A statistics-based sensor selection scheme for continuous probabilistic queries in sensor networks

A common approach to improve the reliability of query results based on error-prone sensors is to introduce redundant sensors. However, using multiple sensors to generate the value for a data item can be expensive, especially in wireless environments where continuous queries are executed. Moreover, some sensors may not be working properly and their readings need to be discarded. In this paper, we propose a statistical approach to decide which sensor nodes to be used to answer a query. In particular, we propose to solve the problem with the aid of continuous probabilistic query (CPQ), which is originally used to manage uncertain data and is associated with a probabilistic guarantee on the query result. Based on the historical data values from the sensor nodes, the query type, and the requirement on the query, we present methods to select an appropriate set of sensors and provide reliable answers for several common aggregate queries. Our statistics-based sensor node selection algorithm is demonstrated in a number of simulation experiments, which shows that a small number of sensor nodes can provide accurate and robust query results.     

Distributed adaptive top-k monitoring in wireless sensor networks

Top-k monitoring queries are useful in many wireless sensor network applications. A query of this type continuously returns a list of k ordered nodes with the highest (or lowest) sensor readings. To process these queries, a well-known approach is to install a filter at each sensor node to avoid unnecessary transmissions of sensor readings. In this paper, we propose a new top-k monitoring method, named Distributed Adaptive Filter-based Monitoring. In this method, we first propose a new query reevaluation algorithm that works distributedly in the network to reduce the communication cost of sending probe messages. Then, we present an adaptive filter updating algorithm which is based on predicted benefits to lower down the transmission cost of sending updated filters to the sensor nodes. Experimental results on real data traces show that our proposed method performs much better than the other existing methods in terms of both network lifetime and average energy consumption.     

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.     

Bandwidth-constrained queries in sensor networks

Sensor networks consist of battery-powered wireless devices that are required to operate unattended for long periods of time. Thus, reducing energy drain is of utmost importance when designing algorithms and applications for such networks. Aggregate queries are often used by monitoring applications to assess the status of the network and detect abnormal behavior. Since radio transmission often constitutes the biggest factor of energy drain in a node, in this paper we propose novel algorithms for the evaluation of bandwidth- constrained queries over sensor networks. The goal of our techniques is, given a target bandwidth utilization factor, to program the sensor nodes in a way that seeks to maximize the accuracy of the produced query results at the monitoring node, while always providing strong error guarantees to the monitoring application. This is a distinct difference of our framework from previous techniques that only provide probabilistic guarantees on the accuracy of the query result. Our algorithms are equally applicable when the nodes have ample power resources, but bandwidth consumption needs to be minimized, for instance in densely distributed networks, to ensure proper operation of the nodes. Our experiments with real sensor data show that bandwidth-constrained queries can substantially reduce the number of messages in the network while providing very tight error bounds on the query result.     

Reliable spatial window aggregation query processing algorithm in wireless sensor networks

After wireless sensor network is deployed, users often submit spatial window aggregation queries to obtain statistical information of the regions of interest, such as maximum temperature, average humidity etc. Existing spatial window aggregation query processing algorithms are based on the assumption that the communication links are ideal which means there are perfect communication links within a given communication range, and none beyond. However, it is not valid in realistic sensor networks, which leads to high retransmissions of data frames. In order to address this problem, a reliable spatial window aggregation query processing algorithm called RESA is proposed in this paper. RESA only requires each node to maintain locations and residual energy of its neighbors and link qualities between them. According to the information, it divides the query area into several sub-regions, followed by collection of sensor readings in each sub-region. RESA traverses all the sub-regions within the query area to ensure the correctness of query result. Based on RESA's energy consumption formula derived, two highly efficient methods for sub-regional division are proposed to reduce packet loss rate during data communication and balance the load of nodes, hence saving energy consumption and extending lifetime. Experimental results show that in most cases RESA outperforms the existing algorithms in terms of energy consumption, quality of query results and lifetime.     

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.     

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.     

无线传感器网络中一种近似Skyline 查询处理算法

由于无线传感器网络的能源有限,且在许多应用中Skyline 查询的部分结果即可满足用户需求,提出了一 种近似Skyline 查询处理算法,在满足用户查询需求的前提下最大化地节省能量.该算法仅需无线传感器网络中的部 分传感器节点回传其感知数据即可计算出Skyline 查询的一个近似结果集.由于该算法在处理查询时,每个传感器节 点只需考察自身数据信息即可决定是否回传其感知数据,而无须与其他传感器节点的感知数据进行比较,因此可以 避免大量的网内通信开销,从而节省网络能源.模拟环境下的大量实验结果表明,该算法可以根据用户的应用需求, 节能地处理传感器网络中的近似skyline 查询.     

Accuracy vs. Lifetime: Linear Sketches for Aggregate Queries in Sensor Networks

The in–network aggregation paradigm in sensor networks provides a versatile approach for evaluating aggregate queries. Traditional approaches need a separate aggregate to be computed and communicated for each query and hence do not scale well with the number of queries. Since approximate query results are sufficient for many applications, we use an alternate approach based on summary data–structures. We consider two kinds of aggregate queries: location range queries that compute the sum of values reported by sensors in a given location range, and value range queries that compute the number of sensors that report values in a given range. We construct summary data–structures called linear sketches, over the sensor data using in–network aggregation and use them to answer aggregate queries in an approximate manner at the base–station. There is a trade–off between accuracy of the query results and lifetime of the sensor network that can be exploited to achieve increased lifetimes for a small loss in accuracy. Most commonly occurring sets of range queries are highly correlated and display rich algebraic structure. Our approach takes full advantage of this by constructing linear sketches that depend on queries. Experimental results show that linear sketching achieves significant improvements in lifetime of sensor networks for only a small loss in accuracy of the queries. Further, our approach achieves more accurate query results than the other classical techniques using Discrete Fourier Transform and Discrete Wavelet Transform. This work was supported in part by NASA under Cooperative Agreement NCC5–315.     

基于高斯模型的不确定数据流Skyline查询方法

由于数据的动态性及不确定性等特征,使得不确定数据流上Skyline查询研究面临挑战.不确定对象一般采用多元概率密度函数(PDF)表示,现有的不确定数据流Skyline查询方法均采用离散型随机变量建模.然而不确定数据流中的对象可能是连续变化的,离散模型对连续性随机变量难以适用.针对连续PDF建模的不确定数据流Skyline查询进行了研究,提出了基于高斯模型的不确定数据流Skyline查询方法(SGMU),该方法包含2个过程:1)动态高斯建模算法(DGM):对滑动窗口采样并建立高斯模型,将原始的数据流转化为不确定对象PDF的参数流;2)提出了基于高斯树的查询算法(GTS)以建立空间索引结构和执行Skyline查询.实验结果表明,SGMU算法不仅能够对连续型不确定对象进行有效建模以辅助Skyline查询,而且能够有效地减少查询对象个数,提高Skyline查询效率.     

Rule-based inference and decomposition for distributed in-network processing in wireless sensor networks

Wireless sensor networks are application specific and necessitate the development of specific network and information processing architectures that can meet the requirements of the applications involved. A common type of application for wireless sensor networks is the event-driven reactive application, which requires reactive actions to be taken in response to events. In such applications, the interest is in the higher-level information described by complex event patterns, not in the raw sensory data of individual nodes. Although the central processing of information produces the most accurate results, it is not an energy-efficient method because it requires a continuous flow of raw sensor readings over the network. As communication operations are the most expensive in terms of energy usage, the distributed processing of information is indispensable for viable deployments of applications in wireless sensor networks. This method not only helps in reducing the total amount of packets transmitted in the network and the total energy consumed by the sensor nodes, but also produces scalable and fault-tolerant networks. For this purpose, we present two schemes that distribute information processing to appropriate nodes in the network. These schemes use reactive rules, which express relations between event patterns and actions, in order to capture reactive behavior. We also share the results of the performance of our algorithms and the simulations based on our approach that show the success of our methods in decreasing network traffic while still realizing the desired functionality.     

Hybrid query execution engine for large attributed graphs

Graphs are widely used for modeling complicated data such as social networks, bibliographical networks and knowledge bases. The growing sizes of graph databases motivate the crucial need for developing powerful and scalable graph-based query engines. We propose a SPARQL-like language, G-SPARQL, for querying attributed graphs. The language enables the expression of different types of graph queries that are of large interest in the databases that are modeled as large graph such as pattern matching, reachability and shortest path queries. Each query can combine both structural predicates and value-based predicates (on the attributes of the graph nodes/edges). We describe an algebraic compilation mechanism for our proposed query language which is extended from the relational algebra and based on the basic construct of building SPARQL queries, the Triple Pattern. We describe an efficient hybrid Memory/Disk representation of large attributed graphs where only the topology of the graph is maintained in memory while the data of the graph are stored in a relational database. The execution engine of our proposed query language splits parts of the query plan to be pushed inside the relational database (using SQL) while the execution of other parts of the query plan is processed using memory-based algorithms, as necessary. Experimental results on real and synthetic datasets demonstrate the efficiency and the scalability of our approach and show that our approach outperforms native graph databases by several factors.     

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。     

传感器网络中基于卡尔曼滤波的能量高效Top—k查询处理技术

无线传感器网络能量受限,如何实现top-k查询处理的能量高效从而延长网络的生命周期是该领域研究的一个重要课题。论文利用传感器节点读数的时空相关性,提出运用卡尔曼滤波根据已知节点读数对未知节点读数估计的时空建模方法,进而提出基于预测机制的区域采样方法（RegionSampling,RS）。实验表明,论文提出的查询方法不但可以满足用户的查询精度要求,而且大大减少了传感器网络的通信次数节省了能量,从而延长了网络的生命周期。     

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.     

Efficient skyline query processing in wireless sensor networks

How to process a skyline query efficiently has received considerable attention in recent years. A skyline query identifies a set of non-dominated data records in a multidimensional dataset. Whereas most previous studies have resolved this problem in a centralized environment, this work considers it in a distributed sensor network environment. An algorithm, known as Skyline Sensor Algorithm (SkySensor), is presented to efficiently retrieve skyline results from a sensor network. A cluster-based architecture is designed in SkySensor to collect all sensor readings. A pruning method is then proposed to progressively sift out the skyline results from the sensor network. SkySensor avoids the need of collecting data from all sensors in the network, which is an extremely expensive action, when searching for the skyline results. The performance study indicates that SkySensor is highly efficient, and significantly outperforms previous methods in processing skyline queries.     

两层传感器网络中安全Top-k查询处理技术综述

无线传感网中安全数据查询技术的研究已引起了广泛的关注,其中以存储节点为中间层的两层传感器网络中安全Top-k查询技术的研究具有重要的现实意义。现有的安全Top-k查询技术主要针对查询过程中数据的隐私保护和查询结果的完整性验证等问题开展研究工作。从安全性能和通信性能两个维度出发对现有的两层传感器网络中的安全Top-k查询技术进行了总结,介绍了网络模型查询模型,以及查询过程中存在的安全性问题;同时分析和总结了现有的各协议所采用的关键技术以及其主要优点和不足,最后指出了未来可能的研究方向。     

路网中双色数据集上连续反向k近邻查询处理的研究

近年来,反向最近邻查询(RNN)算法研究得到了普遍的关注,成为了数据库领域的一个研究热点。欧氏空 间中提出了较多的高效算法,而路网中的反向最近邻处理方面所做的工作不够,有关这方面的成果较少。路网中查询 点和数据对象之间以及不同数据对象之间的距离受到路网连通性的影响,欧氏空间中的反向最近部方法在路网中不 适用。反向最近部查询有两种类型:单色反向最近部查询(Monochromatic RNN, MRNN)和双色反向最近部查询(13i- chromatic RNN,13RNN)。到目前为止,仍然没有有效的算法来处理路网中双色数据集上的连续反向k近部查询。因 此,研究路网中双色数据集上连续反向k近部查询是很有意义的。     

A Novel Data Centric Information Retrieval Protocol for Queries in Delay Tolerant Networks

Information Retrieval (IR) systems aim to retrieve data that satisfies certain requirements and constitute an important service in many types of networks, including Delay/Disruption Tolerant Networks (DTNs). In current DTN based IR systems, the data that satisfies a query is assumed to be stored on a single node. Therefore, once a node receives a query in which it has the corresponding data, the query can be resolved completely. However, in scenarios where a query requires data from multiple nodes, these IR systems may fail. Henceforth, in this paper, we propose Distributed Data-Centric Information Retrieval (DDC-IR), a data centric IR system that supports all query types; e.g., continuous and complex. More importantly, it is designed specifically to operate in DTNs. It also incorporates a new packet, aka Query Reply Packet, that includes both a query and one or more replies. We show how this packet facilitates efficient query resolution and enables data centric routing. In addition, it uses caching so that nodes store popular queries that has the effect of speeding up query resolution. We have conducted an extensive simulation study to compare DDC-IR to state of the art IR systems using the popular Random Waypoint model and a trace-file containing student movements on a campus. The results show that DDC-IR is able to resolve 50 % more queries and has an 80 % lower buffer occupancy level than existing IR systems. We also tested DDC-IR in networks with varying sizes. For networks with 100 nodes, DDC-IR is able to resolve queries while current IR systems fail to resolve any queries. In particular, when the number of nodes increases, current IR systems fail to resolve any queries, whilst DDC-IR is able to resolve complex and continuous queries. The influence of the number of sub-queries on query resolution time is also studied. Specifically, when the number of sub-queries in a complex query increases from five to nine, DDC-IR uses 50 % more time to resolve a query. In comparison, prior IR systems fail to resolve any queries.     

Collection trees for event-monitoring queries

In this paper we present algorithms for building and maintaining efficient collection trees that provide the conduit to disseminate data required for processing monitoring queries in a wireless sensor network. While prior techniques base their operation on the assumption that the sensor nodes that collect data relevant to a specified query need to include their measurements in the query result at every query epoch, in many event monitoring applications such an assumption is not valid. We introduce and formalize the notion of event monitoring queries and demonstrate that they can capture a large class of monitoring applications. We then show techniques which, using a small set of intuitive statistics, can compute collection trees that minimize important resources such as the number of messages exchanged among the nodes or the overall energy consumption. Our experiments demonstrate that our techniques can organize the data collection process while utilizing significantly lower resources than prior approaches.