Delay-aware relay selection with heterogeneous communication range in VANETs

Wang, Sha; Huang, Chuanhe; Wang, Daqing

doi:10.1007/s11276-018-1835-8

Cited by 12 publications

(3 citation statements)

References 21 publications

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“…From the graph, the ultimate figures are almost clearly outperformed and explain the need for the routing protocol. Here, we compare our proposed work with OLSR and existing literature 39 …”

Section: Results and Performance Analysismentioning

confidence: 99%

Performance evaluation on the node mobility with respect to human driver behavior prediction in vehicular ad hoc network using adaptive deer hunting Optimized Link State Routing Protocol

Swamynathan¹,

Palanichamy²,

Jerald³

2021

Int J Communication

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Summary In vehicular ad hoc network (VANET) broadcasting is considered as the critical area of research. The vehicles are connecting in an ad hoc manner to create a network of a wider range. In an intelligent transport system (ITS), with vehicle to vehicle (V2V) communication in the VANET is used to support the network backbone and which the accident prevention technique has been deployed for ensuring the road safety. The major reason for the road accident is the abnormal driving behavior of human drivers. The movement pattern of the vehicle is the main factor of the network topology so that the impact of human driving pattern influences the performance and behavior of the network. Driving behavior can be classified as reckless, normal, drunken, and fatigue driving. During driving, the behavior of human drivers was predicted in this paper, thereby providing the performance analysis on the mobility of the network. For the improvement of a driver behavior, the hybridized mega‐trend diffusion (MTD) with optimized deep belief network–sunflower optimization (DBN‐SFO) is used. Here, the network performance of the adaptive deer hunting Optimized Link State Routing Protocol (ADHOLSR) is analyzed in NS2 simulation platform.

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Section: Results and Performance Analysismentioning

confidence: 99%

Performance evaluation on the node mobility with respect to human driver behavior prediction in vehicular ad hoc network using adaptive deer hunting Optimized Link State Routing Protocol

Swamynathan¹,

Palanichamy²,

Jerald³

2021

Int J Communication

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“…Transmission loss is occur while routing packet to the destination Sha et al [26] High reliable data transmission Lack of parameter consideration in routing induces packet losses…”

Section: Clusteringmentioning

confidence: 99%

An Efficient Delay Aware Emergency Message Dissemination and Data Retrieval in Secure VANET-Cloud Environment

Al-Shabi

2023

Wireless Pers Commun

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Over the recent decades, incorporating Vehicular Ad-hoc Network (VANET) into Cloud computing plays a vital role, since it provides a reliable safety journey to vehicular drivers, passengers, etc.However, attaining security and emergency message dissemination is still major bottleneck in VANET combined Cloud, due to the dynamic nature of vehicles and wireless communication. Our major intention is to provide high level security in VANET-Cloud environment. In addition to it, we also reduce delay in emergency dissemination. Our proposed Delay Aware Emergency Message Dissemination and Data Retrieval in Secure (DEMD 2 RS) VANET-Cloud is composed of four sequential processes: Authentication, Clustering, Data Retrieval and Data dissemination. In regard to maintainingsecurity for both Road Side Unit (RSU) and Vehicles, we propose Hash based Credential Authentication Scheme (HCAS) that affords authentication using Secure Hash Algorithm-3 (SHA-3) and Elliptic Curve Points (ECP). To sustain a stable cluster, Firm Aware Clustering Scheme (FACS) is pursued where Stud Krill Herd (SKH) algorithm is exploited. In the data retrieval process, cloud provides requested information to the RSU in encrypted form using the Twofish algorithm. RSU discover the path to deliver received data through executing Artificial Neural Network (ANN) algorithm. In order to diminish delay in emergency message dissemination, best disseminator is selected by cluster head using Fuzzy-Topsis (FT) algorithm. Our DEMD 2 RSVANET-Cloud network is implemented in Omnet 4.6 and SUMO 0.21.0 simulation tool. Finally, the evaluation of DEMD 2 RS work performance is achieved by computing consequent metrics that are Throughput, Packet Delivery Ratio, Transmission delay, Average delay, Key generation time, Encryption time and Decryption time.

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“…The above two approaches effectively reduce the number of route control packets and the number of forwarding and lower the routing overhead. Greedy perimeter stateless routing (GPSR) [10] is a widely used protocol in the category of geolocation-based routing protocols that requires neither knowledge of the entire network topology nor routing discovery when used, making it ideal for use in a FANET. GPSR works mainly through two forwarding models, that is, under normal circumstances, the greedy mode is used to deliver data as close as possible to the destination node, but when an empty region is encountered, the mode switches to a peripheral one to forward the data.…”

Section: Introductionmentioning

confidence: 99%

QLGR: A Q-learning-based Geographic FANET Routing Algorithm Based on Multi-agent Reinforcement Learning

2021

KSII TIIS

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The utilization of UAVs in various fields has led to the development of flying ad hoc network (FANET) technology. In a network environment with highly dynamic topology and frequent link changes, the traditional routing technology of FANET cannot satisfy the new communication demands. Traditional routing algorithm, based on geographic location, can "fall" into a routing hole. In view of this problem, we propose a geolocation routing protocol based on multi-agent reinforcement learning, which decreases the packet loss rate and routing cost of the routing protocol. The protocol views each node as an intelligent agent and evaluates the value of its neighbor nodes through the local information. In the value function, nodes consider information such as link quality, residual energy and queue length, which reduces the possibility of a routing hole. The protocol uses global rewards to enable individual nodes to collaborate in transmitting data. The performance of the protocol is experimentally analyzed for UAVs under extreme conditions such as topology changes and energy constraints. Simulation results show that our proposed QLGR-S protocol has advantages in performance parameters such as throughput, end-to-end delay, and energy consumption compared with the traditional GPSR protocol. QLGR-S provides more reliable connectivity for UAV networking technology, safeguards the communication requirements between UAVs, and further promotes the development of UAV technology.

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Delay-aware relay selection with heterogeneous communication range in VANETs

Cited by 12 publications

References 21 publications

Performance evaluation on the node mobility with respect to human driver behavior prediction in vehicular ad hoc network using adaptive deer hunting Optimized Link State Routing Protocol

Performance evaluation on the node mobility with respect to human driver behavior prediction in vehicular ad hoc network using adaptive deer hunting Optimized Link State Routing Protocol

An Efficient Delay Aware Emergency Message Dissemination and Data Retrieval in Secure VANET-Cloud Environment

QLGR: A Q-learning-based Geographic FANET Routing Algorithm Based on Multi-agent Reinforcement Learning

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