Nature inspired route optimization in vehicular adhoc network

Bhatt, Mayank; Sharma, Shabnam; Luhach, Ashish Kr.; Prakash, Amit

doi:10.1109/icrito.2016.7784997

Cited by 14 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bhatt et al [23] suggested a model that uses the Bat algorithm to communicate with the destination by performing three stages using an optimized path. e first step of the proposed model is to predict where the destination is.…”

Section: Environmental Informationmentioning

confidence: 99%

An Optimized and Efficient Routing Protocol Application for IoV

Afzal

Tariq

Aadil

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

IoV is the latest application of VANET and is the alliance of Internet and IoT. With the rapid progress in technology, people are searching for a traffic environment where they would have maximum collaboration with their surroundings which comprise other vehicles. It has become a necessity to find such a traffic environment where we have less traffic congestion, minimum chances of a vehicular collision, minimum communication delay, fewer communication errors, and a greater message delivery ratio. For this purpose, a vehicular ad hoc network (VANET) was devised where vehicles were communicating with each other in an infrastructureless environment. In VANET, vehicles communicate in an ad hoc manner and communicate with each other to deliver messages, for infotainment purposes or for warning other vehicles about emergency scenarios. Unmanned aerial vehicle- (UAV-) assisted VANET is one of the emerging fields nowadays. For VANET’s routing efficiency, several routing protocols are being used like optimized link state routing (OLSR) protocol, ad hoc on-demand distance vector (AODV) routing protocol, and destination-sequenced distance vector (DSDV) protocol. To meet the need of the upcoming era of artificial intelligence, researchers are working to improve the route optimization problems in VANETs by employing UAVs. The proposed system is based on a model of VANET involving interaction with aerial nodes (UAVs) for efficient data delivery and better performance. Comparisons of traditional routing protocols with UAV-based protocols have been made in the scenario of vehicle-to-vehicle (V2V) communication. Later on, communication of vehicles via aerial nodes has been studied for the same purpose. The results have been generated through various simulations. After performing extensive simulations by varying different parameters over grid sizes of 300 × 1500 m to 300 × 6000 m, it is evident that although the traditional DSDV routing protocol performs 14% better than drone-assisted destination-sequenced distance vector (DA-DSDV) when we have number of sinks equal to 25, the performance of drone-assisted optimized link state routing (DA-OLSR) protocol is 0.5% better than that of traditional OLSR, whereas drone-assisted ad hoc on-demand distance vector (DA-AODV) performs 22% better than traditional AODV. Moreover, if we increase the number of sinks up to 50, it can be clearly seen that the DA-AODV outperforms the rest of the routing protocols by up to 60% (either traditional routing protocol or drone-assisted routing protocol). In addition, for parameters like MAC/PHY overhead and packet delivery ratio, the performance of our proposed drone-assisted variants of protocols is also better than that of the traditional routing protocols. These results show that our proposed strategy performs better than the traditional VANET protocols and plays important role in minimizing the MAC/PHY and enhancing the average throughput along with average packet delivery ratio.

show abstract

Section: Environmental Informationmentioning

confidence: 99%

An Optimized and Efficient Routing Protocol Application for IoV

Afzal

Tariq

Aadil

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…To accomplish this, it uses three techniques: (i) Zone-Based Routing to provide stability; (ii) Bacterial Foraging Optimization to calculate the shortest routes; and (iii) fuzzy logic to deal with uncertain conditions. In [116], the authors designed an algorithm capable of dealing with traffic-collision-free path-finding and route optimization between two points. With the aim of optimizing performance, this algorithm considers a system composed of three distinct models: prediction of destination points, region formation, and optimized route selection.…”

Section: Traffic Optimization In Vehicular Networkmentioning

confidence: 99%

Network Management and Monitoring Solutions for Vehicular Networks: A Survey

et al. 2020

View full text Add to dashboard Cite

Vehicular networks are emerging as a promising technology that enables reliable and low-cost solutions for intelligent transport systems (ITSs), mainly due to their enormous potential to be considered for multiple purposes and scenarios. These networks are characterized by unique and challenging features such as packet fragmentation, low node density, short contact duration, and network disruption. These features may result in the absence of a path between the source and destination nodes, which is one of the most challenging issues faced by this type of network. To overcome some of these problems, it is necessary to provide vehicular networks with sophisticated tools or methodologies to implement monitoring and management operations. However, designing efficient solutions for this type of network is not an easy task due to its particular characteristics. This paper elaborates on a comprehensive survey focusing on promising proposals to deal with monitoring and management functionalities in vehicular networks. This work aims not only to present the state of the art on monitoring and management solutions but also to analyze their benefits and drawbacks, identify open issues, and provide guidelines for further contributions.

show abstract

“…Bhatt et al . [35] have utilised the bat algorithm for determining the optimal route to reach the destination at the same time avoiding the traffic collision. This method had three modules, namely (i) prediction of the destination location, (ii) formation of the region and (iii) the selection of the optimised route.…”

Section: Motivationmentioning

confidence: 99%