Improving Download Throughput by Saving the Transmission Bandwidth in Vehicular Networks

Patil, Ashish; Shet, N. Shekar V.

doi:10.1007/s13369-019-03762-5

Cited by 3 publications

(4 citation statements)

References 26 publications

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“…Clustering 13,14 is a good example of a VANET concept for minimizing the amount of control packets, traffic on the network congestion, and excessive bandwidth usage. Because bandwidth in VANET is highly constrained, IMERLIN 15 presented a strategy that takes into account both the capacity of channels and the amount of data packet exchanges. This helps to reduce bandwidth use while increasing network throughput.…”

Section: Introductionmentioning

confidence: 99%

A novel swarm intelligence‐based fuzzy logic in efficient connectivity of vehicles

Kumaragurubaran,

Vijayakumar

2024

Int J Communication

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SummaryA vehicular ad hoc network (VANET) is a wireless network composed of a collection of mobility devices that link via a wireless channel without the need for permanent infrastructure or centralized administration. Multicasting is a method of sending data from a single node to multiple recipients at the same time. Because of its energizing network topology and constricted resources, a mobile ad hoc network (MANET) presents a number of issues. Because network execution measurements such as latency, bandwidth, throughput, and energy fluctuate so rapidly owing to node movement, these network properties in a wireless mobile ad hoc network are ambiguous. Because of the influence of these uncertainty issues, determining the top‐grade path from the originating node to a set of recipient nodes is challenging. In order to preserve network resources, we used a Swarm Intelligence‐based fuzzy logic strategy in this study that includes channel caliber factor and connection termination time to try to alleviate these uncertainty issues. This strategy integrates all of the network indicators available for the routes into an individual quantity named as fuzzy cost or cost of communication. The routes with the lowest fuzzy cost will be regarded optimum, and data will be delivered along this route from the originating point to a set of receivers. The simulation was carried out using NS‐3.38 and MATLAB, and the results reveal that the suggested protocol, HACOFLM, outperforms ODMRP, OFAODV, MAODV, and DFMCRP by means of packet delivery ratio, latency, control packet overhead, and throughput. DFMCRP outperforms MAODV by 3% to 6%, OFAODV by 13% to 19%, and ODMRP by 16% to 23%. According to HACOSMO, based on the number of vehicles and velocities, routing overhead is 7% to 10% less than DFMCRP, 9% to 13% less than MAODV, 17% to 29% less than OFAODV, and 22% to 36% less than ODMRP. It improvise throughput by 4% to 6% when contrasted to DFMCRP, 5% to 9% when contrasted to MAODV, 10% to 14% when contrasted to OFAODV, and 14% to 23% when contrasted to ODMRP.

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Section: Introductionmentioning

confidence: 99%

A novel swarm intelligence‐based fuzzy logic in efficient connectivity of vehicles

Kumaragurubaran,

Vijayakumar

2024

Int J Communication

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“…Edge computing is a service and application built on the edge server between the user layer and the data center, which migrates some functions of the user layer and the data center, and provides limited distributed computing, storage and network services. As the medium of Internet of Things (IoT) and cloud computing, edge computing can solve the problems caused by the combination of IoT and cloud computing, such as terminal node request delay, excessive storage and computing burden on cloud servers, and excessive pressure on network transmission bandwidth 11–14 …”

Section: Introductionmentioning

confidence: 99%

“…As the medium of Internet of Things (IoT) and cloud computing, edge computing can solve the problems caused by the combination of IoT and cloud computing, such as terminal node request delay, excessive storage and computing burden on cloud servers, and excessive pressure on network transmission bandwidth. [11][12][13][14] Edge computing extends the service resources of cloud computing to the edge of the network and solves the problems of cloud computing, such as poor mobility, weak geographic information perception, and high latency. However, the rise of edge computing also brings new challenges to the security protection of users, edge nodes, and cloud servers in edge computing networks.…”

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confidence: 99%

Edge‐driven ubiquitous wireless communication for robust cluster intelligent piano presentation platform considering system security

Wang¹

2022

Int J Communication

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Summary With the popularization of 6G‐driven wireless access networks and the development of edge computing, the connection between piano performance and artificial intelligence algorithms is getting closer and closer. 6G was first used to solve large‐capacity, short‐latency communication problems, and research results in this field abound. However, in practical applications, the deployment of 6G high‐frequency network is a major problem faced by the industry. This paper mainly studies the artificial robust cluster piano demonstration platform from the perspective of the fusion of 6G and edge computing. We firstly consider that the edge computing is a technology in which artificial intelligence algorithms are transplanted to edge devices and chips. The vision of the 6G network is to realize ubiquitous wireless intelligence, which includes three dimensions: mobile ultra‐broadband, super Internet of Things, and artificial intelligence, and realizes the conversion of piano scores into digital signals. Artificial Robust Clustering is a comprehensive technique that incorporates many disciplines, including phonetics, artificial intelligence, computer science, and more. This paper analyzes the fusion technology of 6G and edge computing and studies the robust cluster intelligent piano demonstration platform. The mentioned technologies are combined together to make the designed efficient.

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“…Clustering [13,14] is another concept which is widely used in VANET to minimize the number of the control packet, the packet congestion and unnecessary consumption of bandwidth. Since bandwidth is very limited in VANET, IMERLIN [15] proposed a method that considers the channel bandwidth and the number of transmission of the data packet. This helps to minimize the use of bandwidth and also increases the throughput of the network.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy logic-based VANET routing method to increase the QoS by considering the dynamic nature of vehicles

et al. 2021

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Vehicular ad hoc network usually operates in various challenging situations like frequent topology changes, high vehicular mobility and the wide range of communication networks. Due to this it is very hard to maintain a higher data rate and also to achieve low latency during data communication. To overcome these problems, given the dynamic natures of all the vehicles in a given network in the proposed routing method, we have defined two fundamental parameters to determine the forwarding vehicle. The first parameter, which we developed, we call it "Channel quality factor (CQF)" or 'Z'. The other parameter known as "Communication expiration time" or 'T' together with CQF is used in the present method to determine the forwarding vehicle. Fuzzy logic is also used to optimize various Quality of Service matrices. This proposed routing method involves two main parts; one is for forwarding Vehicle selection in the road based on the fuzzy logic. The second one is Road selection at the Road Junction to select the right path to reach the signal to the destination vehicle. The simulation results show that our proposed method performs well compare to other well-known protocols (MoZo, BRAVE, OFAODV) in terms of the average end to end delay, packet delivery ratio and control packet overhead, given any number of vehicles in a set of streets. While we are comparing with VEFR protocol, our proposed method shows higher performance in terms of average E2E delay and control packet overhead. However, it is interesting to see that VEFR gives ∼ 5% better result than our proposed method when the number of vehicles in the streets are lower. But in the limit, when the number of vehicles reaches close to ∼ 1900 the difference between the proposed method and method in VEFR goes to zero. At last we compare our proposed method with junction based two V2I protocols. In every cases, it shows better result even though we change the speed of the vehicles, beacon interval, channel data rate and transmission region.

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Improving Download Throughput by Saving the Transmission Bandwidth in Vehicular Networks

Cited by 3 publications

References 26 publications

A novel swarm intelligence‐based fuzzy logic in efficient connectivity of vehicles

A novel swarm intelligence‐based fuzzy logic in efficient connectivity of vehicles

Edge‐driven ubiquitous wireless communication for robust cluster intelligent piano presentation platform considering system security

Fuzzy logic-based VANET routing method to increase the QoS by considering the dynamic nature of vehicles

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