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

New intelligent network approach for monitoring physiological parameters : the case of Benin

Benin health system is facing many challenges as: (i) affordable high-quality health care to a growing population providing need, (ii) patients’ hospitalization time reduction, (iii) and presence time of the nursing staff optimization. Such challenges can be solved by remote monitoring of patients. To achieve this, five steps were followed. 1) Identification of the Wireless Body Area Network (WBAN) systems’ characteristics and the patient physiological parameters’ monitoring. 2) The national Integrated Patient Monitoring Network (RIMP) architecture modeling in a cloud of Technocenters. 3) Cross-analysis between the characteristics and the functional requirements identified. 4) Each Technocenter’s functionality simulation through: a) the design approach choice inspired by the life cycle of V systems; b) functional modeling through SysML Language; c) the communication technology and different architectures of sensor networks choice studying. 5) An estimate of the material resources of the national RIMP according to physiological parameters. A National Integrated Network for Patient Monitoring (RNIMP) remotely, ambulatory or not, was designed for Beninese health system. The implementation of the RNIMP will contribute to improve patients’ care in Benin. The proposed network is supported by a repository that can be used for its implementation, monitoring and evaluation. It is a table of 36 characteristic elements each of which must satisfy 5 requirements relating to: medical application, design factors, safety, performance indicators and materiovigilance

Analyzing big time series data in solar engineering using features and PCA

In solar engineering, we encounter big time series data such as the satellite-derived irradiance data and string-level measurements from a utility-scale photovoltaic (PV) system. While storing and hosting big data are certainly possible using today’s data storage technology, it is challenging to effectively and efficiently visualize and analyze the data. We consider a data analytics algorithm to mitigate some of these challenges in this work. The algorithm computes a set of generic and/or application-specific features to characterize the time series, and subsequently uses principal component analysis to project these features onto a two-dimensional space. As each time series can be represented by features, it can be treated as a single data point in the feature space, allowing many operations to become more amenable. Three applications are discussed within the overall framework, namely (1) the PV system type identification, (2) monitoring network design, and (3) anomalous string detection. The proposed framework can be easily translated to many other solar engineer applications

The Practice Research of Artificial Intelligence in Computer Network Technology in the New Era

This paper summarizes the practical application and development trend of artificial intelligence technology in the field of computer network. Artificial intelligence has been widely used in network communication, management and security, and other aspects, to achieve the network independent optimization, fault self-healing, threat identification and other intelligent capabilities. Artificial intelligence is a key technology to promote the evolution of the next generation network to the direction of adaptive, intelligence and security. The use of artificial intelligence also faces challenges such as algorithm deviations and regulatory constraints. Collaborative innovation algorithms and systems and the establishment of legal ethics systems are needed to promote the healthy development of AI and the progress of computer networks

Potential Applications and Challenges of Metagenomics in Human Viral Infections

Complex association of human host and pathogenic viruses makes a necessity to understand the overall host and virus interaction network. Identification of virus population and its systematic classification will help in understanding the viral association with the disease outcome. Metagenomics is a recently developing approach for the detection of pathogens in the samples with precise interpretation in a short period of time. Metagenomic approaches have been employed for studying the predominance or spread of the virus within a particular locality and nature of virus during infection. Metagenomics is basically a collective approach of lab-based techniques and in-silico methods for identification of pathogenic viruses without culturing them in specific aseptic conditions. Lack of unique conserved genes in viruses has made metagenomics study difficult in this juncture. Other challenges in the field of metagenomics are like cellular DNA contamination, free environmental DNA contamination and continuous evolution of viruses. Recent studies have shed light on the advancement of this field in virus identification and characterization however still needs further investigations to overcome the challenges. Current chapter focuses on the application and challenges faced in metagenomic analysis of human viral infections

Study on TCM Syndrome Identification Modes of Coronary Heart Disease Based on Data Mining

Coronary heart disease (CHD) is one of the most important types of heart disease because of its high incidence and high mortality. TCM has played an important role in the treatment of CHD. Syndrome differentiation based on information from traditional four diagnostic methods has met challenges and questions with the rapid development and wide application of system biology. In this paper, methods of complex network and CHAID decision tree were applied to identify the TCM core syndromes of patients with CHD, and to establish TCM syndrome identification modes of CHD based on biological parameters. At the same time, external validation modes were also constructed to confirm the identification modes

Towards inter-organizational Enterprise Architecture Management - Applicability of TOGAF 9.1 for Network Organizations

Network organizations and inter-organizational systems (IOS) have recently been the subjects of extensive research and practice. Various papers discuss technical issues as well as several complex business considerations and cultural issues. However, one interesting aspect of this context has only received adequate coverage so far, namely the ability of existing Enterprise Architecture Management (EAM) frameworks to address the diverse challenges of inter-organizational collaboration. The relevance of this question is grounded in the increasing significance of IOS and the insight that many organizations model their architecture using such frameworks. This paper addresses the question by firstly conducting a conceptual literature review in order to identify a set of challenges. An EAM framework was then chosen and its ability to address the challenges was evaluated. The chosen framework is The Open Group Architecture Framework (TOGAF) 9.1 and the analysis conducted with regard to the support of network organizations highlights which issues it deals with. TOGAF serves as a good basis to solve the challenges of “Process and Data Integration” and “Infrastructure and Application Integration”. Other areas such as the “Organization of the Network Organization” need further support. Both the identification of challenges and the analysis of TOGAF assist academics and practitioners alike to identify further research topics as well as to find documentation related to inter-organizational problems in EAM

Disinvestment in healthcare: An overview of HTA agencies and organizations activities at European level

Background: In an era of a growing economic pressure for all health systems, the interest for "disinvestment" in healthcare increased. In this context, evidence based approaches such as Health Technology Assessment (HTA) are needed both to invest and to disinvest in health technologies. In order to investigate the extent of application of HTA in this field, methodological projects/frameworks, case studies, dissemination initiatives on disinvestment released by HTA agencies and organizations located in Europe were searched. Methods: In July 2015, the websites of HTA agencies and organizations belonging to the European network for HTA (EUnetHTA) and the International Network of Agencies for HTA (INAHTA) were accessed and searched through the use of the term "disinvestment". Retrieved deliverables were considered eligible if they reported methodological projects/frameworks, case studies and dissemination initiatives focused on disinvestment in healthcare. Results: 62 HTA agencies/organizations were accessed and eight methodological projects/frameworks, one case study and one dissemination initiative were found starting from 2007. With respect to methodological projects/frameworks, two were delivered in Austria, one in Italy, two in Spain and three in U.K. As for the case study and the dissemination initiative, both came from U.K. The majority of deliverables were aimed at making an overview of existing disinvestment approaches and at identifying challenges in their introduction. Conclusions: Today, in a healthcare context characterized by resource scarcity and increasing service demand, "disinvestment" from low-value services and reinvestment in high-value ones is a key strategy that may be supported by HTA. The lack of evaluation of technologies in use, in particular at the end of their lifecycle, may be due to the scant availability of frameworks and guidelines for identification and assessment of obsolete technologies that was shown by our work. Although several projects were carried out in different countries, most remain constrained to the field of research. Disinvestment is a relatively new concept in HTA that could pose challenges also from a methodological point of view. To tackle these challenges, it is necessary to construct experiences at international level with the aim to develop new methodological approaches to produce and grow evidence on disinvestment policies and practices

Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges

Background: Systems biology has embraced computational modeling in response to the quantitative nature and increasing scale of contemporary data sets. The onslaught of data is accelerating as molecular profiling technology evolves. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) is a community effort to catalyze discussion about the design, application, and assessment of systems biology models through annual reverse-engineering challenges. Methodology and Principal Findings: We describe our assessments of the four challenges associated with the third DREAM conference which came to be known as the DREAM3 challenges: signaling cascade identification, signaling response prediction, gene expression prediction, and the DREAM3 in silico network challenge. The challenges, based on anonymized data sets, tested participants in network inference and prediction of measurements. Forty teams submitted 413 predicted networks and measurement test sets. Overall, a handful of best-performer teams were identified, while a majority of teams made predictions that were equivalent to random. Counterintuitively, combining the predictions of multiple teams (including the weaker teams) can in some cases improve predictive power beyond that of any single method. Conclusions: DREAM provides valuable feedback to practitioners of systems biology modeling. Lessons learned from the predictions of the community provide much-needed context for interpreting claims of efficacy of algorithms described in the scientific literature