Query processing in sensor networks

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.

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.

Research India Publications, 2013

A sensor network is composed of a large number of sensor nodes that are densely deployed either inside the phenomenon or very close to it. The position of sensor nodes does not need to be engineered or predetermined. This allows random deployment in inaccessible terrains or disaster relief operations which in one way or the other is a boon. On the other hand, this also implies that the sensor network protocols and algorithms must possess self-organizing capabilities. Another dispensable feature of sensor networks is the cooperative effort of the sensor nodes. Recent advances in wireless communications and electronics have enabled the development of low-cost, low-power, multifunctional and multitasking sensor nodes that are small in size and communicate undisturbed in short distances. These tiny sensor nodes, which consist of sensing, data processing, and communicating components, leverage the idea of sensor networks. Sensor networks represent a significant improvement over traditional sensors. Wireless sensor networks are formed of tiny, highly energy-constrained sensor nodes that are equipped with wireless transceivers. They may be mobile and are usually deployed in large numbers in unfamiliar environments. The nodes communicate with each other by autonomously creating ad-hoc (inter connected) networks which are actively used to gather sensor data. WSNs also process the data within the network itself and only forward the result to the requesting node.

International Journal of Communication Systems, 2007

In the not too distant future, wireless sensor networks are envisioned to proliferate through the entire spectrum of the 'environmental monitoring' market allowing users to monitor a multitude of environments. Thousands or even millions of sensor nodes may span vast geographical areas enabling various environmental parameters to be monitored with significantly higher spatial and temporal resolutions than what is achievable using existing monitoring technologies. In order to manage the large amount of data that will be generated by these numerous sensor nodes, novel querying methods are needed to extract the required information in an energy-efficient manner. This paper studies the techniques used to manage the queries in a distributed manner and classifies the current state-of-the-art in this field into four main categories: in-network processing, acquisitional query processing, cross-layer optimization and datacentric data/query dissemination. This taxonomy not only illustrates how query management techniques have advanced over the recent past, but also allows researchers to identify the relevant features when designing sensor networks for different applications. * How queries are routed only to the relevant nodes from the node injecting the query into the network (flooding a query to all nodes is not energy-efficient) * How results are routed back to the querying node DISTRIBUTED QUERY MANAGEMENT TECHNIQUES FOR SENSOR NETWORKS 903

2014

Sensor networks comprise of a collection of resource-constrained, low cost, sometimes fragile wireless motes which have the capability to gather information about their surroundings through the use of sensors, and can be conceived as a distributed computing platform for applications ranging from event detection to environmental monitoring. A Sensor Network Query Processor (SNQP) is a means of collecting data from sensor networks where the requirements are defined using a declarative query language with a set of Quality of Service (QoS) expectations. As sensor networks are often deployed in hostile environments, there is a high possibility that the motes could break or that the communication links between the motes become unreliable. SNQP Query Execution Plans (QEPs) are often optimised for a specific network deployment and are designed to be as energy efficient as possible whilst ensuring the QEPs meet the QoS expectations, yet little has been done for handling the situation where t...

2003

We discuss the design of an acquisitional query processor for data collection in sensor networks. Acquisitional issues are those that pertain to where, when, and how often data is physically acquired (sampled) and delivered to query processing operators. By focusing on the locations and costs of acquiring data, we are able to significantly reduce power consumption over traditional passive systems that assume the a priori existence of data. We discuss simple extensions to SQL for controlling data acquisition, and show how acquisitional issues influence query optimization, dissemination, and execution. We evaluate these issues in the context of TinyDB, a distributed query processor for smart sensor devices, and show how acquisitional techniques can provide significant reductions in power consumption on our sensor devices.