Silvia Nittel

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.

Ad hoc shared‐ride trip planning by mobile geosensor networks

Recent developments in miniaturization of computing devices, in location‐sensing technology and in ubiquitous short‐range wireless networks enable new types of social behaviour. This paper investigates one novel application of these technologies, ad hoc inner‐urban shared‐ride trip planning: Transportation clients such as pedestrians are seeking ad hoc shared rides from transportation hosts such as private automobiles, buses, taxi cabs or trains. While centralized trip planners are challenged by assigning clients and hosts in an ad hoc manner, in particular for non‐scheduled hosts, we consider the transportation network as a mobile geosensor network of agents that interact locally by short‐range communication and heuristic wayfinding strategies. This approach is not only fully scalable; we can also demonstrate that with short‐range communication, and hence, incomplete transportation network knowledge a system still can deliver near‐to‐optimal trips.

Monitoring dynamic spatial fields using responsive geosensor networks

Information about dynamic spatial fields, such as temperature, windspeed, or the concentration of gas pollutant in the air, is important for many environmental applications. At the same time, the development of geosensor networks (wirelessly communicating, sensor-enabled, small computing devices distributed throughout a geographic environment) present new opportunities for monitoring dynamic spatial fields in much greater detail than ever before. This paper develops a new model for querying information about dynamic spatial fields using geosensor networks. In order to manage the inherent complexity of dynamic geographic phenomena, our approach is to focus on the qualitative representation of spatial entities, like regions, boundaries, and holes, and of events, like splitting, merging, appearance, and disappearance. Based on combinatorial maps, we present a qualitative model as the underlying data management paradigm for geosensor networks. This model is capable of tracking salient changes in the network in an energy-efficient way. Further, our model enables reconfiguration of the geosensor network in response to changes in the environment. We present an algorithm capable of adapting sensor network granularity according to dynamic monitoring requirements. Regions of high variability can trigger increases in the geosensor network granularity, leading to more detailed information about the dynamic field. Conversely, regions of stability can trigger a coarsening of the sensor network, leading to efficiency increases in particular with respect to power consumption and longevity of the sensor nodes. Querying of this responsive geosensor network is also considered, and the paper concludes with a review of future research directions.

Report from the first workshop on geo sensor networks

Advances in sensor technology and deployment strategies are revolutionizing the way that geospatial information is collected and analyzed. For example, cameras and GPS sensors on-board static or mobile platforms have the ability to provide continuous streams of geospatially-rich information. Furthermore, with the advent of nano-technology it becomes feasible and economically viable to develop and deploy low-cost, low-power devices that are generalpurpose computing platforms with multi-purpose on-board sensing and wireless communications capabilities. Special IT infrastructure challenges are posed by systems consisting of large numbers of unattended, untethered and collaborative sensor nodes that have small, non-renewable power supply and communicate via short range radio frequency with neighboring nodes. All these types of sensors may act collaboratively as nodes within broader network configurations. Such configurations may range in scale from few cameras monitoring traffic to thousands of nodes monitoring an ecosystem. The challenge of sensor networks is to aggregate sensor nodes into computational infrastructures that are able to produce globally meaningful information from raw local data obtained by individual sensor nodes. In geo sensor networks the geospatial content of the information collected, aggregated, analyzed, and monitored by a sensor network is fundamental; this might be performed locally in real-time on the sensor nodes or between sensor nodes, or off-line in a scattered or central repositories. Thus, a geosensor network may be loosely defined as a sensor network that monitors phenomena in a geographic space. This space may range in scale from the confined environment of a room to the highly complex dynamics of a an ecosystem region. The spatial aspect of the overall technology may be of importance in multiple levels of a geo sensor network, as the concepts of space, location, topology, and spatiotemporal events may be recognized on various abstraction levels. For example, the hardware and communication layers handle the physical space of sensor deployment, and communication topologies. The database layer generates execution plans for spatiotemporal queries that relate to sensor node location, and groups of sensors. Applications deal with the relation between sensor networks and phenomena in a geographic space. We feel that the academic and practical expertise of the spatial information theory and engineering domain are crucial to advance the development of sensor networks on all different abstraction levels. The ultimate objective is to develop generic sensor network programming infrastructure that is reusable, and widely applicable in all types of different domains.

Dynamic query re-optimization

Very long running queries in database systems are not uncommon in non traditional application domains such as image processing or data warehousing analysis. Query optimization, therefore, is important. However, estimates of the query characteristics before query execution are usually inaccurate. Further, system configuration and resource availability may change during long evaluation period. As a result, queries are often evaluated with sub-optimal plan configurations. To remedy this situation, we have designed a novel approach to re-optimize suboptimal query plan configurations on-the-fly with Conquest, an extensible and distributed query processing system. A dynamic optimizer considers reconfiguration cost as well as execution cost in determining the best query plan configuration. Experimental results are presented.

Information Dissemination in Mobile Ad-Hoc Geosensor Networks

This paper addresses the issue of how to disseminate relevant information to mobile agents within a geosensor network. Conventional mobile and location-aware systems are founded on a centralized model of information systems, typified by the client-server model used for most location-based services. However, in this paper we argue that a decentralized approach offers several key advantages over a centralized model, including robustness and scalability. We present an environment for simulating information dissemination strategies in mobile ad-hoc geosensor networks. We propose several strategies for scalable, peer-to-peer information exchange, and evaluate their performance with regard to their ability to distribute relevant information to agents and minimize redundancy.

NED: An Efficient Noise-Tolerant Event and Event Boundary Detection Algorithm in Wireless Sensor Networks

Wireless sensor networks provide an advanced platform to observe the physical world. Different users may be interested in different events derived from a same spatial phenomenon. The constrained and noisy environment of sensor networks, however, challenges successful in-network solutions to monitor and detect events and event boundaries. This paper presents an efficient algorithm, named NED, to support event and event boundary detection in wireless sensor networks. NED encodes partial event estimation results into variable length messages exchanged locally among neighboring nodes. Sensor nodes estimate events and event boundaries based on moving averages to eliminate noise effects. Thus, NED is resource-friendly to constrained sensor networks, and scales well to very large networks. Our experiment results illustrate that NED’s communication cost is flexible and moderate to different noise levels, and NED provides high quality estimation results of event and event boundary detection.

Air Pollution Monitoring System based on Geosensor Network

Environment Observation and Forecasting System(EOFS) is a application for monitoring and providing a forecasting about environmental phenomena. We design an air pollution monitoring system which involves a context model and a flexible data acquisition policy. The context model is used for understanding the status of air pollution on the remote place. It can provide an alarm and safety guideline depending on the condition of the context model. It also supports the flexible sampling interval change for effective the tradeoff between sampling rates and battery lifetimes. This interval is changed depending on the pollution conditions derived from the context model. It can save the limited batteries of geosensors, because it reduces the number of data transmission.

A drift-tolerant model for data management in ocean sensor networks

Traditional means of observing the ocean, like fixed mooring stations and radar systems, are difficult and expensive to deploy and provide coarse-grained and data measurements of currents and waves. In this paper, we explore the use of inexpensive wireless drifters as an alternative flexible infrastructure for fine-grained ocean monitoring. Surface drifters are designed specifically to move passively with the flow of water on the ocean surface and they are able to acquire sensor readings and GPS-generated positions at regular intervals. We view the fleet of drifters as a wireless ad-hoc sensor network with two types of nodes:i) a few powerful drifters with satellite connectivity, acting as mobile base-stations, and ii)a large number of low-power drifters with short-range acoustic or radio connectivity. Using real datasets from the Gulf of Maine (US) and the Liverpool Bay (UK), we study connectivity and uniformity properties of the ad-hoc mobile sensor network. We investigate the effect of deployment strategy, weather conditions as well as seasonal changes on the ability of drifters to relay readings to the end-users,and to provide sufficient sensing coverage of the monitored area. Our empirical study provides useful insights on how to design distributed routing and in-network processing algorithms tailored for ocean-monitoring sensor networks.

Detecting Topological Change Using a Wireless Sensor Network

Dynamic geographic phenomena, such as forest fires and oil spills, can have dire environmental, sociopolitical, and economic consequences. Mitigating, if not preventing such events requires the use of advanced spatio-temporal information systems. One such system that has gained widespread interest is the wireless sensor network(WSN), a deployment of sensor nodes--- tiny untethered computing devices, which run on batteries and are equipped with one or more commercial off-the-shelf or custom-made sensors and a radio transceiver. This research deals with initial attempts to detect topological changes to geographic phenomena by an environmentally deployed wireless sensor network (WSN). After providing the mathematical and technical preliminaries, we define topological change and present in-network algorithms to detect such changes and also, to manage the WSNs resources efficiently. The algorithms are compared against a resource-heavy continuous monitoring approach via simulation. The results show that two topological changes, hole loss and hole formation, can be correctly detected in-network and that energy is greatly saved by our event-driven approach. In future work, we hope to test the algorithms over a broader range of topological changes and to relax some of the network assumptions.