In Wireless Sensor Network (WSN) sensor nodes are deployed to sense useful data from environment. Sensors are energy-constrained devices. To prolong the sensor network lifetime, now a days mobile robots (sometimes refer as data sink, data mules, or data collectors) are used for collecting the sensed data from the sensors. In this environment sensor nodes directly transfer their sensed data to t...

Interpreting remotely sensed data typically requires expensive specialized computing machinery capable of stor ing and manipulating large amounts of data quickly In this paper we present a method for accurately analyzing and categorizing remotely sensed data on much smaller less expensive platforms Data size is reduced in such a way as to retain the integrity of the original data where the form...

Interpreting remotely-sensed data typically requires expensive, specialized computing machinery capable of storing and manipulating large amounts of data quickly. In this paper, we present a method for accurately analyzing and categorizing remotely-sensed data on much smaller, less expensive platforms. Data size is reduced in such a way as to retain the integrity of the original data, where the...

YUAN, HUI. Development and Evaluation of Advanced Classification Systems using Remotely Sensed Data for Accurate Land-Use/Land-Cover Mapping. (Under the direction of Dr. Siamak Khorram.) Our general objective in this research was to design, develop, implement, and evaluate advanced classification approaches for more accurate land-use/land-cover mapping using remotely sensed data. The overall re...

In-network data aggregation is one of the most important issues for achieving energy-efficiency in wireless sensor networks since sensor nodes in the surrounding region of an event may generate redundant sensed data. The redundant sensed data should be aggregated before being delivered to the sink to reduce energy consumption. Which node should be selected as a Data Aggregation Node (DAN) for a...

In this position paper we argue that the temporal features of sensed data present a number of fundamental challenges to the development of systems software and programming approaches for the development of pervasive systems, that are yet to be addressed. After an overview of these features, we discuss their impact on the design of infrastructure to support data storage, data access, and reasoni...