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.     

Pattern-based event detection in sensor networks

Many applications of wireless sensor networks monitor the physical world and report events of interest. To facilitate event detection in these applications, in this paper we propose a pattern-based event detection approach and integrate the approach into an in-network sensor query processing framework. Different from existing threshold-based event detection, we abstract events into patterns in sensory data and convert the problem of event detection into a pattern matching problem. We focus on applying single-node temporal patterns, and define the general patterns as well as five types of basic patterns for event specification. Considering the limited storage on sensor nodes, we design an on-node cache manager to maintain the historical data required for pattern matching and develop event-driven processing techniques for queries in our framework. We have conducted experiments using patterns for events that are extracted from real-world datasets. The results demonstrate the effectiveness and efficiency of our approach.     

Detecting proximity events in sensor networks

Sensor networks are often used to perform monitoring tasks, such as animal and vehicle tracking, or the surveillance of enemy forces in military applications. In this paper we introduce the concept of proximity queries, which allow us to report interesting events, observed by nodes in the network that lie within a certain distance from each other. An event is triggered when a user-programmable predicate is satisfied on a sensor node. We study the problem of computing proximity queries in sensor networks and propose several alternative techniques that differ in the number of messages exchanged by the nodes and the quality of the returned answers. Our solutions utilize a distributed routing index, maintained by the nodes in the network, that is dynamically updated as new observations are obtained by the nodes. This distributed index allows us to efficiently process multiple proximity queries involving several different event types within a fraction of the cost that a straightforward evaluation requires. We present an extensive experimental study to show the benefits of our techniques under different scenarios using both synthetic and real data sets. Our results demonstrate that our algorithms scale better and require significantly fewer messages compared to a straightforward execution of the queries.     

In-network wireless sensor network query processors: State of the art,challenges and future directions

In wireless sensor networks (WSNs), energy is valuable because it is scarce. This causes their life time to be determined by their ability to use the available energy in an effective and frugal manner. In most of the earlier sensor network applications, the main requirement consisted mainly of data collection but transmitting all of the raw data out of the network may be prohibitively expensive (in terms of communication) or impossible at given data collection rates.In the last decade, the use of the database paradigm has emerged as a feasible solution to manage data in a WSN context. There are various sensor network query processors (SNQPs) (implementing in-network declarative query processing) that provide data reduction, aggregation, logging, and auditing facilities. These SNQPs view the wireless sensor network as a distributed database over which declarative query processor can be used to program a WSN application with much less effort. They allow users to pose declarative queries that provide an effective and efficient means to obtain data about the physical environment, as users would not need to be concerned with how sensors are to acquire the data, or how nodes transform and/or transmit the data.This paper surveys novel approaches of handling query processing by the current SNQP literature, the expressiveness of their query language, the support provided by their compiler/optimizer to generate efficient query plans and the kind of queries supported. We introduce the challenges and opportunities of research in the field of in-network sensor network query processing as well as illustrate the current status of research and future research scopes in this field.     

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.     

Opportunistic sampling-based query processing in wireless sensor networks

High resolution sampling of physical phenomenon is a prime application of large scale wireless sensor networks (WSNs). With hundreds of nodes deployed over vast tracts of land, monitoring data can now be generated at unprecedented spatio-temporal scales. However, the limited battery life of individual nodes in the network mandates smart ways of collecting this data by maximizing localized processing of information at the node level. In this paper, we propose a WSN query processing method that enhances localized information processing by harnessing the two inherent aspects of WSN communication, i.e., multihop and multipath data transmission. In an active WSN where data collection queries are regularly processed, multihop and multipath routing leads to a situation where a significant proportion of nodes relay and overhear data generated by other nodes in the network. We propose that nodes opportunistically sample this data as they communicate. We model the data communication process in a WSN and show that opportunistic sampling during data communication leads to surprisingly accurate global knowledge at each node. We present an opportunistic query processing system that uses the accumulated global knowledge to limit the data collection requirements for future queries while ensuring temporal freshness of the results.     

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.     

Novel itinerary-based KNN query algorithm leveraging grid division routing in wireless sensor networks of skewness distribution

Wireless sensor networks (WSN) is a key enabling technique for achieving the vision of the Internet of Things. In many applications of WSN such as environmental monitoring and vehicle tracking, they may require to launch spatial queries for collecting and gathering sensory data for achieving certain goals. One such query is the \(K\) nearest neighbor (KNN) query, which aims to collect sensory data from \(k\) sensor nodes nearest to a certain query location. Techniques, namely the itinerary-based KNN query algorithms, are recently developed for facilitating KNN queries. Generally, these techniques propagate queries and collect data along a predetermined itinerary. However, query accuracy and boundary expansion are two challenges that are not well addressed. To mitigate these issues, in this paper, we propose a novel KNN query algorithm based on grid division routing in the setting of skewness distribution, where the itinerary is formed based on the connectivity of adjacent grid cells centers. This technique can achieve better query accuracy and cause less energy consumption by executing the query concurrently in subregions. Besides, the void region problem is well addressed based on the proximity of neighbor grid cells. Experiment result shows that our technique performs better in several aspects including query accuracy, data redundancy, and energy efficiency.     

Adaptive data acquisition strategies for energy-efficient, smartphone-based, continuous processing of sensor streams

There is a growing interest in applications that utilize continuous sensing of individual activity or context, via sensors embedded or associated with personal mobile devices (e.g., smartphones). Reducing the energy overheads of sensor data acquisition and processing is essential to ensure the successful continuous operation of such applications, especially on battery-limited mobile devices. To achieve this goal, this paper presents a framework, called ACQUA, for ‘acquisition-cost’ aware continuous query processing. ACQUA replaces the current paradigm, where the data is typically streamed (pushed) from the sensors to the one or more smartphones, with a pull-based asynchronous model, where a smartphone retrieves appropriate blocks of relevant sensor data from individual sensors, as an integral part of the query evaluation process. We describe algorithms that dynamically optimize the sequence (for complex stream queries with conjunctive and disjunctive predicates) in which such sensor data streams are retrieved by the query evaluation component, based on a combination of (a) the communication cost & selectivity properties of individual sensor streams, and (b) the occurrence of the stream predicates in multiple concurrently executing queries. We also show how a transformation of a group of stream queries into a disjunctive normal form provides us with significantly greater degrees of freedom in choosing this sequence, in which individual sensor streams are retrieved and evaluated. While the algorithms can apply to a broad category of sensor-based applications, we specifically demonstrate their application to a scenario where multiple stream processing queries execute on a single smartphone, with the sensors transferring their data over an appropriate PAN technology, such as Bluetooth or IEEE 802.11. Extensive simulation experiments indicate that ACQUA’s intelligent batch-oriented data acquisition process can result in as much as 80 % reduction in the energy overhead of continuous query processing, without any loss in the fidelity of the processing logic.     

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.     

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.     

Streaming multiple aggregations using phantoms

Data streams characterize the high speed and large volume input of a new class of applications such as network monitoring, web content analysis and sensor networks. Among these applications, network monitoring may be the most compelling one—the backbone of a large internet service provider can generate 1 petabyte of data per day. For many network monitoring tasks such as traffic analysis and statistics collection, aggregation is a primitive operation. Various analytical and statistical needs naturally lead to related aggregate queries. In this article, we address the problem of efficiently computing multiple aggregations over high-speed data streams based on the two-level query processing architecture of GS, a real data stream management system deployed in AT & T. We discern that additionally computing and maintaining fine-granularity aggregations (called phantoms) has the benefit of supporting shared computation. Based on a thorough analysis, we propose algorithms to identify the best set of phantoms to maintain and determine allocation of resources (particularly, space) to compute the aggregations. Experiments show that our algorithm achieves near-optimal computation costs, which outperforms the best adapted algorithm by more than an order of magnitude.     

Data quality and query cost in pervasive sensing systems

This research is motivated by large-scale pervasive sensing applications. We examine the benefits and costs of caching data for such applications. We propose and evaluate several approaches to querying for, and then caching data in a sensor field data server. We show that for some application requirements (i.e., when delay drives data quality), policies that emulate cache hits by computing and returning approximate values for sensor data yield a simultaneous quality improvement and cost saving. This win–win is because when system delay is sufficiently important, the benefit to both query cost and data quality achieved by using approximate values outweighs the negative impact on quality due to the approximation. In contrast, when data accuracy drives quality, a linear trade-off between query cost and data quality emerges. We also identify caching and lookup policies for which the sensor field query rate is bounded when servicing an arbitrary workload of user queries. This upper bound is achieved by having multiple user queries share the cost of a single sensor field query. Finally, we demonstrate that our results are robust to the manner in which the environment being monitored changes using models for two different sensing systems.     

Ranked Query Processing in Uncertain Databases

Recently, many new applications, such as sensor data monitoring and mobile device tracking, raise up the issue of uncertain data management. Compared to "certain” data, the data in the uncertain database are not exact points, which, instead, often reside within a region. In this paper, we study the ranked queries over uncertain data. In fact, ranked queries have been studied extensively in traditional database literature due to their popularity in many applications, such as decision making, recommendation raising, and data mining tasks. Many proposals have been made in order to improve the efficiency in answering ranked queries. However, the existing approaches are all based on the assumption that the underlying data are exact (or certain). Due to the intrinsic differences between uncertain and certain data, these methods are designed only for ranked queries in certain databases and cannot be applied to uncertain case directly. Motivated by this, we propose novel solutions to speed up the probabilistic ranked query (PRank) with monotonic preference functions over the uncertain database. Specifically, we introduce two effective pruning methods, spatial and probabilistic pruning, to help reduce the PRank search space. A special case of PRank with linear preference functions is also studied. Then, we seamlessly integrate these pruning heuristics into the PRank query procedure. Furthermore, we propose and tackle the PRank query processing over the join of two distinct uncertain databases. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed approaches in answering PRank queries, in terms of both wall clock time and the number of candidates to be refined.     

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。     

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.     

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.     

Curracurrong: a stream programming environment for wireless sensor networks

The technological advances in wireless sensor network (WSN) enable the development of complex applications including health monitoring, environmental sampling, and disaster area monitoring. WSN applications deploy battery‐powered sensors at remote locations for long periods. The development of energy‐efficient and complex WSN applications therefore requires in‐depth embedded systems programming skills that are normally not found in domain experts. So that this challenge can be overcome, programming environments for WSN need to offer a high degree of productivity, flexibility, and efficiency at the same time. In this work, we present Curracurrong, a development environment for WSNs that is based on expressing queries with stream programming. A query is represented as a stream graph consisting of stream operators and communication channels. Curracurrong provides an extensible stream operator library that adapts to a wide range of applications. It uses a novel placement algorithm that optimizes the energy consumption on sensor nodes. Through a case study, we demonstrate the productivity and flexibility of our system. We conduct experiments that evaluate the energy efficiency of our optimized operator placement algorithm. Copyright © 2012 John Wiley & Sons, Ltd.     

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.