动态无线传感网中低延迟高可靠的数据查询机制

查询处理作为大规模无线传感器网络中智能服务的一个重要操作,可以根据用户需求对网络中的感知数据进行检索和回传.然而,部署在恶劣环境中的无线传感网络,节点容易遭受外力破坏,或者自身资源(能量、存储等)有限,可能会导致节点发生位移和故障,从而造成网络拓扑不断改变以及部分节点的感知数据失效.同时,由于节点感知数据容量大、传输带宽有限以及网络链路不可靠等情况,可能会造成网络通信时延大大增加.这些因素使得快速、可靠的数据查询处理成为无线传感网中一个难题.为了解决这个难题,提出一种动态网络中低延迟高可靠的数据查询机制.该机制是一种非聚合随机查询方式,通过将传感节点划分为源节点和查询节点来实现数据查询.首先,根据监测事件将网络划分为若干个子区域,每个子区域中的源节点相互协作,并按照时间顺序依次轮流监听该区域的事件信息;接着,源节点根据预估的平均节点故障概率,计算出一个合理的备份数量,并将源数据按照该数量存储到邻居节点中,以降低源数据的失效概率;然后,为了加快数据查询速度,源节点定期对源数据块进行编码压缩,并选取剩余能量和存储空间较小的多个邻居节点作为下一跳接收节点.这些接收节点基于局部区域中节点个数大小,决定是否接收存储该报文.重复上述过程,直至压缩数据均匀地分布在网络中.另一方面,查询节点接收到查询请求时,也使用负载均衡多路分发方式将查询请求传输到部分节点上.为了避免目标数据的冗余回传,当查询请求成功查询到目标数据时,目标节点先修改访问位,再选取与查询节点距离最近的邻居节点作为下一跳接收节点,迭代执行上述操作,直到用户获得所需要的事件信息.在以上过程中,为了节省节点能量,在保证高成功查询率的条件下,建立通信能耗最小化的优化模型,计算出最优的压缩数据副本数和查询消息副本数,之后,源节点和查询节点分别按照该数量进行副本数据分发.最后,理论分析和实验结果表明,与其它四种查询算法相比,提出的查询机制具有更高的查询成功率、更低的通信能耗和通信时延.     

基于物联网技术网络自适应移动节点资源监测研究

针对无线Ad Hoc网络的高度动态性、网络拓扑动态变化、链路不稳定等问题,提出了一种基于物联网技术网络自适应移动节点资源技术.该技术通过建立不同的传感器来探测邻居节点,对无线资源进行动态划分设置请求优先级从而建立动态划分资源优先级机制.实验结果表明,该技术能够快速进行网络节点查询,不仅有效提高了Ad Hoc网络中节点资源发现的性能和系统的稳定性,而且与原有的技术相比在转发率和时延等方面均具有良好的性能.     

基于小世界模型的传感器网络节能查询策略

查询处理是传感器网络中最重要的技术之一。以降低查询能耗为目的,提出了一种基于小世界模型的查询策略（CardSN）。该策略以关联作为长程连接,降低了网络平均路径距离;将各节点的邻居相对定位,使查询处理获得了方向性。仿真实验结果表明：CardSN具有良好的可扩展性,且能耗明显低于ZRP和CAPTURE。     

一种有效的非结构化P2P网络资源搜索策略

针对非结构化P2P网络资源搜索算法中冗余消息数过多、搜索效率低等问题,提出了一种基于邻居-邻居节点的非结构化P2P网络资源搜索策略,该策略可以在一定程度上防止路由环路的产生,同时综合考虑邻居节点的活跃度和资源命中数,选择下一条邻居节点路径转发查询消息,将查询信息发往目标可能存在的区域,因此减少了发往网络的查询信息包数量,节省了网络带宽.实验结果表明,该算法可以减少大量的冗余查询信息,提高资源搜索的成功率,是一种有效的非结构化P2P网络资源搜索策略.     

多传感器网络中的分布式故障检测算法

在传感器网络中,分布式故障检测算法(DFD算法)通过与所有邻居节点的传感器数据的比较判断,实现节点传感器的故障检测。但是,在故障节点聚集的网络区域,故障节点比例的上升将导致该区域的故障检测精度显著下降。针对多传感器网络,本文利用多传感器在相同区域的故障分布差异及传感器之间关联特性对DFD故障检测算法进行改进,提出适用于多传感器网络的MDFD算法,提高了故障聚集区域的检测精度。性能分析和仿真结果表明:在节点故障率高的网络中,与DFD和IDFD算法相比,MDFD提高了故障检测精度,算法适用于节点分布稀疏和传感器故障率较高的网络。     

传感器网络分布式事件触发多目标估计

本文主要研究无线传感器网络中目标数目已知且固定的一类分布式多目标跟踪问题,提出了一种完全分布式的基于事件触发的测量和通信策略使得每个节点在不需要全局信息的情况下实现估计误差和能量消耗之间的平衡.监测区域存在多个移动目标,传感器能否测量到单个目标由事件触发测量机制和节点的测量半径来综合决定.基于节点和邻居的信息采用k-means聚类算法来解决数据关联问题,同时提出了基于最小迹原则的一致性卡尔曼滤波算法.从理论上证明了该事件触发策略不仅在性能指标上优于基于时间触发的算法,而且在网络中如果存在节点对多目标协同可观,系统估计误差在均方意义下是稳定的.最后给出了仿真例子验证了该算法的有效性和可行性.     

基于联合节点行为策略的WSN覆盖控制算法

针对无线传感器网络在对移动目标节点覆盖过程中出现网络能量快速消耗问题，提出了一种基于联合节点行为策略的覆盖算法。根据网络模型建立传感器节点与目标节点从属关系，确定覆盖关联模型；利用概率理论求解邻居节点冗余覆盖度，确定最少传感器节点数量；给出了邻居节点覆盖期望值的求解方法；仿真实验表明，该算法与其他算法在网络覆盖率和网络生存周期两个性能指标上均提升了12.39%和15.01%，从而验证了算法的有效性。     

基于事件驱动机制WSN覆盖算法

针对无线传感器网络覆盖方法自身特点以及在覆盖过程中消耗大量传感器节点能量的不足,提出了一种事件驱动机制的覆盖算法。该算法通过事件驱动机制使节点之间完成了状态转换,同时建立了传感器节点与目标节点之间的关联属性,从而有效地减少节点能量的消耗,延长了网络生存周期,优化了网络资源,确保了以最少的节点完成对目标区域的完全覆盖。仿真实验结果表明,该算法中节点能量的消耗与LEACH协议相比降低了7%,验证了该算法的实效性和稳定性。     

OHQFS:一种非结构化P2P网络中数据查询搜索新策略

为了提高非结构化P2P网络中数据查询搜索的效率,提出一种新型的一跳查询与转发数据搜索新策略（OHQFS）,它是以非结构化P2P网络中的数据查询请求转发策略和非转发策略为基础,将它们整合、集成后得到的一种新策略.OHQFS策略中的查询源结点直接搜索其自身的所有邻居节点,并将查询请求转发给这些邻居节点,使得这些邻居节点再去搜索它们的相邻节点.该策略在数据查询搜索过程中无须维持一个很大的邻居节点信息集合,系统维护开销较小,通过本策略中固有的一步查询转发,使其邻居节点和邻居的邻居节点处于查询搜索范围内.网络仿真的实验结果表明,OHQFS策略相对于转发搜索策略而言,它提高了查询效率;而相对于非转发策略,它获得了较高的成功率.     

基于剩余能量的无线传感网路由算法设计

小规模、分布集中的WSNs(Wireless Sensor Networks,无线传感器网络)适宜采用平面路由协议,但在平面路由协议中,传统的洪泛路由算法以广播的方式在整个网络中传输查询请求和查询结果数据帧,消耗了较多的传感器节点能量,导致节点过早失效.论文对传统的洪泛路由算法进行改进,提出并实现了一种节能路由算法BRE-Flooding(Based on the Remaining Energy Flooding,基于剩余能量的洪泛算法).在算法中,节点依据剩余能量决定是否接收和转发数据帧;节点维护由剩余能量、距离网络中聚合节点(Aggregation Node)的跳数等信息组成的邻居路由信息表,并依据数据帧中表示该查询请求关键程度的属性,结合邻居路由信息表动态确定向哪几个(或全部)邻居节点转发数据帧.经实验仿真,表明所提算法在节省节点能量消耗及网络负载均衡方面具有较好的表现.     

无线传感器网络中基于关联度的多查询优化

无线传感器网络是一种以数据为中心的网络,用户通过基站向网络提出查询请求获取所需数据。如何通过多查询的优化来减少传感器节点的能耗以延长网络生命期是无线传感器网络中需要解决的关键问题之一。提出了基于关联度的多查询优化算法,其基本思想是节点通过节点与候选父亲节点之间的关联度来选择父节点,从而被相同查询覆盖的节点聚集成一个组,多个查询间共享组中节点的数据,在网络中对查询数据进行有效的融合,充分减少了网络的数据传输量,延长了网络的生命期。理论分析和模拟实验表明该算法可以充分减少数据传输量,从而达到节能的目的。     

无线传感器网络Top-k多查询算法

针对无线传感器网络中多个Top-k查询问题,提出了一种Top-k多查询处理的算法,对接收到的多个Top-k查询请求进行预处理,预处理依据是约束条件,得出两类不同的查询集合:单约束条件的多查询和多约束条件的多查询。针对单约束条件的多查询提出了ETOP算法,该算法首先对排在时间序列最前面的Top-k查询请求进行基于网内处理,然后把查询结果存入基站缓存,并把结果的最小值设定为阈值传输到各个节点,再根据后续查询请求的查询范围进行相应的查询,从而快速地获得Top-k查询结果。实验表明:Top-k多查询方法在能够很好地实现查询的同时,减少了无线传感器网络中的传输消耗和能量消耗。     

An analytical model for information retrieval in wireless sensor networks using enhanced APTEEN protocol

Wireless sensor networks are a new class of ad hoc networks that will find increasing deployment in coming years, as they enable reliable monitoring and analysis of unfamiliar and untested environments. The advances in technology have made it possible to have extremely small, low powered sensor devices equipped with programmable computing, multiple parameter sensing, and wireless communication capability. Because of their inherent limitations, the protocols designed for such sensor networks must efficiently use both limited bandwidth and battery energy. We develop an M/G/1 model to analytically determine the delay incurred in handling various types of queries using our enhanced APTEEN (Adaptive Periodic Threshold-sensitive Energy Efficient sensor Network protocol) protocol. Our protocol uses an enhanced TDMA schedule to efficiently incorporate query handling, with a queuing mechanism for heavy loads. It also provides the additional flexibility of querying the network through any node in the network. To verify our analytical results, we have simulated a temperature sensing application with a Poisson arrival rate for queries on the network simulator ns-2. As the simulation and analytical results match perfectly well, this can be said to be the first step towards analytically determining the delay characteristics of a wireless sensor network.     

Towards Spatial Window Queries Over Continuous Phenomena in Sensor Networks

Recent research on sensor networks has focused on the efficient processing of declarative SQL queries over sensor nodes. Users are often interested in querying an underlying continuous phenomenon such as a toxic plume, whereas only discrete readings of sensor nodes are available. Therefore, additional information estimation methods are necessary to process the sensor readings to generate the required query results. Most estimation methods are computationally intensive, even when computed in a traditional centralized setting. Furthermore, energy and communication constraints of sensor networks challenge the efficient application of established estimation methods in sensor networks. In this paper, we present an approach using Gaussian kernel estimation to process spatial window queries over continuous phenomena in sensor networks. The key contribution of our approach is the use of a small number of Hermite coefficients to approximate the Gaussian kernel function for subclustered sensor nodes. As a result, our algorithm reduces the size of messages transmitted in the network by logarithmic order, thus saving resources while still providing high-quality query results.     

一种空间查询高效的无线传感网络路由协议

在以数据为中心的大规模无线传感网络中,感知数据查询通常以空间查询为主,而感知节点携带能量极为有限,因此提高感知数据空间查询能量利用效率尤为重要。 GeoGrid协议是一种完全基于地理位置信息的路由协议,适用于大规模无线传感网络应用场景,但其空间查询效率较低。针对大规模空间查询应用场景,从成簇方式、簇首选举、网络拓扑层次构建及路由策略等方面对 GeoGrid 进行优化,提出一种基于四叉树结构的空间查询能量高效的无线传感网络路由协议———QuadGrid,并对 GeoGrid、QTBDC 及 QuadGrid 的空间查询能耗进行仿真分析。实验结果表明,与 GeoGrid、QTBDC 相比, QuadGrid网络能耗更均衡,网络生命周期更长,空间查询更高效。     

Duty cycle aware spatial query processing in wireless sensor networks

The recent evolution in sensor node location technology has spurred the development of a special type of in-network processing for wireless sensor networks (WSN), called spatial query processing. These queries require data from nodes within a region (called region of interest) defined by the users. The state of the art of spatial query processing considers, in general, that nodes are always on. However, nodes can go to sleep mode (turn off the radio in duty cycles) in order to save energy. This work proposes an energy-efficient in-network spatial query processing mechanism that assumes nodes having no knowledge about their neighbors. The proposed mechanism is able to process spatial queries without the necessity of periodic beacon transmissions for neighbor table updates or for synchronization. Hence, it can work properly over different types of duty cycle algorithms.     

An efficient data-centric storage method using time parameter for sensor networks

In wireless sensor networks, various schemes have been proposed to efficiently store and process sensed data. Among them, the data-centric storage (DCS) scheme is one of the most well-known. The DCS scheme distributes data regions and stores the data in the sensor that is responsible for the region. The DCS based scheme was proposed to reduce the communication cost for transmitting data and to efficiently process exact queries and range queries. Recently, a KDDCS scheme was proposed to overcome storage hot-spots by dynamically readjusting the distributed data regions to sensors based on the K-D tree. However, the existing DCS based schemes including KDDCS suffer from query hot-spots that are formed when query regions are not uniformly distributed. As a result, it reduces the lifetime of the sensor network.In this paper, we propose a new DCS based scheme, called Time-Parameterized Data-Centric Storage (TPDCS), that avoids the problems of storage hot-spots and query hot-spots. To decentralize the skewed data and queries, the data regions are assigned by a time dimension as well as data dimensions in our proposed scheme. Therefore, TPDCS extends the lifetime of sensor networks. It is shown through various experiments that our scheme outperforms the existing schemes.     

Making data-centric storage adaptive and cost-optimal

We present an adaptive and cost-optimal mechanism called Tug-of-War (ToW) for data storage and query mechanism in wireless sensor networks. ToW is based on the concept of data-centric storage (DCS), where a unique home location is used to store events of a given type so that queries of the event type can be directed to the location without flooding. However, rather than using just a single home location, the mechanism can dynamically adjust the number of home locations according to the event and query rates ratio so as to minimize the total communication cost. We also generalize the framework so that different schemes can be used to construct home locations as well as to ensure rendezvous of events and queries, thereby to fine-tune the system performance.     

An efficient mechanism for processing similarity search queries in sensor networks

The similarity search problem has received considerable attention in database research community. In sensor network applications, this problem is even more important due to the imprecision of the sensor hardware, and variation of environmental parameters. Traditional similarity search mechanisms are both improper and inefficient for these highly energy-constrained sensors. A difficulty is that it is hard to predict which sensor has the most similar (or closest) data item such that many or even all sensors need to send their data to the query node for further comparison. In this paper, we propose a similarity search algorithm (SSA), which is a novel framework based on the concept of Hilbert curve over a data-centric storage structure, for efficiently processing similarity search queries in sensor networks. SSA successfully avoids the need of collecting data from all sensors in the network in searching for the most similar data item. The performance study reveals that this mechanism is highly efficient and significantly outperforms previous approaches in processing similarity search queries.     

基于不确定数据的查询处理综述

不确定数据在一些重要应用领域中是固有存在的,如传感器网络和移动物体追踪。在不确定数据上使用传统的查询方法会使查询结果出现偏差,不能满足用户的需求。因此,基于不确定数据的查询处理受到了越来越多的关注。与在确定数据上查询不同,不确定数据上的研究工作将概率引入到数据模型中来衡量不确定对象成为结果集中元素的可能性。由于问题定义和数据模型的不同,不确定数据上的查询类型也多种多样。从问题定义、数据模型、剪枝策略和算法等角度,对基于不确定数据的范围查询、top-k查询以及skyline查询进行了介绍。