无线传感器网络中移动sink节点的路径规划

在无线传感器网络中,大量感知数据汇集到sink节点的采集方法会导致sink节点附近的节点能量耗尽,造成能量空洞。针对该问题,利用移动的sink节点进行数据收集是一种解决方法,其中移动sink的路径规划成为一个重要的问题。提出了一个移动sink路径规划算法,将无线传感器中随机分布的节点划分为不同的子区域,寻找sink节点移动的最佳转向点,最终得到最优的移动路径,以实现无线传感器网络生命周期最大化。仿真实验表明,与现有方案相比,该算法能显著延长网络的生命周期。     

面向智慧园区的WSN路由优化算法

为解决智慧园区中无线传感器网络(WSN)的能耗不均衡问题,构建了路由代价函数,并提出了一种新的能耗均衡路由算法.该算法结合智慧园区中无线传感器网络的特点,综合考虑节点地理位置和剩余能量来构建路由代价函数.传感器节点通过选择其邻居节点中路由代价最小的节点进行数据转发.仿真结果表明,该算法可以有效节约网络能耗,同时延长了网络的生命周期.     

基于离散多目标优化粒子群算法的多移动代理协作规划

无线传感器网络中多移动代理协作能快速高效地完成感知数据汇聚任务,但是随着移动代理访问数据源节点数的增加,移动代理携带的数据分组会逐渐增大,导致传感器节点能量负载不均衡,部分数据源节点能耗过快,网络生存期缩短。目前,针对该问题所设计的能耗均衡算法,多以降低多移动代理总能耗为目标,却未充分考虑部分数据源节点能量消耗过快对网络生存期造成的影响。提出离散多目标优化粒子群算法,以网络的总能耗和移动代理负载均衡作为适应度函数,在多移动代理协作路径规划中寻求近似最优解。通过仿真实验验证,所提出的多移动代理协作路径规划,在网络总能耗和网络生存期方面的性能优于同类其他算法。     

改进Q-Learning的WRSN充电路径规划算法

针对传统无线传感器网络节点能量供应有限和网络寿命短的瓶颈问题,依据无线能量传输技术领域的最新成果,提出了一种基于改进Q-Learning的无线可充电传感器网络的充电路径规划算法。基站根据网络内各节点能耗信息进行充电任务调度,之后对路径规划问题进行数学建模和目标约束条件设置,将移动充电车抽象为一个智能体(Agent),确定其状态集和动作集,合理改进ε-greedy策略进行动作选择,并选择相关性能参数设计奖赏函数,最后通过迭代学习不断探索状态空间环境,自适应得到最优充电路径。仿真结果证明：该充电路径规划算法能够快速收敛,且与同类型经典算法相比,改进的Q-Learning充电算法在网络寿命、节点平均充电次数和能量利用率等方面具有一定优势。     

无线传感器网络动态分簇路由BWAS的算法研究

为加快无线传感器网络(WSN)路径搜索速度,减少了路径寻优能量消耗,提出了基于最优-最差蚂蚁系统(BWAS)算法的无线传感器网络动态分簇路由算法。该算法是基于WSN动态分簇能量管理模式,在簇头节点间运用BWAS算法搜寻从簇头节点到汇聚节点的多跳最优路径,以多跳接力方式将数据发送至汇聚节点。BWAS算法在路径搜寻过程中评价出最优-最差蚂蚁,引入奖惩机制,加强搜寻过程的指导性。结合动态分簇能量管理,避免网络连续过度使用某个节点,均衡了网络节点能量消耗。通过与基于蚁群算法(ACS)路由算法仿真比较,本算法减缓了网络节点的能量消耗,延长了网络寿命,在相同时间里具有较少的死亡节点,具有较强的鲁棒性。     

一种传感器网络的分布式多播路由优化算法

传感器感知的信息需要通过网络传送给感兴趣目标节点,传统网络中的多播技术往往能耗高、实时性不够理想,不利于在传感器网络中使用。针对 WSN中节点对网络拓扑未知,该文先将多播路由问题演化为最优多播路径问题,通过启发式算法求解分布式最优路径,并通过一种基于贪婪思想的裁剪合并策略优化多播路由树,直至整个网络得到最优路径,最后并结合了节点区域集中以及无线多播特性,提出了 DCast 路由算法。最后通过仿真实验与uCast, SenCast等经典的传感器网络的多播路由算法仿真比较,可以得出其算法在时延性以及能耗等方面性能有优势。     

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,采用激光扫描和空间识别方法进行无线传感网络安全防护路径的联合节点路径寻优,在最优覆盖集下实现无线传感网络安全防护路径激光追踪识别。仿真结果表明,采用该方法进行无线传感网络安全防护路径激光追踪的拟合度较高,无线传感网络的负载均衡性和鲁棒性均有提升。     

基于梯度优化的移动机器人路径规划算法

针对目前移动机器人全局路径规划算法中存在规划效率低、路径质量低等问题,提出一种新的基于梯度优化的路径规划算法。首先使用欧氏符号距离场方法表示已知的二维地图环境,构建从障碍物中心向外梯度下降的距离场,以获得机器人到障碍物中心的距离信息来进行碰撞惩罚代价的计算;其次将路径规划问题描述为优化问题,通过引入目标距离信息和障碍物距离信息,设计了一个新的路径规划代价函数,并使用梯度下降方法优化该函数得到一条最优路径;最后通过仿真对比实验表明,所提算法在规划时间、路径长度和最大转向角方面均具有一定的优越性,并通过搭建基于ROS的移动机器人实验平台进行实际环境中的路径规划实验,验证了所提算法的有效性和可行性。     

基于三边测量法的移动锚节点定位方案

确定节点的位置是无线传感器网络对监测区域相关信息进行感知、采集和处理所面临的首要问题,直接影响它在实际中的应用。文中介绍了无线传感器网络的基本概念、节点位置的计算方法以及典型的移动锚节点路径规划,提出一种基于三边测量法的移动锚节点定位方案。在保证遍历所有未知节点的前提下,该方案的路径规划较为简单,定位精度较高。     

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.     

Path planning using a mobile anchor node based on trilateration in wireless sensor networks

In wireless sensor networks (WSNs), many applications require sensor nodes to obtain their locations. Now, the main idea in most existing localization algorithms has been that a mobile anchor node (e.g., global positioning system‐equipped nodes) broadcasts its coordinates to help other unknown nodes to localize themselves while moving according to a specified trajectory. This method not only reduces the cost of WSNs but also gets high localization accuracy. In this case, a basic problem is that the path planning of the mobile anchor node should move along the trajectory to minimize the localization error and to localize the unknown nodes. In this paper, we propose a Localization algorithm with a Mobile Anchor node based on Trilateration (LMAT) in WSNs. LMAT algorithm uses a mobile anchor node to move according to trilateration trajectory in deployment area and broadcasts its current position periodically. Simulation results show that the performance of our LMAT algorithm is better than that of other similar algorithms. Copyright © 2011 John Wiley & Sons, Ltd.     

基于计算机视觉的移动机器人导航

针对跟随路径导引的移动机器人导航方式的灵活性较差、维护成本较高、功能单一的缺点,将计算机视觉用于移动机器人路径识别。首先对视觉传感器获得的视频图像进行处理,获得有用的特征目标,实现机器人对当前路径信息的理解。然后调用直行或转弯功能模块对机器人进行导航控制。实验结果表明,该导航方式具有较好的实时性和鲁棒性。     

Robot path planner based on deep reinforcement learning and the seeker optimization algorithm

Path planning is one of the key technologies for mobile robot applications. However, the traditional robot path planner has a slow planning response, which leads to a long navigation completion time. In this paper, we propose a novel robot path planner (SOA+A2C) that produces global and local path planners with the seeker optimization algorithm (SOA) and the advantage actor-critic (A2C) algorithm, respectively. In addition, to solve the problems of poor convergence performance when training deep reinforcement learning (DRL) agents in complex path planning tasks and path redundancy when metaheuristic algorithms, such as SOA, are used for path planning, we propose the incremental map training method and path de-redundancy method. Simulation results show that first, the incremental map training method can improve the convergence performance of the DRL agent in complex path planning tasks. Second, the path de-redundancy method can effectively alleviate path redundancy without sacrificing the search capability of the metaheuristic algorithm. Third, the SOA+A2C path planner is superior to the Dijkstra & dynamic window approach (Dijkstra+DWA) and the Dijkstra & timed elastic band (Dijkstra+TEB) path planners provided by the robot operating system (ROS) in terms of path length, path planning response time, and navigation completion time. Therefore, the developed SOA+A2C path planner can serve as an effective tool for mobile robot path planning.     

M‐RPSS: A modified RPSS for path scheduling of mobile sink in wireless sensor network

In wireless sensor networks (WSNs), data gathering is the main concern, since it directly affects the network lifetime and data latency. Rendezvous Point Selection Scheme (RPSS) is a mobile sink node approach; it offers superior performance than its preceding mobile sink schemes like Rendezvous Design for Variable Track (RD‐VT), RD‐VT with Steiner Minimum Tree (RD‐VT‐SMT), and Weight Rendezvous Planning with Steiner Minimum Tree (WRP‐SMT). However, a more uniform distribution of the rendezvous node leads to less energy consumption in WSNs. The more optimum path offers less data latency. In the proposed approach, we use particle swarm optimization (PSO) to find the optimum rendezvous point and adaptive PSO (APSO) to find an optimum path by solving the travelling salesman problem. By rigorous simulation, we prove that modified RPSS (M‐RPSS) increases the network lifetime by more than 10% and decreases the data latency.     

Mobile Robot Navigation Using Wireless Sensor Networks Without Localization Procedure

In this paper, algorithms for navigating a mobile robot through wireless sensor networks are presented. The mobile robot can navigate without the need for a map, compass, or GPS module while interacting with neighboring sensor nodes. Two navigation algorithms are proposed in this paper: the first uses the distance between the mobile robot and each sensor node and the second uses the metric calculated from one-hop neighbors’ hop-counts. Periodically measuring the distance or metric, the mobile robot can move toward a point where these values become smaller and finally come to reach the destination. These algorithms do not attempt to localize the mobile robot for navigation, therefore our approach permits cost-effective robot navigation while overcoming the limitations of traditional navigation algorithms. Through a number of experiments and simulations, the performance of the two proposed algorithms is evaluated.     

激光雷达测距的机器人最优移动路径选择机制

针对当前机器人最优移动路径选择机制存在误差,工作效率低的缺陷,设计了基于激光雷达测距的机器人最优移动路径选择机制。首先利用人工势场方构建机器人的移动模型,并采用激光雷达测距技术建立栅格地图,然后将障碍物和机器人间的数据引入到坐标系中,经坐标转换映射到栅格地图中,建立障碍物与栅格地图之间联系;最后采用代价函数对机器人移动方向做出评价,根据评价结果选择移动路径,达到躲避障碍物的目的。结果表明,本文方法可以躲避机器人移动路径上障碍物,避障时间短,获得最优的机器人移动路径,对环境的适应性强。     

Robot path planning using VLSI resistive grids

The resistive grid algorithm for mobile robot path planning is described. A major advantage of the method is that it is capable of a fine-grained parallel analogue VLSI implementation, which offers a fast, low-power solution to the problem of mobile robot navigation. The results from a small-scale test chip are presented, together with their implications for scaling up to a full-sized path-planning chip     

An efficient UE cluster head selection algorithm in wireless sensor networks and cellular networks

Wireless Sensor Networks (WSNs) have been applied in many different areas. Energy efficient algorithms and protocols have become one of the most challenging issues for WSN. Many researchers focused on developing energy efficient clustering algorithms for WSN, but less research has been concerned in the mobile User Equipment (UE) acting as a Cluster Head (CH) for data transmission between cellular networks and WSNs. In this paper, we propose a cellular-assisted UE CH selection algorithm for the WSN, which considers several parameters to choose the optimal UE gateway CH. We analyze the energy cost of data transmission from a sensor node to the next node or gateway and calculate the whole system energy cost for a WSN. Simulation results show that better system performance, in terms of system energy cost and WSNs life time, can be achieved by using interactive optimization with cellular networks.     

WSN多节点决策信息融合在机器人自主导航中的应用

将机器人作为无线传感器网络的（WSN）的移动节点,可实现节点动态自定义部署并扩大其监测范围。建立了接收信号强度（RSSI）势场量化的坐标系描述机器人状态及导航空间,有效避免将RSSI值转换为距离时带来的模型误差。由处于机器人可通信区域内的若干信标节点组成一个分布式导航网络,每个节点都会对机器人做出独立的导航决策,最后由决策控制中心融合各信标节点的输出决定机器人的航向。采用分布式处理技术,绝大部分导航信息数据处理都由信标节点完成,因此很大程度上简化了机器人的设计和硬件成本,仿真和现场实验都表明该系统的有效性。