Game theory-based energy efficiency optimization model for the Internet of Things

Zeng, Xiaoyang

doi:10.1016/j.comcom.2021.12.001

Cited by 16 publications

(6 citation statements)

References 29 publications

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“…Nash equilibrium is essentially a point of strategic balance, where no player has an incentive to unilaterally deviate from their chosen strategy [230]- [232]. In this context, Nash equilibrium provides an approximate optimal solution for the complex task of load balancing in the cloud [233]- [235]. In practical terms, this is like finding the sweet spot where all players (objectives) collectively achieve an outcome that is beneficial for the overall cloud resource management.…”

Section: Game Theory-based Managementmentioning

confidence: 99%

A Comprehensive Survey on Sustainable Resource Management in Cloud Computing Environments

Saxena,

Singh

2024

Preprint

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Section: Game Theory-based Managementmentioning

confidence: 99%

A Comprehensive Survey on Sustainable Resource Management in Cloud Computing Environments

Saxena,

Singh

2024

Preprint

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“…Work [17] suggested a structure for the Industrial Internet gateway that included multiple scalable microcontrollers and a hardware array-based programmable gateway. An adaptive transmission architecture based on an IIoT edge computing network was proposed by author [18] as receiving centralized global support. A review of how effectively edge computing is used in IoT applications like smart cities, farms, and transportation was presented in work [19].…”

Section: Security Enhancement Based Existing Techniquementioning

confidence: 99%

Optimization algorithms in security and privacy-preserving data disturbance for collaborative edge computing social IoT deep learning architectures

Boopathi

Gupta

et al. 2023

Preprint

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Edge computing (EC) has been viewed as a viable option for addressing cloud computing's shortcomings in supporting Internet-of-Things (IoT) applications as an innovative strategy. However, edge-assisted IoT security requires to be better guaranteed due to the network's instability and increase in attack surfaces. Practice of safeguarding a given network from threats that could compromise its availability is referred to as network security. In addition, we can add that network security must address issues such as unauthorized access to network-accessible resources and even their misuse. This study proposes a novel approach to improving edge computing-based data privacy through secure data transmission and deep learning-based optimization with Internet of Things. Edge network privacy preservation is accomplished through collaborative architecture and hybrid federated sever-based stochastic vector networks. The trust based multiple encryption algorithm is used to secure the transmission of the data after it has been optimized using firefly grey optimization. Throughput, network security analysis, PDR, latency, and energy consumption were all examined as part of the experimental analysis for network data privacy and optimization.

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“…Zeng [25] formulated the system energy efficiency problem as a game theory problem. In their work, the sensor devices are modeled to collectively work together to maximize their overall objective function value.…”

Section: Related Workmentioning

confidence: 99%

“…This resulted in a low energy efficiency performance of the system. To address the problem in the work of Zeng [25], we propose a dynamic resource allocation method and an optimization-based method to jointly optimize the allocation of the system resources and improve the overall system energy efficiency in a sequential multi-class WPCN setting.…”

Section: Related Workmentioning

confidence: 99%

Resource Allocation Optimization in IoT-Enabled Water Quality Monitoring Systems

Olatinwo,

Joubert

2023

Sensors

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Water quality monitoring systems that are enabled by the Internet of Things (IoT) and used in water applications to collect and transmit water data to data processing centers are often resource-constrained in terms of power, bandwidth, and computation resources. These limitations typically impact their performance in practice and often result in forwarding their data to remote stations where the collected water data are processed to predict the status of water quality, because of their limited computation resources. This often negates the goal of effectively monitoring the changes in water quality in a real-time manner. Consequently, this study proposes a new resource allocation method to optimize the available power and time resources as well as dynamically allocate hybrid access points (HAPs) to water quality sensors to improve the energy efficiency and data throughput of the system. The proposed system is also integrated with edge computing to enable data processing at the water site to guarantee real-time monitoring of any changes in water quality and ensure timely access to clean water by the public. The proposed method is compared with a related method to validate the system performance. The proposed system outperforms the existing system and performs well in different simulation experiments. The proposed method improved the baseline method by approximately 12.65% and 16.49% for two different configurations, demonstrating its effectiveness in improving the energy efficiency of a water quality monitoring system.

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Game theory-based energy efficiency optimization model for the Internet of Things

Cited by 16 publications

References 29 publications

A Comprehensive Survey on Sustainable Resource Management in Cloud Computing Environments

A Comprehensive Survey on Sustainable Resource Management in Cloud Computing Environments

Optimization algorithms in security and privacy-preserving data disturbance for collaborative edge computing social IoT deep learning architectures

Resource Allocation Optimization in IoT-Enabled Water Quality Monitoring Systems

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