Ecosystems monitoring is essential to properly understand their development and the effects of events, both climatological and anthropological in nature. The amount of data used in these assessments is increasing at very high rates. This is due to increasing availability of sensing systems and the development of new techniques to analyze sensor data. The Enviro-Net Project encompasses several of such sensor system deployments across five countries in the Americas. These deployments use a few different ground-based sensor systems, installed at different heights monitoring the conditions in tropical dry forests over long periods of time. This paper presents our experience in deploying and maintaining these systems, retrieving and pre-processing the data, and describes the Web portal developed to help with data management, visualization and analysis.
These and other initiatives, aiming at integration of ground-based monitoring efforts, are leading to an evolution from single site environmental monitoring into networks for environment observation @cite_8 . This evolution culminates with the current vision for a Sensor Web @cite_9 @cite_39 @cite_47 , encompassing several types of deployments of sensor systems, interconnecting them globally through a Web-based integration strategy using standards developed by the Sensor Web Enablement ( http: www.opengeospatial.org projects groups sensorweb ) Working Group of the Open Geospatial Consortium, Inc. (OGC) ( http: www.opengeospatial.org ).
@cite_9 @cite_47 @cite_39 @cite_8
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This paper provides an overview of sensor webs from the perspective of Earth observation. Definitions of in-situ, proximal, and remote sensing are presented. Sensor webs are also defined and the over-arching concept of integrated Earth sensing is outlined. The paper goes on to describe experiences in the development and deployment of sensor web prototypes in support of vegetation monitoring applications. Recommendations are provided with regard to where the emphasis should be placed for advancing in-situ sensor webs in the context of Earth observation in the future. Finally, the geostrategic importance of integrated Earth sensing, sensor webs and related technologies is discussed. The developments in wireless network technology and miniaturization makes it possible to realistically monitor the natural environment. Within the field of environmental sensor networks, domain knowledge is an essential fourth component. Before designing and installing any system, it is necessary to understand its physical environment and deployment in detail. Sensor networks are designed to transmit data from an array of sensors to a server data repository. They do not necessarily use a simple one way data stream over a communication network rather elements of the system decide what data to pass on, using local area summaries and filtering to minimize power use while maximizing the information content. The Envisense Glacs Web project is developing a monitoring system for a glacial environment. Monitoring the ice caps and glaciers provides valuable information about the global warming and climate change. The Open Geospatial Consortium (OGC) standards activities that focus on sensors and sensor networks comprise an OGC focus area known as Sensor Web Enablement (SWE). Readers interested in greater technical and architecture details can download the OGC SWE Architecture Discussion Paper titled "The OGC Sensor Web Enablement Architecture" (OGC document 06-021r1). The continual advancement in semiconductor technology has enabled vendors to integrate an increasing number of sensors, e.g., accelerometer, gyroscope, digital compass, high resolution cameras and others, in modern mobile phones. Combining with the ever expanding market penetration of mobile phones, the onboard sensors are driving the sensing community towards a new paradigm called participatory sensing in which ordinary citizens voluntarily collect and share information from their local environment using personal mobile devices. The inherent openness of this platform is a double-edged sword. While encouraging user participation on the one hand, it also makes it easy to contribute corrupted data. As such, data trustworthiness becomes a key issue which needs to be addressed to ensure sustainable development of this emerging paradigm. In this paper, we propose a reputation system in which a reputation score is calculated for each device as a reflection of the trustworthiness of its sensor data. We adopt the Gompertz function as a fundamental building block of our system, since it is better suited to deal with the unique characteristics of participatory sensing. We present an application-agnostic implementation of the system, which can work with a wide variety of participatory sensing applications. We evaluate the performance of our reputation system in the context of two real-world participatory sensing applications. Our results show that the proposed reputation system outperforms existing solutions by up to a factor of six. With the price of wireless sensor technologies diminishing rapidly we can expect large numbers of autonomous sensor networks being deployed in the near future. These sensor networks will typically not remain isolated but the need of interconnecting them on the network level to enable integrated data processing will arise, thus realizing the vision of a global "sensor Internet." This requires a flexible middleware layer which abstracts from the underlying, heterogeneous sensor network technologies and supports fast and simple deployment and addition of new platforms, facilitates efficient distributed query processing and combination of sensor data, provides support for sensor mobility, and enables the dynamic adaption of the system configuration during runtime with minimal (zero-programming) effort. This paper describes the global sensor networks (GSN) middleware which addresses these goals. We present GSN's conceptual model, abstractions, and architecture, and demonstrate the efficiency of the implementation through experiments with typical high-load application profiles. The GSN implementation is available from http: gsn.sourceforge.net . Environmental Sensor Networks (ESNs) facilitate the study of fundamental processes and the development of hazard response systems. They have evolved from passive logging systems that require manual downloading, into ‘intelligent’ sensor networks that comprise a network of automatic sensor nodes and communications systems which actively communicate their data to a Sensor Network Server (SNS) where these data can be integrated with other environmental datasets. The sensor nodes can be fixed or mobile and range in scale appropriate to the environment being sensed. ESNs range in scale and function and we have reviewed over 50 representative examples. Large Scale Single Function Networks tend to use large single purpose nodes to cover a wide geographical area. Localised Multifunction Sensor Networks typically monitor a small area in more detail, often with wireless adhoc systems. Biosensor Networks use emerging biotechnologies to monitor environmental processes as well as developing proxies for immediate use. In the future, sensor networks will integrate these three elements (Heterogeneous Sensor Networks). The communications system and data storage and integration (cyberinfrastructure) aspects of ESNs are discussed, along with current challenges which need to be addressed. We argue that Environmental Sensor Networks will become a standard research tool for future Earth System and Environmental Science. Not only do they provide a ‘virtual’ connection with the environment, they allow new field and conceptual approaches to the study of environmental processes to be developed. We suggest that although technological advances have facilitated these changes, it is vital that Earth Systems and Environmental Scientists utilise them. Our work explores the convergence between participatory sensing, political activism and public expressions. Unlike prior research, which focuses on personal sensing, we present low-cost, networked air quality sensors, designed to be repositioned across public landscapes by communities of citizen stakeholders. Our GPS-enabled sensors report dust, exhaust, or VOC's (volatile organic compounds), along with temperature, humidity and light levels to a website that visualizes this data in real time. The sensors can be attached to a variety of surfaces serving as research probes to demarcate ('tag') public spaces with environmental concerns. We deploy our fully functional system with four urban communities - parents, bicyclists, homeless and activists, positioning our system as a tool for studying and supporting community togetherness and public activism. Our findings highlight community sharing of the physical sensors and dialogues surrounding the collected data. The exponentially increasing stream of real time big data produced by Web 2.0 Internet and mobile networks created radically new interdisciplinary challenges for public health and computer science. Traditional public health disease surveillance systems have to utilize the potential created by new situation-aware realtime signals from social media, mobile sensor networks and citizens? participatory surveillance systems providing invaluable free realtime event-based signals for epidemic intelligence. However, rather than improving existing isolated systems, an integrated solution bringing together existing epidemic intelligence systems scanning news media (e.g., GPHIN, MedISys) with real-time social media intelligence (e.g., Twitter, participatory systems) is required to substantially improve and automate early warning, outbreak detection and preparedness operations. However, automatic monitoring and novel verification methods for these multichannel event-based real time signals has to be integrated with traditional case-based surveillance systems from microbiological laboratories and clinical reporting. Finally, the system needs effectively support coordination of epidemiological teams, risk communication with citizens and implementation of prevention measures. However, from computational perspective, signal detection, analysis and verification of very high noise realtime big data provide a number of interdisciplinary challenges for computer science. Novel approaches integrating current systems into a digital public health dashboard can enhance signal verification methods and automate the processes assisting public health experts in providing better informed and more timely response. In this paper, we describe the roadmap to such a system, components of an integrated public health surveillance services and computing challenges to be resolved to create an integrated real world solution. Wireless Sensor Networks (WSN) are often densely deployed in the region of interest in order to continuously monitor physical phenomenon. Due to highly deployment density and the nature of the physical phenomenon, nearby sensor readings are often highly correlated in both space domain and time domain. These spatial and temporal correlations bring significant potential advantages as well as challenges for developing efficient sensing scheduling protocols for WSN. In this paper, a theoretical framework is developed to model the Quality of Monitoring (QoM) by exploiting both spatial and temporal correlations. The objective of this work is to enable the development of efficient sensing scheduling protocols which exploit these advantageous intrinsic features of the WSN paradigm. Specially, we propose two sensing scheduling schemes in order to maximize the overall QoM subject to resource constraints (e.g., under fixed duty cycle). Extensive experiments validate our theoretical results. Abstract: The Sensor Web is a macro-instrument concept that allows for the spatio-temporal understanding of an environment through coordinated efforts between multiple numbers and types of sensing platforms, including both orbital and terrestrial and both fixed and mobile. Each of these platforms, or pods, communicates within their local neighborhood and thus distributes information to the instrument as a whole. Much as intelligence in the brain is a result of the myriad of connections between dendrites, it is anticipated that the Sensor Web will develop a macro-intelligence as a result of its distributed information with the pods reacting and adapting to their environment in a way that is much more than their individual sum. The sharing of data among individual pods will allow for a global perception and purpose of the instrument as a whole. The Sensor Web is to sensors what the Internet is to computers, with different platforms and operating systems communicating via a set of shared, robust protocols. This paper will outline the potential of the Sensor Web concept and describe the Jet Propulsion Laboratory (JPL) Sensor Webs Project (http: sensorwebs.jpl.nasa.gov ). In particular, various fielded Sensor Webs will be discussed. Traditionally, environmental monitoring is achieved by a small number of expensive and high precision sensing unities. Collected data are retrieved directly from the equipment at the end of the experiment and after the unit is recovered. The implementation of a wireless sensor network provides an alternative solution by deploying a larger number of disposable sensor nodes. Nodes are equipped with sensors with less precision, however, the network as a whole provides better spatial resolution of the area and the users can have access to the data immediately. This paper surveys a comprehensive review of the available solutions to support wireless sensor network environmental monitoring applications. Sensor network technology promises a vast increase in automatic data collection capabilities through efficient deployment of tiny sensing devices. The technology will allow users to measure phenomena of interest at unprecedented spatial and temporal densities. However, as with almost every data-driven technology, the many benefits come with a significant challenge in data reliability. If wireless sensor networks are really going to provide data for the scientific community, citizen-driven activism, or organizations which test that companies are upholding environmental laws, then an important question arises: How can a user trust the accuracy of information provided by the sensor networkq Data integrity is vulnerable to both node and system failures. In data collection systems, faults are indicators that sensor nodes are not providing useful information. In data fusion systems the consequences are more dire; the final outcome is easily affected by corrupted sensor measurements, and the problems are no longer visibly obvious. In this article, we investigate a generalized and unified approach for providing information about the data accuracy in sensor networks. Our approach is to allow the sensor nodes to develop a community of trust. We propose a framework where each sensor node maintains reputation metrics which both represent past behavior of other nodes and are used as an inherent aspect in predicting their future behavior. We employ a Bayesian formulation, specifically a beta reputation system, for the algorithm steps of reputation representation, updates, integration and trust evolution. This framework is available as a middleware service on motes and has been ported to two sensor network operating systems, TinyOS and SOS. We evaluate the efficacy of this framework using multiple contexts: (1) a lab-scale test bed of Mica2 motes, (2) Avrora simulations, and (3) real data sets collected from sensor network deployments in James Reserve. Many real-world wireless sensor network applications such as environmental monitoring, structural health monitoring, and smart grid can be formulated as a least-squares problem. In distributed Cyber-Physical System (CPS), each sensor node observes partial phenomena due to spatial and temporal restriction and is able to form only partial rows of least-squares. Traditionally, these partial measurements were gathered at a centralized location. However, with the increase in sensors and their measurements, aggregation is becoming challenging and infeasible. In this paper, we propose distributed randomized kaczmarz that performs in-network computation to solve least-squares over the network by avoiding costly communication. As a case study, we present a volcano monitoring application on a distributed CORE emulator and use real data from Mt. St. Helens to evaluate our proposed method. In the near future, the wireless sensor networks (WSN) consisting of hundreds or thousands of small sensor devices are expected to become increasingly popular due to their low cost and easy use. As a distributed system, they are usually deployed in unattended environments and aim to collect useful information from the sensed area. In this paper, we present a web interface model for habitat monitoring using wireless sensor network, which can be used for wide range of applications. The interface we developed provides a user-friendly environment and a set of functionalities that easies the interaction between the end-users and the WSN. Our web interface is characterized with modularity, which makes applications easily extensible. Intelligent collaborative environments, where heterogenous entities operate together in achieving common mission objectives have been increasingly adopted for monitoring and surveillance of interest areas and physical infrastructures. They can be assembled from multiple existing technologies ranging from wireless sensor networks (WSN), terrestrial remote operated vehicles (ROV) and unmanned aerial vehicles (UAV). In this context, we first introduce a multi-level system framework for multi-sensory robotic surveillance of critical infrastructure protection through communication, data acquisition and processing - MUROS. Leveraging a cognitive radio (CR) scheme is discussed as key point of the paper, arguing that by exploiting in an opportunistic fashion the time, frequency and spatial stream of the wireless environment, increased communication reliability can be achieved with positive impact on the availability and service level at each hierarchical level. The application of CR, given the heterogeneous nature of the application across multiple radio interfaces and protocols, stand outs as a novel and feasible research direction. We argument the advantages of this scheme within the constraints of a working scenario and define a simulation-based approach in order to validate our solution. Sensor nodes have limited sensing range and are not very reliable. To obtain accurate sensing data, many sensor nodes should he deployed and then the collaboration among them becomes an important issue. In W. Zhang and G. Cao, a tree-based approach has been proposed to facilitate sensor nodes collaborating in detecting and tracking a mobile target. As the target moves, many nodes in the tree may become faraway from the root of the tree, and hence a large amount of energy may be wasted for them to send their sensing data to the root. We address the tree reconfiguration problem. We formalize it as finding a min-cost convoy tree sequence, and solve it by proposing an optimized complete reconfiguration scheme and an optimized interception-based reconfiguration scheme. Analysis and simulation are conducted to compare the proposed schemes with each other and with other reconfiguration schemes. The results show that the proposed schemes are more energy efficient than others. 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. Environmental monitoring is one of the driving applications in the domain of sensor networks. The lifetime of such systems is envisioned to exceed several years. To achieve this longevity in unattended operation it is crucial to minimize energy consumption of the battery-powered sensor nodes. This paper proposes Dozer, a data gathering protocol meeting the requirements of periodic data collection and ultra-low power consumption. The protocol comprises MAC-layer, topology control, and routing all coordinated to reduce energy wastage of the communication subsystem. Using a tree-based network structure, packets are reliably routed towards the data sink. Parents thereby schedule precise rendezvous times for all communication with their children. In a deployed network consisting of 40 TinyOS- enabled sensor nodes, Dozer achieves radio duty cycles in the magnitude of 0.2 . This brief introduces a class of problems and models for the prediction of the scalar field of interest from noisy observations collected by mobile sensor networks. It also introduces the problem of optimal coordination of robotic sensors to maximize the prediction quality subject to communication and mobility constraints either in a centralized or distributed manner. To solve such problems, fully Bayesian approaches are adopted, allowing various sources of uncertainties to be integrated into an inferential framework effectively capturing all aspects of variability involved. The fully Bayesian approach also allows the most appropriate values for additional model parameters to be selected automatically by data, and the optimal inference and prediction for the underlying scalar field to be achieved. In particular, spatio-temporal Gaussian process regression is formulated for robotic sensors to fuse multifactorial effects of observations, measurement noise, and prior distributions for obtaining the predictive distribution of a scalar environmental field of interest. New techniques are introduced to avoid computationally prohibitive Markov chain Monte Carlo methods for resource-constrained mobile sensors. Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks starts with a simple spatio-temporal model and increases the level of model flexibility and uncertainty step by step, simultaneously solving increasingly complicated problems and coping with increasing complexity, until it ends with fully Bayesian approaches that take into account a broad spectrum of uncertainties in observations, model parameters, and constraints in mobile sensor networks. The book is timely, being very useful for many researchers in control, robotics, computer science and statistics trying to tackle a variety of tasks such as environmental monitoring and adaptive sampling, surveillance, exploration, and plume tracking which are of increasing currency. Problems are solved creatively by seamless combination of theories and concepts from Bayesian statistics, mobile sensor networks, optimal experiment design, and distributed computation. Wireless Sensor Networks (WSNs) allow for untethered sensing of the environment. It is anticipated that, within the next few years, sensors will be deployed in a variety of scenarios, ranging from environmental monitoring to health care, from public to private sector and other areas. This paper investigate the utilization of receiver-based Medium Access Control (MAC) protocol in enhancing the performance of routing protocols such as IETF ROLL's RPL. Receiver-Based MAC (RB-MAC) is a preamble-sampling MAC protocol which dynamically elects the next-hop among a number of potential relay neighbors, based on current channel conditions and status of the sensor nodes. The proposed scheme is resilient to lossy links by nature, and hence reduces the number of retransmissions. We show by analysis how it outperforms the state-of-the-art sender-based preamble sampling MAC protocols in terms of energy and delay.