Ixent Galpin

A semantically enabled service architecture for mashups over streaming and stored data

Sensing devices are increasingly being deployed to monitor the physical world around us. One class of application for which sensor data is pertinent is environmental decision support systems, e.g. flood emergency response. However, in order to interpret the readings from the sensors, the data needs to be put in context through correlation with other sensor readings, sensor data histories, and stored data, as well as juxtaposing with maps and forecast models. In this paper we use a flood emergency response planning application to identify requirements for a semantic sensor web. We propose a generic service architecture to satisfy the requirements that uses semantic annotations to support well-informed interactions between the services. We present the SemSor- Grid4Env realisation of the architecture and illustrate its capabilities in the context of the example application.

A Semantics for a Query Language over Sensors, Streams and Relations

We introduce a query language over sensors, streams and relations and formally describe its semantics. Although the language was specifically designed for sensor network querying, where data is pulled into streams, the semantics contributed in the paper also encompasses the case in which data is pushed onto streams or else lies stored in classical relations. The approach taken is that continuous queries over streams are an extension of classical queries over stored extents. Apart from the fact that query evaluation over streams is reactive, or periodic, the main difference is the conception of windows as an additional collection type with the consequent use of type converter operations to and from streams and windows (which, as bounded collections of tuples, can be operated on in a relational-algebraic setting). The language and the semantics we provide for it advance on previous work in being more comprehensive with respect to the collection types allowed and in being more flexible as to the number and content of the windows contributing to the result at each evaluation event of a continuous query. The formalization advances on previous work in clarifying the implementation onus.

A semantic sensor web for environmental decision support applications

Sensing devices are increasingly being deployed to monitor the physical world around us. One class of application for which sensor data is pertinent is environmental decision support systems, e.g., flood emergency response. For these applications, the sensor readings need to be put in context by integrating them with other sources of data about the surrounding environment. Traditional systems for predicting and detecting floods rely on methods that need significant human resources. In this paper we describe a semantic sensor web architecture for integrating multiple heterogeneous datasets, including live and historic sensor data, databases, and map layers. The architecture provides mechanisms for discovering datasets, defining integrated views over them, continuously receiving data in real-time, and visualising on screen and interacting with the data. Our approach makes extensive use of web service standards for querying and accessing data, and semantic technologies to discover and integrate datasets. We demonstrate the use of our semantic sensor web architecture in the context of a flood response planning web application that uses data from sensor networks monitoring the sea-state around the coast of England.

Comprehensive Optimization of Declarative Sensor Network Queries

We present a sensor network query processing architecture that covers all the query optimization phases that are required to map a declarative query to executable code. The architecture is founded on the view that a sensor network truly is a distributed computing infrastructure, albeit a very constrained one. As such, we address the problem of how to develop a comprehensive optimizer for an expressive declarative continuous query language over acquisitional streams as one of finding extensions to classical distributed query processing techniques that contend with the peculiarities of sensor networks as an environment for distributed computing.

An Architecture for Query Optimization in Sensor Networks

We present a novel sensor network query processing architecture that (a) covers all the query optimization phases that are required to map a declarative query to executable code; and (b) does so for a more expressive query language than has heretofore been supported over sensor networks. The architecture is founded on the view that a sensor network truly is a distributed computing infrastructure, albeit a very constrained one. As such, we address the problem of how to develop a comprehensive optimizer for an expressive declarative continuous query language over acquisitional streams as one of finding extensions to a classical distributed query processing architecture that contend with the peculiarities of sensor networks as an environment for distributed computing.

Validated cost models for sensor network queries

Generating a good execution plan for a declarative query has long been a central problem in data management research. With the rise in interest in wireless sensor networks (WSNs) as query processing platforms, it was quickly noticed that the corresponding optimization problem is even more challenging than the classical one, since, in comparison to classical platforms, a WSN is a very constrained computational infrastructure (in terms of memory, processing, and communication capabilities, and, crucially, depletable energy stocks). Optimizing a declarative query for execution in WSNs is thereby made both more important and more challenging. One of the requirements for effective query optimization is the availability of effective models for estimating the cost of alternative execution plans. This paper describes how query cost models for space, time and energy were methodically derived and validated for an expressive algebra for continuous queries over sensor streams.

QoS-aware optimization of sensor network queries

The resource-constrained nature of mote-level wireless sensor networks (WSNs) poses challenges for the design of a general-purpose sensor network query processors (SNQPs). Existing SNQPs tend to generate query execution plans (QEPs) that are selected on the basis of a fixed, implicit expectation, for example, that energy consumption should be kept as small as possible. However, in WSN applications, the same query may be subject to several, possibly conflicting, quality-of-service (QoS) expectations concomitantly (for example maximizing data acquisition rates subject to keeping energy consumption low). It is also not uncommon for the QoS expectations to change over the lifetime of a deployment (for example from low to high data acquisition rates). This paper describes optimization algorithms that respond to stated QoS expectations (about acquisition rate, delivery time, energy consumption and lifetime) when making routing, placement, and timing decisions for in-WSN query processing. The paper shows experimentally that QoS-awareness offers significant benefits in responding to, and reconciling, diverse QoS expectations, thereby enabling QoS-aware SNQPs to generate efficient QEPs for a broader range WSN applications than has hitherto been possible.

Extending query languages for in-network query processing

Sensor networks have become ubiquitous and their proliferation in day-to-day life provides new research challenges. Sensors deployed at forest sites, high performance facilities, or areas striken by environmental, or other, phenomena, are only a few representative examples. More recently, mobile sensor networks have made their presence and are rapidly growing in numbers, such as the successful ZebraNet project or PDAs and smartphones. Nevertheless, such networks have mainly been used for data acquisition and data are being processed externally instead of in-network. Basic research problems that arise in the in-network setting include how to adjust in a timely and efficient manner to changing conditions and network topology. In this paper, we present a methodology, based on declarative query processing to alleviate the aforementioned problems, by making the deployment and optimization of a data analysis application as automatic as possible, which also helps execution in mobile environments. Our proposed solution focuses on extending a state-of-the-art sensor network platform, SNEE, with builtin data analysis capabilities.

SensorBench: benchmarking approaches to processing wireless sensor network data

Wireless sensor networks enable cost-effective data collection for tasks such as precision agriculture and environment monitoring. However, the resource-constrained nature of sensor nodes, which often have both limited computational capabilities and battery lifetimes, means that applications that use them must make judicious use of these resources. Research that seeks to support data intensive sensor applications has explored a range of approaches and developed many different techniques, including bespoke algorithms for specific analyses and generic sensor network query processors. However, all such proposals sit within a multi-dimensional design space, where it can be difficult to understand the implications of specific decisions and to identify optimal solutions. This paper presents a benchmark that seeks to support the systematic analysis and comparison of different techniques and platforms, enabling both development and user communities to make well informed choices. The contributions of the paper include: (i) the identification of key variables and performance metrics; (ii) the specification of experiments that explore how different types of task perform under different metrics for the controlled variables; and (iii) an application of the benchmark to investigate the behavior of several representative platforms and techniques.

Deploying In-Network Data Analysis Techniques in Sensor Networks

Sensor Networks have received considerable attention recently, as they provide manifold benefits. Not only are they a means for data acquisition and monitoring of unexplored or inaccessible areas, they are also a low-cost alternative for sensing the environment, which greatly aids to better understand our surroundings. A major motivation in either occasion is to acknowledge endangering situations and take action(s) accordingly. To this end, we would like to enable data mining or analysis techniques on top or, even better, within such networks, due to the prohibitive cost of communication in this setting. In this work, we demonstrate running data mining algorithms on a set of sensors, which are of low-processing power. In addition to showcasing the execution of data analysis algorithms on resource-constrained hardware, our demo is intended to show how to take advantage of the properties of each algorithm to make better use of the sensors and their capabilities. We support the execution and monitoring of these algorithms with a graphical user interface (GUI).