73,474 research outputs found

    Cyber-Physical Systems: a multi-criteria assessment for Internet-of-Things (IoT) systems

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    This research work was partially supported by funds provided by the European Commission in the scope of FoF/H2020-636909 C2NET, FoF/H2020-723710 vf-OS and ICT/H2020-825631 ZDMP.This article addresses a multi-criteria decision problem regarding the more suitable device (system) to perform a task for cyber-physical systems. New embedded systems provided everyday makes engineers’ decision very difficult. Components are proposed to formally describe solutions, criteria, constraints and priorities, taking into account users’ specific aspects. To materialise all formal descriptions, a model-driven approach is followed, allowing the design of enablers for interoperability with standards. It is enabled the use of different software languages and decision methods. Proposed framework enables a better Internet-of-Things system selection, and therefore stakeholders can perform a more suitable design of their cyber-physical enterprise systems.authorsversioninpres

    A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

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    In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

    Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

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    Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

    Choosing Wearable Internet of Things Devices for Managing Safety in Construction Using Fuzzy Analytic Hierarchy Process as a Decision Support System

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    Many safety and health risks are faced daily by workers in the field of construction. There is unpredictability and risk embedded in the job and work environment. When compared with other industries, the construction industry has one of the highest numbers of worker injuries, illnesses, fatalities, and near-misses. To eliminate these risky events and make worker performance more predictable, new safety technologies such as the Internet of Things (IoT) and Wearable Sensing Devices (WSD) have been highlighted as effective safety systems. Some of these Wearable Internet of Things (WIoT) and sensory devices are already being used in other industries to observe and collect crucial data for worker safety in the field. However, due to limited information and implementation of these devices in the construction field, Wearable Sensing Devices (WSD) and Internet of Things (IoT) are still relatively underdeveloped and lacking. The main goal of the research is to develop a conceptual decision-making framework that managers and other appropriate personnel can use to select suitable Wearable Internet of Things (WIoT) devices for proper application/ implementation in the construction industry. The research involves a literature review on the aforementioned devices and the development and demonstration of a decision-making framework using the Fuzzy Analytic Hierarchy Process (FAHP)
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