An effect of big data technology with ant colony optimization based routing in vehicular ad hoc networks: Towards smart cities

Lakshmanaprabu, S.K.; Shankar, K.; Rani, Sudesh; Abdulhay, Enas; Arunkumar, N.; Ramı́rez, Gustavo; Uthayakumar, J.

doi:10.1016/j.jclepro.2019.01.115

Cited by 92 publications

(53 citation statements)

References 11 publications

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“…In 2019, Lakshmanaprabu et al [32] have introduced an effect of big data technology by ant colony optimisation based on routing in VANET towards smart cities. To enhance the quality of city life the concept of a smart city was introduced.…”

Section: Literature Reviewmentioning

confidence: 99%

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Optimal routing in VANET using improved meta‐heuristic approach: a variant of Jaya

Gnanasekar¹,

Samiappan

2020

IET Communications

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Section: Literature Reviewmentioning

confidence: 99%

“…However, still, it suffers from the limitations of less reliability. SAFERACO [32] offers a high level of robustness and it is more stable. Still, it contains some drawbacks of more packet collisions and higher delay and overhead.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal routing in VANET using improved meta‐heuristic approach: a variant of Jaya

Gnanasekar¹,

Samiappan

2020

IET Communications

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“…Furthermore, Ant ACO was deployed for routing in VANETS over HMR distributed framework. At last, the analysis results make certain that the computational time of the adopted scheme was considered with respect to the rise in the count of a node in the HMR framework [8]. Table 1 shows the reviews based on the optimal routing in VANETs.…”

Section: A Related Workmentioning

confidence: 99%

“…DCF was implemented which offers better PDR and it also minimizes e2e, but it has to concern more on centralized solutions [7]. At last, ACO was suggested that reduces the time for processing and it raises the count of nodes [8]. However, other metaheuristic schemes have to be considered.…”

Section: B Reviewmentioning

confidence: 99%

Hybridization of Monarch Butterfly and Grey Wolf Optimization for Optimal Routing in VANET

Santhosh¹,

Dhandapani²

2019

IJEAT

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The routing process in Vehicular Ad hoc Networks (VANET) remains a more demanding task in city backgrounds. Identifying an optimal end-to-end path that satisfies reduced overhead and delay control is still facing a lot of difficulties and limitations in recent days. These limitations are owing to the increased movement of vehicles, the repeated failures of a path, and the varied obstacles that might have an effect on the consistency of the data routing and transmission. Hence, this paper intends to present an enhanced VANET routing model by considering the network quality metrics including congestion, travel, collision and QoS awareness cost. Accordingly, in the proposed work, a cost model is modeled as the solution for the vehicle routing problem by taking into account the above-mentioned constraints. For determining the optimal route, this research work establishes a new hybrid algorithm known as Grey Updated Butterfly Operator (GU-BO) that links both the concepts of Monarch Butterfly Optimization (MBO) Algorithm and Grey Wolf Optimization (GWO). Finally, the performance of the implemented approach is compared over other conventional approaches with respect to congestion and cost analysis, and proves its superiority of proposed work over others.

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“…possible. Such a network must be implemented without the limitations of client-server network connection structures [1]. Each vehicle equipped with a VANET device is similar to a node in Ad-hoc networks and will be able to send and receive others' messages through the wireless network.…”

Section: Introductionmentioning

confidence: 99%

Location-Aware Distributed Clustering with Eliminating GPS in Vehicular Ad-hoc Networks

Beheshti¹,

Adabi²,

Rezaee³

2020

Preprint

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Vehicular ad-hoc network (VANET) is a type of mobile network which is used for establishing connection between vehicles (M2M) and also between vehicles and nearby stationary equipment which are often road-side equipment. The main target of VANET is to provide security and convenience for the passengers. In order to achieve this goal, a special electronic device called OBU (On-Board Unit) is embedded in each vehicle which makes the connection between vehicles and between the vehicles and the road-side equipment possible. In this paper, the Location-Aware Clustering in Vehicular Ad-hoc Networks (LAC-VANET) is proposed. We try to achieve the main and major goal in VANET networks, i.e. fast propagation of security and urgent messages in ITS systems, using clustering and selecting the best cluster head based on Fuzzy logic such that the cluster head can transfer important information such as the obstacles and accidents detected on the road with a suitable speed and without creating a large traffic load in the vehicle network in order to notify other vehicles and prevent the danger and vehicle accidents. Moreover, LAC-VANET method is evaluated here via extensive simulations carried out in NS-2. The simulation results indicate that the VANET network performance metrics are improved in terms of average throughput, Packet Delivery Ratio (PDR), end to end delay, and packet loss rate.

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An effect of big data technology with ant colony optimization based routing in vehicular ad hoc networks: Towards smart cities

Cited by 92 publications

References 11 publications

Optimal routing in VANET using improved meta‐heuristic approach: a variant of Jaya

Optimal routing in VANET using improved meta‐heuristic approach: a variant of Jaya

Hybridization of Monarch Butterfly and Grey Wolf Optimization for Optimal Routing in VANET

Location-Aware Distributed Clustering with Eliminating GPS in Vehicular Ad-hoc Networks

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