Conceptual Evaluation of a 5G Network Slicing Technique for Emergency Communications and Preliminary Estimate of Energy Trade-Off

Kourtis, Michail-Alexandros; Sarlas, Thanos; Xilouris, Giorgios; Batistatos, Michael C.; Zarakovitis, Charilaos C.; Chochliouros, Ioannis P.; Koumaras, Harilaos

doi:10.3390/en14216876

Cited by 10 publications

(6 citation statements)

References 23 publications

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“…The received GPS data can be influenced in urban areas by building occlusions, making the data less accurate [ 13 ]. On the other hand, post-accident techniques use a GPS to detect and find the location of the accident, with GSM and 5G technology for messaging to emergency service centers [ 51 , 52 ]. These techniques require internet connection.…”

Section: Related Workmentioning

confidence: 99%

Smart Roads for Autonomous Accident Detection and Warnings

Mateen

Hanif

Khatri

et al. 2022

Sensors

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An increasing number of vehicles on the roads increases the risk of accidents. In bad weather (e.g., heavy rainfall, strong winds, storms, and fog), this risk almost doubles due to bad visibility as well as road conditions. If an accident happens, especially in bad weather, it is important to inform approaching vehicles about it. Otherwise, there might be another accident, i.e., a multiple-vehicle collision (MVC). If the Emergency Operations Center (EOC) is not informed in a timely fashion about the incident, fatalities might increase because they do not receive immediate first aid. Detecting humans or animals would undoubtedly provide us with a better answer for reducing human fatalities in traffic accidents. In this research, an accident alert light and sound (AALS) system is proposed for auto accident detection and alerts with all types of vehicles. No changes are required in non-equipped vehicles (nEVs) and EVs because the system is installed on the roadside. The idea behind this research is to make smart roads (SRs) instead of equipping each vehicle with a separate system. Wireless communication is needed only when an accident is detected. This study is based on different sensors that are used to build SRs to detect accidents. Pre-saved locations are used to reduce the time needed to find the accident’s location without the help of a global positioning system (GPS). Additionally, the proposed framework for the AALS also reduces the risk of MVCs.

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Section: Related Workmentioning

confidence: 99%

Smart Roads for Autonomous Accident Detection and Warnings

Mateen

Hanif

Khatri

et al. 2022

Sensors

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“…T round ≤ T NSR (8) where f residual is the total residual flow (in terms of link capacity) for path i from the source node to the target node for NSR i,t , ψ length i is the length of path i, Ψ µ is the average path length for all node connections, and T round is the total roundtrip time for data transmission from the source to target node expressed as,…”

Section: Node Selection and Slice Mapping Modelmentioning

confidence: 99%

“…Equation (7) guarantees that the residual flow meets the required link capacity for NSR i,t . In addition, Equation (8) ensures that the total time needed to transmit NSR i,t from n α to n Ω (vice-versa) does not exceed the specified lifetime of the NSR. Furthermore, all identified augmenting paths are used as links for candidate network slices by the DCB-NS agent for NSR slice provisioning.…”

Section: Node Selection and Slice Mapping Modelmentioning

confidence: 99%

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A Deep Contextual Bandit-Based End-to-End Slice Provisioning Approach for Efficient Allocation of 5G Network Resources

Dayot

Kim

2022

Network

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5G networks have been experiencing challenges in handling the heterogeneity and influx of user requests brought upon by the constant emergence of various services. As such, network slicing is considered one of the critical technologies for improving the performance of 5G networks. This technology has shown great potential for enhancing network scalability and dynamic service provisioning through the effective allocation of network resources. This paper presents a Deep Reinforcement Learning-based network slicing scheme to improve resource allocation in 5G networks. First, a Contextual Bandit model for the network slicing process is created, and then a Deep Reinforcement Learning-based network slicing agent (NSA) is developed. The agent’s goal is to maximize every action’s reward by considering the current network state and resource utilization. Additionally, we utilize network theory concepts and methods for node selection, ranking, and mapping. Extensive simulation has been performed to show that the proposed scheme can achieve higher action rewards, resource efficiency, and network throughput compared to other algorithms.

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“…Network slicing technology is the core part of 5G network [ 4 ]. The definition of 5G network slicing creates a broad field for communication service innovation [ 5 ]. The vertical market targeted by 5G networks supports multiple network slices on general and programmable infrastructure [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

5G Network Slicing: Methods to Support Blockchain and Reinforcement Learning

2022

Computational Intelligence and Neuroscience

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With the advent of the 5G era, due to the limited network resources and methods before, it cannot be guaranteed that all services can be carried out. In the 5G era, network services are not limited to mobile phones and computers but support the normal operation of equipment in all walks of life. There are more and more scenarios and more and more complex scenarios, and more convenient and fast methods are needed to assist network services. In order to better perform network offloading of the business, make the business more refined, and assist the better development of 5G network technology, this article proposes 5G network slicing: methods to support blockchain and reinforcement learning, aiming to improve the efficiency of network services. The research results of the article show the following: (1) In the model testing stage, the research results on the variation of the delay with the number of slices show that the delay increases with the increase of the number of slices, but the blockchain + reinforcement learning method has the lowest delay. The minimum delay can be maintained. When the number of slices is 3, the delay is 155 ms. (2) The comparison of the latency of different types of slices shows that the latency of 5G network slicing is lower than that of 4G, 3G, and 2G network slicing, and the minimum latency of 5G network slicing using blockchain and reinforcement learning is only 15 ms. (3) In the detection of system reliability, reliability decreases as the number of users increases because reliability is related to time delay. The greater the transmission delay, the lower the reliability. The reliability of supporting blockchain + reinforcement learning method is the highest, with a reliability of 0.95. (4) Through the resource utilization experiment of different slices, it can be known that the method of blockchain + reinforcement learning has the highest resource utilization. The resource utilization rate of the four slices under the blockchain + reinforcement learning method is all above 0.8 and the highest is 1. (5) Through the simulation test of the experiment, the results show that the average receiving throughput of video stream 1 is higher than that of video stream 2, IOT devices and mobile devices, and the average cumulative receiving throughput under the blockchain + reinforcement learning method. The highest is 1450 kbps. The average QOE of video stream 1 is higher than that of video stream 2, IOT devices and mobile devices, and the average QOE is the highest under the blockchain + reinforcement learning method, reaching 0.83.

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Conceptual Evaluation of a 5G Network Slicing Technique for Emergency Communications and Preliminary Estimate of Energy Trade-Off

Cited by 10 publications

References 23 publications

Smart Roads for Autonomous Accident Detection and Warnings

Smart Roads for Autonomous Accident Detection and Warnings

A Deep Contextual Bandit-Based End-to-End Slice Provisioning Approach for Efficient Allocation of 5G Network Resources

5G Network Slicing: Methods to Support Blockchain and Reinforcement Learning

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