Survey on Quality of Observation within Sensor Web Systems

The Sensor Web vision refers to the addition of a middleware layer between sensors and applications. To bridge the gap between these two layers, Sensor Web systems must deal with heterogeneous sources, which produce heterogeneous observations of disparate quality. Managing such diversity at the application level can be complex and requires high levels of expertise from application developers. Moreover, as an information-centric system, any Sensor Web should provide support for Quality of Observation (QoO) requirements. In practice, however, only few Sensor Webs provide satisfying QoO support and are able to deliver high-quality observations to end consumers in a specific manner. This survey aims to study why and how observation quality should be addressed in Sensor Webs. It proposes three original contributions. First, it provides important insights into quality dimensions and proposes to use the QoO notion to deal with information quality within Sensor Webs. Second, it proposes a QoO-oriented review of 29 Sensor Web solutions developed between 2003 and 2016, as well as a custom taxonomy to characterise some of their features from a QoO perspective. Finally, it draws four major requirements required to build future adaptive and QoO-aware Sensor Web solutions

From Sensor to Observation Web with Environmental Enablers in the Future Internet

This paper outlines the grand challenges in global sustainability research and the objectives of the FP7 Future Internet PPP program within the Digital Agenda for Europe. Large user communities are generating significant amounts of valuable environmental observations at local and regional scales using the devices and services of the Future Internet. These communities’ environmental observations represent a wealth of information which is currently hardly used or used only in isolation and therefore in need of integration with other information sources. Indeed, this very integration will lead to a paradigm shift from a mere Sensor Web to an Observation Web with semantically enriched content emanating from sensors, environmental simulations and citizens. The paper also describes the research challenges to realize the Observation Web and the associated environmental enablers for the Future Internet. Such an environmental enabler could for instance be an electronic sensing device, a web-service application, or even a social networking group affording or facilitating the capability of the Future Internet applications to consume, produce, and use environmental observations in cross-domain applications. The term ?envirofied? Future Internet is coined to describe this overall target that forms a cornerstone of work in the Environmental Usage Area within the Future Internet PPP program. Relevant trends described in the paper are the usage of ubiquitous sensors (anywhere), the provision and generation of information by citizens, and the convergence of real and virtual realities to convey understanding of environmental observations. The paper addresses the technical challenges in the Environmental Usage Area and the need for designing multi-style service oriented architecture. Key topics are the mapping of requirements to capabilities, providing scalability and robustness with implementing context aware information retrieval. Another essential research topic is handling data fusion and model based computation, and the related propagation of information uncertainty. Approaches to security, standardization and harmonization, all essential for sustainable solutions, are summarized from the perspective of the Environmental Usage Area. The paper concludes with an overview of emerging, high impact applications in the environmental areas concerning land ecosystems (biodiversity), air quality (atmospheric conditions) and water ecosystems (marine asset management)

The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms

Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

Estimating Fire Weather Indices via Semantic Reasoning over Wireless Sensor Network Data Streams

Wildfires are frequent, devastating events in Australia that regularly cause significant loss of life and widespread property damage. Fire weather indices are a widely-adopted method for measuring fire danger and they play a significant role in issuing bushfire warnings and in anticipating demand for bushfire management resources. Existing systems that calculate fire weather indices are limited due to low spatial and temporal resolution. Localized wireless sensor networks, on the other hand, gather continuous sensor data measuring variables such as air temperature, relative humidity, rainfall and wind speed at high resolutions. However, using wireless sensor networks to estimate fire weather indices is a challenge due to data quality issues, lack of standard data formats and lack of agreement on thresholds and methods for calculating fire weather indices. Within the scope of this paper, we propose a standardized approach to calculating Fire Weather Indices (a.k.a. fire danger ratings) and overcome a number of the challenges by applying Semantic Web Technologies to the processing of data streams from a wireless sensor network deployed in the Springbrook region of South East Queensland. This paper describes the underlying ontologies, the semantic reasoning and the Semantic Fire Weather Index (SFWI) system that we have developed to enable domain experts to specify and adapt rules for calculating Fire Weather Indices. We also describe the Web-based mapping interface that we have developed, that enables users to improve their understanding of how fire weather indices vary over time within a particular region.Finally, we discuss our evaluation results that indicate that the proposed system outperforms state-of-the-art techniques in terms of accuracy, precision and query performance.Comment: 20pages, 12 figure

Future internet enablers for VGI applications

This paper presents the authors experiences with the development of mobile Volunteered Geographic Information (VGI) applications in the context of the ENVIROFI project and Future Internet Public Private Partnership (FI-PPP) FP7 research programme.FI-PPP has an ambitious goal of developing a set of Generic FI Enablers (GEs) - software and hardware tools that will simplify development of thematic future internet applications. Our role in the programme was to provide requirements and assess the usability of the GEs from the point of view of the environmental usage area, In addition, we specified and developed three proof of concept implementations of environmental FI applications, and a set of specific environmental enablers (SEs) complementing the functionality offered by GEs. Rather than trying to rebuild the whole infrastructure of the Environmental Information Space (EIS), we concentrated on two aspects: (1) how to assure the existing and future EIS services and applications can be integrated and reused in FI context; and (2) how to profit from the GEs in future environmental applications.This paper concentrates on the GEs and SEs which were used in two of the ENVIROFI pilots which are representative for the emerging class of Volunteered Geographic Information (VGI) use-cases: one of them is pertinent to biodiversity and another to influence of weather and airborne pollution on users’ wellbeing. In VGI applications, the EIS and SensorWeb overlap with the Social web and potentially huge amounts of information from mobile citizens needs to be assessed and fused with the observations from official sources. On the whole, the authors are confident that the FI-PPP programme will greatly influence the EIS, but the paper also warns of the shortcomings in the current GE implementations and provides recommendations for further developments

Observation Centric Sensor Data Model

Management of sensor data requires metadata to understand the semantics of observations. While e-science researchers have high demands on metadata, they are selective in entering metadata. The claim in this paper is to focus on the essentials, i.e., the actual observations being described by location, time, owner, instrument, and measurement. The applicability of this approach is demonstrated in two very different case studies