We envision participatory texture documentation (PTD) as a process in which a group of participants (dedicated individuals and/or general public) with camera-equipped mobile phones participate in collaborative/social collection of the urban texture information. PTD enables inexpensive, scalable and high resolution urban texture documentation. PTD is implemented in two steps. In the first step, minimum number of points in the urban environment are selected from which collection of maximum urban texture is possible. This step is called viewpoint selection. In the next step, the selected viewpoints are assigned to users (based on their preferences and constraints) for texture collection. This step is termed viewpoint assignment. In this paper, we focus on the viewpoint selection problem. We prove that this problem is NP-hard, and accordingly, propose a scalable (and efficient) heuristic with approximation guarantee for viewpoint selection. We study, profile and verify our proposed solution by extensive experiments.
Sensor networks pose a number of challenging conceptual and optimization problems. A fundamental problem in sensor networks is the clustering of the nodes into groups served by a high powered relay head, then forming a backbone among the relay heads for data transfer to the base station. We address this problem with a genetic algorithm (GA) as a search technique.
We present an algorithm that makes an appropriate use of a Kalman filter combined with a geometric computation with respect to the localisation of a pollutant-emitting point source. Assuming resource-constrained inexpensive nodes and no specific placement distance to the source, our approach has been shown to perform well in estimating the coordinates and intensity of a source. Using local gossip to directionally propagate estimates, our algorithm initiates a real-time exchange of information that has as an ultimate goal to lead a packet from a node that initially sensed the event to a destination that is as close to the source as possible. The coordinates and intensity measurement of the destination comprise the final estimate. In this paper, we assert that this low-overhead coarse localisation method can rival more sophisticated and computationally-hungry solutions to the source estimation problem.
The understanding of complex environmental phenomena, such as deforestation and epidemics, requires observations at multiple scales. This scale dependency is not handled well by today's rather technical sensor definitions. Geosensor networks are normally defined as distributed ad-hoc wireless networks of computing platforms serving to monitor phenomena in geographic space. Such definitions also do not admit animals as sensors. Consequently, they exclude human sensors, which are the key to volunteered geographic information, and they fail to support connections between phenomena observed at multiple scales. We propose definitions of sensors as information sources at multiple aggregation levels, relating physical stimuli to observations. An algebraic formalization shows their behavior as well as their aggregations and generalizations. It is intended as a basis for defining consistent application programming interfaces to sense the environment at multiple scales of observations and with different types of sensors.
Game Theory (GT) is a branch of applied mathematics that models situations where players (participants in a game) participate in a strategic situation (the game) in which they perform different actions attempting to maximise their profits, while at the same time minimise losses. As nodes in Wireless Sensor Networks (WSN) can be abstracted as the players in such games where energy and information are valuable resources it is obvious that Game Theory provides a solid framework for both the modelling and the induction of node behaviour in such networks. The proposed algorithm induces an energy-aware and efficient collaborative behaviour to the nodes using sensor centric information, by making them aware of their interdependency, without compromising the main purpose of the network - the collection of information.
Considerable progress has been made on the technical development of sensor networks. However increasing attention is now also required for the broad diversity of end-user requirements for the deployment of sensor networks. An expert survey on the user requirements and future expectations for sensor networks was carried out. Both technology and applications are seen as main drivers for sensor network deployment however harmonization of (open) standards to collect, access, manage, and integrate sensor data are considered crucial for further development. Although sensor based applications are increasingly used in every day life, their use in decision making requires further improvement of aspects like privacy, data quality, etc. Finally, next to formal sensor networks, standardization will allow voluntary sensor information to become a significant sensor data source.
En-route data compression is fundamental to reduce the power consumed for data gathering in sensor networks. Typical in-network compression schemes involve the distributed computation of some decorrelating transform on the data; the structure along which the transform is computed influences both coding performance and transmission cost of the computed coefficients, and has been widely explored in the literature. However, few works have studied this interaction in the practical case when the routing configuration of the network is also built in a distributed manner. In this paper we aim at expanding this understanding by specifically considering the impact of distributed routing tree initialization algorithms on coding and transmission costs, when a tree-based wavelet lifting transform is adopted. We propose a simple modification to the collection tree protocol (CTP) which can be tuned to account for a vast range of spatial correlations. In terms of costs and coding efficiency, our methods do not improve the performance of more sophisticated routing trees such as the shortest path tree, but they entail an easier manageability in case of node reconfigurations and update.
The increasing availability of huge amounts of "thin" data, i.e. data pertaining to time and positions generated by different sources with a wide variety of technologies (e.g., RFID tags, GPS, GSM networks) leads to large spatio-temporal data collections. Mining such amounts of data is challenging, since the possibility of extracting useful information from this particular type of data is crucial in many application scenarios such as vehicle traffic management, hand-off in cellular networks and supply chain management. In this paper, we address the issue of clustering spatial trajectories. In the context of trajectory data, this problem is even more challenging than in classical transactional relationships, as here we deal with data (trajectories) in which the order of items is relevant. We propose a novel approach based on a suitable regioning strategy and an efficient clustering technique based on edit distance. Experiments performed on real world datasets have confirmed the efficiency and effectiveness of the proposed techniques.
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.
We propose energy-efficient compressed sensing for wireless sensor networks using spatially-localized sparse projections. To keep the transmission cost for each measurement low, we obtain measurements from clusters of adjacent sensors. With localized projection, we show that joint reconstruction provides significantly better reconstruction than independent reconstruction. We also propose a metric of energy overlap between clusters and basis functions that allows us to characterize the gains of joint reconstruction for different basis functions. Compared with state of the art compressed sensing techniques for sensor network, our simulation results demonstrate significant gains in reconstruction accuracy and transmission cost.
Sensor networks have become a highly active research area due to their potential for providing diverse new capabilities for a wide variety of real world applications. Distributed applications require to collect information from a lot of different sensor systems, retrieved data are usually heterogeneous from many points of view and they need to be integrated to share a common objective. In this paper we present the SeNsIM framework, a scalable software architecture for the integration of heterogeneous sensor systems. SeNsIM enables the deployment of applications based on multiple sensor systems by providing a standard way to manage, query, and interact with sensors. We propose the architectural and data model of the SeNsIM framework and provide a method to describe sensor systems using XML as modeling language in order to facilitate sharing of structured data across them.
A wireless sensor network deployment on a glacier in Iceland is described. The system uses power management as well as power harvesting to provide long-term environment sensing. Advances in base station and sensor node design as well as initial results are described.
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.
Structured peer-to-peer overlay networks such as Chord, CAN, Tapestry, and Pastry, operate as distributed hash tables (DHTs). However, since every node is assigned a unique identifier in the basic design of DHT (randomly hashed), "locality-awareness" is not inherent due to the topology mismatching between the P2P overlay network and the physical underlying network. In this paper, we propose to incorporate physical locality into a Chord system. To potentially benefit from some level of knowledge about the relative proximity between peers, a network positioning model is necessary for capturing physical location information of network nodes. Thus, we incorporate GNP (Global Network Positioning) into Chord (Chord-GNP) since peers can easily maintain geometric coordinates that characterize their locations in the Internet. Next, we identify and explore three factors affecting Chord-GNP performance: distance between peers, message timeout calculation and lookup latency. The measured results show that Chord-GNP efficiently locates the nearest available node providing a locality property. In addition, both the number of the messages necessary to maintain routing information and the time taken to retrieve data in Chord-GNP is less than that in Chord.
Recently, wireless sensor networks providing fine-grained spatio-temporal observations have become one of the major monitoring platforms for geo-applications. Along side data acquisition, outlier detection is essential in geosensor networks to ensure data quality, secure-monitoring and reliable detection of interesting and critical events. A key challenge for outlier detection in these geosensor networks is accurate identification of outliers in a distributed and online manner while maintaining low resource consumption. In this paper, we propose an online outlier detection technique based on one-class hyperellipsoidal SVM and take advantage of spatial and temporal correlations that exist between sensor data to cooperatively identify outliers. Experiments with both synthetic and real data show that our online outlier detection technique achieves better detection accuracy compared to the existing SVM-based outlier detection techniques designed for sensor networks. We also show that, understanding data distribution and correlations among sensor data is essential to select the most suitable outlier detection technique.
In this paper we present algorithms for building and maintaining efficient aggregation trees that provide the conduit to disseminate data required for processing monitoring queries in a wireless sensor network. While prior techniques base their operation on the assumption that the sensor nodes that collect data relevant to a specified query need to include their measurements in the query result at every query epoch, in many event monitoring applications such an assumption is not valid. We introduce and formalize the notion of event monitoring queries and demonstrate that they can capture a large class of monitoring applications. We then show techniques which, using a small set of intuitive statistics, can compute aggregation trees that minimize important resources such as the number of messages exchanged among the nodes or the overall energy consumption. Our experiments demonstrate that our techniques can organize the data aggregation process while utilizing significantly lower resources than prior approaches.
Radio frequency identification (RFID) technology is gaining popularity for many IT related applications. Nevertheless, an immediate adoption of RFID solutions by the existing IT infrastructure is a formidable task because of the volume of data that can be collected in a large-scale deployment of RFIDs. In this paper we present algorithms for temporal and spatial aggregation of RFID data streams, as a means to reduce their volume in an application controllable manner. We propose algorithms of increased complexity that can aggregate the temporal records indicating the presence of an RFID tag using an application-defined storage upper bound. We further present complementary techniques that exploit the spatial correlations among RFID tags. Our methods detect multiple tags that are moved as a group and replace them with a surrogate group ID, in order to further reduce the size of the representation. We provide an experimental study using real RFID traces and demonstrate the effectiveness of our methods.
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This book constitutes the thoroughly refereed proceedings of the Second GeoSensor Networks Conference, held in Boston, Massachusetts, USA, in October 2006. The conference addressed issues related to the collection, management, processing, analysis, and delivery of real-time geospatial data using distributed geosensor networks. This represents an evolution of the traditional static and centralized geocomputational paradigm. The 13 carefully reviewed and selected papers included in the volume constitute extended versions of the papers presented at the conference. They are preceded by an introduction written by the volume editors. The book is structured in sections on Data Acquisition and Processing, Data Analysis and Integration, and Applications. The papers represent key research areas that are fundamental in order to realize the full potential of the emerging geosensor network paradigm. The contributions cover theentire spectrum of the field from low-level energy consumption issues at the individual sensor level to the high-level abstraction of events and ontologies or models to recognize and monitor phenomena using geosensor networks.
