Distributed Maximum Likelihood for Simultaneous Self-localization and Tracking in Sensor Networks

We show that the sensor self-localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we implement fully decentralized versions of the Recursive Maximum Likelihood and on-line Expectation-Maximization algorithms to localize the sensor network simultaneously with target tracking. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a novel message passing algorithm. The latter allows each node to compute the local derivatives of the likelihood or the sufficient statistics needed for Expectation-Maximization. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we demonstrate that the developed algorithms are able to learn the localization parameters.Comment: shorter version is about to appear in IEEE Transactions of Signal Processing; 22 pages, 15 figure

Gravitational Clustering: A Simple, Robust and Adaptive Approach for Distributed Networks

Distributed signal processing for wireless sensor networks enables that different devices cooperate to solve different signal processing tasks. A crucial first step is to answer the question: who observes what? Recently, several distributed algorithms have been proposed, which frame the signal/object labelling problem in terms of cluster analysis after extracting source-specific features, however, the number of clusters is assumed to be known. We propose a new method called Gravitational Clustering (GC) to adaptively estimate the time-varying number of clusters based on a set of feature vectors. The key idea is to exploit the physical principle of gravitational force between mass units: streaming-in feature vectors are considered as mass units of fixed position in the feature space, around which mobile mass units are injected at each time instant. The cluster enumeration exploits the fact that the highest attraction on the mobile mass units is exerted by regions with a high density of feature vectors, i.e., gravitational clusters. By sharing estimates among neighboring nodes via a diffusion-adaptation scheme, cooperative and distributed cluster enumeration is achieved. Numerical experiments concerning robustness against outliers, convergence and computational complexity are conducted. The application in a distributed cooperative multi-view camera network illustrates the applicability to real-world problems.Comment: 12 pages, 9 figure

Secure data exchange in IIoT

Dupla diplomação com a National Polytechnic University of ArmeniaIndustrial Internet of Things (IIoT) plays a central role for the Fourth Industrial Revolution. In the scope of Industry 4.0 many specialists of the field are working together towards implementing large scalable, reliable and secure Industrial environments. However, existing environments are lacking security standards and have limited resources per component which results in various security britches such as trust in between the components, partner factories or remote control units with the system. Due to the resilience and it’s security properties, combining blochchain-based solutions with IIoT environments is gaining popularity. Despite that, chain-structured classic blockchain solutions are extremely resource-intensive and are not suitable for power-constrained IoT devices. To mitigate the security challenges presented above a secure architecture is proposed by using a DAG-structured asynchronous blockchain which can provide system security and transactions efficiency at the same time. Use-cases and sequence diagrams were created to model the solution and a security threat analysis of the architecture is made. Threat analysis is performed based on STRIDE methodology and provides us in depth understanding how our security architecture mitigates the threats and reveals also open challenges. The results are robust, supported by extensive security evaluation, which foster future development over the proposed architecture. Therefore, the contributions made are valid, and as the architecture is generic, will be possible to deploy it in diverse custom industrial environments. The flexibility of the architecture will allow incorporation of future hardware and software development in the field.A Internet das Coisas Industriais (IIoT) tem um papel central na quarta revolução industrial. Na área da Indústria 4.0 muitos especialistas colaboram com o objetivo de criar ambientes industriais escaláveis, confiáveis e seguros. No entanto, os cenários existentes carecem de normas de segurança, os recursos dos componentes são limitados, que levam a várias falhas de segurança que impedem a confiança entre dos diversos componentes, entre fábricas parceiras e entre unidades de controlo remoto de sistemas. Soluções suportadas por blockchain em ambientes IIoT estão a ganhar popularidade, principalmente devido à resiliência e propriedades de segurança da blockchain. No entanto, as soluções baseadas em blockchain clássicas estruturadas em cadeia fazem uso intensivo dos recursos, o que as torna não adequadas pra dispositivos IoT com restrição de energia. Para mitigar os desafios apresentados, propõe-se uma arquitetura segura que recorre a uma blockchain assíncrona com uma estrutura DAG, que procura fornecer segurança e eficiência nas transações. Casos de uso e diagramas sequência foram criados para modelar a solução e é realizada uma análise de ameaças de segurança à arquitetura. A análise recorre à metodologia STRIDE e fornece informação de como a nossa proposta mitiga as ameaças e revela também os desafios em aberto. Os resultados da avaliação demonstram que esta abordagem é robusta permitindo o desenvolvimento futuro da arquitetura proposta. As contribuições deste trabalho são validas, e como a arquitetura é genérica, será possível a implantar em diversas ambientes indústrias específicos. A flexibilidade da arquitetura permitirá a incorporar os futuros desenvolvimentos na área sejam hardware e/ou software

A service-oriented middleware for integrated management of crowdsourced and sensor data streams in disaster management

The increasing number of sensors used in diverse applications has provided a massive number of continuous, unbounded, rapid data and requires the management of distinct protocols, interfaces and intermittent connections. As traditional sensor networks are error-prone and difficult to maintain, the study highlights the emerging role of “citizens as sensors” as a complementary data source to increase public awareness. To this end, an interoperable, reusable middleware for managing spatial, temporal, and thematic data using Sensor Web Enablement initiative services and a processing engine was designed, implemented, and deployed. The study found that its approach provided effective sensor data-stream access, publication, and filtering in dynamic scenarios such as disaster management, as well as it enables batch and stream management integration. Also, an interoperability analytics testing of a flood citizen observatory highlighted even variable data such as those provided by the crowd can be integrated with sensor data stream. Our approach, thus, offers a mean to improve near-real-time applications