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Bio-inspired VANET routing optimization: an overview

A taxonomy of notable VANET routing problems, overview, advancement state, and future perspective under the bio-inspired optimization approaches

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Abstract

This paper demonstrates a recapitulated historic evolution further to a future overview of all vehicular ad-hoc network (VANET) routing problems that concern either directly related routing tasks or targeting a set of diverse routing-related techniques with the aid of the bio-inspired approaches. In this lecture, we serialize, in a synchronous observation, the evolution and tendencies of the VANET routing problem’s solving simultaneously with the emergence of different classes of nature-based meta-heuristics, by bringing a proposed taxonomy of different major VANET routing problems seen their nature, studied range and metaheuristic types used for their optimization. Then, we follow with a visionary deduction of the other appearing routing issues of VANETs that can be approached or already began to be solved by nature-inspired optimization algorithms. Noting that each spread routing problem is illustrated with notable related works, describing initially realized conventional protocols to vulgarize different routing modules, then detailing bio-inspired protocols for VANET routing to explain the utility of nature-inspired optimization techniques. The motivation of this work came from the lack of a reference classifying the VANET-related routing problems within the notion of nature-inspired optimization. That’s further to giving and up-to-date literature on the context for opening out a visionary opinion on the tendencies of either emerging recent bio-inspired optimization approaches or the different metaheuristic-based combinations on specific VANET routing problems.

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Abbreviations

5G:

Fifth generation

ABC:

Artificial bee colony

ACO:

Ant colony optimization

ADR:

Average delivery rate

AE2ED:

Average end-to-end delay

AISs:

Artificial immune systems

AODV:

Ad-hoc on-demand distance vector

AOMDV:

Ad-hoc on-demand multipath distance vector

BCO:

Bee colony optimization

BFOA:

Bacterial foraging optimization algorithm

CBR:

Cluster based routing

CBRP:

Cluster based routing protocol

CH:

Cluster head

CM:

Cluster member

CMs:

Cluster members

CN:

Cluster node

CNs:

Cluster nodes

CSA:

Cuckoo search algorithm

DE:

Differential evolution

DSDV:

Destination sequenced distance vector

DSR:

Dynamic source routing

DTN:

Delay-tolerant network

E2ED:

End-to-end delay

EA:

Evolutionary algorithm

ED:

Euclidean distance

GA:

Genetic algorithm

GPS:

Geographic positioning system

GPCR:

Greedy perimeter coordinator routing

GPSR:

Greedy perimeter stateless routing

Greedy-V2V:

Greedy vehicle-to-vehicle

GRP:

Geography-based routing protocol

GSR:

Geographic source routing

IARP:

IntrA-routing protocol

ID:

IDentifier

IERP:

IntEr-routing protocol

IoT:

Internet-of-things

IRC:

Inter-roadside communication

MANETs:

Mobile ad-hoc networks

MAS:

Multi-agent system

MOPs:

Multi-objective optimization problems

MRP:

Multicast routing problem

NRL:

Normalized routing load

NS2:

Network simulator

OBU:

On-board unit

OLSR:

Optimized link state routing

PDR:

Packet delivery ratio

PSO:

Particle swarm optimization

QoS:

Quality of service

RREQ:

Route REQuest

RSSI:

Received signal strength indicator

RSU:

Road side unit

SA:

Simulated annealing

SCF:

Store-carry-and-forward

TCP:

Transmission control protocol

TS:

Tabu search

TSP:

Travel salesman problem

TTL:

Time-to-live

UAV:

Unmanned aerial vehicle

V2C:

Vehicle-to-cloud

V2I:

Vehicle-to-infrastructure

V2R:

Vehicle-to-roadside

V2U:

Vehicle-to-UAV

V2V:

Vehicle-to-vehicle

VCC:

Vehicular cloud computing

VDTN:

Vehicular delay-tolerant network

VNI:

Virtual navigation interface

VRP:

Vehicle routing problem

ZBR:

Zone based routing

ZRP:

Zone routing protocol

WANETs:

Wireless ad-hoc networks

WSNs:

Wireless sensor networks

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    Youcef Azzoug & Abdelmadjid Boukra

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Azzoug, Y., Boukra, A. Bio-inspired VANET routing optimization: an overview. Artif Intell Rev 54, 1005–1062 (2021). https://doi.org/10.1007/s10462-020-09868-9

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