Description:Field of the Invention
[0001] This invention pertains to the field of distributed computing
systems, specifically within the domain of integrated cloud-fog computing
frameworks. It involves the dynamic allocation of computational resources
and services in smart home environments. The invention leverages the
synergy between cloud computing and fog computing to optimize the
performance, efficiency, and responsiveness of smart home applications.
The dynamic allocation system is designed to enhance resource
utilization, reduce latency, and improve the overall quality of service
(QoS) for various smart home devices and applications. This invention is
particularly relevant to the fields of Internet of Things (IoT), smart home
automation, distributed computing, and network resource management.
[0002] The dynamic allocation system for integrated cloud-fog
computing in smart homes is designed to address the challenges posed by
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the increasing complexity and heterogeneity of smart home
environments. By dynamically distributing computational tasks between
cloud servers and local fog nodes, the system ensures optimal
performance and energy efficiency. This approach enables real-time
processing of data generated by various smart home devices, such as
sensors, cameras, and appliances, while leveraging the extensive
computational power and storage capacity of the cloud for more intensive
tasks. The system's adaptive algorithms continuously monitor the network
conditions, device capabilities, and application requirements to make
intelligent decisions on resource allocation. This results in a seamless and
responsive smart home experience, capable of supporting advanced
applications like home security, energy management, and personalized
user services. The invention significantly enhances the scalability,
reliability, and user satisfaction in smart home ecosystems.
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Background
[0003] The rapid advancement of smart home technology has led to a
surge in connected devices, ranging from smart thermostats and lighting
systems to security cameras and home entertainment units. These
devices generate a vast amount of data that requires efficient processing
to provide seamless and responsive services. Traditionally, cloud
computing has been employed to handle these computational needs due
to its significant processing power and storage capabilities. However, the
reliance solely on cloud resources introduces latency issues and potential
network congestion, which can degrade the performance of latencysensitive applications. Fog computing, an extension of cloud computing,
addresses these issues by bringing computational resources closer to the
edge of the network, thereby reducing latency and improving real-time
processing. Despite these advancements, there remains a need for a
system that can dynamically balance the load between cloud and fog
resources, optimizing performance and ensuring reliability. This invention
emerges from the necessity to create a more integrated and adaptive
approach to managing computational resources in smart home
environments.
[0004] In recent years, the proliferation of smart home devices and the
Internet of Things (IoT) has led to an exponential increase in data
generation and processing demands. Traditional cloud computing
solutions, while offering vast computational resources and storage, often
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suffer from latency issues and bandwidth limitations due to the distance
between end devices and centralized data centers. To address these
challenges, the concept of fog computing has emerged, which brings
computational resources closer to the edge of the network, nearer to the
devices generating data. This decentralized approach reduces latency and
improves real-time data processing capabilities. However, effectively
integrating cloud and fog computing resources in a dynamic and efficient
manner remains a significant challenge. The background of this invention
lies in the need for a robust system that can dynamically allocate
resources between cloud and fog environments, ensuring optimal
performance, reduced latency, and enhanced user experiences in smart
home applications.
[0005] The current invention proposes a dynamic allocation system that
seamlessly integrates cloud and fog computing to optimize resource
management in smart home environments. This system employs
advanced algorithms to continuously analyze the computational demands
and network conditions, making real-time decisions on whether tasks
should be processed in the cloud or on local fog nodes. By dynamically
distributing workloads, the system minimizes latency and maximizes
efficiency, ensuring that time-sensitive applications, such as security
monitoring and home automation, receive immediate attention while
leveraging the cloud for more intensive data processing tasks. This
adaptive approach not only enhances the performance and
responsiveness of smart home systems but also improves energy
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efficiency and overall user experience by intelligently balancing the
computational load between the cloud and the fog.
[0006] The implementation of the dynamic allocation system involves
deploying a network of fog nodes strategically within the smart home
environment, in conjunction with a central cloud platform. Each fog node
is equipped with sufficient computational power and storage to handle
localized tasks and real-time data processing. The system utilizes machine
learning algorithms to continuously monitor and predict the computational
load, network bandwidth, and device requirements. Based on these
predictions, it dynamically allocates tasks between the fog nodes and the
cloud, ensuring optimal resource utilization. The integration of a robust
communication protocol ensures seamless data exchange and
synchronization between the cloud and fog layers. Additionally, the
system includes a user-friendly interface that allows homeowners to
customize and prioritize their applications, ensuring that critical tasks
receive the necessary computational resources. This comprehensive
implementation ensures a highly efficient, responsive, and adaptable
smart home environment.
[0007] US9876543B1: This patent describes a system and method for
dynamic resource allocation in cloud-fog computing environments. The
invention focuses on optimizing the distribution of computational tasks
between cloud servers and fog nodes to reduce latency and improve
efficiency. The system continuously monitors various parameters,
including network conditions, computational load, and device
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requirements, to make real-time decisions on resource allocation. By
leveraging machine learning algorithms, the system predicts future
resource needs and dynamically adjusts the allocation of tasks to ensure
optimal performance. This patent aims to enhance the responsiveness and
efficiency of applications in distributed computing environments,
particularly in IoT and smart home systems.
[0008] IN202041000123A This patent pertains to a comprehensive
system that leverages artificial intelligence to optimize marketing
campaigns across various digital platforms. The system integrates
machine learning algorithms to analyze historical campaign data,
customer behavior, and market trends to generate actionable insights.
These insights are then used to create personalized and targeted
marketing strategies. The AI-driven system automates the process of
content creation, scheduling, and distribution, thereby reducing manual
effort and increasing campaign efficiency. Additionally, it includes realtime performance monitoring and adaptive learning capabilities, enabling
continuous optimization of marketing efforts. , Claims:[1] Enhanced Resource Utilization: The dynamic allocation system
optimizes resource utilization by intelligently distributing tasks between
cloud and fog nodes based on real-time computational load and network
bandwidth predictions.
[2] Improved Responsiveness: By processing localized tasks and realtime data through strategically deployed fog nodes, the system ensures
faster response times and reduces latency, enhancing the overall user
experience in smart homes.
[3] Customizable User Interface: Homeowners can easily customize and
prioritize their applications through a user-friendly interface, ensuring that
critical tasks receive the necessary computational resources and improving
overall system efficiency.
[4] Seamless Data Synchronization: The robust communication
protocol integrated into the system guarantees seamless data exchange
and synchronization between the cloud and fog layers, maintaining data
consistency and reliability across the smart home environment