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Internet of Vehicles and Intelligent Routing: A Survey-
Based Study
Abeer Hassan1, Radwa Attia1, and Rawya Rizk1
1Electrical Engineering Department, Port Said University, Egypt, e-mails:
abeerhassan648@gmail.com, radwa_yousef@eng.psu.edu.eg,
r.rizk@eng.psu.edu.eg
Abstract. In the rapid development of Intelligent Transport Systems, new vehi-
cles are expected to be involved (ITS). Vehicular ad hoc networks (VANET)
are the main component that is used recently for the development of Intelligent
Transportation Systems (ITs), which has a highly dynamic network topology
due to the rapid movement of vehicles. Different types of applications can be
supported by vehicular networks such as vehicle and road safety, traffic man-
agement, navigation-based applications, and internet-based services. The grow-
ing number of connected vehicles and the need for real-time data processing has
increased the demand for transforming real VANETs into the Internet of Vehi-
cles (IoV) to achieve the goal of an effective and smart future transportation
system. Routing in IoV faces serious challenges as the movement of vehi-
cles keeps changing the network topology. Therefore, developing a routing pro-
tocol that can deliver data packets in the shortest time and with the least packet
loss is crucial to improve vehicle safety, and win user satisfaction. This paper
introduces a survey on different routing protocols in IoV taking into account
new bio-inspired routing protocols and how it can improve routing performance
in IoV.
Keywords: ITS, VANET, IoV, V2V, V2I, Vehicle to Everything, bio-inspired
algorithms, Routing, Bee Colony Optimization, geographic-based routing, in-
tersection-based routing.
1 Introduction
VANET [5] are a special type of Mobile Ad hoc NETworks (MANETs) [6] where an
On-Board Unit (OBU) like vehicles can act as data exchange nodes; this data can vary
depending on different applications such as : online vehicle status checking, intelli-
gent route navigation and rescue, and avoiding illegal cyber operations[1]. Generally,
VANET communication modes are categorized as Vehicle to Vehicle (V2V) and
Vehicle to Infrastructure (V2I) [7,8]. The Road Side Units (RSUs) [9] operate as as-
sistants to reinforce the transmission procedure if the V2V wireless communication
mode is not accessible. The challenges contributed to VANET accessibility [10] such
as dynamic topology, high mobility that causes less scalability, and signal losses have
led to a growing demand for turning real VANET into the automotive IoV for achiev-
ing a goal of the effective and smart future transportation system. The structure of the
IoV network is seen in Figure 1.1. In order to efficiently and effectively deliver the
packets to the ultimate destination, many routing protocols have been proposed for
IoV [11]. However, the existing IoV routing protocols are not efficient in every traffic
scenario. Due to the high mobility of the vehicles, the unstable connectivity and the
uneven distribution of the vehicles, it is a challenge to develop efficient routing proto-
cols to route information between network nodes to improve traffic safety and trans-
portation efficiency
Fig. 1. Structure of IoV network.
This paper provides a survey of using bio-inspired routing protocols to achieve ef-
ficient routing in IoV. The remainder of the paper is organized as follows; Section 2
describes the architecture and main characteristics of IoV. An overview of the func-
tionality for different communication proposals in IoV is provided in Section 3. Sec-
tion 4 introduces a review of heuristic bio-inspired routing protocols and their evalua-
tion approaches. Finally, Section 5 concludes the paper and defines topics for future
research.
2 IoV
In IoV environments, vehicles, RSUs, and pedestrians become data exchange nodes.
They are connected in a mesh network topology and construct Vehicle to everything
(V2X) communication networks using various wireless access technologies. Many
papers discussed IoV architecture as several layers [18], but the most commonly di-
vided into three levels; the perception layer, the network layer, and the application
layer, as shown in Figure 2.1.
The perception layer: The first level contains all the sensors within the vehicle
that gather environmental data and detect specific events of interest.
The network layer: The communication layer that supports different wireless
communication modes such as V2V, V2I and Vehicle-to-Sensor (V2S) which en-
sures seamless connectivity to emerging networks such as Wi-Fi, LTE, etc.
The application layer: This is responsible for storage, analysis, processing, and
decision making about different risk situations such as traffic congestion, danger-
ous road conditions.
Fig. 2. IoV architecture layers
2.1 Communication Technology in IoV
Inter-vehicular communication protocols in VANET play an essential role in IoV as
they enable different levels of interaction among vehicles, humans and RSUs. They
can provide alternate routes efficiently and quickly if a problem arises with the cur-
rent route. At present, there are many existing wireless access technologies such as:
1. Inter-Vehicle Access Technologies.
Wireless Access in Vehicular Environment (WAVE) [20,21] is Wi-Fi standards con-
tain IEEE 802.11a/b/g/n/p protocol, which has achieved great acceptance in the mar-
ket because it supports short-range, relatively high-speed data transmission, to allow
high-speed communications between VANET nodes, which also known as Dedicated
Short-Range Communication (DSRC). As shown in Figure 2.3, the frequency band in
WAVE is divided into six service channels that are used for different application
types.
2. Mobile Internet Access Technologies
Wi-MAX [2]: contains IEEE 802.16 a/e/m standards which able to cover a large
geographical area, up to 50km, and can deliver significant bandwidth to end-users -
up to 72 Mbps and supports speeds up to 160 km/h and different classes of QoS,
even for non-line-of-sight transmissions.
Cellular wireless (4G/5G and LTE) [4]: is the most efficient technology to
launch the intra-vehicle network and to activate the IoV. It has been deployed by
most countries to provide access services, also in the high buildings and complex
city environment, the performance of 4G or LTE is the best among all wireless ac-
cess technologies. In [22], an analysis of DSRC as opposed to 4G-LTE for con-
nected vehicle applications explores various advantages and disadvantages of both
technologies regarding communication performance.
Fig. 3. Channel allocation in WAVE protocol [21]
2.2 IoV Applications
IoV has huge services, which offer drivers convenience in the traffic, reduce energy
consumption and minimize the costs and time of travel as following:
Driving safety services: These related applications are mainly related to Coopera-
tive Collision Avoidance Systems (CCAS) [25], which augment the Collision
Avoidance System (CAS) by sharing CAS information between neighboring vehi-
cles, usually via V2V communication [26], to rescue injured people in accidents.
Navigation service: The navigation integrated system brings the current location
of vehicles and obtains the final specified destination in the map from the driver,
also finding an available parking space in an urban environment with the help of
GPS systems is also an interesting problem [27].
Commercial services: Deliver commercial advertisements to people on the street,
such as Restaurant services, gas price announcements and the nearest hotel ser-
vices.
Infotainment services: Include mainly internet access services and file sharing
among vehicles, especially video sharing, these applications can improve the com-
fort of traffic for driver.
3 Routing Protocol Classification
Routing is a key study area in IoV, which is the network-layer protocol that aims to
improve throughput while minimizing packet loss and overhead. The main IoV com-
munication protocols are classified as follows:
3.1 Topology-Based Routing
The main principle of topology-based routing considers topological links between
nodes along the source-destination path to determine routes, and is categorized to
[29]:
Proactive (Table-Driven). Aims to find paths for all source and destination pairs in
each node in advance and store them in the form of tables. For rapid topology changes
in the IoV; Proactive routing generates many control packets and therefore incurs
more overhead. The best-known proactive protocols are Dynamic Destination Se-
quenced Distance-Vector Routing (DSDV) [30] and Optimized Link State Routing
(OLSR) protocols [31].
Reactive (On-Demand). The required paths are available only when needed and are
designed to reduce transmission and transmission delays when new routes are re-
quired. However, the route acquisition process causes significant delays, which is not
easy in case of emergency information. Dynamic Source Routing (DSR) [32] and Ad-
hoc On-Demand Distance Vector (AODV) [14] are the two most popular reactive
routing protocols.
Fig. 4. Routing protocols classification
3.2 Multicast Routing Protocols
Cluster-Based Routing In order to ensure scalability in the VANETs, cluster-based
routing is used. This category is based on creating a virtual partial network infrastruc-
ture called a cluster; the cluster has one cluster head that is responsible for intra and
inter-cluster control management. After clustering and head election, the source node
is decided to select the vehicle that plays a gateway role. In [15], various clustering
algorithms are analyzed in view of VANETs.
Geo-cast Routing It is a type of location-based multicast routing [11] that aims to
send a packet from a source node to all other nodes within a defined geographical
region; known as the Zone of Relevance (ZoR). Geo-cast routing in urban VANETs
routing protocol [16] is a GPS-based inter-vehicle communication protocol used for
alarm message dissemination among vehicles in a highway in risk situations.
3.3 Broadcast Routing
In this protocol, each node can broadcast messages to all other nodes. Broadcast is a
common routing mechanism in IoV for sharing traffic, weather, emergency, and road
condition information among cars and other applications [11].
3.4 Position-Based Routing
Geographic-Based Routing
All nodes discover their own location and the location of neighboring nodes using
pointing devices such as a Global Positioning System (GPS) [13]. The best known
geography-based protocol is Greedy Perimeter Stateless Routing (GPSR) [15], where
the data packet is sent to the node that is geographically closest to the destination.
1. Greedy Perimeter Stateless Routing (GPSR) [50]: greedily chooses the next
hop towards the destination. If greedy mode fails, the algorithm switches to perim-
eter mode and the next forwarding node is chosen using the right-hand rule [39].
GPSR applies only the distance to the destination to greedily select the next hop
towards the destination, which creates the local maximum problem. The local
maximum occurs when the distance between the source nodes from the destination
node is less than the neighboring node to the destination, so GPSR may not work
well in some network situations.
2. Greedy Perimeter Stateless Routing Junction (GPSRJ+) [40] is proposed to
further improve the packet delivery ratio based on a minimal modification of
GPSR. GpsrJ+ utilizes two-hop beaconing to predict the next road segment in
which the packet should be forwarded toward a destination. If the current forward-
ing node has the same direction as a coordinator node, the prediction mechanism
bypasses the intersection and forwards the packet to the node ahead of the junction
node. In comparison with GPSR, the GpsrJ+ increases the packet delivery ratio
and reduces the number of hops in the perimeter mode of packet forwarding.
3. Path Aware GPSR (PA-GPSR) [41] aims to improve the overall performance of
IoV, which is improved from the GPSR routing protocol. PA-GPSR introduces ad-
ditional extension tables to select the optimal route, and also proposes avoiding
routing loops algorithm. Simulation results on SUMO and NS3 simulations soft-
ware indicate that the PA-GPSR outperforms the GPSR in terms of; packet deliv-
ery ratio, delay time, and other performance metrics [39] in different density and
mobility scenarios.
4. Link-State aware Geographical and Opportunistic (LSGO) [42] is a routing
protocol that applies link-state information along with the position information of
nodes to make routing decisions. Also, to select the next forwarding node, LSGO
applies a timer-based priority scheduling algorithm. The main drawback of LSGO
is that, it does not take into consideration the node’s moving speed and direction
information to calculate the priority of nodes.
5. Link-State aware Geographic (LSGR) Routing protocol [43] designed for
VANET. For enhancing the greedy forwarding, a novel metric called Expected
One-transmission Advance (EOA) is used to show the geographic distance that a
packet can make towards the destination, to calculate the EOA, both link quality
and packet’s advance are considered. The drawback of LSGO is that it does not
consider the node’s moving speed and direction information when selecting the
next forwarding node.
6. A hybrid Opportunistic and Position Based Protocol (OPBR) is proposed in
[45] select optimal candidate nodes and determines appropriate priority for trans-
mitting data, it can estimate link failure by evaluating link quality and predict the
location of the nodes using different metrics like minimum hop number to destina-
tion but fails to ensure reliable communication at higher transmission rate.
Infrastructure Localization protocol
Many other protocols are integrated with infrastructure-based routing protocols to
eliminate the conventional geographical routing protocol limitations and adapt to
variable traffic conditions [46,47].
1. Connectivity Awareness quality Transmission Guaranteed Routing protocol
(CTGR) [48] presents a novel geographic routing in urban IoV. Each road seg-
ment is assigned a weight based on the collected connectivity and transmission
quality information. Using the weight information, the road segment can be dy-
namically selected one at a time to include the best routing path.
2. Traffic aware and Link quality sensitive Routing Protocol (TLRP) [49] is a
geographic protocol used for urban IoV with the help of introducing intersection
backbone nodes, each road segment is assigned to a different weight according to
the designed link transmission quality to select the routing path for data transmis-
sion.
3. Distance weighted Back-pressure Dynamic Routing protocol (DBDR) [50] is
proposed in, it prioritizes the vehicles that are closed to the destination and have a
large backlog differential of buffer queues to provide dynamic hop-by-hop for-
warding and is jointly designed with multi-hop internet gateway discovery proce-
dure and vehicle mobility management for the internet services.
4. Vehicular Environment Fuzzy Router (VEFR)[] proposed which is a distribut-
ed-based protocol modelling the routing process as an aggregation of multi-criteria
by using a fuzzy inference system and runs in two main processes; road segment
selection and relay vehicle selection. The purpose of the road selection is to select
multiple successive junctions whereby the packets are transmitted to the destina-
tion, while the relay selection process is designed to select relay vehicles from the
selected road segment.
4 Bio-Inspired Routing Protocols
Bio-inspired methods are more efficient for large-scale IoV because; the behavior of
species during the discovery of the food is very similar to the identification of IoV
communication routes [13,19], also, because of the low complexity of bio-inspired
protocols in performing the computational problems. Bio-inspired algorithms are
typically divided into three main classes, as shown in Figure 3.2. The IoV routing
optimization problems give rise to a number of challenges [11]:
Network Scalability: With the large network size and the frequent IoV topology
changes, the performance is quickly degraded, especially when more vehicles want
to communicate at the same time.
Computational Complexity: In such large-scale networks, computational costs
like; run time and the number of resources utilized to solve routing problems, are
very high, hence bio-inspired approaches can be successfully used to provide opti-
mal pathways with low complexity.
Adaptability: Self-organizing and making non-user-based routing is an important
challenge. Bio-inspired approaches enable the deployment of adaptive routing so-
lutions better than traditional techniques.
Network Robustness: This is the ability to provide secure paths between source
and destination against network failures, disruptions, and attack threats.
Quality of Services: QoS implies ensuring the successful delivery of transmitted
messages with a minimum number of dropped packets, with high bandwidth.
Fig. 5. Taxonomy of bio-inspired Algorithms for IoV
4.1 Evolutionary Algorithms
The evolution of species inspires Genetic Algorithm (GA) [19] that is an optimization
approach based on a population of chromosomes to generate another population dur-
ing iteration. GAs can be further investigated in wireless networks to find the best
routing strategy; however, it has some disadvantages such as; the computation time
and the excessive growth of its individuals [8]. In [20], the author presented the Adap-
tive Weighted Clustering Protocol (AWCP), as an optimized clustering protocol that
takes into account the highway number, vehicle direction, position, speed, and the
number of neighboring cars to improve network topology stability. In [21], the author
proposed a software-defined geographic protocol, where an improved genetic method
is used to create an optimal forwarding path that identifies the shortest path based on
shorter road length and higher vehicles density. In [77], author implements intersec-
tion based geographical Protocol that is based on an effective selection of road inter-
sections through which a packet must pass to reach the gateway to the Internet. The
selection among the road intersections is made by GA depending on the probability of
connectivity as an objective function while satisfying QoS constraints on tolerable
delay, bandwidth, and error rate.
GA can also be applied in a parallel manner that allows reaching high-quality re-
sults in an acceptable execution time. In [22], the author proposed an energy-aware
OLSR routing to reduce the power consumption of OLSR [12] protocol in IoV by
using a parallel GA algorithm, this protocol makes OLSR compatible with large real-
istic IoV scenarios, experimental analysis proved substantial reductions in power
consumption and significantly enhance the network overload, while the modified
protocol only suffered abounded degradation in the packet delivery metric. The draw-
back of using parallel GA is unaffectedness to cover huge search space frequently,
even with the help of local search compared to other hybrid approaches.
4.2 Swarm Intelligence
Ant Colony Optimization (ACO) It is inspired by the foraging behavior of ants. In
ACO, artificial ants communicate with each other indirectly through the pheromones
that deposit in their path, once a subsequent ant follows the path, it lays down new
pheromone over the path, then depending on the amount of pheromone previously
deposited, the best paths are selected and the others are ignored [19]. Many modifica-
tions are made on ACO to solve the route optimization for existing dynamic routing
problems [23]. Mobility zone based routing is presented in [54] which use reactive
and proactive approaches to find the path between source and destination. The param-
eters used for efficient routing are vehicle movement, density and speed. Periodic
control packets are sent to maintain routes within the zones, increasing overhead. In
[24], the author presented an adaptive routing protocol for IoV with ACO, which can
determine the QoS route intersection through which data packets can be transferred
from source to destination on a periodic basis. However, this protocol misses two
important features self-organization and the ability to adapt to the failure of RSUs. In
[25], an ACO-based delay-sensitive routing protocol is proposed which utilizes pher-
omone information of transmission delay and heuristic information of vehicles. The
authors in [26] introduced a reactive route setup technique, which relies on ACO to
choose the shortest path from source to destination, based on connectivity, delay, and
packet delivery ratio, However, increasing network size can increase the network
congestion, which generates a very important overhead.
Particle Swarm Optimization (PSO) is inspired by the social behavior of bird flock-
ing. PSO is a swarm of particles that fly around in a multidimensional search space.
During flying, each particle adjusts its movement based on its best prior position as
well as the swarm's best previous position. Thus, the PSO system combines local and
global search methods, attempting to balance exploration and exploitation [27]. Many
researchers have used the ACO and PSO algorithms for local and global optimization;
respectively [23]. The author presented a PSO algorithm to obtain an optimal combi-
nation of parameters used in the Ad-hoc On demand Multipath Distance Vector
(AOMDV) protocol [58], which is an extension of AODV routing a real scenario of
VANET. The simulation results show a significant improvement in QoS compared to
the average delay, normalized overhead and transmission rate parameters of the
standard AOMDVs; respectively. The only flaw is that there is a decrease in the de-
livery rate for large cards. In [28], the author proposed PSO based routing method for
VANETs that considers several objective criteria of vehicles' location, distance, and
vehicle speed to determine the next forwarding vehicle. But the density parameter is
not considered. The author in [29] presents clustering V2V routing based on PSO in
IoV, which consists of three components; cluster creation, route particle coding, and
routing within or between clusters. This protocol can improve the stability of the net-
work; however, it has the limitation of tuning network stability with delay.
Artificial Bee Colony (ABC) It is an optimization algorithm that mimics the food
foraging behavior of bee colonies. Some bees (called scouts) explore the region in
search of food, if they are discovered; they return to the hive performing a waggle
dance to inform their mates of their finding, some bees (called foragers ( are recruited
to exploit this discovery [8]. ABC algorithm uses few control parameters, and has fast
convergence; so many researchers use it in optimization problems for IoV [30-32]. In
[33], a QoS-based routing protocol is proposed; it uses fuzzy logic to determine a
feasible path from multiple possible ones discovered by the ABC algorithm, where
the path must meet requirements such as bandwidth, latency, jitter, and link expiration
time, however this protocol may overhead the network with control packets. A Clus-
ter-Based algorithm is proposed in [63] to apply the QoS routing protocol in VANET.
Clustering method is used to optimize routing data transfer and ABC algorithm is
used to find the best path from source to destination. A hybrid protocol proposed in
[78] that is a unicast and multipath routing protocol and guarantees the delay, delivery
ratio and overhead of security application requirements. The protocol applies topolo-
gy-based routing in dense networks and geography-based routing in sparse networks.
4.3 Other Bio-inspired Approaches
Firefly Algorithm (FA) It is an optimization algorithm based on the flashing behav-
ior of fireflies, which acts as a signal system to attract other fireflies. It is able to solve
multi-dimensional problems with fast convergence, and it can be used for both global
and local search problems [27]. In [34], the author proposed a Reputation-based
Weighted Clustering Protocol (RWCP) for stabilizing the IoV topology using FA
algorithm, taking into account the vehicles' direction, position, velocity, and other
parameters, however, comparison with IoV clustering is not considered. In [35], the
author concentrates on the QoS multicast routing problem by using FA with the Levy
distribution algorithm to prevent the local optima convergence.
Cuckoo Search Algorithm (CS) It is a meta-heuristic algorithm inspired by the
cuckoo bird's method of existence; these birds are known as "Brood parasites". Cuck-
oos eject one of the host bird's eggs and lay eggs that more closely resemble the host's
eggs. If the host bird identifies the different eggs, it either throws that eggs away from
its nest or leaves its nest and builds a new one. The advantage of CS over other opti-
mization algorithms is its simplicity of using fewer control parameters [27]. In [36],
the authors proposed a clustering technique that uses velocity and distance of vehicles
to create a stable cluster structure, CS algorithm is triggered to select the super clus-
ter-head while achieving optimum distance, minimum delay, and high network life-
time. In [37], the author developed a NetCLEVER protocol to support the intelligent
routing of the data packets using broadcast communication in order to avoid the
broadcast storm problem, in this approach, CS technique is used by considering the
impact of a road intersection and traffic lights on link stability. Adaptive protocol
based on the CS algorithm presented in [79] that provides reliable and secure routes
between source and destination nodes with optimal distance and low routing over-
heads.
Gray Wolf Algorithm (GWO) It is a population-based algorithm that uses the grey
wolf social intelligence to pick the best prey for the hunt. In particular, the three best
candidate solutions α (the first one), β (the second one), γ (the third one) are randomly
generated respecting the constraints, after this; other possible solutions are generated
according to these three solutions and adjusted their positions accordingly [38]. In
[39], a GWO clustering protocol is proposed to solve the vehicle clustering problem,
the proposed algorithm reduces the transmission cost of the entire network by effec-
tively reducing the number of clusters, but it did not consider the bandwidth metric
that affects the network efficiency. In [40], a social-based routing scheme is proposed
based on the GWO algorithm to diminish the social network services and enhance the
throughput.
Whale Optimization Algorithm (WOA) It is inspired by the bubble net feeding
behavior of humpback whales in the ocean. In [41], authors proposed an enhanced
WOA, they use Adaptive Weighted Clustering Protocol (AWCP) for grouping the
network topology. In [42], Optimal Adaptive Data Dissemination Protocol (OADDP)
for vehicle road safety is proposed; it uses the WOA for clustering and predictor-
based decision-making algorithm for control overhead messages reduction. A Modi-
fied Cognitive Tree Routing Protocol (MCTRP) is proposed in [76] which incorporate
a routing protocol with the cognitive radio technology for efficient channel assign-
ment. The proposed technique involves a genetic WOA which helps in choosing a
root channel for transmission. The analytical results show that MCTRP promises
minimum overheads with effective channel utilization than the OLSR [31] protocol
that causes more overhead and it takes more time to discover the broken link.
In order to have better vision of discussed routing protocols, Table 3 summarized
the main features of these protocols. This comparison is based on routing protocols
mobility model, simulated area, and performance metrics. In [32], Advanced Greedy
Hybrid Bio-Inspired (AGHBI) routing protocol is proposed to improve the perfor-
mance of IoV. AGHBI uses modified hybrid routing scheme with the help of ABC to
select the highest QoS route and keep the route with minimum overflow.
5 Performance Comparison and Discussions
The performance of AGHBI protocol is compared with three other protocols AODV
[14], GPSR [15] as traditional routing algorithms and VEFR [16] as intersection
based algorithms. The performance parameters are Packet Delivery Ratio (PDR),
Normalized network overhead, and end-to-end delay.
AGHBI
VSIM
GPSR
AODV
Characteristics
hybrid
hybrid
Position/map
network topology
Forwarder selection
information
multicast
multicast
multicast
broadcast
Target destination
hybrid
hybrid
greedy
hybrid
Routing strategy
Heterogeneous
Homogeneous
Homogeneous
Homogeneous
Target network
2D/3D
3D
1D
1D/2D
Scenario dimension
Insecure
Insecure
Insecure
Insecure
Security sensitivity
Optimum-path
Optimum-path
Path-based
Path-less
Dissemination
strategy
infotainment
infotainment
safety
safety
Application
hybrid
V2V
V2V
V2V
Communication
system
Multi-objective:
High packet delivery
ratio
Low delay
Low overhead
Multi-objective:
High packet delivery
ratio
Low latency
Objective
ABC
FL
Optimization
method
5.1 Simulation Setup
The protocols are simulated in DOT NET 4.5 environment using C# [13] and
OMNET++ [91] platforms. A Port-said grid is used for simulating urban network. To
generate random movement of vehicles, Simulation of Urban Mobility (SUMO) is
used. The vehicle speed ranges between 60 and 90 km/h. The wireless technology
used is IEEE802.11p and the HELLO packet interval is set to 2 s.
5.2 Discussion
The network density effect on PDR, delay, and overhead is given in Figs. 5, 6, and 7,
respectively. From Fig. 5, PDR gets increased when the network density increases for
AGHBI and its PDR is high compared to other protocols. The least PDR is for GPSR.
From Fig. 6, end-to-end delay is lowest for AGHBI when network density is lesser
and the delay gets increased and is highest for AODV. From Fig. 7, total overhead is
highest for VEFR and overhead for AGHBI, VEFR, AODV and GPSR gets increased
as the network density increases. The least overhead is for AODV.
Fig. 6. Packet delivery ratio versus Number of vehicles
Fig. 7. Delay versus Number of vehicles
Fig. 8. Overhead versus Number of vehicles
6 Conclusion and Future Work
A solid analysis of the most significant IoV routing proposals with a summary of
features of existing approaches is introduced. The survey proves that bio-inspired
approaches such as ACO, PSO, and ABC are more efficient for large-scale IoV,
which disseminate data packets with low complexity and improve routing perfor-
mances metrics. After that, several geographic and topology based routing protocols
that are suitable for IoV environment in urban scenarios are covered. For urban sce-
narios, AGHBI protocol is more appropriate considering end-to-end delay, latency,
and packet delivery ratio since it uses an artificial intelligence in selecting best next
hop. Compared to the protocols—AGHBI, VEFR, AODV, and GPSR, AGHBI is
better according to PDR, end-to-end delay, and normalized overhead. On the horizon,
there is still a lot of work to be done. From a technical point of view, achieving IoV
still presents many challenges to be solved related to devices, protocols, applications,
and services. The investigations concerning the routing and handoff decision are still
below expectations.
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