Conference Papers

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    Heuristic-based iot application modules placement in the fog-cloud computing environment
    (Institute of Electrical and Electronics Engineers Inc., 2018) Natesha, B.V.; Guddeti, R.M.
    Nowadays many Smart City applications make use of Internet of Things (IoT) devices for monitoring the environment. The increase in use of IoT for smart city applications causes exponential increase in the volume of data. Using centralised cloud for time sensitive IoT applications is not feasible due to more delay because of the network congestion. Hence, fog computing is used for processing the data near to the edge of the network, where processing is done by distributed network nodes. But, there is a challenge to select the fog nodes which can host and process the application modules. The placement of application module on these fog devices is known as NP-hard problem. Hence, we need better placement strategies to decide placement of application modules in fog infrastructure to minimize the application latency. In this paper, we design a First-Fit Decreasing (FFD) heuristic based approach for placing IoT application modules on Fog-Cloud and carried out the experiment using iFogsim simulator. The simulation results demonstrate that the proposed method shows significant decrease in both the application latency and energy consumption of Fog-Cloud as compared to the benchmark method. © 2018 IEEE.
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    Fuzzy Reinforcement Learning based Microservice Allocation in Cloud Computing Environments
    (Institute of Electrical and Electronics Engineers Inc., 2019) Joseph, C.T.; Martin, J.P.; Chandrasekaran, K.; Kandasamy, A.
    Nowadays the Cloud Computing paradigm has become the defacto platform for deploying and managing user applications. Monolithic Cloud applications pose several challenges in terms of scalability and flexibility. Hence, Cloud applications are designed as microservices. Application scheduling and energy efficiency are key concerns in Cloud computing research. Allocating the microservice containers to the hosts in the datacenter is an NP-hard problem. There is a need for efficient allocation strategies to determine the placement of the microservice containers in Cloud datacenters to minimize Service Level Agreement violations and energy consumption. In this paper, we design a Reinforcement Learning-based Microservice Allocation (RL-MA) approach. The approach is implemented in the ContainerCloudSim simulator. The evaluation is conducted using the real-world Google cluster trace. Results indicate that the proposed method reduces both the SLA violation and energy consumption when compared to the existing policies. © 2019 IEEE.
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    Development of IoT-Based Smart Home Application with Energy Management
    (Institute of Electrical and Electronics Engineers Inc., 2023) Prathyusha, M.R.; Bhowmik, B.
    IoT Evolution is a trusted source in the Internet of Things (IoT) that describes an ecosystem of connected devices. Interconnected smart devices have become a ubiquitous part of daily lives. In other words, the rising popularity of the IoT in day-to-day life has led to people incorporating innovative applications, e.g., smart home environments, to improve convenience, comfort, energy efficiency, and safety. However, these intelligent appliances provide additional energy costs. Thus, energy management is essential to minimize this energy cost in an intellectual environment. The cost incurred for improved energy or other innovative services varies with the design of an intelligent system. This paper presents a smart home automation system designed along with energy management. In this work, each room of the proposed smart home is modeled with a set of selected smart things and simulated. Also, their energy consumption is estimated and analyzed for efficient energy management. © 2023 IEEE.
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    Recent Advances in Building Materials and Technologies–An Introduction
    (Springer Science and Business Media Deutschland GmbH, 2024) Kolathayar, S.; Sreekeshava, K.S.; Vinod Chandra Menon, N.; Shekhawat, P.; Bhargavi, C.
    This volume underscores the critical influence of building materials on construction projects, emphasizing their role in progress, quality, and operational durability. The construction industry's explosive growth, aligning with economic development, is noted as a positive force for industrialization and modernization. Amidst climate change considerations, the imperative for sustainable and resilient building materials is highlighted. Alternative materials, whether fully or partially replacing aggregates or cement, emerge as vital for sustainable and resilient construction. These include diverse industrial wastes (e.g., plastics, construction by-products) and fibers/ashes (e.g., jute, steel, sugarcane bagasse). A notable innovation is the geopolymer, an alkali-activated binder offering superior durability and mechanical strength with lower energy consumption and CO2 emissions than traditional cement. In waste material utilization, studies explore plastic, waste tea, scrap ceramic tiles, and construction waste in concrete, addressing both sustainable waste management and high-performance structures. Ash applications consider wood ash, palm oil fuel ash, and agricultural waste ashes as sustainable alternatives to traditional cement. Geopolymer advancements encompass mechanical behavior, heat conditions, and novel applications like using iron ore tailings. Durability assessment explores nanotechnology to enhance concrete properties and reduce energy consumption. Fiber-reinforced materials and compressed stabilized earth blocks reinforced with coconut fiber aim for enhanced mechanical properties and reduced carbon emissions. The volume also touches on construction project investigations, addressing safety, progress tracking, and construction delay analysis techniques. In essence, this synthesis offers a panoramic view of recent advances in building materials and technologies, contributing to a holistic understanding of sustainable and resilient construction practices. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.