made it an interesting subject for many studies. One area of research is the controlled node placement in which the location of nodes is not random but predetermined. Controlled node placement can be very effective when either the price of the sensor nodes is high or the sensor coverage is of a specific type and it is necessary to provide special characteristics such as coverage, lifetime, reliability, delay, efficiency or other performance aspects of a wireless sensor network by using the minimum number of nodes. Since node placement algorithms are NP-Hard problems, and characteristics of a network are often in conflict with each other, the use of multi-objective evolutionary optimization algorithms in controlled node placement can be helpful. Previous research on node placement has assumed a uniform pattern of events, but this study shows if the pattern of events in the environment under investigation is geographically dependent, the results may lose their effectiveness drastically. In this study, a controlled node placement algorithm is proposed that aims to increase network lifetime and improve sensor coverage and radio communication, assuming that the event pattern is not uniform and has a geographical dependency. The proposed placement algorithm can be used for the initial placement or, for repairing a segmented network over time. In this study, multi-objective evolutionary optimization algorithms based on decomposition (MOEA/D) have been used, and the performance results have been compared with other node placement methods through simulation under different conditions.

     

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.     

Coverage control algorithm for wireless sensor networks based on non-cooperative game

For the redundancy coverage of nodes leads to the phenomenon of low energy efficiency,Non-cooperative game theory was used to solve it.A revenue function was proposed,which considering the coverage of nodes and the residual energy.The lifetime of the node and network path gain were applied to revenue function.The network topology was built by nodes with the appropriate work strategy.Control algorithm coverage in wireless sensor network was proposed based on Non-cooperative game theory.A Nash equilibrium between the coverage rate and the residual energy was proved,and the return function converged to the Pareto optimal.Experiments show that the algorithm can provide reasonable coverage of network nodes and ensure energy efficiency.     

移动无线传感网的生存时间优化算法研究

当sink节点位置固定不变时,分布在sink 节点周围的传感节点很容易成为枢纽节点,因转发较多的数据而过早失效。为解决上述问题,提出移动无线传感网的生存时间优化算法（LOAMWSN）。LOAMWSN算法考虑sink节点的移动,采用减聚类算法确定sink节点移动的锚点,采用最近邻插值法寻找能遍历所有锚点的最短路径近似解,采用分布式非同步Bellman-Ford算法构建sink节点k跳通信范围内的最短路径树。最终,传感节点沿着最短路径树将数据发送给sink节点。仿真结果表明：在节点均匀分布和非均匀分布的无线传感网中,LOAMWSN算法都可以延长网络生存时间、平衡节点能耗,将平均节点能耗保持在较低水平。在一定的条件下,比Ratio_w、TPGF算法更优。