Philippe Bonnet

Towards Sensor Database Systems

Sensor networks are being widely deployed for measurement, detection and surveillance applications. In these new applications, users issue long-running queries over a combination of stored data and sensor data. Most existing applications rely on a centralized system for collecting sensor data. These systems lack flexibility because data is extracted in a predefined way; also, they do not scale to a large number of devices because large volumes of raw data are transferred regardless of the queries that are submitted. In our new concept of sensor database system, queries dictate which data is extracted from the sensors. In this paper, we define the concept of sensor databases mixing stored data represented as relations and sensor data represented as time series. Each long-running query formulated over a sensor database defines a persistent view, which is maintained during a given time interval. We also describe the design and implementation of the COUGAR sensor database system.

Querying the physical world

In the next decade, millions of sensors and small-scale mobile devices will integrate processors, memory, and communication capabilities. Networks of devices will be widely deployed for monitoring applications. In these new applications, users need to query very large collections of devices in an ad hoc manner. Most existing systems rely on a centralized system for collecting device data. These systems lack flexibility because data is extracted in a predefined way. Also, they do not scale to a large number of devices because large volumes of raw data are transferred. In our new concept of a device database system, distributed query execution techniques are applied to leverage the computing capabilities of devices, and to reduce communication. We define an abstraction that allows us to represent a device network as a database and we describe how distributed query processing techniques are applied in this new context.

Adaptive and Decentralized Operator Placement for In-Network Query Processing

In-network query processing is critical for reducing network traffic when accessing and manipulating sensor data. It requires placing a tree of query operators such as filters and aggregations but also correlations onto sensor nodes in order to minimize the amount of data transmitted in the network. In this paper, we show that this problem is a variant of the task assignment problem for which polynomial algorithms have been developed. These algorithms are however centralized and cannot be used in a sensor network. We describe an adaptive and decentralized algorithm that progressively refines the placement of operators by walking through neighbor nodes. Simulation results illustrate the potential benefits of our approach. They also show that our placement strategy can achieve near optimal placement onto various graph topologies despite the risks of local minima.

Bluetooth and sensor networks: a reality check

The current generation of sensor nodes rely on commodity components. The choice of the radio is particularly important as it impacts not only energy consumption but also software design (e.g., network self-assembly, multihop routing and in-network processing). Bluetooth is one of the most popular commodity radios for wireless devices. As a representative of the frequency hopping spread spectrum radios, it is a natural alternative to broadcast radios in the context of sensor networks. The question is whether Bluetooth can be a viable alternative in practice. In this paper, we report our experience using Bluetooth for the sensor network regime. We describe our tiny Bluetooth stack that allows TinyOS applications to run on Bluetooth-based sensor nodes, we present a multihop network assembly procedure that leverages Bluetooths device discovery protocol, and we discuss how Bluetooth favorably impacts in-network query processing. Our results show that despite obvious limitations the Bluetooth sensor nodes we studied exhibit interesting properties, such as a good energy per bit sent ratio. This reality check underlies the limitations and some promises of Bluetooth for the sensor network regime.

Smart-tag based data dissemination

Monitoring wide, hostile areas requires disseminating data between fixed, disconnected clusters of sensor nodes. It is not always possible to install long-range radios in order to cover the whole area. We propose to leverage the movement of mobile individuals, equipped with smart-tags, to disseminate data across disconnected static nodes spread across a wide area. Static nodes and mobile smart-tags exchange data when they are in the vicinity of each other; smart-tags disseminate data as they move around. In this paper, we propose an algorithm for update propagation and a model for smart-tag based data dissemination. We use simulation to study the characteristics of the model we propose. Finally, we present an implementation based on Bluetooth smart-tags.

Adaptive and decentralized operator placement for in-network query processing

In-network query processing is critical for reducing network traffic when accessing and manipulating sensor data. It requires placing a tree of query operators such as filters and aggregations but also correlations onto sensor nodes in order to minimize the amount of data transmitted in the network. In this paper, we show that this problem is a variant of the task assignment problem for which polynomial algorithms have been developed. These algorithms are however centralized and cannot be used in a sensor network. We describe an adaptive and decentralized algorithm that progressively refines the placement of operators by walking through neighbor nodes. Simulation results illustrate the potential benefits of our approach. They also show that our placement strategy can achieve near optimal placement onto various graph topologies despite the risks of local minima.

The distributed information search component (Disco) and the World Wide Web

The Distributed Information Search COmponent (DISCO) is a prototype heterogeneous distributed database that accesses underlying data sources. The DISCO prototype currently focuses on three central research problems in the context of these systems. First, since the capabilities of each data source is different, transforming queries into subqueries on data source is difficult. We call this problem the weak data source problem. Second, since each data source performs operations in a generally unique way, the cost for performing an operation may vary radically from one wrapper to another. We call this problem the radical cost problem. Finally, existing systems behave rudely when attempting to access an unavailable data source. We call this problem the ungraceful failure problem. DISCO copes with these problems. For the weak data source problem, the database implementor defines precisely the capabilities of each data source. For the radical cost problem, the database implementor (optionally) defines cost information for some of the operations of a data source. The mediator uses this cost information to improve its cost model. To deal with ungraceful failures, queries return partial answers. A partial answer contains the part of the final answer to the query that was produced by the available data sources. The current working prototype of DISCO contains implementations of these solutions and operations over a collection of wrappers that access information both in files and on the World Wide Web.

Linux block IO: introducing multi-queue SSD access on multi-core systems

The IO performance of storage devices has accelerated from hundreds of IOPS five years ago, to hundreds of thousands of IOPS today, and tens of millions of IOPS projected in five years. This sharp evolution is primarily due to the introduction of NAND-flash devices and their data parallel design. In this work, we demonstrate that the block layer within the operating system, originally designed to handle thousands of IOPS, has become a bottleneck to overall storage system performance, specially on the high NUMA-factor processors systems that are becoming commonplace. We describe the design of a next generation block layer that is capable of handling tens of millions of IOPS on a multi-core system equipped with a single storage device. Our experiments show that our design scales graciously with the number of cores, even on NUMA systems with multiple sockets.

Computational reproducibility: state-of-the-art, challenges, and database research opportunities

Computational experiments have become an integral part of the scientific method, but reproducing, archiving, and querying them is still a challenge. The first barrier to a wider adoption is the fact that it is hard both for authors to derive a compendium that encapsulates all the components needed to reproduce a result and for reviewers to verify the results. In this tutorial, we will present a series of guidelines and, through hands-on examples, review existing tools to help authors create of reproducible results. We will also outline open problems and new directions for database-related research having to do with querying computational experiments.

Device Database Systems

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