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/)

Monitoring Dynamic Spatial Fields Using Responsive Geosensor Networks

Many environmental phenomena (e.g., changes in global levels of atmospheric carbon dioxide) can be modeled as variations of attributes over regions of space and time, called dynamic spatial fields. The goal of this project is to provide efficient ways for sensor networks to monitor such fields, and to report significant changes in them. The focus is on qualitative changes, such as splitting of areas or emergence of holes in a region of study. The approach is to develop qualitative and topological methods to deal with changes. Qualitative properties form a small, discrete space, whereas quantitative values form a large, continuous space, and this enables efficiencies to be gained over traditional quantitative methods. The combinatorial map model of the spatial embedding of the sensor network is rich enough so that for each sensor, its position, and the distances and bearings of neighboring sensors, are easily computed. The sensors are responsive to changes to the spatial field, so that sensors are activated in the vicinity of interesting developments in the field, while sensors are deactivated in quiescent locations. All computation and message passing is local , with no centralized control. Optimization is addressed through use of techniques in qualitative representation and reasoning, and efficient update through a dynamic and responsive underlying spatial framework. Effective deployment of very large arrays of sensors for environmental monitoring has important scientific and societal benefits. The project is integrated with the NSF IGERT program on Sensor Science, Engineering, and Informatics at the University of Maine, which will enhance educational and outreach opportunities. The project Web site (http://www.spatial.maine.edu/~worboys/sensors.html) will be used for broad results dissemination

A Survey of Geosensor Networks: Advances in Dynamic Environmental Monitoring

In the recent decade, several technology trends have influenced the field of geosciences in significant ways. The first trend is the more readily available technology of ubiquitous wireless communication networks and progress in the development of low-power, short-range radio-based communication networks, the miniaturization of computing and storage platforms as well as the development of novel microsensors and sensor materials. All three trends have changed the type of dynamic environmental phenomena that can be detected, monitored and reacted to. Another important aspect is the real-time data delivery of novel platforms today. In this paper, I will survey the field of geosensor networks, and mainly focus on the technology of small-scale geosensor networks, example applications and their feasibility and lessons learnt as well as the current research questions posed by using this technology today. Furthermore, my objective is to investigate how this technology can be embedded in the current landscape of intelligent sensor platforms in the geosciences and identify its place and purpose

Physical Pointer Swizzling

Most of todays object oriented database systems (OODBS) employ a two-level architecture consisting of an object level and a storage level containing database objects in different formats. Before objects can be used by an application program, they have to be converted from their storage format to an in-memory format. An important aspect in this process is the conversion of references between objects to in-memory pointers so that object access is accelerated. These conversion mechanisms for references are so-called pointer swizzling strategies. Existing pointer swizzling strategies consist of many processing steps, and thus, are cumbersome and timeconsuming. We introduce the physical pointer swizzling strategy which is based on a different kind of persistent references representation in the storage format, and results in a reduction of the number of conversion steps. By using the OO7 benchmark, we show that the physical pointer swizzling strategy achieves a twofold speedup in the convers..

Shared Ride Trip Planning in Large Transportation Networks

We present and discuss a specification for a simulation of shared ride trip planning in ad-hoc mobile geosensor networks. In this scenario, the nodes - clients with transportation demand, and hosts with transportation supply - have to plan routes and manage bookings collaboratively. The specification enables to compare different communication strategies for that purpose, with the goal to find an efficient communication strategy that still guarantees planning of acceptable trips in a continuously changing environment. In particular it makes the route planning strategies and booking mechanisms transparent, and shows their dependence on communication strategies.November 200