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)

Localisation and tracking of people using distributed UWB sensors

In vielen Überwachungs- und Rettungsszenarien ist die Lokalisierung und Verfolgung von Personen in Innenräumen auf nichtkooperative Weise erforderlich. Für die Erkennung von Objekten durch Wände in kurzer bis mittlerer Entfernung, ist die Ultrabreitband (UWB) Radartechnologie aufgrund ihrer hohen zeitlichen Auflösung und Durchdringungsfähigkeit Erfolg versprechend. In dieser Arbeit wird ein Prozess vorgestellt, mit dem Personen in Innenräumen mittels UWB-Sensoren lokalisiert werden können. Er umfasst neben der Erfassung von Messdaten, Abstandschätzungen und dem Erkennen von Mehrfachzielen auch deren Ortung und Verfolgung. Aufgrund der schwachen Reflektion von Personen im Vergleich zum Rest der Umgebung, wird zur Personenerkennung zuerst eine Hintergrundsubtraktionsmethode verwendet. Danach wird eine konstante Falschalarmrate Methode zur Detektion und Abstandschätzung von Personen angewendet. Für Mehrfachziellokalisierung mit einem UWB-Sensor wird eine Assoziationsmethode entwickelt, um die Schätzungen des Zielabstandes den richtigen Zielen zuzuordnen. In Szenarien mit mehreren Zielen kann es vorkommen, dass ein näher zum Sensor positioniertes Ziel ein anderes abschattet. Ein Konzept für ein verteiltes UWB-Sensornetzwerk wird vorgestellt, in dem sich das Sichtfeld des Systems durch die Verwendung mehrerer Sensoren mit unterschiedlichen Blickfeldern erweitert lässt. Hierbei wurde ein Prototyp entwickelt, der durch Fusion von Sensordaten die Verfolgung von Mehrfachzielen in Echtzeit ermöglicht. Dabei spielen insbesondere auch Synchronisierungs- und Kooperationsaspekte eine entscheidende Rolle. Sensordaten können durch Zeitversatz und systematische Fehler gestört sein. Falschmessungen und Rauschen in den Messungen beeinflussen die Genauigkeit der Schätzergebnisse. Weitere Erkenntnisse über die Zielzustände können durch die Nutzung zeitlicher Informationen gewonnen werden. Ein Mehrfachzielverfolgungssystem wird auf der Grundlage des Wahrscheinlichkeitshypothesenfilters (Probability Hypothesis Density Filter) entwickelt, und die Unterschiede in der Systemleistung werden bezüglich der von den Sensoren ausgegebene Informationen, d.h. die Fusion von Ortungsinformationen und die Fusion von Abstandsinformationen, untersucht. Die Information, dass ein Ziel detektiert werden sollte, wenn es aufgrund von Abschattungen durch andere Ziele im Szenario nicht erkannt wurde, wird als dynamische Überdeckungswahrscheinlichkeit beschrieben. Die dynamische Überdeckungswahrscheinlichkeit wird in das Verfolgungssystem integriert, wodurch weniger Sensoren verwendet werden können, während gleichzeitig die Performanz des Schätzers in diesem Szenario verbessert wird. Bei der Methodenauswahl und -entwicklung wurde die Anforderung einer Echtzeitanwendung bei unbekannten Szenarien berücksichtigt. Jeder untersuchte Aspekt der Mehrpersonenlokalisierung wurde im Rahmen dieser Arbeit mit Hilfe von Simulationen und Messungen in einer realistischen Umgebung mit UWB Sensoren verifiziert.Indoor localisation and tracking of people in non-cooperative manner is important in many surveillance and rescue applications. Ultra wideband (UWB) radar technology is promising for through-wall detection of objects in short to medium distances due to its high temporal resolution and penetration capability. This thesis tackles the problem of localisation of people in indoor scenarios using UWB sensors. It follows the process from measurement acquisition, multiple target detection and range estimation to multiple target localisation and tracking. Due to the weak reflection of people compared to the rest of the environment, a background subtraction method is initially used for the detection of people. Subsequently, a constant false alarm rate method is applied for detection and range estimation of multiple persons. For multiple target localisation using a single UWB sensor, an association method is developed to assign target range estimates to the correct targets. In the presence of multiple targets it can happen that targets closer to the sensor induce shadowing over the environment hindering the detection of other targets. A concept for a distributed UWB sensor network is presented aiming at extending the field of view of the system by using several sensors with different fields of view. A real-time operational prototype has been developed taking into consideration sensor cooperation and synchronisation aspects, as well as fusion of the information provided by all sensors. Sensor data may be erroneous due to sensor bias and time offset. Incorrect measurements and measurement noise influence the accuracy of the estimation results. Additional insight of the targets states can be gained by exploiting temporal information. A multiple person tracking framework is developed based on the probability hypothesis density filter, and the differences in system performance are highlighted with respect to the information provided by the sensors i.e. location information fusion vs range information fusion. The information that a target should have been detected when it is not due to shadowing induced by other targets is described as dynamic occlusion probability. The dynamic occlusion probability is incorporated into the tracking framework, allowing fewer sensors to be used while improving the tracker performance in the scenario. The method selection and development has taken into consideration real-time application requirements for unknown scenarios at every step. Each investigated aspect of multiple person localization within the scope of this thesis has been verified using simulations and measurements in a realistic environment using M-sequence UWB sensors

Information Fusion for 5G IoT: An Improved 3D Localisation Approach Using K-DNN and Multi-Layered Hybrid Radiomap

Indoor positioning is a core enabler for various 5G identity and context-aware applications requiring precise and real-time simultaneous localisation and mapping (SLAM). In this work, we propose a K-nearest neighbours and deep neural network (K-DNN) algorithm to improve 3D indoor positioning. Our implementation uses a novel data-augmentation concept for the received signal strength (RSS)-based fingerprint technique to produce a 3D fused hybrid. In the offline phase, a machine learning (ML) approach is used to train a model on a radiomap dataset that is collected during the offline phase. The proposed algorithm is implemented on the constructed hybrid multi-layered radiomap to improve the 3D localisation accuracy. In our implementation, the proposed approach is based on the fusion of the prominent 5G IoT signals of Bluetooth Low Energy (BLE) and the ubiquitous WLAN. As a result, we achieved a 91% classification accuracy in 1D and a submeter accuracy in 2D

Accurate and efficient localisation in wireless sensor networks using a best-reference selection

Many wireless sensor network (WSN) applications depend on knowing the position of nodes within the network if they are to function efficiently. Location information is used, for example, in item tracking, routing protocols and controlling node density. Configuring each node with its position manually is cumbersome, and not feasible in networks with mobile nodes or dynamic topologies. WSNs, therefore, rely on localisation algorithms for the sensor nodes to determine their own physical location. The basis of several localisation algorithms is the theory that the higher the number of reference nodes (called “references”) used, the greater the accuracy of the estimated position. However, this approach makes computation more complex and increases the likelihood that the location estimation may be inaccurate. Such inaccuracy in estimation could be due to including data from nodes with a large measurement error, or from nodes that intentionally aim to undermine the localisation process. This approach also has limited success in networks with sparse references, or where data cannot always be collected from many references (due for example to communication obstructions or bandwidth limitations). These situations require a method for achieving reliable and accurate localisation using a limited number of references. Designing a localisation algorithm that could estimate node position with high accuracy using a low number of references is not a trivial problem. As the number of references decreases, more statistical weight is attached to each reference’s location estimate. The overall localisation accuracy therefore greatly depends on the robustness of the selection method that is used to eliminate inaccurate references. Various localisation algorithms and their performance in WSNs were studied. Information-fusion theory was also investigated and a new technique, rooted in information-fusion theory, was proposed for defining the best criteria for the selection of references. The researcher chose selection criteria to identify only those references that would increase the overall localisation accuracy. Using these criteria also minimises the number of iterations needed to refine the accuracy of the estimated position. This reduces bandwidth requirements and the time required for a position estimation after any topology change (or even after initial network deployment). The resultant algorithm achieved two main goals simultaneously: accurate location discovery and information fusion. Moreover, the algorithm fulfils several secondary design objectives: self-organising nature, simplicity, robustness, localised processing and security. The proposed method was implemented and evaluated using a commercial network simulator. This evaluation of the proposed algorithm’s performance demonstrated that it is superior to other localisation algorithms evaluated; using fewer references, the algorithm performed better in terms of accuracy, robustness, security and energy efficiency. These results confirm that the proposed selection method and associated localisation algorithm allow for reliable and accurate location information to be gathered using a minimum number of references. This decreases the computational burden of gathering and analysing location data from the high number of references previously believed to be necessary.Thesis (PhD(Eng))--University of Pretoria, 2011.Electrical, Electronic and Computer Engineeringunrestricte

VIGILANT: "Situation-Aware" Quality of Information Interest Groups for Wireless Sensor Network Surveillance Applications

Effective situation awareness is a critical element for decision support in a wide range of military and para-military operational surveillance scenarios. Effective situation awareness in a surveillance scenario can greatly increase operational effectiveness, by improving the quality and timeliness of decisions. In this paper we outline a three level integrated design approach to promote situation awareness. Our approach allows deployed wireless sensor nodes to efficiently self-organise into dynamic clusters, based on a current common perceived threat situation ( context). Firstly our distributed predator aware situation assessment system ( PORTENT) models, detects and presents, in terms of quality of information (QoI), potential situations occurring within an uncertain environment. Secondly, we utilise a Bayesian belief network to understand the significance associated with the potential situation. Finally in order to obtain a better shared awareness we have developed a "context aware" service protocol that supports group formation and efficient management of sensor network assets. By combining this three level approach, we present our VIGILANT "situation aware" QoI interest group system. Extensive simulations have been undertaken to verify the VIGILANT concept, to demonstrate the effectiveness of our approach, in improving performance for network management efficiency, through utilisation of a shared "context" service provision time and QoI surveillance presentation