Sensors are often employed to monitor continuously changing entities like locations of moving ob-jects and temperature. The sensor readings are reported to a database system, and are subsequently used to answer queries. Due to continuous changes in these values and limited resources (e.g., net-work

based on discrete sensor measurements. Kriging is a traditional spatial interpolation method with a guaranteed minimal estimation error, and a smooth, unbiased estimation. However, its execution is computationally expensive. In this paper, we present an approach to Kriging that allows inexpensive in-network

on application specific solutions, whereas our innetwork aggregation approach is driven by a general purpose, SQL-style interface that can execute queries over any type of sensor data while providing opportunities for significant optimization. We present a variety of techniques to improve the reliability

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

In sensor networks, data acquisition frequently takes place at low-capability devices. The acquired data is then transmitted through a hierarchy of nodes having progressively increasing network bandwidth and computational power. We consider the problem of executing queries over these data streams

Wireless sensor networks with thousands of tiny sensor nodes, are expected to find wide applicability and increas-ing deployment in coming years, as they enable reliable monitoring and analysis of the environment. In this paper, we propose a hybrid routing protocol (APTEEN) which al

is that individual sensor readings are inherently unreliable. In order to address these two aspects, sensor database systems like TinyDB and Cougar enable in-network data aggregation to reduce the communication cost and improve reliability. The existing data aggregation techniques, however, are limited to relatively

Abstract—In this paper we introduce MINT (Materialized In-Network Top-k) Views, a novel framework for optimizing the execution of continuous monitoring queries in sensor networks. A typical materialized view V maintains the complete results of a query Q in order to minimize the cost of future query

not favor energy efficiency. In this paper, we propose a distributed scheme called DNN for in-network processing of nearest neighbor queries. A cost model is built to analyze the performance of DNN. Experimental results show that DNN outperforms the centralized scheme significantly in terms of energy

This article introduces a sensor information networking architecture, called SINA, that facilitates querying, monitoring, and tasking of sensor networks. SINA plays the role of a middleware that abstracts a network of sensor nodes as a collection of massively distributed objects. The SINA