the cougar approach to innetwork query processing in sensor

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Active Query Forwarding in Sensor Networks (ACQUIRE)

Bonnet, Gehrke, and Seshadri [10, 11] as well as Yao and Gehrke [16] present the COUGAR approach which treats sensor networks as distributed databases, with users tasking the net-work with declarative queries which are then converted by a front-end query processor into an efficient query plan for in-network processing.

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Continuous Location Dependent Queries in Mobile Wireless

Continuous Location Dependent Queries in Mobile Continuous Location Dependent Queries The query processing approach based on TinyDB [16] focuses on simple range queries over stationary sensors in which sensor data are aggregated in the network and sent back through

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In

The in-network query processing paradigm in sensor networks postulates that a query is routed among sensors and collects the answers from the sensors on its trajectory. It works for static and connected sensor networks. However, when the network consists of mobile sensors and is sparse, a different approach is necessary.

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The quantity diversity and accuracy of information

might be harsh, hostile, inaccessible, and remote. The quantity, diversity and accuracy of information extracted from the deployed WSN can be optimized based on variety of reliable, high-performance, and cost-effective technologies [4]. In recent years, there is a great interest in WSNs among researchers in academia and industry as well as by

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A Sensor Network Data Aggregation Technique

implement our distributed in-network aggregation approach. III. THE EEIA APPROACH Our approach consists of providing a new distributed algorithm for query processing in wireless sensor networks which is an optimized energy efficient distributed algorithm with respect to all the sensor's resource constraints. Some

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Implementation and Result Analysis of Secure Strategy for

Sensor networks are usually deployed in hostile and unattended environment where an adversary can read and modify the content of the data packet. September 2002. The cougar approach to in-network query processing in sensor networks. In SIGMOD Record. analysis defenses. In IPSN'04: Proceedings of the third international symposium on

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Area Query Processing Based on Gray Code in Wireless

Area query is a new kind of query in wireless sensor networks. Intuitively, Base Station can collect data from all sensor nodes and then process area queries in a centralized manner; however, sensor nodes will drain energy quickly due to frequent reports of sensed values. The existing techniques of in-network query processing

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Active query forwarding in sensor networks

energy constraints in sensor networks, a one-size-fits-all approach is unlikely to provide efficient solutions for other types of queries. In this paper we propose a new data-centric querying mechanism, ACtive QUery forwarding In sensoR nEtworks (ACQUIRE). Fig. 1 shows the different categories of queries and the kinds of

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Query Processing in Sensor Networks

Distributed in network query processor 3 TinyDB or Cougar Wireless sensor network Query input Result collection 2 Query parsing Query optimization 1 Query result storage and display Figure 1. Architecture of a sensor network query processor. Numbers indicate the sequence of steps involved in processing a query.

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Processing approximate aggregate queries in wireless

3. In-network data aggregation. Depending on their application, sensor nodes typically operate under one of two possible modes. In the batch processing mode sensor nodes collect data until either the collected data reaches a specified size, or until a maximum amount of time since the last transmission has elapsed. The collected data is then processed locally and periodically forwarded to a base station for further

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How to setup the SQL v2 sensors in PRTG? Is there a guide

Jul 03, 2019Under the Data section, select the SQL Query File (the file you've saved in Step 1), define the Transaction approach, and if you want PRTG to read/evaluate the results of the query, set the Data Processing option to Process data table. For this example we work with Process data table. After selecting this option a whole new section will appear.

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1 Multi

abstraction for sensor networks: Clients "program" the sensors through queries in a high-level declarative language (such as a variant of SQL) permitting the system to perform the low-level optimizations necessary for energy-efficient query processing. In this paper we consider multi-query optimization for aggregate queries on sensor networks.

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Operator Placement for In

Operator Placement for In-Network Stream Query Processing Utkarsh Srivastava Kamesh Munagala Jennifer Widom Stanford University Duke University Stanford University usrivcs.stanford.edu kameshcs.duke.edu widomcs.stanford.edu Abstract In sensor networks, data acquisition frequently takes place at low-capability devices. The acquired data is

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The Cougar Approach to In

Applications range from environmental control, warehouse inventory, and health care to military environments. Existing sensor networks assume that the sensors are preprogrammed and send data to a central frontend where the data is aggregated and stored for offline querying and analysis. This approach has two major drawbacks.

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PRESTO: Feedback

query processing is handled at (or close to) the data source, thereby reducing communication needs. How-ever, the approach assumes that remote sensors have suf-ficient processing resources to handle query processing, an assumption that may not hold in untethered networks of inexpensive sensors (e.g., Berkeley Motes [19]). In

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Operator Placement for In

Operator Placement for In-Network Stream Query Processing Sensor Data Processing may exhibit similar general characteristics. Typically, queries are posed and results collected at the root of the hierarchy. One simple approach is to transmit all data acquired at the sensors through the hierarchy to the root, then perform all query pro-

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA

oriented query processing in sensor networks. The COUGAR project [3] introduces a sensor database system and deals with three types of event queries: historical queries, snapshot queries, and long-running queries. The system employs threshold-based detection logic and en-capsulates it into a set of asynchronous functions provided for users.

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The Cougar Approach to In

Applications range from environmental control, warehouse inventory, and health care to military environments. Existing sensor networks assume that the sensors are preprogrammed and send data to a central frontend where the data is aggregated and stored for offline querying and analysis. This approach has two major drawbacks.

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Location

Short Paper: Location-Aware Event-Driven Query Processing in Sensor Database Management Soon J. Hyun Prof. Department of Computer Science Korea Advanced Institute of Science and Technology DEBS 2012 The 6thACM InternationalConferenceon DistributedEvent-BasedSystems Contents 1 • Introduction • Related Works • Event-driven query in SNQL • Processing of Event-driven SNQL •

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Complex Event Processing in Wireless Sensor Networks

uses the declarative queries that are similar to SQL to query sensor nodes. Additionally, sensor data in cougar is consid-ered like a "virtual" relational database. Cougar places on each node an additional query layer that lies between the network and application layers which has

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Energy

that is built during the query flooding phase on a per-query basis. This approach, pioneered by early systems such as TinyDB [10] and Cougar [1], is efficient for continuous (long-running) queries, where the high energy cost incurred during the query flooding and per-query data aggregation phases is amortized over time.

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Query Processing in Sensor Networks

Distributed in network query processor 3 TinyDB or Cougar Wireless sensor network Query input Result collection 2 Query parsing Query optimization 1 Query result storage and display Figure 1. Architecture of a sensor network query processor. Numbers indicate the sequence of steps involved in processing a query.

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The quantity diversity and accuracy of information

might be harsh, hostile, inaccessible, and remote. The quantity, diversity and accuracy of information extracted from the deployed WSN can be optimized based on variety of reliable, high-performance, and cost-effective technologies [4]. In recent years, there is a great interest in WSNs among researchers in academia and industry as well as by

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The cougar approach to in

Given a user query, a query optimizer generates an efficient query plan for in-network query processing, which can vastly reduce resource usage and thus extend the lifetime of a sensor network. In addition, since queries are asked in a declarative language, the user is shielded from the physical characteristics of the network.

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StreamGlobe Adaptive Query Processing and Optimization in

"PIPES – A Public Infrastructure for Processing and Exploring Streams". SIGMOD 2004 Lser, Siberski, Wolpers, Nejdl. "Information Integration in Schema-Based Peer-To-Peer Networks". CAiSE 2003 Yang, Garcia-Molina. "Designing a Super-Peer Network". ICDE 2003 Yao, Gehrke. "The Cougar Approach to In-Network Query Processing in Sensor

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A Sensor Network Data Aggregation Technique

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.

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Complex Event Processing in Wireless Sensor Networks

uses the declarative queries that are similar to SQL to query sensor nodes. Additionally, sensor data in cougar is consid-ered like a "virtual" relational database. Cougar places on each node an additional query layer that lies between the network and application layers which has the responsibility of in-network processing.

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Cornell Database Group

Sensoria Node Demo. A Tracking GUI is used to specify simulated motion tracks, and then the main COUGAR GUI detects these motion tracks and display them onscreen. In addition, we will describe how the Sensoria nodes can be used in conjunction with Mica motes to form a large sensor network.

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Approximating Sensor Network Queries Using In

of our approach to changes in the data and query workload. 1.1 Related Work The idea of Semantic Routing Trees was proposed in [16] as an overlay index in the network, to allow for routing de-cisions to those leaves relevant to the query. Our in-network summaries

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The Cougar Approach On In

Why in-network processing? Sensor networks are power constrained. Local computation is much cheaper than communication. A lot of sensor data but few queries, and only a subset of data is involved in queries In-network processing provides a trade-off between computation and communication. Cougar prolongs the lifetime of sensor networks through distributed in-network processing and carefully designed sensor coordination.

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