Query processing in sensor networks

2005

Although advances in sensor node hardware, which comprises sensors, embedded processors, and communication components, have made the large-scale deployment of sensor networks a reality, sensor networks are quite limited in resources. A sensor network includes a numerous battery-operated wireless sensor nodes, which have limited processing and storage capabilities, and a few base stations, which are powered PCs that are possibly connected to the Internet. Furthermore, typical sensor network applications, ranging from monitoring to military and health care, generate various complex continuous queries. The querying of sensor networks requires a rich set of abstractions, techniques, and heuristics.

2007

Greater availability and affordability of wireless technology has led to an increase in the number of wireless sensor network(WSN) applications where sense data is collected at a central user point, commonly outside (geographically and topologically) the network, for processing. Resource constraints on nodes in the network coupled with considerable redundancy in the data generated mean that applications have to be developed with an eye to maximising energy efficiency in order to extend network life. In-network computation, and in particular in-network information extraction from data, has been promoted as a technique for achieving this aim. In-network processing, however, has been limited to systems where at most, simple aggregate queries are evaluated, the results of which are communicated to the outside world. There is currently little research into how the idea of in-network processing can be extended and implemented to allow more complex queries to be resolved within the network. This paper examines key applicative query-based approaches that utilise in-network processing for query resolution, identifies their strengths and limitations and puts forward ideas for facilitating innetwork complex query processing in WSNs. Finally, preliminary results of experiments, where in-network attribute-based logical abstractions are used for processing complex queries, are presented.

2009

this paper proposes a new approach to obtain optimality in distributed query processing in a wireless sensor network environment. The goal of this paper is to design a scheme which supports multiple data acquisition and aggregation queries and minimizes the amount of radio transmissions, average transmission time, and energy consumption. Co-related queries will be automatically rewritten into synthetic queries to minimize communication and computation costs.

Signal Processing, 2007

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.

Sensors, 2011

One of main features in sensor networks is the function that processes real time state information after gathering needed data from many domains. The component technologies consisting of each node called a sensor node that are including physical sensors, processors, actuators and power have advanced significantly over the last decade. Thanks to the advanced technology, over time sensor networks have been adopted in an all-round industry sensing physical phenomenon. However, sensor nodes in sensor networks are considerably constrained because with their energy and memory resources they have a very limited ability to process any information compared to conventional computer systems. Thus query processing over the nodes should be constrained because of their limitations. Due to the problems, the join operations in sensor networks are typically processed in a distributed manner over a set of nodes and have been studied. By way of example while simple queries, such as select and aggregate queries, in sensor networks have been addressed in the literature, the processing of join queries in sensor networks remains to be investigated. Therefore, in this paper, we propose and describe an Incremental Join Algorithm (IJA) in Sensor Networks to reduce the overhead caused by moving a join pair to the final join node or to minimize the communication cost that is the main consumer of the battery when processing the distributed queries in sensor networks

IEEE Conference …, 2004

Sensor networks are promising in many applications; however, we have not yet seen its wide acceptance and deployment. We envision that a big obstacle to solving this dilemma is the lack of an easily usable tool for applications scientists to manage and use sensor networks. In this paper, we take an initial step to tackle this problem by developing a general tool to fill the gap between applications and sensor network protocols, which would also pave the way for the wide acceptance and deployment of sensor networks. Our approach includes two components: a general network programming interface abstracted from common usage patterns, and a transparent query to interface converter which will ease the burden on application scientists significantly. Using this automatic converter, application scientists need to use a high level SQL-like language for plugging and playing wireless sensor network operations. These two components will be integrated and implemented into a general query processing tool called QueryAgent. This tool also maps efficient sensor network protocols to different requirements of end-users in an intelligent way.

Recently, wireless sensor network (WSN) research community has proposed to model WSN as a distributed database. This enables users to interrogate the WSNs through simple SQL like queries. First, this approach significantly reduces the development and implementation cost of the system. Furthermore, various demonstrations have been quickly reported, showed that this approach performs substantial energy saving. These particular database systems are requently referred as sensor network query processing systems (SNQP). During the last years, various flavors of SNQPs have been introduced and investigated. However, one of them completely answers to the capital performance requirements including spatial and temporal data management. In this paper, we present a survey of key state of art of SNQP systems existing in the literature, analyze their key characteristics and discuss their drawbacks and challenges.Open related issues of research are also summarized.

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.

Proceedings. 13th International Conference on Computer Communications and Networks (IEEE Cat. No.04EX969), 2004

Advances in sensor node hardware, which comprises sensors, embedded processors, and communication components, have made the large-scale deployment of sensor networks a reality. Various sensor network applications ranging from monitoring applications to military applications require sensor nodes to collect data over a continuous time period. The placement, management, and processing of sensor network data necessitates an effective data storage, management, and query processing policy. This paper attempts to identify the key query processing techniques used in sensor networks. Design goals and challenges for query processing techniques are identified. The techniques are evaluated in terms of efficiency, scalability, applicability, and reliability. The evaluation of the techniques is guided by the distinctive query processing features supported by both types of sensor networks: conventional and wide area. Moreover, we argued for the integration of conventional and wide area sensor networks and addressed the integration issues and design goals. In particular, a query processing architecture is proposed to meet the emerging needs of sensor networks. The architecture addressed the requirements for different layers of the integrated sensor network components: base stations, sensor nodes and wide area workstations. Additionally, the future research directions for query processing are outlined.