FGWSO‐TAR: Fractional glowworm swarm optimization for traffic aware routing in urban VANET

Abstract

SummaryIn mobile distributed applications, such as traffic alert dissemination, dynamic route planning, file sharing, and so on, vehicular ad hoc network (VANET) has emerged as a feasible solution in recent years. However, the performance of the VANET depends on the routing protocol in accord with the delay and throughput requirements. Many of the routing protocols have been extensively studied in the literature. Although there are exemptions, they escalate research challenges in traffic aware routing (TAR) protocol of VANET. This paper introduces the fractional glowworm swarm optimization (FGWSO) for the TAR protocol of VANET in an urban scenario that can identify the optimal path for the vehicle with less traffic density and delay time. The proposed FGWSO searches the optimal routing path based on the fitness function formulated in this paper. Fractional glowworm swarm optimization is the combination of the GWSO and fractional theory. Moreover, exponential weighted moving average is utilized to predict the traffic density and the speed of the vehicle, which is utilized as the major constraints in the fitness function of the optimization algorithm to find the optimal traffic aware path. Simulation of FGWSO shows the significant improvement with a minimal end-to-end delay of 6.6395 seconds and distance of 17.3962 m, respectively, in comparison with the other existing routing approaches. The simulation also validates the optimality of the proposed TAR protocol.KEYWORDS fractional theory, glowworm optimization, traffic aware routing protocol, traffic density, VANET | INTRODUCTIONNetworking concept has received increasing attention in the recent years with the advancement in the wireless communication technology. 1 The vehicular network has the advanced applications in the field of car manufacturing. The vehicular network has a wireless technology and has major advantages, such as intelligent transportation, urban monitoring, and internal access. 2 The vehicular network consists of vehicles with short range wireless communication devices 3,4 in a different environment. These substantial advantages made the wireless vehicular networks as an active area of research. Vehicular ad hoc network (VANET) is a new type of vehicular network, and it is a form of mobile ad hoc network.

Optimal Shortest Path Selection Base on Multi-Criteria Decision Making in Vehicular Ad Hoc Network

“…In reference (Rewadkar & Doye, 2018) recommended Fractional glowworm swarm optimization for traffic-aware routing in urban VANETs. They were making the protocol with the combination of fractional theory and GSWO known as FGWSO.…”

“…The single router End-to-end delay, explore the total delay between the sender and receiver. To become manage on the opinion; assume there are N-1 routers amongst the source and destination host (Rewadkar & Doye, 2018). (EED = (start time − ended time) (4) 4.1.4 Distance: It will show the length of choosing the way for the vehicles which will reach the destination.…”

“…(EED = (start time − ended time) (4) 4.1.4 Distance: It will show the length of choosing the way for the vehicles which will reach the destination. The equation for a distance as given below (Rewadkar & Doye, 2018). As we know that in physic the distance formula is…”

“…It predicts the directions of vehicles' movement. But it doesn't predict the future environment change (Rewadkar & Doye, 2018).…”

“…The single router End-to-end delay, explore the total delay between the sender and receiver. To become manage on the opinion; assume there are N-1 routers amongst the source and destination host (Rewadkar & Doye, 2018).…”