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2006
To process aggregation queries issued through different sensors as access points in sensor networks, existing algorithms handle queries independently and perform in-network aggregation only at the query time. As a result of ad-hoc and independent execution of queries, no partial result is sharable and reusable among the queries. Consequently, scarce sensor network resources can be easily overconsumed, particularly, those sensors commonly accessed by queries. In this paper, we address this issue by examining strategies to maintain Materialized In-Network Views (MINVs) that pre-compute and store commonly used aggregation results in the sensor network. With MINVs, aggregated sensed results for some spatial regions are available and sharable to queries. Thus, the number of sensor accesses is greatly reduced. Through simulations, we validate the effectiveness of proposed strategies.
Computer Communications, 2006
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.
2004
algorithm to reduce the number of duplicate/overlapping queries and save overall energy consumption 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.
2006 10th International Database Engineering and Applications Symposium (IDEAS'06), 2006
In this paper, we present SURCH, a novel decentralized algorithm for efficient processing of queries generated in sensor networks. Unlike existing techniques, SURCH is fully distributed and does not require the existence or construction of a communication infrastructure. It exploits the broadcast nature of wireless communication to optimize query propagation and evaluation. In SURCH, partial results are aggregated en route while the query spreads through the network. The key features of SURCH include its ability to avoid unnecessary communication, balanced node workload, and resilience to node failures. Performance results illustrate that SURCH outperforms alternative techniques for a variety of aggregation and selection queries.
Journal of Systems and Software, 2008
A wireless sensor network (WSN) is composed of tens or hundreds of spatially distributed autonomous nodes, called sensors. Sensors are devices used to collect data from the environment related to the detection or measurement of physical phenomena. In fact, a WSN consists of groups of sensors where each group is responsible for providing information about one or more physical phenomena (e.g., group for collecting temperature data). Sensors are limited in power, computational capacity, and memory. Therefore, a query engine and query operators for processing queries in WSNs should be able to handle resource limitations such as memory and battery life. Adaptability has been explored as an alternative approach when dealing with these conditions. Adaptive query operators (algorithms) can adjust their behavior in response to specific events that take place during data processing. In this paper, we propose an adaptive innetwork aggregation operator for query processing in sensor nodes of a WSN, called ADAGA (ADaptive AGgregation Algorithm for sensor networks). The ADAGA adapts its behavior according to memory and energy usage by dynamically adjusting data-collection and data-sending time intervals. ADAGA can correctly aggregate data in WSNs with packet replication. Moreover, ADAGA is able to predict non-performed detection values by analyzing collected values. Thus, ADAGA is able to produce results as close as possible to real results (obtained when no resource constraint is faced). The results obtained through experiments prove the efficiency of ADAGA.
2009
Existing sensor network data aggregation techniques assume that the nodes are preprogrammed and send data to a central sink for offline querying and analysis. This approach faces two major drawbacks. First, the system behavior is preprogrammed and cannot be modified on the fly. Second, the increased energy wastage due to the communication overhead will result in decreasing the overall system lifetime. Thus, energy conservation is of prime consideration in sensor network protocols in order to maximize the network's operational lifetime. In this paper, we give an energy efficient approach to query processing by implementing new optimization techniques applied to in-network aggregation. We first discuss earlier approaches in sensors data management and highlight their disadvantages. We then present our approach and evaluate it through several simulations to prove its efficiency, competence and effectiveness.
Sensor network is a term used to refer to a heterogeneous system combining tiny sensors and actuators with general/special-purpose processors. Sensor networks are assumed to grow in size to include hundreds or thousands of low-power, low-cost, static or mobile nodes. This system is created by observing that for any densely deployed sensor network, high redundancy exists in the gathered information from the sensor nodes that are close to each other we have exploited the redundancy and designed schemes to secure different kinds of aggregation processing against both inside and outside attacks.
2003
Sensor networks represent a non traditional source of information, as readings generated by sensors flow continuously, leading to an infinite stream of data. Traditional DBMSs, which are based on an exact and detailed representation of information, are not suitable in this context, as all the information carried by a data stream cannot be stored within a bounded storage space. Thus, compressing data (by possibly loosing less relevant information) and storing their compressed representation, rather than the original one, becomes mandatory. This approach aims to store as much information carried by the stream as possible, but makes it unfeasible to provide exact answers to queries on the stream content. However, exact answers to queries are often not necessary, as approximate ones usually suffice to get useful reports on the world monitored by the sensors. In this paper we propose a technique for providing fast approximate answers to aggregate queries on sensor data streams. Our proposal is based on a hierarchical summarization of the data stream embedded into a flexible indexing structure, which permits us to both access and update compressed data efficiently. The compressed representation of data is updated continuously, as new sensor readings arrive. When the available storage space is not enough to store new data, some space is released by compressing the "oldest" stored data progressively, so that recent information (which is usually the most relevant to retrieve) is represented with more detail than old one.
Encyclopedia of Database Systems, 2009
Distributed and Parallel Databases, 2010
Hardware for sensor nodes that combine physical sensors, actuators, embedded processors, and communication components has advanced significantly over the last decade, and made the large-scale deployment of such sensors a reality. Applications range from monitoring applications such as inventory maintenance over health care to military applications. In this paper, we evaluate the design of a query layer for sensor networks. The query layer accepts queries in a declarative language that are then optimized to generate efficient query execution plans with in-network processing which can significantly reduce resource requirements. We examine the main architectural components of such a query layer, concentrating on in-network aggregation, interaction of in-network aggregation with the wireless routing protocol, and distributed query processing. Initial simulation experiments with the ns-2 network simulator show the tradeoffs of our system.
Information Systems, 2006
In-network data aggregation has been recently proposed as an effective means to reduce the number of messages exchanged in wireless sensor networks. Nodes of the network form an aggregation tree, in which parent nodes aggregate the values received from their children and propagate the result to their own parents. However, this schema provides little flexibility for the end-user to control the operation of the nodes in a data sensitive manner. For large sensor networks with severe energy constraints, the reduction (in the number of messages exchanged) obtained through the aggregation tree might not be sufficient. In this paper we present new algorithms for obtaining approximate aggregate statistics from large sensor networks. The user specifies the maximum error that he is willing to tolerate and, in turn, our algorithms program the nodes in a way that seeks to minimize the number of messages exchanged in the network, while always guaranteeing that the produced estimate lies within the specified error from the exact answer. A key ingredient to our framework is the notion of the residual mode of operation that is used to eliminate messages from sibling nodes when their cumulative change to the computed aggregate is small. We introduce two new algorithms, based on potential gains, which adaptively redistribute the error thresholds to those nodes that benefit the most and try to minimize the total number of transmitted messages in the network. Our techniques significantly reduce the number of messages, often by a factor of 10 for a modest 2% relative error bound, and consistently outperform previous techniques for computing approximate aggregates, which we have adapted for sensor networks.
On the Move to Meaningful …, 2004
The problem of representing and querying sensor-network data issues new research challenges, as traditional techniques and architectures used for managing relational and object oriented databases are not suitable in this context. In this paper we present a Grid-based architecture that supports aggregate query answering on sensor network data, and uses a summarization technique to efficiently accomplish this task. In particular, grid nodes are used either to collect, compress and store sensor readings, and to extract information from stored data. Grid nodes can exchange information among each other, so that the same piece of information can be stored (with a different degree of accuracy) into several nodes. Queries are evaluated by locating the grid nodes containing the needed information, and choosing (among these nodes) the most convenient ones, according to a cost model.
2007
Ef cient in-networking processing of higher-level query types such as range and aggregate queries are a major challenge in distributed, data-intensive, and sensor networks. In this paper we propose a novel data management infrastructure based on multidimensional indexing techniques to support fast aggregate and non-aggregate query processing. Our approach applies to stationary and mobile environments and is based on an overlay structure, called AGGINDEX. AGGINDEX organizes the sensors in a tree structure of virtual processors which continuously compute both precise and approximate aggregations. Our experiments show that AGGINDEX provides a signi cant gain in latency and message costs over gossip-based aggregation and spanning-tree based aggregation techniques as used by TAG and Cougar.
Data & Knowledge Engineering, 2011
This study proposes a method of in-network aggregate query processing to reduce the number of messages incurred in a wireless sensor network. When aggregate queries are issued to the resource-constrained wireless sensor network, it is important to efficiently perform these queries. Given a set of multiple aggregate queries, the proposed approach shares intermediate results among queries to reduce the number of messages. When the sink receives multiple queries, it should be propagated these queries to a wireless sensor network via existing routing protocols. The sink could obtain the corresponding topology of queries and views each query as a query tree. With a set of query trees collected at the sink, it is necessary to determine a set of backbones that share intermediate results with other query trees (called non-backbones). First, it is necessary to formulate the objective cost function for backbones and non-backbones. Using this objective cost function, it is possible to derive a reduction graph that reveals possible cases of sharing intermediate results among query trees. Using the reduction graph, this study first proposes a heuristic algorithm BM (standing for Backbone Mapping). This study also develops algorithm OOB (standing for Obtaining Optimal Backbones) that exploits a branch-and-bound strategy to obtain the optimal solution efficiently. This study tests the performance of these algorithms on both synthesis and real datasets. Experimental results show that by sharing the intermediate results, the BM and OOB algorithms significantly reduce the total number of messages incurred by multiple aggregate queries, thereby extending the lifetime of sensor networks.
Distributed Computing and Networking, 2008
This paper presents a novel approach to processing continuous aggregate queries in sensor networks, which lifts the assumption of tree-based routing. Given a query workload and a special-purpose gateway node where results are expected, the query optimizer exploits query correlations in order to generate an energy-efficient distributed evaluation plan. The proposed optimization algorithms identify common query sub-aggregates, and propose common routing structures to share the sub-aggregates at an early stage. Moreover, they avoid routing sub-aggregates of the same query through long-disjoint paths, thus further reducing the communication cost of result propagation. The proposed algorithms are fully-distributed, and are shown to offer significant communication savings compared to existing tree-based approaches. A thorough experimental evaluation shows the benefits of the proposed techniques for a variety of query workloads and network topologies.
This paper proposes an efficient data aggregation algorithm with range query capability for sensor networks. The proposed aggregation and query mechanisms are based on a virtual grid. In each grid, a head node is selected to be a manager. When a head node detects a generated event, it announces that to all other head nodes. A user, i.e. mobile sink, queries the interesting event via a head node, called agent, within the same grid. According to the received event type, the user issues an enquiry message to query the sensor network with a specific range. The user queries and aggregates the data in the regular-shape and irregular-shape range. The irregular-shape range is to aggregate the data of continuous event occurred. While the information is collected from the sensors, the information will be sent back to the user. Furthermore, we propose efficient approaches to gather the information from sensor networks while the void exists. Finally, experimental results show that our proposed approaches are more energy-efficiency than the existing approach.
2009
Application-specific data aggregation can play a significant role in energy-efficient operation of wireless sensor networks. Existing aggregation techniques rely heavily on the routing protocol to build shortest paths to route node measurements to the base station and are limited in the types of supported queries. We propose an aggregation scheme that utilizes the inherent information gradients present in the network. The query is directed to the source of information, resulting in better load sharing in the network. We support a variety of queries ranging from simple maximum, minimum or average of the readings of sensor nodes to more complex quantile queries such as k highest values or k th highest value through a generic query algorithm. The query algorithm shifts the computation to the querying agent, thus eliminating any in-network aggregation.
International Journal of Sensor Networks, 2006
This paper explores in-network aggregation as a power-efficient mechanism for collecting data in wireless sensor networks. In particular, we focus on sensor network scenarios where a large number of nodes produce data periodically. Such communication model is typical of monitoring applications, an important application domain sensor networks target. The main idea behind in-network aggregation is that, rather than sending individual data items from sensors to sinks, multiple data items are aggregated as they are forwarded by the sensor network. Through simulations, we evaluate the performance of different in-network aggregation algorithms, including our own cascading timers, in terms of the trade-offs between energy efficiency, data accuracy and freshness. Our results show that timing, i.e., how long a node waits to receive data from its children (downstream nodes in respect to the information sink) before forwarding data onto the next hop (toward the sink) plays a crucial role in the performance of aggregation algorithms for applications that generate data periodically. By carefully selecting when to aggregate and forward data, cascading timers achieves considerable energy savings while maintaining data freshness and accuracy. We also study in-network aggregation's cost-efficiency using simple mathematical models.
Query processing in sensor networks is critical for several sensor based monitoring applications and poses several challenging research problems. The in-network aggregation paradigm in sensor networks provides a versatile approach for evaluating simple aggregate queries, in which an aggregation-tree is imposed on the sensor network that is rooted at the base-station and the data gets aggregated as it gets forwarded up the tree. In this paper we consider an two kinds of aggregate queries: value range queries that compute the number of sensors that report values in the given range, and location range queries that compute the sum of values reported by sensors in a given location range. Such queries can be answered by using the in-network aggregation approach where only sensors that fall within the range contribute to the aggregate being maintained. However it requires a separate aggregate to be computed and communicated for each query and hence does not scale well with the number of queries.
Lecture Notes in Computer Science, 2009
In this paper we present algorithms for building and maintaining efficient aggregation 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 aggregation 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 aggregation process while utilizing significantly lower resources than prior approaches.
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