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

Collaborative Information Processing in Wireless Sensor Networks for Diffusive Source Estimation

In this dissertation, we address the issue of collaborative information processing for diffusive source parameter estimation using wireless sensor networks (WSNs) capable of sensing in dispersive medium/environment, from signal processing perspective. We begin the dissertation by focusing on the mathematical formulation of a special diffusion phenomenon, i.e., an underwater oil spill, along with statistical algorithms for meaningful analysis of sensor data leading to efficient estimation of desired parameters of interest. The objective is to obtain an analytical solution to the problem, rather than using non-model based sophisticated numerical techniques. We tried to make the physical diffusion model as much appropriate as possible, while maintaining some pragmatic and reasonable assumptions for the simplicity of exposition and analytical derivation. The dissertation studies both source localization and tracking for static and moving diffusive sources respectively. For static diffusive source localization, we investigate two parametric estimation techniques based on the maximum-likelihood (ML) and the best linear unbiased estimator (BLUE) for a special case of our obtained physical dispersion model. We prove the consistency and asymptotic normality of the obtained ML solution when the number of sensor nodes and samples approach infinity, and derive the Cramer-Rao lower bound (CRLB) on its performance. In case of a moving diffusive source, we propose a particle filter (PF) based target tracking scheme for moving diffusive source, and analytically derive the posterior Cramer-Rao lower bound (PCRLB) for the moving source state estimates as a theoretical performance bound. Further, we explore nonparametric, machine learning based estimation technique for diffusive source parameter estimation using Dirichlet process mixture model (DPMM). Since real data are often complicated, no parametric model is suitable. As an alternative, we exploit the rich tools of nonparametric Bayesian methods, in particular the DPMM, which provides us with a flexible and data-driven estimation process. We propose DPMM based static diffusive source localization algorithm and provide analytical proof of convergence. The proposed algorithm is also extended to the scenario when multiple diffusive sources of same kind are present in the diffusive field of interest. Efficient power allocation can play an important role in extending the lifetime of a resource constrained WSN. Resource-constrained WSNs rely on collaborative signal and information processing for efficient handling of large volumes of data collected by the sensor nodes. In this dissertation, the problem of collaborative information processing for sequential parameter estimation in a WSN is formulated in a cooperative game-theoretic framework, which addresses the issue of fair resource allocation for estimation task at the Fusion center (FC). The framework allows addressing either resource allocation or commitment for information processing as solutions of cooperative games with underlying theoretical justifications. Different solution concepts found in cooperative games, namely, the Shapley function and Nash bargaining are used to enforce certain kinds of fairness among the nodes in a WSN

Mining a Small Medical Data Set by Integrating the Decision Tree and t-test

[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Human-in-the-Loop Cyber-Physical-Systems based on Smartphones

Tese de doutoramento em Ciências e Tecnologias da Informação, apresentada ao Departamento de Engenharia Informática da Faculdade de Ciências e Tecnologia da Universidade de CoimbraTechnological devices increasingly become smaller, more mobile, powerful and efficient. However, each time we have to hurdle through unintuitive menus, errors and incompatibilities we become stressed by our technology. As first put forward by the renowned computer scientist Mark Weiser, the ultimate form of computers may be an extension of our subconscious. The ideal computer would be capable of truly understanding people's unconscious actions and desires. Instead of humans adapting to technology and learning how to use it, it would be technology that would adapt to the disposition and uniqueness of each human being. This thesis focuses on the realm of Human-in-the-loop Cyber-Physical Systems (HiTLCPSs). HiTLCPSs infer the users’ intents, psychological states, emotions and actions, using this information to determine the system's behavior. This involves using a large variety of sensors and mobile devices to monitor and evaluate human nature. Therefore, this technology has strong ties with wireless sensor networks, robotics, machine-learning and the Internet of Things. In particular, our work focuses on the usage of smartphones within these systems. It begins by describing a framework to understand the principles and theory of HiTLCPSs. It provides some insights into current research being done on this topic, its challenges, and requirements. Another of the thesis' objectives is to present our innovative taxonomy of human roles, where we attempt to understand how a human may interact with HiTLCPSs and how to best explore this resource. This thesis also describes concrete examples of the practical usage of HiTL paradigms. As such, we included a comprehensive description of our research work and associated prototypes, where the major theoretical concepts behind HiTLCPS were applied and evaluated to specific scenarios. Finally, we discuss our personal view on the future and evolution of these systems.A tecnologia tem vindo a tornar-se cada vez mais pequena, móvel, poderosa e eficiente. No entanto, lidar com menus pouco intuitivos, erros, e incompatibilidades, causa frustração aos seus utilizadores. Segundo o reconhecido cientista Mark Weiser, os computadores do futuro poderão vir a existir como se fossem uma extensão do nosso subconsciente. O computador ideal seria capaz de entender, em toda a sua plenitude, as ações e os desejos inconscientes dos seres humanos. Em vez de serem os humanos a adaptarem-se à tecnologia e a aprender a usá-la, seria a tecnologia a aprender a adaptar-se à disposição e individualidade de cada ser humano. Esta tese foca-se na área dos Human-in-the-loop Cyber-Physical Systems (HiTLCPSs). Os HiTLCPSs inferem as intenções, estados psicológicos, emoções e ações dos seus utilizadores, usando esta informação para determinar o comportamento do sistema ciber-físico. Isto envolve a utilização de uma grande variedade de sensores e dispositivos móveis que monitorizam e avaliam a natureza humana. Assim sendo, esta tecnologia tem fortes ligações com redes de sensores sem fios, robótica, algoritmos de aprendizagem de máquina e a Internet das Coisas. Em particular, o nosso trabalho focou-se na utilização de smartphones dentro destes sistemas. Começamos por descrever uma estrutura para compreender os princípios e teoria associados aos HiTLCPSs. Esta análise permitiu-nos adquirir alguma clareza sobre a investigação a ser feita sobre este tópico, e sobre os seus desafios e requisitos. Outro dos objetivos desta tese é o de apresentar a nossa inovadora taxonomia sobre os papeis do ser humano nos HiTLCPSs, onde tentamos perceber as possíveis interações do ser humano com estes sistemas e as melhores formas de explorar este recurso. Esta tese também descreve exemplos concretos da utilização prática dos paradigmas HiTL. Desta forma, incluímos uma descrição do nosso trabalho experimental e dos protótipos que lhe estão associados, onde os conceitos teóricos dos HiTLCPSs foram aplicados e avaliados em diversos casos de estudo. Por fim, apresentamos a nossa perspetiva pessoal sobre o futuro e evolução destes sistemas.Fundação Luso-Americana para o DesenvolvimentoFP7-ICT-2007-2 GINSENG projectiCIS project (CENTRO-07-ST24-FEDER-002003)SOCIALITE project (PTDC/EEI-SCR/2072/2014

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC