稀疏水下传感网中AUV数据移动收集技术研究

由于水下传感器节点的水声通信距离有限、价格昂贵,水下传感器网络中一般采用稀疏方式部署,因此很难保证整体网络的连通性及数据采集效率。自主水下航行器AUV（Autonomous Underwater Vehicle）作为天然的移动数据采集平台,可以弥补固定Sink节点数据采集方式的缺陷。提出了一种基于移动AUV的水下传感网移动数据收集机制。以AUV覆盖区域内的传感器节点作为临时Sink节点,其他传感器节点以临时Sink节点为根节点,采用最小生成树MST（Minimum Spanning Tree）方法将传感数据传输到这些临时Sink节点,然后通过临时Sink节点将汇聚数据传输给AUV。随着AUV的自主移动轨迹,水下传感网的传感数据都能简单高效地被收集起来。仿真结果验证了该方法在保证网络能耗的前提下提高了数据采集效率。     

移动传感器网络实验平台的设计与实现

本文针对无线传感器网络机动能力欠缺、无重部署能力的问题,设计并实现了一种具有机动能力的无线传感网络平台.该平台主要由一个汇聚节点和多个移动节点组成,移动节点和汇聚节点间通过多跳的方式进行数据交换.每个移动节点均具有传感、计算、通讯和机动能力,用户可以根据需要实时地改变节点的部署情况.演示实验结果表明,该平台在摄像头的监控下具有节点重部署的能力.     

基于Dubins曲线的无线传感网聚类移动数据采集算法

利用类车型Sink节点实现无线传感器网络的移动数据采集,可以有效延长网络生命周期。考虑类车型移动Sink节点的Kinematic约束,本文提出了一种基于聚类间Dubins平滑曲线的移动数据采集算法。整个无线传感器网络被划分为多个不重叠的聚类区间,聚类内采用最小生成树路由方法实现传感器节点的无线多跳式数据传输,Sink节点按聚类之间规划的Dubins曲线寻访数据采集点进行移动数据采集,从而兼顾移动数据采集的平滑性和节点能耗的优化性。仿真结果验证了本文算法可以在保证移动路径平滑的约束条件下提高无线传感器网络的能耗效率。     

多移动汇聚节点的无线传感网中基于服务质量的能耗

针对无线传感网（WSN）中传感节点能耗过高、传输时延过长、数据完整性过差的问题,提出一种基于多移动汇聚节点考虑服务质量（QoS）的路由算法——时延敏感和数据完整性（MSTSDI）算法。首先,通过从基站接收信号的强度判断节点的密度,用K-means聚类算法将传感网划分成自治区域;其次,给每个自治区域分配一个移动汇聚节点,利用支持向量回归（SVR）的方法确定移动汇聚节点的轨迹;最后,引入深度引力域和队列引力域,通过Improved-IDDR算法对时延敏感数据包和数据完整性要求高的数据包进行传输。理论分析和仿真表明,与GLRM算法和LEACH算法相比,Improved-IDDR算法的路由策略的能耗下降幅度分别为21.2%和23.7%;而该算法的时延分别降低了15.23%和17.93%;该算法的所传输数据包的完整性也更好。实验结果表明,MSTSDI在传感网络中能够有效提高系统的性能。     

基于二次Bezier曲线的无线传感网避障路径规划研究

用固定Sink节点进行无线传感网内数据采集的传统方式会导致热点区域(hot spot)问题,而采用移动Sink节点进行数据采集可以克服这个问题,从而达到均衡网络能量分布与延长网络生命周期的效果.本文针对类车型机器人作为无线传感网中移动数据汇聚节点的应用场景,提出了一种基于Bezier连续曲线的移动Sink节点避障路径规划算法.本文构建了连续分段Bezier曲线为巡航轨迹,采用人工势场中的斥力场理论实现对多个障碍物的智能躲避,动态调节二次Bezier曲线的内部控制点位置,将障碍物排斥在二次Bezier曲线之外.仿真结果验证本文提出的算法可以实现移动Sink节点规划路径的避障功能,同时Bezier曲线规划算法简单,计算量较小.     

多层三角网格轨迹模型的移动传输控制策略

针对新型多层移动传感网络结构下的传输移动控制问题,提出一种三角网格轨迹模型。在三角网格模型下设计F-connect移动策略,给出动态二锚节点定位算法triangle-grid,绘制区域三角网格节点分布图,结合EHCC—communication性质控制F-collect移动以收集S节点的数据信息。模拟实验结果表明,该模型能较好地解决随机移动节点带来的网络不稳定、信息丢失率高等问题,且适用于大规模的无线传感网络,只需布置少量F-collect节点就能获得较高的网络覆盖率和较低的数据丢失率。     

无线传感网络移动节点位置并行微粒群优化策略

网络节点位置优化是无线传感网络研究的核心问题之一.无线传感网络通常由固定节点和少量移动节点构成,传统的虚拟力导向算法无法解决固定节点对移动节点优化的约束.该文针对这一问题,提出了基于并行微粒群算法的优化策略.微粒群算法具有适于解决连续空间多维函数优化问题、能快速收敛至全局最优解的特点.并行框架提高了算法的运行效率,降低了算法的运算复杂度,使算法能够满足无线传感网络的需求.通过并行微粒群算法搜索不同状态下无线传感节点的最优位置,使无线传感网络能够利用移动节点实现网络结构的动态重组,最大化网络覆盖范围,提高网络测量可靠性.实验证明,并行微粒群优化策略能快速有效地实现无线传感网络移动节点位置优化.     

移动传感网络汇聚节点动态选址

目前绝大多数汇聚节点选址策略都是基于静态传感器网络,但这些算法应用在移动传感器网络中有很大的局限性。本文提出了一种分布式和集中式的SCSN模型,该模型实现了结构意识自适应算法,能在移动传感网中动态实时的维护汇聚节点在Steiner中心位置。其基于边结构设计,实现了凸壳更新节点个数复杂度由 下降到 ,结合kalman滤波预测方法准确的预测汇聚节点的移动轨迹。实验对比了Steiner和其它几何中心更新的偏移量,结果表明Steiner有使汇聚节点具有高稳定低偏心的特征。     

在有洞的无线传感器网络中移动基站轨迹的研究

数据收集是无线传感器网络的一个基本功能,由于部署在基站周围的传感器节点承担着网络内大部分的负载,因此导致能量迅速耗尽.但是现有的负载平衡的基站移动策略只能在传感器节点被均匀布撒的情况下才能有效地延长网络寿命.本文针对在含有洞的无线传感器网络区域中,提出了基站移动的4种轨迹,基站沿着边界之间的中轴线进行移动,把中轴线上的节点作为汇聚节点,可以降低包的丢失率.实验仿真结果表明,该算法不仅能平衡节点负载,提高包的接收率,也降低了网络能耗,延长了网络寿命.     

移动Sink的传感器网络路径优化策略

在无线传感器网络（WSNs）中引入移动 Sink 可以避免网络拥塞和能量空洞并降低网络能耗,但由于移动速度的限制导致时延较大。针对这一问题,提出了时延约束下的移动 Sink 路径优化策略,根据时延和网络能耗之间的关系设计了可调节的节点权重,通过模拟退火遗传算法得到最优节点权重,并依据此权重通过迭代得到汇聚节点和最佳移动路径。仿真结果表明：该策略能保证在满足时延约束的前提下降低网络能耗,且收敛速度快。     

An efficient trajectory design for mobile sink in a wireless sensor network

Mobile sink trajectory plays a pivotal role for network coverage, data collection and data dissemination in wireless sensor networks. Considering this, we propose a novel approach for mobile sink trajectory in wireless sensor networks. Our proposed approach is based on Hilbert Space Filling Curve, however, the proposed approach is different from the previous work in a sense that the curve order changes according to node density. In this paper, we investigate the mobile sink trajectory based on Hilbert Curve Order which depends upon the size of the network. Second, we calculate the Hilbert Curve Order based on node density to re-dimension the mobile sink trajectory. Finally, we perform extensive simulations to evaluate the effectiveness of proposed approach in terms of network coverage and scalability. Simulation results confirm that our proposed approach outperforms with size based Hilbert Curve in terms of network coverage, packet delivery ratio and average energy consumption.     

基于可预测移动汇聚节点的无线传感网分簇算法研究

在汇聚节点移动可预测情况下,提出一种无线传感网分簇算法。该算法将subsink节点引入到HEED分簇算法中,以较快感知移动路径变化,快速形成分簇拓扑;采用sink节点注册机制,实现汇聚节点移动过程中的信息交互。实例分析表明,该算法能快速形成合理网络拓扑,延长无线传感网的生存期。     

Energy efficient secure data collection with path-constrained mobile sink in duty-cycled unattended wireless sensor network

This paper addresses the problem of secure data transmission and balanced energy consumption in an unattended wireless sensor network (UWSN) comprising of multiple static source nodes and a mobile sink in the presence of adversaries. The proposed system comprises of three phases: the identification of data collection points (convex nodes), path planning by the mobile sink, and secure data transmission. An energy-aware convex hull algorithm is used for the identification of data collection points for data transmission to the mobile sink. Data transmission from sensor nodes to the nearest data collection point is performed using multihop communication and from sensor nodes to the mobile sink in a single hop. Data are securely transmitted through an elliptic curve cryptography based ElGamal scheme for message authentication. A data packet is associated with a digital signature. The variation in a digital signature and threshold energy obtained using support vector machine is used to determine the presence of malicious nodes in the network. The performance of the proposed system is evaluated using Cooja simulator by Contiki for various node counts under a static sink and mobile sink, with different threat scenarios. The results indicate that the proposed system is resilient against threats and provides satisfactory performance     

面向动态传感器网络的数据汇集算法

传感器网络中移动终端广泛存在,针对无线传感器网络移动Sink场景,提出一种移动Sink代理机制和网络质量评估策略.根据网络质量决定Sink的移动路径,并在此基础上,从能量均衡的角度提出一种基于角度的数据汇集算法ADC-MS(Angle-based Data Collection algorithm for Mobile...     

Decomposition algorithms for maximizing the lifetime of wireless sensor networks with mobile sinks

We address the problem of maximizing the lifetime of a wireless sensor network with energy-constrained sensors and a mobile sink. The sink travels among discrete locations to gather information from all the sensors. Data can be relayed among sensors and then to the sink location, as long as the sensors and the sink are within a certain threshold distance of each other. However, sending information along a data link consumes energy at both the sender and the receiver nodes. A vital problem that arises is to prescribe sink stop durations and data flow patterns that maximally prolong the life of the network, defined as the amount of time until any node exhausts its energy. We describe linear programming and column generation approaches for this problem, and also for a version in which data can be delayed in its transmission to the sink. Our column generation approach exploits special structures of the linear programming formulations so that all subproblems are shortest path problems with non-negative costs. Computational results demonstrate the efficiency of the proposed algorithms.     

能量有效和延迟敏感的无线传感器网络数据收集协议

利用移动Sink进行数据收集是无线传感器网络数据收集的一个趋势。本文提出一种能量有效、延迟敏感的移动数据收集协议（Energy—efficient and Delay—Sensitive Data Gathering Protocol for Wireless Sensor Networks,简称EEDS）。EEDS中,移动Sink在网络中穿行,从代理节点收集传感器节点监测到的数据。为了减少数据收集的延迟,采用类TSP（Traveling Salesman Problem）的解决方法,确保移动Sink在各个代理节点中收集数据时,始终选择一条最短路径在网络中行走。模拟仿真表明,提出的数据收集协议在延长网络生命周期以及减少数据收集延迟方面都有显著的优势。     

面向移动汇聚节点的无线传感器网络自适应路由方法

针对无线传感器网络面向移动汇聚节点的自适应路由问题,为实现路由过程中对节点能量以及计算、存储、通信资源的优化利用,并对数据传输时延和投递率等服务质量进行优化,提出一种基于强化学习的自适应路由方法,设计综合的奖赏函数以实现对能量、时延和投递率等多个指标的综合优化。从报文结构、路由初始化、路径选择等方面对路由协议进行详细设计,采用汇聚节点声明以及周期性洪泛机制加速收敛速度,从而支持汇聚节点的快速移动。理论分析表明基于强化学习的路由方法具备收敛快、协议开销低以及存储计算需求小等特点,能够适用于能量和资源受限的传感器节点。在仿真平台中通过性能评估和对比分析验证了所述自适应路由算法的可行性和优越性。     

Particle swarm optimization based clustering algorithm with mobile sink for WSNs

Wireless sensor networks with fixed sink node often suffer from hot spots problem since sensor nodes close to the sink usually have more traffic burden to forward during transmission process. Utilizing mobile sink has been shown as an effective technique to enhance the network performance such as energy efficiency, network lifetime, and latency, etc. In this paper, we propose a particle swarm optimization based clustering algorithm with mobile sink for wireless sensor network. In this algorithm, the virtual clustering technique is performed during routing process which makes use of the particle swarm optimization algorithm. The residual energy and position of the nodes are the primary parameters to select cluster head. The control strategy for mobile sink to collect data from cluster head is well designed. Extensive simulation results show that the energy consumption is much reduced, the network lifetime is prolonged, and the transmission delay is reduced in our proposed routing algorithm than some other popular routing algorithms.     

An improved ant colony optimization-based approach with mobile sink for wireless sensor networks

Traditional wireless sensor networks (WSNs) with one static sink node suffer from the well-known hot spot problem, that of sensor nodes near the static sink bear more traffic load than outlying nodes. Thus, the overall network lifetime is reduced due to the fact some nodes deplete their energy reserves much faster compared to the rest. Recently, adopting sink mobility has been considered as a good strategy to overcome the hot spot problem. Mobile sink(s) physically move within the network and communicate with selected nodes, such as cluster heads (CHs), to perform direct data collection through short-range communications that requires no routing. Finding an optimal mobility trajectory for the mobile sink is critical in order to achieve energy efficiency. Taking hints from nature, the ant colony optimization (ACO) algorithm has been seen as a good solution to finding an optimal traversal path. Whereas the traditional ACO algorithm will guide ants to take a small step to the next node using current information, over time they will deviate from the target. Likewise, a mobile sink may communicate with selected node for a relatively long time making the traditional ACO algorithm delays not suitable for high real-time WSNs applications. In this paper, we propose an improved ACO algorithm approach for WSNs that use mobile sinks by considering CH distances. In this research, the network is divided into several clusters and each cluster has one CH. While the distance between CHs is considered under the traditional ACO algorithm, the mobile sink node finds an optimal mobility trajectory to communicate with CHs under our improved ACO algorithm. Simulation results show that the proposed algorithm can significantly improve wireless sensor network performance compared to other routing algorithms.     

Multi-constraint multi-objective QoS aware routing heuristics for query driven sensor networks using fuzzy soft sets

In this paper, a fuzzy based distributed power aware routing scheme considering both energy and bandwidth constraints, especially for query driven applications in the asynchronous duty-cycled wireless sensor networks are devised. The proposed multi-constraint, multi-objective routing optimization approach under strict resource constraints guarantees reliability and fast data delivery along with efficient power management in spite of unreliable wireless links and limited power supply. In query driven applications, the request from the sink to the individual sensor node will be a broadcast message, whereas the individual sensor nodes replies back to sink as unicast messages. In the proposed work, the fuzzy approach and “A Star” algorithm are utilized for satisfying energy and bandwidth constraints to route the broadcast messages of the sink while querying all the sensor nodes in the network. Every node will be provided with a guidance list, which is used to decide the next best neighbor node with good route quality for forwarding the received multi-hop broadcast messages. The route quality of the every node is estimated with fuzzy rules based on the network parameters such as maximum remaining energy, minimum traffic load and better link quality to increase the network lifetime. The provision of overhearing the broadcast messages and acknowledgements within the transmission range minimizes the effort to search for the active time of nodes while routing the broadcast messages with asynchronous scheduling. Further, in the proposed work only the time slot of its nearest neighbor relay node (to which packets are to be forwarded) is learnt to reduce the number of message transmissions in the network. For the unicast message replies, the fuzzy membership function is modified and devised based on the routing metrics such as higher residual energy, minimum traffic loads and minimum hop count under energy and bandwidth constraints. Also, the multi-hop heuristic routing algorithm called Nearest Neighbor Tree is effectively used to reduce the number of neighbors in the guidance list that are elected for forwarding. This helps to increase the individual sensor node’s lifetime, thereby maximizes the network lifetime and guarantees increased network throughput. The simulation results show that the proposed technique reduces repeated transmissions, decreases the number of transmissions, shortens the active time of the sensor nodes and increases the network lifetime for query driven sensor network applications invariant to total the number of sensor nodes and sinks in the network. The proposed algorithm is tested in a small test bed of sensor network with ten nodes that monitors the room temperature.