Associative Search Network for RSSI-basedTarget Localization in Unknown Environments

International audienceReceived Signal Strength Indicator (RSSI) is commonly consideredand is very popular for target localization applications, since itdoes not require extra-circuitry and is always available on current devices.Unfortunately, target localizations based on RSSI are aected withmany issues, above all in indoor environments. In this paper, we focus onthe pervasive localization of target objects in an unknown environment.In order to accomplish the localization task, we implement an AssociativeSearch Network (ASN) on the robots and we deploy a real test-bedto evaluate the eectiveness of the ASN for target localization. The ASNis based on the computation of weights, to "dictate" the correct directionof movement, closer to the target. Results show that RSSI through anASN is eective to localize a target, since there is an implicit mechanismof correction, deriving from the learning approach implemented in theASN

Fuzzy Mobile-Robot Positioning in Intelligent Spaces Using Wireless Sensor Networks

This work presents the development and experimental evaluation of a method based on fuzzy logic to locate mobile robots in an Intelligent Space using Wireless Sensor Networks (WSNs). The problem consists of locating a mobile node using only inter-node range measurements, which are estimated by radio frequency signal strength attenuation. The sensor model of these measurements is very noisy and unreliable. The proposed method makes use of fuzzy logic for modeling and dealing with such uncertain information. Besides, the proposed approach is compared with a probabilistic technique showing that the fuzzy approach is able to handle highly uncertain situations that are difficult to manage by well-known localization methods

Investigations of Dempster-Shafer theory in the context of WLAN-based indoor localization

Accurate user's locations and real-time location estimations in indoor environments, are important parameters to achieve reliable Location Based Services (LBSs). Non-Bayesian frameworks are gaining more and more interest in order to improve the location accuracy indoors when WLAN positioning is used. The main objective of this thesis is to study the feasibility of Dempster Shafer non-Bayesian combining in the context of received signal strength (RSS)-based indoor WLAN localization. The motivation of our work has been to look for new approaches in order to try to deal better with the incomplete or erroneous data measurements used in the training phase of any WLAN positioning algorithm. State-of-art studies show that the accuracy of mobile position estimation by WLAN localization algorithms with the Bayesian framework is not satisfactory. Thus, it makes sense to try to investigate non-Bayesian approaches and to see their usefulness in the context of WLAN localization. First, a comprehensive analysis of various DST combining rules with RSS-based positioning methods has been performed. Then, the idea has been implemented via MATLAB simulator and the outputs were compared to the Bayesian approaches. The comparison is in terms of root mean square errors, correct floor detection probabilities and error radius and we used real-field data measurements as test data. Typically, the current published research work based on non-Bayesian frameworks in the context of wireless localization is limited to fingerprinting methods. Both the fingerprinting and the path-loss model using the DST frameworks are carried out in this thesis. The thesis results contain two parts. The first one examines the fingerprinting with various DST combination while the other one deals with the path-loss and DST combination. The positioning accuracy estimated by Bayesian framework is compared to the DST and a high correlation between these two has been observed. As expected, the Bayesian framework results are slightly less accurate (on average) than the DST, because the DST fuse RSS from multiple access points with different beliefs or underlying uncertainty and allows the uncertainty to be a model parameter

Smart Monitoring and Control in the Future Internet of Things

The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things

Space-partitioning with cascade-connected ANN structures for positioning in mobile communication systems

The world around us is getting more connected with each day passing by – new portable devices employing wireless connections to various networks wherever one might be. Locationaware computing has become an important bit of telecommunication services and industry. For this reason, the research efforts on new and improved localisation algorithms are constantly being performed. Thus far, the satellite positioning systems have achieved highest popularity and penetration regarding the global position estimation. In spite the numerous investigations aimed at enabling these systems to equally procure the position in both indoor and outdoor environments, this is still a task to be completed. This research work presented herein aimed at improving the state-of-the-art positioning techniques through the use of two highly popular mobile communication systems: WLAN and public land mobile networks. These systems already have widely deployed network structures (coverage) and a vast number of (inexpensive) mobile clients, so using them for additional, positioning purposes is rational and logical. First, the positioning in WLAN systems was analysed and elaborated. The indoor test-bed, used for verifying the models’ performances, covered almost 10,000m2 area. It has been chosen carefully so that the positioning could be thoroughly explored. The measurement campaigns performed therein covered the whole of test-bed environment and gave insight into location dependent parameters available in WLAN networks. Further analysis of the data lead to developing of positioning models based on ANNs. The best single ANN model obtained 9.26m average distance error and 7.75m median distance error. The novel positioning model structure, consisting of cascade-connected ANNs, improved those results to 8.14m and 4.57m, respectively. To adequately compare the proposed techniques with other, well-known research techniques, the environment positioning error parameter was introduced. This parameter enables to take the size of the test environment into account when comparing the accuracy of the indoor positioning techniques. Concerning the PLMN positioning, in-depth analysis of available system parameters and signalling protocols produced a positioning algorithm, capable of fusing the system received signal strength parameters received from multiple systems and multiple operators. Knowing that most of the areas are covered by signals from more than one network operator and even more than one system from one operator, it becomes easy to note the great practical value of this novel algorithm. On the other hand, an extensive drive-test measurement campaign, covering more than 600km in the central areas of Belgrade, was performed. Using this algorithm and applying the single ANN models to the recorded measurements, a 59m average distance error and 50m median distance error were obtained. Moreover, the positioning in indoor environment was verified and the degradation of performances, due to the crossenvironment model use, was reported: 105m average distance error and 101m median distance error. When applying the new, cascade-connected ANN structure model, distance errors were reduced to 26m and 2m, for the average and median distance errors, respectively. The obtained positioning accuracy was shown to be good enough for the implementation of a broad scope of location based services by using the existing and deployed, commonly available, infrastructure

Perception for context awareness of agricultural robots

Context awareness is one key point for the realisation of robust autonomous systems in unstructured environments like agriculture. Robots need a precise description of their environment so that tasks could be planned and executed correctly. When using a robot system in a controlled, not changing environment, the programmer maybe could model all possible circumstances to get the system reliable. However, the situation gets more complex when the environment and the objects are changing their shape, position or behaviour. Perception for context awareness in agriculture means to detect and classify objects of interest in the environment correctly and react to them. The aim of this cumulative dissertation was to apply different strategies to increase context awareness with perception in mobile robots in agriculture. The objectives of this thesis were to address five aspects of environment perception: (I) test static local sensor communication with a mobile vehicle, (II) detect unstructured objects in a controlled environment, (III) describe the influence of growth stage to algorithm outcomes, (IV) use the gained sensor information to detect single plants and (V) improve the robustness of algorithms under noisy conditions. First, the communication between a static Wireless Sensor Network and a mobile robot was investigated. The wireless sensor nodes were able to send local data from sensors attached to the systems. The sensors were placed in a vineyard and the robot followed automatically the row structure to receive the data. It was possible to localize the single nodes just with the exact robot position and the attenuation model of the received signal strength with triangulation. The precision was 0.6 m and more precise than a provided differential global navigation satellite system signal. The second research area focused on the detection of unstructured objects in point clouds. Therefore, a low-cost sonar sensor was attached to a 3D-frame with millimetre level accuracy to exactly localize the sensor position. With the sensor position and the sensor reading, a 3D point cloud was created. In the workspace, 10 individual plant species were placed. They could be detected automatically with an accuracy of 2.7 cm. An attached valve was able to spray these specific plant positions, which resulted in a liquid saving of 72%, compared to a conventional spraying method, covering the whole crop row area. As plants are dynamic objects, the third objective of describing the plant growth with adequate sensor data, was important to characterise the unstructured agriculture domain. For revering and testing algorithms to the same data, maize rows were planted in a greenhouse. The exact positions of all plants were measured with a total station. Then a robot vehicle was guided through the crop rows and the data of attached sensors were recorded. With the help of the total station, it was possible to track down the vehicle position and to refer all data to the same coordinate frame. The data recording was performed over 7 times over a period of 6 weeks. This created datasets could afterwards be used to assess different algorithms and to test them against different growth changes of the plants. It could be shown that a basic RANSAC line following algorithm could not perform correctly under all growth stages without additional filtering. The fourth paper used this created datasets to search for single plants with a sensor normally used for obstacle avoidance. One tilted laser scanner was used with the exact robot position to create 3D point clouds, where two different methods for single plant detection were applied. Both methods used the spacing to detect single plants. The second method used the fixed plant spacing and row beginning, to resolve the plant positions iteratively. The first method reached detection rates of 73.7% and a root mean square error of 3.6 cm. The iterative second method reached a detection rate of 100% with an accuracy of 2.6 - 3.0 cm. For assessing the robustness of the plant detection, an algorithm was used to detect the plant positions in six different growth stages of the given datasets. A graph-cut based algorithm was used, what improved the results for single plant detection. As the algorithm was not sensitive against overlaying and noisy point clouds, a detection rate of 100% was realised, with an accuracy for the estimated height of the plants with 1.55 cm. The stem position was resolved with an accuracy of 2.05 cm. This thesis showed up different methods of perception for context awareness, which could help to improve the robustness of robots in agriculture. When the objects in the environment are known, it could be possible to react and interact smarter with the environment as it is the case in agricultural robotics. Especially the detection of single plants before the robot reaches them could help to improve the navigation and interaction of agricultural robots.Kontextwahrnehmung ist eine Schlüsselfunktion für die Realisierung von robusten autonomen Systemen in einer unstrukturierten Umgebung wie der Landwirtschaft. Roboter benötigen eine präzise Beschreibung ihrer Umgebung, so dass Aufgaben korrekt geplant und durchgeführt werden können. Wenn ein Roboter System in einer kontrollierten und sich nicht ändernden Umgebung eingesetzt wird, kann der Programmierer möglicherweise ein Modell erstellen, welches alle möglichen Umstände einbindet, um ein zuverlässiges System zu erhalten. Jedoch wird dies komplexer, wenn die Objekte und die Umwelt ihr Erscheinungsbild, Position und Verhalten ändern. Umgebungserkennung für Kontextwahrnehmung in der Landwirtschaft bedeutet relevante Objekte in der Umgebung zu erkennen, zu klassifizieren und auf diese zu reagieren. Ziel dieser kumulativen Dissertation war, verschiedene Strategien anzuwenden, um das Kontextbewusstsein mit Wahrnehmung bei mobilen Robotern in der Landwirtschaft zu erhöhen. Die Ziele dieser Arbeit waren fünf Aspekte von Umgebungserkennung zu adressieren: (I) Statische lokale Sensorkommunikation mit einem mobilen Fahrzeug zu testen, (II) unstrukturierte Objekte in einer kontrollierten Umgebung erkennen, (III) die Einflüsse von Wachstum der Pflanzen auf Algorithmen und ihre Ergebnisse zu beschreiben, (IV) gewonnene Sensorinformation zu benutzen, um Einzelpflanzen zu erkennen und (V) die Robustheit von Algorithmen unter verschiedenen Fehlereinflüssen zu verbessern. Als erstes wurde die Kommunikation zwischen einem statischen drahtlosen Sensor-Netzwerk und einem mobilen Roboter untersucht. Die drahtlosen Sensorknoten konnten Daten von lokal angeschlossenen Sensoren übermitteln. Die Sensoren wurden in einem Weingut verteilt und der Roboter folgte automatisch der Reihenstruktur, um die gesendeten Daten zu empfangen. Es war möglich, die Sendeknoten mithilfe von Triangulation aus der exakten Roboterposition und eines Sendesignal-Dämpfung-Modells zu lokalisieren. Die Genauigkeit war 0.6 m und somit genauer als das verfügbare Positionssignal eines differential global navigation satellite system. Der zweite Forschungsbereich fokussierte sich auf die Entdeckung von unstrukturierten Objekten in Punktewolken. Dafür wurde ein kostengünstiger Ultraschallsensor auf einen 3D Bewegungsrahmen mit einer Millimeter Genauigkeit befestigt, um die genaue Sensorposition bestimmen zu können. Mit der Sensorposition und den Sensordaten wurde eine 3D Punktewolke erstellt. Innerhalb des Arbeitsbereichs des 3D Bewegungsrahmens wurden 10 einzelne Pflanzen platziert. Diese konnten automatisch mit einer Genauigkeit von 2.7 cm erkannt werden. Eine angebaute Pumpe ermöglichte das punktuelle Besprühen der spezifischen Pflanzenpositionen, was zu einer Flüssigkeitsersparnis von 72%, verglichen mit einer konventionellen Methode welche die gesamte Pflanzenfläche benetzt, führte. Da Pflanzen sich ändernde Objekte sind, war das dritte Ziel das Pflanzenwachstum mit geeigneten Sensordaten zu beschreiben, was wichtig ist, um unstrukturierte Umgebung der Landwirtschaft zu charakterisieren. Um Algorithmen mit denselben Daten zu referenzieren und zu testen, wurden Maisreihen in einem Gewächshaus gepflanzt. Die exakte Position jeder einzelnen Pflanze wurde mit einer Totalstation gemessen. Anschließend wurde ein Roboterfahrzeug durch die Reihen gelenkt und die Daten der angebauten Sensoren wurden aufgezeichnet. Mithilfe der Totalstation war es möglich, die Fahrzeugposition zu ermitteln und alle Daten in dasselbe Koordinatensystem zu transformieren. Die Datenaufzeichnungen erfolgten 7-mal über einen Zeitraum von 6 Wochen. Diese generierten Datensätze konnten anschließend benutzt werden, um verschiedene Algorithmen unter verschiedenen Wachstumsstufen der Pflanzen zu testen. Es konnte gezeigt werden, dass ein Standard RANSAC Linien Erkennungsalgorithmus nicht fehlerfrei arbeiten kann, wenn keine zusätzliche Filterung eingesetzt wird. Die vierte Publikation nutzte diese generierten Datensätze, um nach Einzelpflanzen mithilfe eines Sensors zu suchen, der normalerweise für die Hinderniserkennung benutzt wird. Ein gekippter Laserscanner wurde zusammen mit der exakten Roboterposition benutzt, um eine 3D Punktewolke zu generieren. Zwei verschiedene Methoden für Einzelpflanzenerkennung wurden angewendet. Beide Methoden nutzten Abstände, um die Einzelpflanzen zu erkennen. Die zweite Methode nutzte den bekannten Pflanzenabstand und den Reihenanfang, um die Pflanzenpositionen iterativ zu erkennen. Die erste Methode erreichte eine Erkennungsrate von 73.7% und damit einen quadratischen Mittelwertfehler von 3.6 cm. Die iterative zweite Methode erreichte eine Erkennungsrate von bis zu 100% mit einer Genauigkeit von 2.6-3.0 cm. Um die Robustheit der Pflanzenerkennung zu bewerten, wurde ein Algorithmus zur Erkennung von Einzelpflanzen in sechs verschiedenen Wachstumsstufen der Datasets eingesetzt. Hier wurde ein graph-cut basierter Algorithmus benutzt, welcher die Robustheit der Ergebnisse für die Einzelpflanzenerkennung erhöhte. Da der Algorithmus nicht empfindlich gegen ungenaue und fehlerhafte Punktewolken ist, wurde eine Erkennungsrate von 100% mit einer Genauigkeit von 1.55 cm für die Höhe der Pflanzen erreicht. Der Stiel der Pflanzen wurde mit einer Genauigkeit von 2.05 cm erkannt. Diese Arbeit zeigte verschiedene Methoden für die Erkennung von Kontextwahrnehmung, was helfen kann, um die Robustheit von Robotern in der Landwirtschaft zu erhöhen. Wenn die Objekte in der Umwelt bekannt sind, könnte es möglich sein, intelligenter auf die Umwelt zu reagieren und zu interagieren, wie es aktuell der Fall in der Landwirtschaftsrobotik ist. Besonders die Erkennung von Einzelpflanzen bevor der Roboter sie erreicht, könnte helfen die Navigation und Interaktion von Robotern in der Landwirtschaft verbessern

Symmetry-Adapted Machine Learning for Information Security

Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis

BLE-based Indoor Localization and Contact Tracing Approaches

Internet of Things (IoT) has penetrated different aspects of modern life with smart sensors being prevalent within our surrounding indoor environments. Furthermore, dependence on IoT-based Contact Tracing (CT) models has significantly increased mainly due to the COVID-19 pandemic. There is, therefore, an urgent quest to develop/design efficient, autonomous, trustworthy, and secure indoor CT solutions leveraging accurate indoor localization/tracking approaches. In this context, the first objective of this Ph.D. thesis is to enhance accuracy of Bluetooth Low Energy (BLE)-based indoor localization. BLE-based localization is typically performed based on the Received Signal Strength Indicator (RSSI). Extreme fluctuations of the RSSI occurring due to different factors such as multi-path effects and noise, however, prevent the BLE technology to be a reliable solution with acceptable accuracy for dynamic tracking/localization in indoor environments. In this regard, first, an IoT dataset is constructed based on multiple thoroughly separated indoor environments to incorporate the effects of various interferences faced in different spaces. The constructed dataset is then used to develop a Reinforcement Learning (RL)-based information fusion strategy to form a multiple-model implementation consisting of RSSI, Pedestrian dead reckoning (PDR), and Angle-of-Arrival (AoA)-based models. In the second part of the thesis, the focus is devoted to application of multi-agent Deep Neural Networks (DNN) models for indoor tracking. DNN-based approaches are, however, prone to overfitting and high sensitivity to parameter selection, which results in sample inefficiency. Moreover, data labelling is a time-consuming and costly procedure. To address these issues, we leverage Successor Representations (SR)-based techniques, which can learn the expected discounted future state occupancy, and the immediate reward of each state. A Deep Multi-Agent Successor Representation framework is proposed that can adapt quickly to the changes in a multi-agent environment faster than the Model-Free (MF) RL methods and with a lower computational cost compared to Model-Based (MB) RL algorithms. In the third part of the thesis, the developed indoor localization techniques are utilized to design a novel indoor CT solution, referred to as the Trustworthy Blockchain-enabled system for Indoor Contact Tracing (TB-ICT) framework. The TB-ICT is a fully distributed and innovative blockchain platform exploiting the proposed dynamic Proof of Work (dPoW) approach coupled with a Randomized Hash Window (W-Hash) and dynamic Proof of Credit (dPoC) mechanisms

A heuristic approach for the computation of individual trajectories of a fleet of robots under connectivity constraints

International audienc