WITHDRAWN: Smart internet of vehicle maintenance system

Anusha, P.; Shabanabegum, S.K.; Pavaiyarkarasi, R.; Seethalakshmi, E.; Vadivukkarasi, K.; Ponnusamy, Vijayakumar

doi:10.1016/j.matpr.2020.11.303

Cited by 4 publications

(3 citation statements)

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“…(4) (5) where Q(s,a) represents the cumulative discount reward when action at is taken for the state s t , while V(s) represents the baseline, this technique allows updating the policy network in a direction that chooses better actions. PPO uses the trust-region (TRPO) method to ensure that the new updated policy never goes far away from the current policy, making it more stable and reliable.…”

Section: Reinforcement Learningmentioning

confidence: 99%

“…Vehicle-to-Vehicle (V2V), which enables the vehicular nodes to contact each other directly, and Vehicle-to-Infrastructure (V2I), Volume 13, Number 3,2022 in which vehicles can communicate with RSUs [3]. Providing vehicles with a permanent internet connection has become an urgent necessity to feed drivers with relevant road information and offer a comfortable trip for passengers [4] [5]. Providing the Internet for vehicles requires finding a suitable gateway.…”

Section: Introductionmentioning

confidence: 99%

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Reinforcement Learning based Gateway Selection in VANETs

Alabbas

Huszák

2022

Int. j. electr. comput. eng. syst. (Online)

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In vehicular ad hoc networks (VANETs), providing the Internet has become an urgent necessity, where mobile gateways are used to ensure network connection to all customer vehicles in the network. However, the highly dynamic topology and bandwidth limitations of the network represent a significant issue in the gateway selection process. Two objectives are defined to overcome these challenges. The first objective aims to maximize the number of vehicles connected to the Internet by finding a suitable gateway for them depending on the connection lifetime. The second objective seeks to minimize the number of connected vehicles to the same gateway to overcome the limitation of gateways' bandwidth and distribute the load in the network. For this purpose, A gateway discovery system assisted by the vehicular cloud is implemented to find a fair trade-off between the two conflicting objectives. Proximal Policy Optimization, a well-known reinforcement learning strategy, is used to define and train the agent. The trained agent was evaluated and compared with other multi-objective optimization methods under different conditions. The obtained results show that the proposed algorithm has better performance in terms of the number of connected vehicles, load distribution over the mobile gateways, link connectivity duration, and execution time.

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Section: Reinforcement Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning based Gateway Selection in VANETs

Alabbas

Huszák

2022

Int. j. electr. comput. eng. syst. (Online)

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“…Therefore, there was a lack of studies that used cloud computing to benefit from the ability to share the systems that control the process more widely. Several studies proposed cloud computing products, like artificial intelligence (AI) [ 78 ], and the internet of things (IoT) [ 5 , 79 , 80 , 81 ] in their models, however, all of the studies were focusing only on the smart vehicles’ batteries [ 77 , 78 , 82 , 83 , 84 ]. Therefor this research focuses on the problem of centralized parallel processing and diagnosis of power system condition data based on cloud computing and big data technology because difficult to meet the real-time requirements of the power system and construction of a safe, stable, cost-effective, green and environmentally friendly smart grid.…”

Section: Introductionmentioning

confidence: 99%

Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations

AL-Jumaili

Muniyandi

Hasan

et al. 2023

Sensors

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Traditional parallel computing for power management systems has prime challenges such as execution time, computational complexity, and efficiency like process time and delays in power system condition monitoring, particularly consumer power consumption, weather data, and power generation for detecting and predicting data mining in the centralized parallel processing and diagnosis. Due to these constraints, data management has become a critical research consideration and bottleneck. To cope with these constraints, cloud computing-based methodologies have been introduced for managing data efficiently in power management systems. This paper reviews the concept of cloud computing architecture that can meet the multi-level real-time requirements to improve monitoring and performance which is designed for different application scenarios for power system monitoring. Then, cloud computing solutions are discussed under the background of big data, and emerging parallel programming models such as Hadoop, Spark, and Storm are briefly described to analyze the advancement, constraints, and innovations. The key performance metrics of cloud computing applications such as core data sampling, modeling, and analyzing the competitiveness of big data was modeled by applying related hypotheses. Finally, it introduces a new design concept with cloud computing and eventually some recommendations focusing on cloud computing infrastructure, and methods for managing real-time big data in the power management system that solve the data mining challenges.

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