Processing Multiple Aggregation Queries in Geo-Sensor Networks

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.

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.