Progressive Processing of Continuous Range Queries in Hierarchical Wireless Sensor Networks

In this paper, we study the problem of processing continuous range queries in a hierarchical wireless sensor network. Contrasted with the traditional approach of building networks in a "flat" structure using sensor devices of the same capability, the hierarchical approach deploys devices of higher capability in a higher tier, i.e., a tier closer to the server. While query processing in flat sensor networks has been widely studied, the study on query processing in hierarchical sensor networks has been inadequate. In wireless sensor networks, the main costs that should be considered are the energy for sending data and the storage for storing queries. There is a trade-off between these two costs. Based on this, we first propose a progressive processing method that effectively processes a large number of continuous range queries in hierarchical sensor networks. The proposed method uses the query merging technique proposed by Xiang et al. as the basis and additionally considers the trade-off between the two costs. More specifically, it works toward reducing the storage cost at lower-tier nodes by merging more queries, and toward reducing the energy cost at higher-tier nodes by merging fewer queries (thereby reducing "false alarms"). We then present how to build a hierarchical sensor network that is optimal with respect to the weighted sum of the two costs. It allows for a cost-based systematic control of the trade-off based on the relative importance between the storage and energy in a given network environment and application. Experimental results show that the proposed method achieves a near-optimal control between the storage and energy and reduces the cost by 0.989~84.995 times compared with the cost achieved using the flat (i.e., non-hierarchical) setup as in the work by Xiang et al.Comment: 41 pages, 20 figure

Smart Query Answering for Marine Sensor Data

We review existing query answering systems for sensor data. We then propose an extended query answering approach termed smart query, specifically for marine sensor data. The smart query answering system integrates pattern queries and continuous queries. The proposed smart query system considers both streaming data and historical data from marine sensor networks. The smart query also uses query relaxation technique and semantics from domain knowledge as a recommender system. The proposed smart query benefits in building data and information systems for marine sensor networks

An Optimal Query Assignment for Wireless Sensor Networks

A trade-off between two QoS requirements of wireless sensor networks: query waiting time and validity (age) of the data feeding the queries, is investigated. We propose a Continuous Time Markov Decision Process with a drift that trades-off between the two QoS requirements by assigning incoming queries to the wireless sensor network or to the database. To compute an optimal assignment policy, we argue, by means of non-standard uniformization, a discrete time Markov decision process, stochastically equivalent to the initial continuous process. We determine an optimal query assignment policy for the discrete time process by means of dynamic programming. Next, we assess numerically the performance of the optimal policy and show that it outperforms in terms of average assignment costs three other heuristics, commonly used in practice. Lastly, the optimality of the our model is confirmed also in the case of real query traffic, where our proposed policy achieves significant cost savings compared to the heuristics.Comment: 27 pages, 20 figure

A statistics-based sensor selection scheme for continuous probabilistic queries in sensor networks

An approach to improve the reliability of query results based on error-prone sensors is to use redundant sensors. However, this approach is expensive; moreover, some sensors may malfunction and their readings need to be discarded. In this paper, we propose a statistical approach to decide which sensors to be used to answer a query. In particular, we propose to solve the problem with the aid of continuous probabilistic query (CPQ), which is originally used to manage uncertain data and is associated with a probabilistic guarantee on the query result. Based on the historical data values from the sensors, the query type, and the requirement on the query, we present methods to select an appropriate set of sensors and provide reliable answers for aggregate queries. Our algorithm is demonstrated in simulation experiments to provide accurate and robust query results. © 2005 IEEE.published_or_final_versionThe 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA 2005), Hong Kong, China, 17-19 August 2005. In Proceedings of the 11th RTCSA, 2005, p. 331-33

Area Query Processing Based on Gray Code in Wireless Sensor Networks

Area query processing is significant for various applications of wireless sensor networks since it can request information of particular areas in the monitored environment. Existing query processing techniques cannot solve area queries. Intuitively centralized processing on Base Station can accomplish area queries via collecting information from all sensor nodes. However, this method is not suitable for wireless sensor networks with limited energy since a large amount of energy is wasted for reporting useless data. This motivates us to propose an energy-efficient in-network area query processing scheme. In our scheme, the monitored area is partitioned into grids, and a unique gray code number is used to represent a Grid ID (GID), which is also an effective way to describe an area. Furthermore, a reporting tree is constructed to process area merging and data aggregations. Based on the properties of GIDs, subareas can be merged easily and useless data can be discarded as early as possible to reduce energy consumption. For energy-efficiently answering continuous queries, we also design an incremental update method to continuously generate query results. In essence, all of these strategies are pivots to conserve energy consumption. With a thorough simulation study, it is shown that our scheme is effective and energy-efficient

CAREER: Data Management for Ad-Hoc Geosensor Networks

This project explores data management methods for geosensor networks, i.e. large collections of very small, battery-driven sensor nodes deployed in the geographic environment that measure the temporal and spatial variations of physical quantities such as temperature or ozone levels. An important task of such geosensor networks is to collect, analyze and estimate information about continuous phenomena under observation such as a toxic cloud close to a chemical plant in real-time and in an energy-efficient way. The main thrust of this project is the integration of spatial data analysis techniques with in-network data query execution in sensor networks. The project investigates novel algorithms such as incremental, in-network kriging that redefines a traditional, highly computationally intensive spatial data estimation method for a distributed, collaborative and incremental processing between tiny, energy and bandwidth constrained sensor nodes. This work includes the modeling of location and sensing characteristics of sensor devices with regard to observed phenomena, the support of temporal-spatial estimation queries, and a focus on in-network data aggregation algorithms for complex spatial estimation queries. Combining high-level data query interfaces with advanced spatial analysis methods will allow domain scientists to use sensor networks effectively in environmental observation. The project has a broad impact on the community involving undergraduate and graduate students in spatial database research at the University of Maine as well as being a key component of a current IGERT program in the areas of sensor materials, sensor devices and sensor. More information about this project, publications, simulation software, and empirical studies are available on the project\u27s web site (http://www.spatial.maine.edu/~nittel/career/)

An optimal query assignment for wireless sensor networks

With the increased use of large-scale real-time embedded sensor networks, new control mechanisms are needed to avoid congestion and meet required Quality of Service (QoS) levels. In this paper, we propose a Markov Decision Problem (MDP) to prescribe an optimal query assignment strategy that achieves a trade-off between two QoS requirements: query response time and data validity. Query response time is the time that queries spend in the sensor network until they are solved. Data validity (freshness) indicates the time elapsed between data acquisition and query response and whether that time period exceeds a predefined tolerance. We assess the performance of the proposed model by means of a discrete event simulation. Compared with three other heuristics, derived from practical assignment strategies, the proposed policy performs better in terms of average assignment costs. Also in the case of real query traffic simulations, results show that the proposed policy achieves cost gains compared with the other heuristics considered. The results provide useful insight into deriving simple assignment strategies that can be easily used in practice

Towards Spatial Queries over Phenomena in Sensor Networks

Today, technology developments enable inexpensive production and deployment of tiny sensing and computing nodes. Networked through wireless radio, such senor nodes form a new platform, wireless sensor networks, which provide novel ability to monitor spatiotemporally continuous phenomena. By treating a wireless sensor network as a database system, users can pose SQL-based queries over phenomena without needing to program detailed sensor node operations. DBMS-internally, intelligent and energyefficient data collection and processing algorithms have to be implemented to support spatial query processing over sensor networks. This dissertation proposes spatial query support for two views of continuous phenomena: field-based and object-based. A field-based view of continuous phenomena depicts them as a value distribution over a geographical area. However, due to the discrete and comparatively sparse distribution of sensor nodes, estimation methods are necessary to generate a field-based query result, and it has to be computed collaboratively ‘in-the-network’ due to energy constraints. This dissertation proposes SWOP, an in-network algorithm using Gaussian Kernel estimation. The key contribution is the use of a small number of Hermite coefficients to approximate the Gaussian Kernel function for sub-clustered sensor nodes, and processes the estimation result efficiently. An object-based view of continuous phenomena is interested in aspects such as the boundary of an ‘interesting region’ (e.g. toxic plume). This dissertation presents NED, which provides object boundary detection in sensor networks. NED encodes partial event estimation results based on confidence levels into optimized, variable length messages exchanged locally among neighboring sensor nodes to save communication cost. Therefore, sensor nodes detect objects and boundaries based on moving averages to eliminate noise effects and enhance detection quality. Furthermore, the dissertation proposes the SNAKE-based approach, which uses deformable curves to track the spatiotemporal changes of such objects incrementally in sensor networks. In the proposed algorithm, only neighboring nodes exchange messages to maintain the curve structures. Based on in-network tracking of deformable curves, other types of spatial and spatiotemporal properties of objects, such as area, can be provided by the sensor network. The experimental results proved that our approaches are resource friendly within the constrained sensor networks, while providing high quality query results

A Survey on IT-Techniques for a Dynamic Emergency Management in Large Infrastructures

This deliverable is a survey on the IT techniques that are relevant to the three use cases of the project EMILI. It describes the state-of-the-art in four complementary IT areas: Data cleansing, supervisory control and data acquisition, wireless sensor networks and complex event processing. Even though the deliverable’s authors have tried to avoid a too technical language and have tried to explain every concept referred to, the deliverable might seem rather technical to readers so far little familiar with the techniques it describes