20,551 research outputs found

    A Smart Game for Data Transmission and Energy Consumption in the Internet of Things

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    The current trend in developing smart technology for the Internet of Things (IoT) has motivated a lot of research interest in optimizing data transmission or minimizing energy consumption, but with little evidence of proposals for achieving both objectives in a single model. Using the concept of game theory, we develop a new MAC protocol for IEEE 802.15.4 and IoT networks in which we formulate a novel expression for the players' utility function and establish a stable Nash equilibrium (NE) for the game. The proposed IEEE 802.15.4 MAC protocol is modeled as a smart game in which analytical expressions are derived for channel access probability, data transmission probability, and energy used. These analytical expressions are used in formulating an optimization problem (OP) that maximizes data transmission and minimizes energy consumption by nodes. The analysis and simulation results suggest that the proposed scheme is scalable and achieves better performance in terms of data transmission, energy-efficiency, and longevity, when compared with the default IEEE 802.15.4 access mechanism.Peer reviewe

    Machine Learning in Wireless Sensor Networks for Smart Cities:A Survey

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    Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of the internet of things (IoT) nodes deployed in smart cities. In smart cities, the major IoT applications are smart traffic monitoring, smart waste management, smart buildings and patient healthcare monitoring. The small size IoT nodes based on low power Bluetooth (IEEE 802.15.1) standard and wireless sensor networks (WSN) (IEEE 802.15.4) standard are generally used for transmission of data to a remote location using gateways. The WSN based IoT (WSN-IoT) design problems include network coverage and connectivity issues, energy consumption, bandwidth requirement, network lifetime maximization, communication protocols and state of the art infrastructure. In this paper, the authors propose machine learning methods as an optimization tool for regular WSN-IoT nodes deployed in smart city applications. As per the author’s knowledge, this is the first in-depth literature survey of all ML techniques in the field of low power consumption WSN-IoT for smart cities. The results of this unique survey article show that the supervised learning algorithms have been most widely used (61%) as compared to reinforcement learning (27%) and unsupervised learning (12%) for smart city applications

    Internet of Things and Intelligent Technologies for Efficient Energy Management in a Smart Building Environment

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    Internet of Things (IoT) is attempting to transform modern buildings into energy efficient, smart, and connected buildings, by imparting capabilities such as real-time monitoring, situational awareness and intelligence, and intelligent control. Digitizing the modern day building environment using IoT improves asset visibility and generates energy savings. This dissertation provides a survey of the role, impact, and challenges and recommended solutions of IoT for smart buildings. It also presents an IoT-based solution to overcome the challenge of inefficient energy management in a smart building environment. The proposed solution consists of developing an Intelligent Computational Engine (ICE), composed of various IoT devices and technologies for efficient energy management in an IoT driven building environment. ICE’s capabilities viz. energy consumption prediction and optimized control of electric loads have been developed, deployed, and dispatched in the Real-Time Power and Intelligent Systems (RTPIS) laboratory, which serves as the IoT-driven building case study environment. Two energy consumption prediction models viz. exponential model and Elman recurrent neural network (RNN) model were developed and compared to determine the most accurate model for use in the development of ICE’s energy consumption prediction capability. ICE’s prediction model was developed in MATLAB using cellular computational network (CCN) technique, whereas the optimized control model was developed jointly in MATLAB and Metasys Building Automation System (BAS) using particle swarm optimization (PSO) algorithm and logic connector tool (LCT), respectively. It was demonstrated that the developed CCN-based energy consumption prediction model was highly accurate with low error % by comparing the predicted and the measured energy consumption data over a period of one week. The predicted energy consumption values generated from the CCN model served as a reference for the PSO algorithm to generate control parameters for the optimized control of the electric loads. The LCT model used these control parameters to regulate the electric loads to save energy (increase energy efficiency) without violating any operational constraints. Having ICE’s energy consumption prediction and optimized control of electric loads capabilities is extremely useful for efficient energy management as they ensure that sufficient energy is generated to meet the demands of the electric loads optimally at any time thereby reducing wasted energy due to excess generation. This, in turn, reduces carbon emissions and generates energy and cost savings. While the ICE was tested in a small case-study environment, it could be scaled to any smart building environment

    Efficient energy management for the internet of things in smart cities

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    The drastic increase in urbanization over the past few years requires sustainable, efficient, and smart solutions for transportation, governance, environment, quality of life, and so on. The Internet of Things offers many sophisticated and ubiquitous applications for smart cities. The energy demand of IoT applications is increased, while IoT devices continue to grow in both numbers and requirements. Therefore, smart city solutions must have the ability to efficiently utilize energy and handle the associated challenges. Energy management is considered as a key paradigm for the realization of complex energy systems in smart cities. In this article, we present a brief overview of energy management and challenges in smart cities. We then provide a unifying framework for energy-efficient optimization and scheduling of IoT-based smart cities. We also discuss the energy harvesting in smart cities, which is a promising solution for extending the lifetime of low-power devices and its related challenges. We detail two case studies. The first one targets energy-efficient scheduling in smart homes, and the second covers wireless power transfer for IoT devices in smart cities. Simulation results for the case studies demonstrate the tremendous impact of energy-efficient scheduling optimization and wireless power transfer on the performance of IoT in smart cities

    6LoWPAN in Wireless Sensor Network with IoT in 5G Technology for Network Secure Routing and Energy Efficiency

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    Today, interconnection and routing protocols must discover the best solution for secure data transformation with a variety of smart devices due to the growing influence of information technology, such as Internet of Things (IoT), in human life. In order to handle routing concerns with regard to new interconnection approaches like the 6LoWPAN protocol, it is required to offer an improved solution. This research propose novel technique in 6LoWPAN network secure routing and energy efficiency (EE) for WSN in IoT application based on 5G technology. Here the energy optimization has been carried out using clustered channel aware least square support vector machine (Cl_CHLSSVM). Then the secure routing has been carried out using fuzzy based Routing Protocol for low-power and Lossy Networks with kernel-particle swarm optimization (Fuz_RPL_KPSO). To serve needs of IoT applications, proposed method is cognizant of both node priorities as well as application priorities. Applications' sending rate allocation is modeled as a constrained optimization issue.Pxperimental analysis is carried out in terms of throughput of 96%, weighted fairness index of 77%, end-to-end delay of 59%, energy consumption of 86%, and buffer dropped packets of 51%

    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

    Improving energy consumption of commercial building with IoT and machine learning

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    Computing server power modeling in a data center: survey,taxonomy and performance evaluation

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    Data centers are large scale, energy-hungry infrastructure serving the increasing computational demands as the world is becoming more connected in smart cities. The emergence of advanced technologies such as cloud-based services, internet of things (IoT) and big data analytics has augmented the growth of global data centers, leading to high energy consumption. This upsurge in energy consumption of the data centers not only incurs the issue of surging high cost (operational and maintenance) but also has an adverse effect on the environment. Dynamic power management in a data center environment requires the cognizance of the correlation between the system and hardware level performance counters and the power consumption. Power consumption modeling exhibits this correlation and is crucial in designing energy-efficient optimization strategies based on resource utilization. Several works in power modeling are proposed and used in the literature. However, these power models have been evaluated using different benchmarking applications, power measurement techniques and error calculation formula on different machines. In this work, we present a taxonomy and evaluation of 24 software-based power models using a unified environment, benchmarking applications, power measurement technique and error formula, with the aim of achieving an objective comparison. We use different servers architectures to assess the impact of heterogeneity on the models' comparison. The performance analysis of these models is elaborated in the paper
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