An Investigation into Trust and Reputation Frameworks for Autonomous Underwater Vehicles

As Autonomous Underwater Vehicles (AUVs) become more technically capable and economically feasible, they are being increasingly used in a great many areas of defence, commercial and environmental applications. These applications are tending towards using independent, autonomous, ad-hoc, collaborative behaviour of teams or fleets of these AUV platforms. This convergence of research experiences in the Underwater Acoustic Network (UAN) and Mobile Ad-hoc Network (MANET) fields, along with the increasing Level of Automation (LOA) of such platforms, creates unique challenges to secure the operation and communication of these networks. The question of security and reliability of operation in networked systems has usually been resolved by having a centralised coordinating agent to manage shared secrets and monitor for misbehaviour. However, in the sparse, noisy and constrained communications environment of UANs, the communications overheads and single-point-of-failure risk of this model is challenged (particularly when faced with capable attackers). As such, more lightweight, distributed, experience based systems of “Trust” have been proposed to dynamically model and evaluate the “trustworthiness” of nodes within a MANET across the network to prevent or isolate the impact of malicious, selfish, or faulty misbehaviour. Previously, these models have monitored actions purely within the communications domain. Moreover, the vast majority rely on only one type of observation (metric) to evaluate trust; successful packet forwarding. In these cases, motivated actors may use this limited scope of observation to either perform unfairly without repercussions in other domains/metrics, or to make another, fair, node appear to be operating unfairly. This thesis is primarily concerned with the use of terrestrial-MANET trust frameworks to the UAN space. Considering the massive theoretical and practical difference in the communications environment, these frameworks must be reassessed for suitability to the marine realm. We find that current single-metric Trust Management Frameworks (TMFs) do not perform well in a best-case scaling of the marine network, due to sparse and noisy observation metrics, and while basic multi-metric communications-only frameworks perform better than their single-metric forms, this performance is still not at a reliable level. We propose, demonstrate (through simulation) and integrate the use of physical observational metrics for trust assessment, in tandem with metrics from the communications realm, improving the safety, security, reliability and integrity of autonomous UANs. Three main novelties are demonstrated in this work: Trust evaluation using metrics from the physical domain (movement/distribution/etc.), demonstration of the failings of Communications-based Trust evaluation in sparse, noisy, delayful and non-linear UAN environments, and the deployment of trust assessment across multiple domains, e.g. the physical and communications domains. The latter contribution includes the generation and optimisation of cross-domain metric composition or“synthetic domains” as a performance improvement method

Physical Behaviours for Trust Assessment in Autonomous Underwater MANETs

This paper proposes a new approach to determine trust in resource-constrained networks of autonomous systems based on their physical behaviour, using the motion of nodes within a team to detect and identify malicious or failing operation within their cohort. This is accomplished by looking at operations in the underwater marine environment. We present a series of composite metrics based on physical movement, and apply these metrics to the detection and discrimination of sample physical misbehaviours. This approach opens the possibility of bringing information about both the physical and communications behaviours of autonomous MANETs together to strengthen and expand the application of future Trust Management Frameworks in sparse and/or resource constrained environment

Hierarchical Classification of Scientific Taxonomies with Autonomous Underwater Vehicles

Autonomous Underwater Vehicles (AUVs) have catalysed a significant shift in the way marine habitats are studied. It is now possible to deploy an AUV from a ship, and capture tens of thousands of georeferenced images in a matter of hours. There is a growing body of research investigating ways to automatically apply semantic labels to this data, with two goals. The task of manually labelling a large number of images is time consuming and error prone. Further, there is the potential to change AUV surveys from being geographically defined (based on a pre-planned route), to permitting the AUV to adapt the mission plan in response to semantic observations. This thesis focusses on frameworks that permit a unified machine learning approach with applicability to a wide range of geographic areas, and diverse areas of interest for marine scientists. This can be addressed through the use of hierarchical classification; in which machine learning algorithms are trained to predict not just a binary or multi-class outcome, but a hierarchy of related output labels which are not mutually exclusive, such as a scientific taxonomy. In order to investigate classification on larger hierarchies with greater geographic diversity, the BENTHOZ-2015 data set was assembled as part of a collaboration between five Australian research groups. Existing labelled data was re-mapped to the CATAMI hierarchy, in total more than 400,000 point labels, conforming to a hierarchy of around 150 classes. The common hierarchical classification approach of building a network of binary classifiers was applied to the BENTHOZ-2015 data set, and a novel application of Bayesian Network theory and probability calibration was used as a theoretical foundation for the approach, resulting in improved classifier performance. This was extended to a more complex hidden node Bayesian Network structure, which permits inclusion of additional sensor modalities, and tuning for better performance in particular geographic regions

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

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

IoT Anomaly Detection Methods and Applications: A Survey

Ongoing research on anomaly detection for the Internet of Things (IoT) is a rapidly expanding field. This growth necessitates an examination of application trends and current gaps. The vast majority of those publications are in areas such as network and infrastructure security, sensor monitoring, smart home, and smart city applications and are extending into even more sectors. Recent advancements in the field have increased the necessity to study the many IoT anomaly detection applications. This paper begins with a summary of the detection methods and applications, accompanied by a discussion of the categorization of IoT anomaly detection algorithms. We then discuss the current publications to identify distinct application domains, examining papers chosen based on our search criteria. The survey considers 64 papers among recent publications published between January 2019 and July 2021. In recent publications, we observed a shortage of IoT anomaly detection methodologies, for example, when dealing with the integration of systems with various sensors, data and concept drifts, and data augmentation where there is a shortage of Ground Truth data. Finally, we discuss the present such challenges and offer new perspectives where further research is required.Comment: 22 page

Proceedings of the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

This book is a collection of 15 reviewed technical reports summarizing the presentations at the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory. The covered topics include image processing, optical signal processing, visual inspection, pattern recognition and classification, human-machine interaction, world and situation modeling, autonomous system localization and mapping, information fusion, and trust propagation in sensor networks

Smart Wireless Sensor Networks

The recent development of communication and sensor technology results in the growth of a new attractive and challenging area - wireless sensor networks (WSNs). A wireless sensor network which consists of a large number of sensor nodes is deployed in environmental fields to serve various applications. Facilitated with the ability of wireless communication and intelligent computation, these nodes become smart sensors which do not only perceive ambient physical parameters but also be able to process information, cooperate with each other and self-organize into the network. These new features assist the sensor nodes as well as the network to operate more efficiently in terms of both data acquisition and energy consumption. Special purposes of the applications require design and operation of WSNs different from conventional networks such as the internet. The network design must take into account of the objectives of specific applications. The nature of deployed environment must be considered. The limited of sensor nodes� resources such as memory, computational ability, communication bandwidth and energy source are the challenges in network design. A smart wireless sensor network must be able to deal with these constraints as well as to guarantee the connectivity, coverage, reliability and security of network's operation for a maximized lifetime. This book discusses various aspects of designing such smart wireless sensor networks. Main topics includes: design methodologies, network protocols and algorithms, quality of service management, coverage optimization, time synchronization and security techniques for sensor networks