A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Key Perspectives in Power Aware Ad-hoc Internet of Things with Advanced Networks and Real Time Scenarios

Smart gadgets with integrated power optimization segments are the key perspectives that use Internet of Things (IoT) enabled technology to promote lifestyle advancements. It has an influence on a number of sectors in academia and/or business thanks to its strong integration with the current Cloud architecture. Recently, the Internet of Things has been acknowledged as a disruptive technology for the aerial ad hoc network. IoT may be thought of as a network inside a network. IoT-based networks rely heavily on the so-called self-organizing capability working in a dispersed manner in ad hoc networks, with users travelling at speeds ranging from walking pace to automobile, rail, or airline speed. IoT applications that assist logistics and the administration of ad hoc networks may be found in a broad variety. Utility companies are under pressure now to produce ever more enormous amounts of electricity. In megacities, there is an exponential rise in the number of people and energy users. Thus, the need for energy conservation is growing significantly on a global scale. The best way to optimise the rising energy demands and consumptions is as a consequence of the development of energy-monitoring systems. These solutions can cut current utilisation levels, stop energy waste, and make better use of our resources

Smart Grid Economics: Policy Guidance through Competitive Simulation

Sustainable energy systems of the future will need more than efficient, clean, low-cost, renewable energy sources; they will also need efficient price signals that motivate sustainable energy consumption as well as a better real-time alignment of energy demand and supply

Cloud manufacturing system for sheet metal processing

Cloud computing is changing the way industries and enterprises run their businesses. Cloud manufacturing is emerging as an approach to transform the traditional manufacturing business model, while helping the manufacturer to align production efficiency with its business strategy, and creating intelligent factory networks that enable collaboration across the whole enterprise. Many production planning and control (PPC) problems are essentially optimisation problems, where the objective is to develop a plan that meets the demand at minimum cost or maximum profit. Because the underlying optimisation problem will vary in the different business and operation phases, it is important to think about optimisation in a dynamic mechanism and in a number of interlinked sub-problems at the same time. Cloud manufacturing has the potential to offer decision support as a service and medium of communication in PPC. To solve these problems and produce collaboration across the supply chain, this paper provides an overview of the state of the art in cloud manufacturing and presents a model of cloud-based production planning and production system for sheet metal processing.fi=vertaisarvioitu|en=peerReviewed

An Internet of Things based framework to enhance just-in-time manufacturing

Just-in-time manufacturing is a main manufacturing strategy used to enhance manufacturers’ competitiveness through inventory and lead time reduction. Implementing just-in-time manufacturing has a number of challenges, for example, effective, frequent and real-time information sharing and communication between different functional departments, responsive action for adjusting the production plan against the continually changing manufacturing situation. Internet of Things technology has the potential to be used for capturing desired data and information from production environment in real time, and the collected data and information can be used for adjusting production schedules corresponding to the changing production environment. This article presents an Internet of Things based framework to support responsive production planning and scheduling in just-in-time manufacturing. The challenges of implementing just-in-time manufacturing are identified first and then an Internet of Things based solution is proposed to address these challenges. A framework to realise the proposed Internet of Things solution is developed and its implementation plan is suggested based on a case study on automotive harness parts manufacturing. This research contributes knowledge to the field of just-in-time manufacturing by incorporating the Internet-of-Things technology to improve the connectivity of production chains and responsive production scheduling capability

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

Multiple Case Comparison of the In-Transit Visibility Business Process

Over the past decade, the Department of Defense has developed an In-transit Visibility capability. Despite significant funding and research in developing this capability, the initial deployment in support of Operation ENDURING FREEDOM (OEF) in 2001 highlighted an ongoing problem to achieve ITV within the U.S. Air Force. Initial results from Headquarters USAF initiated studies point to a need to focus on business processes related ITV management. This research employed a multiple case study design embedded in a functional benchmarking process to solicit ITV management best practices from leaders in the civilian logistics industry and to identify gaps between their practices and those of the Air Force. The data collection method used electronic mail as a portal to conducting subject matter expert interviews. Using the data collected from the benchmarking partners, the research recognized 19 best practices and compared the civilian and military environments in 41 areas. This evaluation highlighted gaps between practices used in the civilian industry and those used by the Air Force. These gaps served as areas of opportunity in which the Air Force can evaluate alternative management practices in an effort to improve the ITV process. Using these gaps as a foundation, the research proposed fourteen recommendations for action

Design Principles for Diffusion of Reports and Innovative Use of Business Intelligence Platforms

In order to innovate and respond quickly to new requirements, employees frequently supplement their information systems. This particularly applies to the context of business intelligence (BI) because many users supplement their BI platforms with individually tinkered spreadsheets. Unfortunately, these supplements bear numerous threats such as limited report reuse across all potential users. To address this gap, we establish a design science project. First, we qualitatively explore impediments to diffusion of reports and impediments to innovative use. Second, upon our findings and extant literature, we derive meta-requirements for BI platforms that foster diffusion of reports and innovative use. Third, we develop and discuss principles for how to design a BI platform that would meet the identified meta-requirements. The resulting design principles emphasize (1) permanent user sandboxes to improve innovative use and (2) hybrid recommendation agents based on user interaction, collaborative-filtering, and users\u27 social influence to improve diffusion of reports

Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability

Internet-of-Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive and Scalable Communication Network

From Data Flows to Privacy Issues: A User-Centric Semantic Model for Representing and Discovering Privacy Issues

In today\u27s highly connected cyber-physical world, people are constantly disclosing personal and sensitive data to different organizations and other people through the use of online and physical services. Such data disclosure activities can lead to unexpected privacy issues. However, there is a general lack of tools that help to improve users\u27 awareness of such privacy issues and to make more informed decisions on their data disclosure activities in wider contexts. To fill this gap, this paper presents a novel user-centric, data-flow graph based semantic model, which can show how a given user\u27s personal and sensitive data are disclosed to different entities and how different types of privacy issues can emerge from such data disclosure activities. The model enables both manual and automatic analysis of privacy issues, therefore laying the theoretical foundation of building data-driven and user-centric software tools for people to better manage their data disclosure activities in the cyber-physical world