链路感知的传感器网络空间范围查询处理算法

在无线传感器网络环境中,用户经常提交空间范围查询以获取网络某局部区域的统计信息,如最大温度、平均湿度等。现有的基于路线的空间范围查询处理算法假设节点通信模型为理想的圆盘模型,而实际的网络并不满足该假设,导致其能量消耗大且查询结果质量差。提出了一种链路感知的空间范围查询处理算法LSA,它根据网络拓扑和链路质量动态地将查询区域划分为若干个网格,依次收集各网格中节点的感知数据,以生成最终的查询结果。LSA算法通过遍历查询区域内的所有网格,保证了算法查询结果的质量。提出了启发式的网格划分方法以降低节点间数据通信的丢包率,给出链路感知的数据收集算法,以减少算法的能量消耗,提高查询结果的质量。通过仿真实验系统地分析和比较了LSA算法和现有的IWQE算法的能量消耗及查询结果质量,结果表明,在绝大多数情况下,LSA算法优于IWQE算法。     

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.     

抗窃听攻击的传感器网络空间范围聚集查询处理算法

现有传感器网络聚集查询隐私保护方法采用加解密的形式保护节点感知数据,且需要网络中的所有节点参与查询处理。过多加解密操作会大量消耗节点能量,且用户可能只对其中部分区域的聚集结果感兴趣。针对这些问题,提出一种抗窃听攻击的传感器网络空间范围聚集查询处理算法PCPDA。该算法沿着既定路线,一边查询一边聚集,使得算法不依赖于预先构造好的拓扑结构,适用于网络拓扑结构动态变化的传感器网络,节省了维护拓扑结构的开销。该算法在未采用任何加密措施情况下保证了节点感知数据的隐私性。理论分析和仿真结果表明,PCPDA在能量损耗和隐私保护方面都优于现有算法。     

EQOWSN: Evolutionary-based query optimization over self-organized wireless sensor networks

In this paper, a new approach has been introduced that integrates an evolutionary-based mechanism with a distributed query sensor cover algorithm for optimal query execution in self-organized wireless sensor networks (WSN). An algorithm based on an evolutionary technique is proposed, with problem-specific genetic operators to improve computing efficiency. Redundancy within a sensor network can be exploited to reduce the communication cost incurred in execution of spatial queries. Any reduction in communication cost would result in an efficient use of battery energy, which is very limited in sensors. Our objective is to self-organize the network, in response to a query, into a topology that involves an optimal subset of sensors that is sufficient to process the query subject to connectivity, coverage, energy consumption, cover size and communication overhead constraints. Query processing must incorporate energy awareness into the system by reducing the total energy consumption and hence increasing the lifetime of the sensor cover, which is beneficial for large long running queries. Experiments have been carried out on networks with different sensors Transmission radius, different query sizes, and different network configurations. Through extensive simulations, we have shown that our designed technique result in substantial energy savings in a sensor network. Compared with other techniques, the results demonstrated a significant improvement of the proposed technique in terms of energy-efficient query cover with lower communication cost and lower size.     

Effective query aggregation for data services in sensor networks

Providing efficient data services has been required by many sensor network applications. While most existing work in this area focuses on data aggregation, not much attention has been paid to query aggregation. For many applications, especially ones with high query rates, query aggregation is very important. In this paper, we study a query aggregation-based approach to provide efficient data services. In particular: (1) we propose a multi-layer overlay-based framework consisting of a query manager and access points (nodes), where the former provides the query aggregation plan and the latter executes the plan; (2) we design an effective query aggregation algorithm to reduce the number of duplicate/overlapping queries and save overall energy consumption in the sensor network. We also design protocols to effectively deliver aggregated queries and query results in the sensor network. Our performance evaluations show that by applying our query aggregation algorithm, the overall energy consumption can be significantly reduced and the sensor network lifetime can be prolonged correspondingly.     

无线传感器网络中的节点关联规则挖掘

信息处理是无线传感器网络的核心技术,必须采取简单、高效的处理策略.本文通过对传统Apriori算法的改进,提出了一种节点关联规则挖掘方法来发现大量节点之间的有用关联或相关联系,以此消除节点之间信息的冗余.这种方法可以帮助用户对数据进行有效的融合、分类、查询、分析、理解和决策.仿真结果表明该方法能够有效减少信息处理中通信和计算所消耗的能量,缩短数据查询响应的时间,从而延长整个网络的寿命.     

无线传感器网络中基于聚簇结构的Skyline查询方法

现有的基于单服务器的Skyline查询算法已经不能很好地应用于无线传感器网络这类分布式多跳自组织网络中。基于聚簇结构的Skyline查询算法就是针对 这类特定的网络结构而提出的。该算法采用基于聚簇的路由结构,为了减少Skyline查询处理过程中传感器节点的通信开销,挑选具有最大支配力的数据元组作为全局过滤元组来过滤不满足Skyline条件的数据。同时,在Skyline查询处理过程中引入滑动窗口机制,该机制也能有效地降低通信开销。大量的仿真实验结果显示,所提Skyline查询算法在确保能耗的基础上仍然具有很好的性能。     

基于节点分布密度的传感器网络查询处理机制

为了使传感器网络在进行数据查询时降低能耗和提高网络生命期,引入了一种分布式查询处理机制。这种机制是先将查询分发到网络后再进行优化,这种方法更具有针对性,优化效果也更明显。分簇路由协议与分布式查询有着天然的结合点。每个簇头相当于传统数据库中的一个索引,负责查询的分析、优化和数据融合。簇头根据本区域的节点分布和数据特性可以自主地选择区域内结构而不受其他区域的影响,这样就可以把每个区域看成一个自治系统,而整个传感器网络就是多个自治系统的集合。结果表明：设计查询处理机制时考虑这些因素可以降低能耗和提高网络生命期。     

无线传感器网络上的极值区域查询处理

提出了无线传感器网络上的一种查询——极值区域查询(peak region query,简称PRQ),即用户指定查询区域的大小和形状,例如半径为R的圆形区域,然后需要查询传感器网络中的某个区域,使得区域内传感器节点数据的某种聚集值最大.定义了极值区域查询的概念,并提出一种集中式算法以求解查询结果.由于传感器节点的能量有限,为了降低查询处理过程中的能耗,提出了分布式算法EXQ(an algorithm for extreme value query processing).与集中式算法相比,EXQ不但显著降低了能耗,而且使得每个传感器的能耗更加平均,从而延长了网络的使用寿命.EXQ的基本思想是,将整个网络划分为若干相互重叠的子区域,对每个子区域通过本地数据聚集得到一个本地结果,然后对这些结果再进行全局数据聚集从而得到查询结果.从理论和实验两方面分析和比较了集中式算法和EXQ的能耗和节点负载分布.     

无线传感器网络中能量有效自适应路由算法研究

针对无线传感器网络寿命最大化问题,基于无线传感器节点能耗分布特点和数据传输能耗模型,建立无线传感器网络生存周期的数学优化模型,并针对最小能耗路由的能耗不均衡问题和能量均衡路由的能耗开销问题,综合考虑网络中节点的剩余能量和节点间发送数据的能耗,提出一个适合无线多跳传感器网络的自适应路由算法。仿真结果表明,提出的路由算法能充分地利用有限的能量资源,较大地延长网络生存周期。     

传感器网络中健壮数据聚集算法

节约能量以提高网络寿命是传感器网络研究面临的重要挑战.网内聚集查询在中间节点对数据进行预处理,可以减少消息传送的数量或者大小,从而实现能量的有效利用,但是,目前的聚集查询研究假设采样数据都是正确的.而目前的异常检测算法以检测率作为首要目标,不考虑能量的消耗,也不考虑查询的特点.所以将两方面的研究成果简单地结合在一起并不能产生很好的效果.分析了错误和异常数据可能对聚集结果造成的影响,提出了健壮聚集算法RAA(robust aggregation algorithm).RAA 对传统聚集查询进行了改进,在聚集的同时利用读向量相似性判断数据是否发生了错误或异常,删除错误数据,聚集正常数据并报告异常,使用户可以对网络目前状况有清晰的理解.最后,比较了RAA 和TAGVoting(在使用TAG(tiny aggregation)算法聚集的同时利用Voting算法进行异常检测),实验结果表明,RAA 算法在能量消耗和异常检测率方面都优于TAGVoting.     

传感器网络中节点个数约束查询处理算法

对传感器网络中一类新查询--节点个数约束查询,提出能量有效的查询处理算法.算法主要由查询下发和结果回收两部分构成.查询下发算法首先根据节点个数约束查询的特点提出相关节点选择以及基于Steiner树的查询下发算法.然后对该下发算法以及一种基于洪泛的能量有效查询下发算法的能量消耗进行分析,并对比两种算法的能量消耗从中选择适当的下发算法.结果回收算法提出直接和间接两种结果回收方式,并给出两种方式在进行结果回收时能够节省能量的条件.仿真实验表明,提出的能量有效节点个数约束查询处理算法能够在满足用户查询精度的同时,使其能量消耗低于其他查询处理算法.     

Localized monitoring of kNN queries in wireless sensor networks

Wireless sensor networks have been widely used in civilian and military applications. Primarily designed for monitoring purposes, many sensor applications require continuous collection and processing of sensed data. Due to the limited power supply for sensor nodes, energy efficiency is a major performance concern in query processing. In this paper, we focus on continuous kNN query processing in object tracking sensor networks. We propose a localized scheme to monitor nearest neighbors to a query point. The key idea is to establish a monitoring area for each query so that only the updates relevant to the query are collected. The monitoring area is set up when the kNN query is initially evaluated and is expanded and shrunk on the fly upon object movement. We analyze the optimal maintenance of the monitoring area and develop an adaptive algorithm to dynamically decide when to shrink the monitoring area. Experimental results show that establishing a monitoring area for continuous kNN query processing greatly reduces energy consumption and prolongs network lifetime.     

无线传感器网络中基于模型拟合的可信近似查询处理算法

无线传感器网络的一个重要应用是可信地查询网络中所有节点的监测数据.目前,多数研究主要集中在如何利用节点之间的时空相关性,节省能量地查询感知数据.但是这些方法的查询结果不能满足某些应用对数据的高可信要求,也不能适用于节点之间不存在空间相关性或空间相关性不稳定的情况.针对这一问题,提出了基于模型拟合的可信近似查询处理方法.该方法在感知数据集合上寻找具有最小数据传输比的拟合模型,通过传输模型及其参数来代替传输实际的监测数据.理论分析和实验结果证明,基于模型拟合的可信近似查询处理方法不仅能够节省大量能源而且能够返回满足用户精度要求的可信查询结果.     

QoS-aware optimization of sensor network queries

The resource-constrained nature of mote-level wireless sensor networks (WSNs) poses challenges for the design of a general-purpose sensor network query processors (SNQPs). Existing SNQPs tend to generate query execution plans (QEPs) that are selected on the basis of a fixed, implicit expectation, for example, that energy consumption should be kept as small as possible. However, in WSN applications, the same query may be subject to several, possibly conflicting, quality-of-service (QoS) expectations concomitantly (for example maximizing data acquisition rates subject to keeping energy consumption low). It is also not uncommon for the QoS expectations to change over the lifetime of a deployment (for example from low to high data acquisition rates). This paper describes optimization algorithms that respond to stated QoS expectations (about acquisition rate, delivery time, energy consumption and lifetime) when making routing, placement, and timing decisions for in-WSN query processing. The paper shows experimentally that QoS-awareness offers significant benefits in responding to, and reconciling, diverse QoS expectations, thereby enabling QoS-aware SNQPs to generate efficient QEPs for a broader range WSN applications than has hitherto been possible.     

BARC: A Battery Aware Reliable Clustering algorithm for sensor networks

Clustering in wireless sensor networks (WSNs) provides scalability and robustness for the network; it allows spatial reuse of the bandwidth, simpler routing decisions, and results in decreased energy dissipation of the whole system by minimizing the number of nodes that take part in long distance communication. Clustering allows for data aggregation which reduces congestion and energy consumption. Recent study in battery technology reveals that batteries tend to discharge more power than needed and reimburse the over-discharged power if they are recovered. In this paper, we first provide an online mathematical battery model suitable for implementation in sensor networks. Using our battery model, we propose a new Battery Aware Reliable Clustering (BARC) algorithm for WSNs. BARC incorporates many features which are missing in many other clustering algorithms. It rotates cluster heads (CHs) according to a battery recovery scheme and it also incorporates a trust factor for selecting cluster heads thus increasing reliability. Most importantly, our proposed algorithm relaxes many of the rigid assumptions that the other algorithms impose such as the ability of the cluster head to communicate directly with the base station and having a fixed communication radius for intra-cluster communication. BARC uses Z-MAC which has several advantages over other MAC protocols. Simulation results show that using BARC prolongs the network lifetime greatly in comparison to other clustering techniques.     

基于簇的无线传感器网络交会路由协议

针对现有无线传感器网络(WSN)协议中更多消耗sink附近节点能量导致网络寿命短的问题,本文提出一种基于簇的无线传感器网络交会路由协议(Cluster-based Rendezvous Routing Protocol, CRRP)。该协议是基于交会的路由协议,其中在网络的中间构建交会区域,该交会区域划分整个网络区域并在传感器节点之间分配网络负载,这延长了网络寿命。此交会区域内的节点分为不同的簇,每个簇的簇头(CH)负责不同簇之间的通信,sink在此交会区域内发送其更新的位置信息,并且当传感器节点想要发送数据时,会从该交会区域检索sink的当前位置信息并直接将数据发送到sink。仿真实验结果表明,在能耗与网络寿命性能方面,本文CRRP协议优于Rendezvous协议、LBDD协议、Railroad协议和Ring协议。     

基于最优刚性图的链路质量与能量的拓扑控制算法

针对目前无线传感器网络研究中网络能量利用率低和通信链路不可靠等问题, 提出一种基于最优刚性图的网络拓扑优化算法. 该算法通过建立包含链路质量和能量两方面内容的链路权值函数来构建链路可靠性强、能量利用率高的网络拓扑结构. 研究结果表明, 所构建的拓扑具有平均节点度低和链路性能好等优越特性. 仿真结果表明, 与现有拓扑控制算法相比, 所提出的算法能够更有效地减少能量消耗, 从而延长网络寿命.

     

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.     

Low-complexity and energy efficient image compression scheme for wireless sensor networks

Currently most energy-constrained wireless sensor networks are designed with the object of minimizing the communication power at the cost of more computation. To achieve high compression efficiency, the main image compression algorithms used in wireless sensor networks are the high-complexity, state-of-the-art image compression standards, such as JPEG2000. These algorithms require complex hardware and make the energy consumption for computation comparable to communication energy dissipation. To reduce the hardware cost and the energy consumption of the sensor network, a low-complexity and energy efficient image compression scheme is proposed. The compression algorithm in the proposed scheme greatly lowers the computational complexity and reduces the required memory, while it still achieves required PSNR. The proposed implementation scheme of the image compression algorithm overcomes the computation and energy limitation of individual nodes by sharing the processing of tasks. And, it applies transmission range adjustment to save communication energy dissipation. Performance of the proposed scheme is investigated with respect to image quality and energy consumption. Simulation results show that it greatly prolongs the lifetime of the network under a specific image quality requirement.