Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

Integration of Cost andWork Breakdown Structures in the Management of Construction Projects

Scope management allows project managers to react when a project underperforms regarding schedule, budget, and/or quality at the execution stage. Scope management can also minimize project changes and budget omissions, as well as improve the accuracy of project cost estimates and risk responses. For scope management to be effective, though, it needs to rely on a robust work breakdown structure (WBS). A robust WBS hierarchically and faithfully reflects all project tasks and work packages so that projects are easier to manage. If done properly, the WBS also allows meeting the project objectives while delivering the project on time, on budget, and with the required quality. This paper analyzes whether the integration of a cost breakdown structure (CBS) can lead to the generation of more robust WBSs in construction projects. Over the last years, some international organizations have standardized and harmonized different cost classification systems (e.g., ISO 12006-2, ISO 81346-12, OmniClass, CoClass, UniClass). These cost databases have also been introduced into building information modeling (BIM) frameworks. We hypothesize that in BIM environments, if these CBSs are used to generate the project WBS, several advantages are gained such as sharper project definition. This enhanced project definition reduces project contradictions at both planning and execution stages, anticipates potential schedule and budget deviations, improves resource allocation, and overall it allows a better response to potential project risks. The hypothesis that the use of CBSs can generate more robust WBSs is tested by the response analysis of a questionnaire survey distributed among construction practitioners and project managers. By means of structural equation modeling (SEM), the correlation (agreement) and perception differences between two 250-respondent subsamples (technical project staff vs. project management staff) are also discussed. Results of this research support the use of CBSs by construction professionals as a basis to generate WBSs for enhanced project management (PM)

Analysis and Synthesis of Metadata Goals for Scientific Data

The proliferation of discipline-specific metadata schemes contributes to artificial barriers that can impede interdisciplinary and transdisciplinary research. The authors considered this problem by examining the domains, objectives, and architectures of nine metadata schemes used to document scientific data in the physical, life, and social sciences. They used a mixed-methods content analysis and Greenberg’s (2005) metadata objectives, principles, domains, and architectural layout (MODAL) framework, and derived 22 metadata-related goals from textual content describing each metadata scheme. Relationships are identified between the domains (e.g., scientific discipline and type of data) and the categories of scheme objectives. For each strong correlation (\u3e0.6), a Fisher’s exact test for nonparametric data was used to determine significance (p \u3c .05). Significant relationships were found between the domains and objectives of the schemes. Schemes describing observational data are more likely to have “scheme harmonization” (compatibility and interoperability with related schemes) as an objective; schemes with the objective “abstraction” (a conceptual model exists separate from the technical implementation) also have the objective “sufficiency” (the scheme defines a minimal amount of information to meet the needs of the community); and schemes with the objective “data publication” do not have the objective “element refinement.” The analysis indicates that many metadata-driven goals expressed by communities are independent of scientific discipline or the type of data, although they are constrained by historical community practices and workflows as well as the technological environment at the time of scheme creation. The analysis reveals 11 fundamental metadata goals for metadata documenting scientific data in support of sharing research data across disciplines and domains. The authors report these results and highlight the need for more metadata-related research, particularly in the context of recent funding agency policy changes

The Case for Developing and Deploying an Open Source Electronic Logistics Management Information System

Summarizes efforts to strengthen health information systems in low- and lower-middle-income countries, including development of common requirements. Outlines models for collaboration among stakeholders, national leaders, and health information users

Data DNA: The Next Generation of Statistical Metadata

Describes the components of a complete statistical metadata system and suggests ways to create and structure metadata for better access and understanding of data sets by diverse users