Geosensor Networks

Efficient data collection in wireless sensor networks (SNs) plays a key role in power conservation. It has spurred a number of research projects focusing on effective algorithms that reduce power consumption with effective in-network aggregation techniques. Up to now, most approaches are based on the assumption that data collection involves all nodes of a network. There is a large number of queries that in fact select only a subset of the nodes in a SN. Thus, we concentrate on selective queries, i.e., queries that request data from a subset of a SN. The task of optimal data collection in such queries is an instance of the NP-hard minimal Steiner tree problem. We argue that selective queries are an important class of queries that can benefit from algorithms that are tailored for partial node participation of a SN. We present an algorithm, called Pocket Driven Trajectories (PDT), that optimizes the data collection paths by approximating the global minimal Steiner tree using solely local spatial knowledge. We identify a number of spatial factors that play an important role for efficient data collection, such as the distribution of participating nodes over the network, the location and dispersion of the data clusters, the location of the sink issuing a query, as well as the location and size of communication holes. In a series of experiments, we compare performance of well-known algorithms for aggregate query processing against the PDT algorithm in partial node participation scenarios. To measure the efficiency of all algorithms, we also compute a near-optimal solution, the globally approximated minimal Steiner tree. We outline future research directions for selective queries with varying node participation levels, in particular scenarios in which node participation is the result of changing physical phenomena as well as reconfigurations of the SN itself.

Efficient data collection in wireless sensor networks (SNs) plays a key role in power conservation. It has spurred a number of research projects focusing on effective algorithms that reduce power consumption with effective in-network aggregation techniques. Up to now, most approaches are based on the assumption that data collection involves all nodes of a network. There is a large number of queries that in fact select only a subset of the nodes in a SN. Thus, we concentrate on selective queries, i.e., queries that request data from a subset of a SN. The task of optimal data collection in such queries is an instance of the NP-hard minimal Steiner tree problem. We argue that selective queries are an important class of queries that can benefit from algorithms that are tailored for partial node participation of a SN. We present an algorithm, called Pocket Driven Trajectories (PDT), that optimizes the data collection paths by approximating the global minimal Steiner tree using solely local spatial knowledge. We identify a number of spatial factors that play an important role for efficient data collection, such as the distribution of participating nodes over the network, the location and dispersion of the data clusters, the location of the sink issuing a query, as well as the location and size of communication holes. In a series of experiments, we compare performance of well-known algorithms for aggregate query processing against the PDT algorithm in partial node participation scenarios. To measure the efficiency of all algorithms, we also compute a near-optimal solution, the globally approximated minimal Steiner tree. We outline future research directions for selective queries with varying node participation levels, in particular scenarios in which node participation is the result of changing physical phenomena as well as reconfigurations of the SN itself.

In-network aggregation has been proposed as one method for reducing energy consumption in networked sensors. In this paper, we explore the idea of influencing the construction of the routing trees for sensor networks with the goal of reducing the size of transmitted data for networks with in-network aggregation involving Group By queries. Toward this, we propose a group-aware network configuration method and present two algorithms, that "cluster" along the same path sensor nodes which belong to the same group. We evaluate our proposed scheme experimentally, in the context of existing innetwork aggregation schemes, with respect to energy consumption and quality of data. Overall, our routing tree construction scheme provides energy savings over existing network configuration schemes and improves quality of data in systems with imperfect quality of data such as TiNA.

In this paper, a general model of an Early Detection of Environmental Events System is presented. It follows established technologies and it is based on wireless sensor network nodes connected to a central computer using IEEE 802.15.4 and the General Radio Packet Service. In order to test its feasibility, the model is applied to the early detection of forest fires. The contribution of this paper is to show that although the system is theoretically apt to work well in small grid squares, its deployment over the totality of large areas is unfeasible due to the sheer number of sensors necessary, and their present costs and deployment difficulties. Instead, a second best alternative, giving not an early warning of an impending fire but an early notice that the fire is in its beginning phase, is shown to be feasible, from both technical and economic points of view.

This paper describes the requirements engineering methodology used for the definition of semantic sensors in a Crisis and Disaster Management framework. The goal of the framework is effective management of emergencies which depends on timely information availability, reliability and intelligibility. To achieve this, different Command and Control (C2) Systems and Sensor Systems have to cooperate and interoperate. Unless standards and well-defined specifications are used, however, the interoperability of these systems can be very complex. To address this challenge, in the C2-SENSE project, a "profiling" approach will be used to achieve seamless interoperability by addressing all the layers of the communication stack in the security field. The main objective is to develop a profile based Emergency Interoperability framework by the use of existing standards and semantically enriched Web services to expose the functionalities of C2 Systems, Sensor Systems and other Emergency and Crisis Management systems. We introduce the concepts of Semantic Sensors, describe the characteristics of Sensor Systems in Emergency Management, and the methodology of requirements engineering for such a framework.

The EMSODEV[1] (European Multidisciplinary Seafloor and water column Observatory DEVelopment) is an EU project whose general objective is to set up the full implementation and operation of the EMSO distributed Research Infrastructure (RI), through the development, testing and deployment of an EMSO Generic Instrument Module (EGIM). This research infrastructure will provide accurate records on marine environmental changes from distributed local nodes around Europe. These observations are critical to respond accurately to the social and scientific challenges such as climate change, changes in marine ecosystems, and marine hazards. In this paper we present the design and development of the EGIM data acquisition system. EGIM is able to operate on any EMSO node, mooring line, sea bed station, cabled or non-cabled and surface buoy. In fact a central function of EGIM within the EMSO infrastructure is to have a number of ocean locations where the same set of core variables are measured homogeneously: using the same hardware, same sensor references, same qualification methods, same calibration methods, same data format and access, and same maintenance procedures.

We consider the issue of query and data propagation in the context of geosensor networks over geo-aware sensors. In such networks, techniques for efficient propagation of queries and data play a significant role in reducing energy consumption. Georouting is a new technique for the broadcasting of localized data and queries in geo-aware sensor networks; it makes use of the existing query routing tree, and does not involve the creation of any additional communication channels. In addition to localized broadcasting, georouting is useful for (nonlocalized) broadcasting spatial data, greatly reducing the amount of communication, and hence energy consumption, during broadcasts. We demonstrate its effectiveness empirically, having implemented this technique. In addition to broadcasting queries and data to the sensors, we consider data gathering, where data is being transmitted from the sensors back towards the central processor. Delta-gathering is a new technique for reducing the amount of communication during data gathering. Finally, we apply our delta-gathering approach toward the problem of sensor data visualization. We present sensor terrains as a preferable alternative to isoline-based visualization (contour maps) for this problem.

In this paper, a general model of an Early Detection of Environmental Events System is presented. It follows established technologies and it is based on wireless sensor network nodes connected to a central computer using IEEE 802.15.4 and the General Radio Packet Service. In order to test its feasibility, the model is applied to the early detection of forest fires. The contribution of this paper is to show that although the system is theoretically apt to work well in small grid squares, its deployment over the totality of large areas is unfeasible due to the sheer number of sensors necessary, and their present costs and deployment difficulties. Instead, a second best alternative, giving not an early warning of an impending fire but an early notice that the fire is in its beginning phase, is shown to be feasible, from both technical and economic points of view.

is working on the topic of spatio-temporal data streams, especially the data stream model support of sensor data streams capturing continuous phenomena. He will defend his thesis proposal in June 2011. Name: Nural, Arda Worked for more than 160 Hours: Yes Contribution to Project: Arda Nural has started as a Ph.D. student at the University of Maine in Fall 2003. His research topic is in the area of mobile geosensor networks and data streams management systems. In particular, he focuses on the detection of emerging flock patterns in crowds of moving objects and the tracking of topological behavior such as splitting and merging of flocks, and the identification of leader patterns. He defended successfully defended his thesis proposal in April 2009. Name: Jin, Guang Worked for more than 160 Hours: Yes Contribution to Project: Guang Jin was a Ph.D. student and graduated successfully in April 2009. His research area was in the realm of quantitative and qualitative detection of events in geosensor networks. Name: Xiao, Danqing Worked for more than 160 Hours: Yes Contribution to Project: The female student is currently funded by the correlated NSF project 'Monitoring dynamic spatial fields using responsive geosensor networks', PI M. Worboys, Co-PI S. Nittel. She successfully defended her MS thesis in April 2010, and her topic was detection of non-topological changes in geosensor networks.

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.

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. Figure 1: If the bus is missed riding with cars becomes an alternative, which requires ad-hoc trip planning.

Ocean currents monitoring is a crucial research area in marine sciences as it has a significant impact on marine ecology. Currents carry nutrients; they affect diffusion of various marine species, and knowledge of currents can help energy savings in naval vessels. Today, large-scale sensing platforms such as stationary buoys, research vessels, gliders, single drifters and high frequency coastal radars are utilized in ocean current monitoring. In the near future, novel technologies will be added, such as small-scale, cost effective networks of drifters and/or autonomous underwater vehicles (AUVs) such as gliders. Such networks of drifter are equipped with GPS devices to collect location data and can use acoustic, optical and/or radio communications systems to be in coordination with other instruments and each other; furthermore, we assume some drifters will be use satellite communication to send their data to onshore base stations or research vessels. Deploying a fleet of passively propelled drifters presents novel research questions in spatio-temporal modeling and querying. It raises challenges with regard to in-network data collection, and aggregation to provide continuous ocean current observations on a much finer scale as is possible today. Our proposed model relies on a fleet deployment of passive, current-propelled, smallsized ocean drifters as an alternative or support to CODAR systems. We simulated the connectivity of the drifters and uniformity of monitoring area using real data from the Gulf of Maine (US) and Liverpool Bay (UK) in our previous work. In this paper, we develop a formal model for fleets based on the relative motion patterns of drifters as a group, which is connected over time. The drifters are conceptualized as geo-spatial lifelines (i.e. trajectories) with spatio-temporal relationships between the relative movements of the objects. This leads to the formalization of various moving patterns types such as flock, leadership, convergence, and encounter patterns.

We propose and investigate a model for shared ride trip planning in ad-hoc mobile geosensor networks. Our focus is on the communication strategies between the network nodes. In a dynamically changing network of autonomous nodes all trip plans and provisions need to be kept up-to-date. At the same time, energy consumption by broadcasting messages needs to be minimized. Hence, we have to solve an optimization problem: find an efficient communication strategy that still guarantees planning of acceptable trips in a continuously changing environment.

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. Figure 1: If the bus is missed riding with cars becomes an alternative, which requires ad-hoc trip planning.

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 spatiotemporal information systems. One such system that has gained widespread interest is the wireless sensor network (WSN), a deployment of sensor nodestiny 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 WSN's 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.

Traditional means of observing the ocean, like fixed moorings and radar systems, are expensive to deploy and provide coarse-grained data measurements of ocean currents and waves. In this paper, we explore the use of an inexpensive wireless mobile ocean sensor network 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 basestations, and ii) a large number of low-power drifters with short-range acoustic or radio connectivity. We study connectivity and uniformity properties of the ad-hoc mobile sensor network. We investigate the effect of deployment strategy. The objective of this paper is to address the following challenge: how can we trade the usage of resources (e.g. number of drifters, and number of basestations vs. communication range) and which deployment strategy should be chosen (e.g. grid-like, star-like, etc.) to minimize energy costs, whilst satisfying application requirements for network connectivity and sensing density. Using simulation and real dataset, we investigate the effects of deploying drifters with regard to the following questions: i) where/when should drifters be placed initially? ii) how many drifters should initially be deployed?, iii) the effect of the number of basestations (drifters with satellite connectivity) on the overall network connectivity, and iv) the optimal communication range of the basic drifters. Our empirical study provides useful insights on how to design distributed routing and in-network processing algorithms tailored for ocean-monitoring sensor 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.

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.

Recently technology developments enable the production and deployment of sensor networks that provide outstanding ability to monitor discrete and continuous phenomena in physical space. From a database perspective, a sensor network can be seen as a virtual ...

This study presents an on-road driver monitoring system, which is implemented on a stand-alone in-vehicle embedded system and driven by effective solar cells. The driver monitoring function is performed by an efficient eye detection technique. Through the driver’s eye movements captured from the camera, the attention states of the driver can be determined and any fatigue states can be avoided. This driver monitoring technique is implemented on a low-power embedded in-vehicle platform. Besides, this study also proposed monitoring machinery that can detect the brightness around the car to effectively determine whether this in-vehicle system is driven by the solar cells or by the vehicle battery. On sunny days, the in-vehicle system can be powered by solar cell in places without the vehicle battery. While in the evenings or on rainy days, the ambient solar brightness is insufficient, and the system is powered by the vehicle battery. The proposed system was tested under the conditions t...

Topological changes to regions, such as merging/splitting and hole formation/elimination, are significant events in their evolution. Information about such salient changes is useful in many applications. The research reported in this paper provides theoretical foundations for such topological change detection in sensor networks. A local tree model is proposed in the spatial domain, based on which a set of types of topological changes is specified. We also present a sensor network framework which captures the necessary information required by the tree model. Both the local tree model and the sensor network framework form the foundations for detection approaches that allow sensor networks to report topological changes.

We propose and investigate a model for shared ride trip planning in ad-hoc mobile geosensor networks. Our focus is on the communication strategies between the network nodes. In a dynamically changing network of autonomous nodes all trip plans and provisions need to be kept up-to-date. At the same time, energy consumption by broadcasting messages needs to be minimized. Hence, we have to solve an optimization problem: find an efficient communication strategy that still guarantees planning of acceptable trips in a continuously changing environment.

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. Figure 1: If the bus is missed riding with cars becomes an alternative, which requires ad-hoc trip planning.

This paper describes the concept of a Wide Area Alerting System (WAAS), a flexible ICT platform for building alerting and notification applications. It is tailored to the generation and dissemination of complex alerts or notifications derived from geospatial-temporal data originating from heterogeneous sensors, models and/or monitoring processes. A WAAS implementation explicitly recognises and harnesses spatial location and time and is interesting for its ability to receive and combine observed events of multiple phenomena, over wide geographic areas, into highly targeted, rich notifications/alerts, delivered to potentially large numbers of users over convenient communication channels. It can be deployed in a variety of scenarios that require functionality for notifying users about phenomena or events occurring at geographical scales. Furthermore, events that could trigger alerts or notifications may be fast-moving or slow to unfold-even over a period of hours or days. This paper describes the functional and non-functional design goals, system architecture and software infrastructure, usage scenarios and implementation experiences to date of the WAAS.

We propose and investigate a model for shared ride trip planning in ad-hoc mobile geosensor networks. Our focus is on the communication strategies between the network nodes. In a dynamically changing network of autonomous nodes all trip plans and provisions need to be kept up-to-date. At the same time, energy consumption by broadcasting messages needs to be minimized. Hence, we have to solve an optimization problem: find an efficient communication strategy that still guarantees planning of acceptable trips in a continuously changing environment.

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. Figure 1: If the bus is missed riding with cars becomes an alternative, which requires ad-hoc trip planning.

In this paper, a general model of an Early Detection of Environmental Events System is presented. It follows established technologies and it is based on wireless sensor network nodes connected to a central computer using IEEE 802.15.4 and the General Radio Packet Service. In order to test its feasibility, the model is applied to the early detection of forest fires. The contribution of this paper is to show that although the system is theoretically apt to work well in small grid squares, its deployment over the totality of large areas is unfeasible due to the sheer number of sensors necessary, and their present costs and deployment difficulties. Instead, a second best alternative, giving not an early warning of an impending fire but an early notice that the fire is in its beginning phase, is shown to be feasible, from both technical and economic points of view.

Sensor networks represent a non traditional source of information, as readings generated by sensors flow continuously, leading to an infinite stream of data. Traditional DBMSs, which are based on an exact and detailed representation of information, are not suitable in this context, as all the information carried by a data stream cannot be stored within a bounded storage space. Thus, compressing data (by possibly loosing less relevant information) and storing their compressed representation, rather than the original one, becomes mandatory. This approach aims to store as much information carried by the stream as possible, but makes it unfeasible to provide exact answers to queries on the stream content. However, exact answers to queries are often not necessary, as approximate ones usually suffice to get useful reports on the world monitored by the sensors. In this paper we propose a technique for providing fast approximate answers to aggregate queries on sensor data streams. Our proposal is based on a hierarchical summarization of the data stream embedded into a flexible indexing structure, which permits us to both access and update compressed data efficiently. The compressed representation of data is updated continuously, as new sensor readings arrive. When the available storage space is not enough to store new data, some space is released by compressing the "oldest" stored data progressively, so that recent information (which is usually the most relevant to retrieve) is represented with more detail than old one.

Sensor technologies and capabilities have an effect on observational data quality. Typically, data management begins, at best, when a data manager obtains the data and needs to describe it sufficiently to data consumers. Often, the sensing methods are not adequately described and the data manager does not know the appropriate questions to ask or where to direct questions about sensors, their configuration, and the deployment. Consequently, knowledge often remains buried in sensor manuals and field operator logs. Thus, most metadata requirements have been simplified to accommodate this gap in knowledge. When information is captured where it is best understood and tools are created to easily capture this knowledge, machine-actionable descriptions can be provided to adequately describe the processes taken in generating observations. The information can be associated with the data and thus be accessible, discoverable and used in data quality control by data providers and in data quality assessment by the data consumers. Here, we define actors and actions to promote role-based creation of fully-described, standards-based documents. These documents can be created in SensorML (OGC SWE) that includes links to resolvable term definitions (W3C Semantic Web), enabling the creation of associated mappings and ontologies to extend and resolve the meaning of each term.

We propose and investigate a model for shared ride trip planning in ad-hoc mobile geosensor networks. Our focus is on the communication strategies between the network nodes. In a dynamically changing network of autonomous nodes all trip plans and provisions need to be kept up-to-date. At the same time, energy consumption by broadcasting messages needs to be minimized. Hence, we have to solve an optimization problem: find an efficient communication strategy that still guarantees planning of acceptable trips in a continuously changing environment.

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. Figure 1: If the bus is missed riding with cars becomes an alternative, which requires ad-hoc trip planning.

The Dagstuhl Seminar, "Social Issues in Computational Transportation Science" (13512) took place from 15 to 19 December 2103, attracting 27 participants active in a wide range of academic, commercial, and public sector areas. CTS is an emerging discipline that combines advances in computer science and engineering with the modeling, planning, social, and economic aspects of transportation in order to improve the safety, mobility, and sustainability of transportation systems. The aim of this seminar was to focus on the social computing aspects of CTS, including such areas as social networks and crowd-sourcing for transportation, as well as the integration of persuasive technologies and behavioral economics in social computing. In their time at the workshop, participants discussed and debated these and other topics, as shown in the workshop's summary report.

We propose and investigate a model for shared ride trip planning in ad-hoc mobile geosensor networks. Our focus is on the communication strategies between the network nodes. In a dynamically changing network of autonomous nodes all trip plans and provisions need to be kept up-to-date. At the same time, energy consumption by broadcasting messages needs to be minimized. Hence, we have to solve an optimization problem: find an efficient communication strategy that still guarantees planning of acceptable trips in a continuously changing environment.

The aim of this article is to compare possibilities of the ontology usage in automation. In the first part there is the ontology definition and its generic form discussed as well as the connection between ontology and semantic web. In the second part, there are shown the possibilities of the ontology usage in automation, especially usage of ontology as metadata storage. The last part of the article is showing ontology work directly on sensors. Cooperating with computing system, this ontology should be able to work with sensor process data and to analyze these data.

A method for estimating mobile robot egomotion is presented, which relies on tracking contours in real-time images acquired with a calibrated monocular video system. After fitting an active contour to an object in the image, 3D motion is derived from the affine deformations suffered by the contour in an image sequence. More than one object can be tracked at the same time yielding some different pose estimations. Then, improvements in pose determination are achieved by fusing all these different estimations. Inertial information is used to obtain better estimates, as it introduces in the tracking algorithm a measure of the real velocity. Inertial information is also used to eliminate some ambiguities arising from the use of a monocular image sequence. As the algorithms developed are intended to be used in real-time control systems, considerations on computation costs are taken into account.

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. Figure 1: If the bus is missed riding with cars becomes an alternative, which requires ad-hoc trip planning.

Technologies for capturing large amounts of real-time and high-detail data about the environment have advanced rapidly; our ability to use this data for understanding the monitored settings for decision-making has not. Visual analytics, creating suitable tools and interfaces that combine computational powers with the human's capabilities for visual sense making, is a promising approach. Geosensor networks monitor a range of different complex environmental settings, collecting heterogeneous data at different spatial and temporal scales. Similarly domain experts with specific preferences and requirements use the collected data. Additionally, long-term monitoring networks may aim to increase sensor node longevity by minimizing storage and communication load. Based on these aspects, four key challenges for the extraction of knowledge about environmental objects and events from geosensor data are identified: dynamics and uncertainty of the continuous stream of recorded data; different scales in data collection but also data analysis at a range of aggregation levels; decentralized data processing and storage; and evaluation of the effectiveness, efficiency and completeness of implemented decentralized visual analytics approaches.

We are inevitably moving into a realm where small and inexpensive wireless devices would be seamlessly embedded in the physical world and form a wireless sensor network in order to perform complex monitoring and computational tasks. Such networks pose new challenges in data processing and dissemination due to the con ict between (i) the abundance of information that can be collected and processed in a distributed fashion among thousands of nodes and (ii) the limited resources (bandwidth, energy) that such devices possess. In this paper we propose a new data reduction technique that exploits the correlation and redundancy among multiple measurements on the same sensor and achieves high degree of data reduction while managing to capture even the smallest details of the recorded measurements. The key to our technique is the base signal, a series of values extracted from the real measurements, used for encoding piece-wise linear correlations among the collected data values. We provide e cient algorithms for extracting the base signal features from the data and for encoding the measurements using these features. Our experiments demonstrate that our method by far outperforms standard approximation techniques like Wavelets, Histograms and the Discrete Cosine Transform, on a variety of error metrics and for real datasets from di erent domains.

Augmenting heavy and power-hungry data collection equipment with lighter, smaller wireless sensor network nodes leads to faster, larger deployments. Arrays comprising dozens of wireless sensor nodes are now possible, allowing scientific studies that aren't feasible with traditional instrumentation. Designing sensor networks to support volcanic studies requires addressing the high data rates and high data fidelity these studies demand. The authors' sensor-network application for volcanic data collection relies on triggered event detection and reliable data retrieval to meet bandwidth and data-quality demands.

IEEE-1451(1) and OGC Sensor Web Enable- ment (OGC SWE)(2) define standard protocols to oper- ate instruments, including methods to calibrate, config- ure, trigger data acquisition, and retrieve instrument data based on specified temporal and geospatial criteria. These standards also provide standard ways to describe instru- ment capabilities, properties, and data structures produced by the instrument. These standard operational protocols and descriptions enable observing systems to manage very diverse instruments as well as to acquire, process, and interpret their data in a uniform and automated manner. We refer to this property as "instrument interoperability". This paper describes integration and evaluation of MBARI PUCK protocol (3) at OBSEA (4,5) in Spain.

This paper introduces an approach illustrating how the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) framework can be used in order to build a franco-lebanese observatory. We present the practical application of SWE services as a source of real-time observation data and the associated technical architecture for making near real-time observations available to end users on the Web. We discuss the question of crossing sensor data with other data sources, e.g., data provided by human observations. We illustrate our approach by describing the methodology to integrate a first illustration case to monitor snow weather stations in the Lebanese Mountains.

This paper describes the integration of IEEE 1451 smart transducers and Open Geospatial Consortium -Sensor Web Enablement (OGC-SWE) using the Smart Transducer Web Service (STWS). An integration architecture and a prototype system are presented. The integration is illustrated via case studies of the prototype system.

Mobile sensor networks consist of a number of sensor nodes which are capable of sensing, processing, communicating and moving. These mobile sensor nodes move around and explore their surrounding areas. Top-k queries are useful in many mobile sensor network applications. However, the mobility of sensor nodes incurs new challenges in addition to the problems of static sensor networks (i.e., resource constraints). Since mobile sensor nodes tend to move continuously, the network condition changes frequently and they consume considerably more energy than static sensor nodes. In this paper, we propose an efficient top-k query processing framework in a mobile sensor network environment called mSensor. To construct an efficient routing topology, we devise a mobility-aware routing method. Using the semantics of the top-k query, we develop a filter-based data collection method which can save the energy consumption and provide more accurate query results. We also devise a data compression method for disconnected sensor nodes to deal with the problem of limited memory space of sensor nodes. The performance of our proposed approach is extensively evaluated using synthetic data sets and real data sets. The results show the effectiveness of our approach.

Abstract. In fast OLAP applications it is often advantageous to provide approximate answers to range queries in order to achieve very high performances. A possible solution is to inquire summary data rather than the original ones and to perform suitable interpolations. Approximate answers become mandatory in situations where only aggregate data are available. This paper studies the problem of estimating range queries (namely, sum and count) over aggregate data using a probabilistic approach for computing expected value and variance of the answers. The novelty of this approach is the exploitation of possible integrity constraints about the presence of elements in the range that are known to be null or nonnull. Closed formulas for all results are provided, and some interesting applications for query estimations on histograms are discussed. 1