On routing algorithms in the internet of vehicles: a survey

Abstract

Internet of Vehicles (IoV) provides an overview of the Internet of Things (IoT) and the Internet of Everything (IoE). Generally, it connects the different items, Vehicles, and Environments, which transfers data between the network. The various challenges associated with IoV are routing security and data transmission. It is a different form of the traditional intelligent transportation system, and thus, many researchers have studied routing protocols and their simulation tools. Finding all Such methodologies and their development are unavailable at a single source. Focusing on this objective, our research explores routing protocols within the Internet of Vehicles (IoV) context. Our study comprehensively reviews diverse routing algorithms and their associated assessment methodologies. To systematically categorise these protocols, we employ a multi-tiered taxonomy. Firstly, we classify them into three main groups based on their transmission strategies: unicast, geo-cast, and broadcast. Secondly, we categorise them into four classes: topology-based, position-based, map-based, and path-based. Thirdly, we organise them according to their dimensions, differentiating between 1-D, 2-D, and 3-D approaches. Finally, we classify these protocols based on their applicability to homogeneous or heterogeneous network environments. The combination of classical routing protocols with the emerging heterogeneous network paradigm is of particular interest in our research, representing a compelling area for future exploration. By presenting this extensive framework, we aim to inspire researchers in the IoV field to develop innovative and efficient routing protocols and technologies.

Keywords:

• Internet of vehicles
• routing
• vehicle to everything communication
• challenges
• layered architecture

1. Introduction

IoV is the modified version of Vehicular networks. In IoV, different types of routing algorithms and protocols are used. Three protocols, known as Unicast, Geocast, and Broadcast, are used depending on the transmission strategies. The necessary information can be categorised into four types: Position-Based, Topology-Based, path-Based, and map-based. The IoT connects different types of connecting devices, such as sensors, Actuators, and Gateways. As IoV is identified from IoT, It provides the connection between things, Vehicles, and the Environment and transfers the data between the networks (J. Cheng et al., 2015; Z. Xu et al., 2021). Figure 1 depicts the structure of IoV. In today's world, the use of automobiles and vehicles has become increasingly prevalent in the daily lives of many individuals. However, this widespread increase in vehicles on the road has led to a significant rise in road accidents, presenting a grave issue that modern society must grapple with. As societal lifestyles evolve, various networking solutions have been proposed to address this challenge. The Internet of Vehicles (IoV) superset the Vehicle Ad-Hoc Network (VANET). IoV extends not only the scale and structure but also the scope of applications compared to VANET. In contrast to the conventional Intelligent Transportation System (ITS), IoV strongly emphasises fostering information exchange among vehicles, humans, and roadside units (RSU). Its primary objective is to facilitate a comprehensive understanding of road traffic information, thus safeguarding travel convenience and enhancing the overall travel experience. To provide network access to the driver, people, and traffic management person, IoV is used in many urban areas. As the transportation system increases daily, it becomes inefficient and costly. A recent report shows that IoV is used worldwide and will have over three billion users by 2030. The rapid growth in the number of vehicles causes more road accidents due to traffic congestion (Diao et al., 2022; Priyan & Devi, 2019). The IoV can be a feasible solution for heavily congested roads. Mainly, in metro cities, traffic congestion becomes unavoidable during office hours. Routing in IoV plays a vital role in day-to-day life (Yang et al., 2014). Routing involves the determination of a pathway within a traffic network or across multiple interconnected networks. This critical function is executed in various network contexts, mainly when dynamic alterations in the network's topology exist. The job of IoV is to detect the drawbacks, analyse the data, and make decisions to drive the vehicles. In IoV, Intelligent devices combined with an embedded processor and wireless technologies. It transfers the signals between the vehicles (Liang et al., 2019; Liu et al., 2016). The number of traffic accidents can be significantly reduced by using IoV networks. Different types of Communication are used in modern technology, such as device-to-device and machine-to-machine, which have a significant role in the IoV environment (Meng et al., 2021; Y. Wang & Li, 2009).

Figure 1. Structure of internet of vehicles.

IoV has many other applications, such as Edge Computing, 5G, and IoT. The future growth of IoT supports vehicle-to-everything communication (V2X). Intra-vehicular contact can monitor vehicles' internal performance through OBU and no of sensors (Contreras-Castillo et al., 2017; H. Y. Lin et al., 2018). V2I communication is used to access the extra information from the Internet, which can be accessed through a wireless 5G network. V2P communication is used for the awareness of roads, which uses pedestrian and cyclist communication.

IoV network, which provides a distributed system like vehicles, roadside units, and sensors, is known as distributed nodes. These nodes are performing the local communication. The growth of the distributed system provides an edge computing framework and interaction between communication and computation. Artificial Intelligence has the leading role in accessing the IoV network (Kaiwartya et al., 2016; H.-Y. Lin et al., 2017). The job of the IoV network is to gather information from the roadside unit known as the mobile application. The 5G mobile network is identified, which has the excellent bandwidth used to increase the communication between the Internet (Al-Sultan et al., 2014; T. Xiao et al., 2021). IoV can be used in Traffic management systems and Industrial applications. The future growth of IoV research may be used in many Big data algorithms used to process IoT devices. IoV is a vital domain that attracts the researcher. It is the most recent field used in networking systems. According to the part of human life, the different routing protocols are used for the IoV environment (Altayeb & Mahgoub, 2013; Yu et al., 2021). Among them, SL-ZRP(Stable-Link state zone routing protocol) is a vital communication protocol that keeps the node updated with information about its routing area. SL-ZRP is a function-based protocol based on speed, destination, and delay to find the roots among the vehicles. It can reduce the network representation and network overhead. Nowadays, large-scale and heterogeneous networks are introduced to the VANET. It provides more services than safety information, e.g. entertainment and environmental protection. In this, we propose the taxonomy of routing and different applications of IoV (Dai et al., 2019; Y. Jiang et al., 2021).

The significant contribution of this paper is:

1. In-depth examination of routing protocols, including wireless technology, its attributes, challenges, and prerequisites.

2. Categorisation of Routing Protocols in Highly Demanding and Intricate Urban Settings.

3. Addressing the demand for diverse routing algorithms within the IoV framework, capable of accommodating both low and high-density networks while minimising throughput variations and delays.

4. Comparison between the Routing Protocols

5. Listing of Existing surveys in the same field of research

6. Lesson learned from Researchers based on different challenges

The rest of the paper is organised in the following manner: Section 2 describes the Architecture of IoV, encompassing six distinct layers. Section 3 presents the Background and Literature survey of IoV. Routing protocols based on their routing strategies are presented in Section 4. Routing protocols based on their routing strategies are presented in Section 5. Section 6 summarises the Routing protocols. Section 7 addresses various challenges and prospects, and Section 8 concludes the paper.

2. Architecture of IoV

Recent advancements in integrated communication technologies, including Dedicated Short-Range Communication (DSRC), Long-Term Evolution (LTE), WiMax, Cloud Computing, and IEEE 802.11p, have significantly influenced the primary objective and design of the Internet of Vehicles (IoV) architecture. The vision and motivation behind IoV are vast and ambitious. However, IoV faces many challenges related to its architecture, interconnected nodes, resource allocation, data integration, and liability issues. These challenges are a compelling impetus for me to formulate a more precise and relevant architectural framework. Some of the existing challenges within the IoV network include:

1. Complex, diverse, and heterogeneous networks

2. Provision of Internet

3. Challenges encompass constrained computing and storage resources, service precision, security, and dependability.

4. Localization Issues, disruptive tolerant communication, addressing, tracking, and network fragmentation.

5. The substantial mobility of vehicle nodes and the ever-changing network topologies.

6. Scalability.

This architectural framework is fundamental in any network that integrates all devices for data communication services. Crafting a heterogeneous network poses a challenge due to the diverse elements involved. The IoV network has different components, which is also quite challenging. The architecture of IoV requires other functionalities and various methods to satisfy the network requirement (Jiao et al., 2021; Naser & Kadhim, 2020). The six layers of architecture are explained here. Figure 2 depicts the architecture of IoV.

1. User Interface Layer

2. Systemization Layer

3. Intermixture Layer

4. Service Layer

5. Commerce Layer

6. Security Layer

Figure 2. Architecture of IoV.

2.1. User interface (UI) layer

This architecture layer encompasses various devices installed within the IoV and roadside units, including RSUs, OBUs, actuators, mobile devices, hand-held devices, and sensor nodes. Its primary function is to capture and gather pertinent traffic information, as detailed in Marzak et al. (2016). This information includes speed, location, direction, traffic density, conditions (dense and sparse), accident reports, and weather alerts.

Additionally, this layer serves as an interface and is responsible for transmitting data to higher layers. It also plays a crucial role in ensuring secure data transmission between devices. Furthermore, it addresses challenges inherent in diverse networks that collect captured data. Moreover, this layer reduces risk factors within the IoV through its visual and interactive display elements integrated into the network.

2.2. Systemization layer

This represents the second layer tasked with managing network coordination, encompassing technologies like WiFi, WAVE, RFID, Bluetooth, and LTE. It receives data or information from the first layer, forwarding it to the subsequent layer for further processing. However, it's worth noting that this layer faces interoperability challenges owing to the diverse array of networks employed to sustain IoV network activities. Notably, this layer is crucial in extracting information from heterogeneous networks and reassembling it into a predefined structure tailored for each candidate network, as detailed in Euchi (2017).

2.3. Intermixture layer

This constitutes the third layer within the IoV Architecture, dedicated to the cloud-based infrastructure. This layer enables the storage, processing, and analysis of data received from the lower layers. It functions as an Information Management System (IMS), where cloud computing, extensive data analysis, and expert systems play pivotal roles. Notably, this layer grapples with the substantial challenge of service management, primarily stemming from the diverse services within cloud-based systems, as elaborated in Oubbati et al. (2017).

2.4. Service layer

This constitutes the IoV architecture's fourth layer, often called the Service Layer. Many intelligent applications are available within this layer, including those related to services, multimedia, web-based interactions, and cloud-based functionalities, as detailed in Gupta and Patel (2016).

Moreover, this layer is critical in thoroughly analysing all services, enhancing their intelligence, and rendering them more user-friendly. It primarily focuses on commercial and marketing applications within the IoV architecture while also facilitating interactions between the different layers. Given the diverse range of commercial applications at its disposal, selecting the most suitable and reliable service for users can sometimes be a challenging task. Importantly, this layer is pivotal in advancing IoV networks and meeting current demands.

2.5. Commerce layer

This layer provides operational management of the system, which is used to handle commerce-level operations such as application data, i.e. statistically handled. Business tools such as flow charts,3D graphics, comparison tables, and case studies are used. These analysis tools are utilised for decision-making for future investment, business growth, and proper utilisation of resources in IoV networks. This layer has prices and demands, so it maintains all commercial dealings with third parties and allows the IoV network users to decide the best option (Senouci et al., 2019).

2.6. Security layer

This layer plays a pivotal role in managing the intricacies of large-scale IoV networks, where a substantial volume of data is present, potentially giving rise to various security threats. Within this layer, devices such as sink nodes, base stations, access points, and data backbone points are deployed to facilitate data collection and ensure the seamless execution of security functions.

Security within this layer necessitates meeting specific requirements, including authentication, integrity, confidentiality, and authorisation. Authentication processes are implemented to identify vehicles and sink nodes for data centres, while integrity measures to safeguard data from destruction and unauthorised modification, as detailed in Cunha et al. (2016). Secure data collection is the primary requirement for large-scale IoV networks within this layer.

3. Background

This section presents the various research work regarding routing in IoV. The main research goal of IoV is its routing protocols. In the literature survey section, we have discussed the primary focus of routing in IoV, which routing technology is used for which purposes, what the topics discussed depending on routing technology, and what remark we got from it. Different routing protocols like Destination sequence vector routing (DSDV), Dynamic source routing (DSR), and ad hoc on-demand distance vector (AODV) come from the VANET study. By considering the different properties of vehicles, We have also proposed some Geographical protocols, such as Greedy perimeter Coordinator routing (GPCR) and Greedy perimeter stateless routing (GPSR). Next, we provide the taxonomy of routing protocol in IoV based on different perspectives. The significant works of the survey are presented in Table 1.

Table 1. Literature survey in IoV.

Source	Concepts Discussed	Remark
J. Cheng et al. (2015)	Discussed Routing and Types of Routing Protocol	How Routing can be done in a real-world scenario, and how different types of Networks can make IoV more effective daily.
Yang et al. (2014)	Network Model of IoV technology and application of IoV	IoV routing interconnect the people within and around vehicles
Liu et al. (2016)	VANET Architecture, Protocols stack	Position-based Routing protocol comparison, Different positioning method used in Urban and IoV environment.
Al-Sultan et al. (2014)	VANET communication Domain, Wireless technology in IoV, Challenges and requirement of IoV	Major focus of IoV includes what kind of Architecture, technology, application and simulation used in a new paradigm.
Altayeb and Mahgoub (2013)	Routing used in Packet forwarding, Different types of routing	Comparison of different routing protocols, Vanet routing problem in V2V communication.
Bujari et al. (2018)	Routing in 3D area, Performance of routing protocol in urban environment	Some basic behaviour of different routing protocols on different network graph which represents the IoV scenario.
C. Wang et al. (2018)	Routing protocol in a vehicular network, Reinforcement learning, Architecture of SDCIV, Q-learning algorithm	It Proposes SDCIV and design of QCR to find out an optimal solution based on different Traffic scenario QCR compared with GPSR and AODV.
C. Chen et al. (2020)	Position-based routing used in IoV.	Traffic-aware and Link transmission quality-based routing protocol and the best route can be out using link preference routing protocol.
Han et al. (2020)	Network stability Analysis, Network Model Implementation of DBDR vehicle network	Network stability, The HTTP request and response success rate is always higher than DSDV and GPSR.
M. T. Abbas et al. (2020)	SD-IoV road ware approach, Functionality of IoV, Path failure notification, path duration estimation	Different protocols compared with different metrics are considered to evaluate the performance of proposed protocol for SDN-based IoV.
K. Z. Ghafoor et al. (2018)	Comparison of routing solutions in SDIoV, QRA algorithm for route discovery, LCA algorithm for discrete optimisation	QRA algorithm is used to find the optimal route(not shortest path) for reliable and stable data delivery.
Gasmi et al. (2020)	Zone routing protocol, Inter, Intra, SL-ZRP algorithm	SL-ZRP is used to achieve the expected Quality of service.
Kadadha et al. (2018)	VANET entities, Application, Different routing mechanism	Comparison of different routing protocol, Transmission strategy, and routing maintenance.
Venkatramana et al. (2017)	Different types of routing protocols like topology-based, Proactive, and Hybrid protocols and different types of routing such as OLSR, GPSR, and CBR	Design an efficient routing protocol for all VANET applications, Comparison of different classes of VANET routing protocols.

3.1. Literature survey

4. Routing protocols according to their routing strategy

Here, we have proposed the VANET used in small-scale and homogeneous IoV and the taxonomy of the routing and evaluation approach. The taxonomy of routing is considered from five different perspectives, and the main perspective is transmission strategy. The author decides the transmission strategy from the routing taxonomy. The routing taxonomy indicates transmission strategy, Information required, scenarios routing, and network types. The critical challenge of IoV is its routing protocols. Many routing protocols, such as DSDV, DSR, and AODV, come from the MANET. Here, the author discussed some geographical routing protocols like GPSR and GPCR. Routing protocols are classified depending on the transmission strategy. Location-based routing protocols are classified into four types: Hierarchical, Geographic, Broadcast, and Geocast. Geographical protocols are divided into unicast, Broadcast, and Geocast. Depending on the author's transmission strategy, it is divided into the unicast, Geocast, and Broadcast routing protocols (Kadadha et al.,2018).

4.1. Transmission strategy

Depending on the transmission strategy, routing protocols in IoV are categorised into three primary types.

Unicast routing protocol The primary objective of this protocol is to transmit data from a single source to a single destination through a multi-hop approach facilitated by a greedy forwarding mechanism. Intermediate vehicles along the routing path relay the data from the source to the destination based on a specific routing algorithm. Notably, this category specifically refers to unicast routing protocols. Examples of unicast routing protocols include OLSR (Optimised Link State Routing), which is a critical ad hoc proactive routing protocol and an enhancement of pure link-state routing. On the other hand, DSDV (Destination Sequenced Distance Vector) routing belongs to unicast and table-driven routing algorithms. Each node maintains a routing table to reach any other node in the destination. It periodically transmits its routing table to direct neighbours to maintain table consistency in a rapidly changing topology. FSR (Fisheye State Routing) is another efficient link-state routing protocol that maintains a topology map in each node and propagates link-state updates to immediate neighbours only. Based on the information provided, routing protocols in IoV are further classified into four distinct types: (1) topology-based, (2) position-based, (3) map-based, and (4) path-based routing protocols, as detailed in Tassoult et al. (2019). Figure 3 illustrates the Routing strategy and different Routing protocols in the context of IoV.

Figure 3. Routing strategy and different routing protocol.

Geocast routing protocol This protocol aims to send the data from a single source node to all other destination nodes within a specific geographical region known as the Zone of Relevance. It is implemented with a multicast service known as location-based multicast routing. Many of the Vanet applications can benefit from Geocast routing. Here, the vehicles receive and drop packets only according to their location. For Geocast routing, the Traffic lights are helpful (Li et al., 2020). GPSR, GPCR, and GSR are examples of Geocast routing protocols.GPSR (Greedy Perimeter Stateless Routing): GPSR operates within Non-DTN Routing algorithms and is primarily a unicast routing protocol.

GPCR (Greedy Perimeter Co-ordinator Routing): GPCR, on the other hand, represents a hybrid routing approach that combines elements of both DTN and Non-DTN methodologies, incorporating both greedy and perimeter-based strategies. In GPCR, data packets are forwarded to the nearest nearest neighbour to the destination node. However, the forwarding strategy may encounter challenges if no nodes are present in the direction of the destination.

GSR (Geographic Source Routing): GSR is another unicast routing protocol designed to mitigate certain shortcomings associated with geographic routing. It amalgamates position-based routing with topological knowledge and employs a static street map in urban environments to circumvent potential issues. By utilising this static street map and location information for each node, GSR calculates a route to the destination by forwarding messages along the streets.

These protocols exemplify various approaches to geocast routing within IoV networks.

Broadcast routing protocol Generally, this protocol shares the vehicles' traffic, Weather emergencies, and road conditions. It can also provide deliver advertisements and announcements. This protocol is also used in Unicast Routing protocol and finds the route to the destination. Another example of broadcast routing protocol is DHVN (Dissemination protocol for heterogeneous Cooperative networks) (Abuashour & Kadoch, 2017). It requires two things: road topology and network connectivity. UMB and BSMP are examples of broadcast routing protocols. It is used to find an efficient route to the node destination during the routing discovery phase of the unicast routing protocol. This protocol is signed to communicate safety messages to all the nodes in the network.UMB(Urban Multi-hop Broadcast protocol) addresses the issue related to broadcast storms, hidden nodes, and the reliability of multi-hop broadcasts in urban areas.

4.2. Information required

Depending upon the information required, It is classified into four types of routing: (1) Topology-based routing, (2) Position-based routing, (3) Map-based routing, and (4) Path-based routing.

4.2.1. Topology based routing

DSDV is an example of topology-based routing. This type of routing is used to solve the loop problem. Every column in the routing table contains a sequence number, sequence id, and this id is even if the link is present; otherwise, it is odd (Ren et al., 2017). Another topology-based routing protocol is DSR. It uses the table-driven approach, eliminating the periodic table-update message required by this approach (Mershad et al., 2012). The significant difference between this and other on-demand protocols is that it is beacon-less and does not require the transmission of the periodic hello packets(beacons), which a node uses to inform the neighbours of its presence. Another topology-based protocol is AODV. This protocol is silent until the connection is needed. In this context, routing protocols are categorised into three main types: proactive, reactive, and hybrid.

• Reactive Routing Protocols: Examples of reactive routing protocols include AODV, DSR, and TORA.

• AODV (Adhoc On-Demand Distance Vector): AODV routing is tailored for general-purpose mobile ad-hoc networks and only maintains routes when necessary.

• DSR (Dynamic Source Routing): DSR represents a unicast routing approach where routes are established on-demand, i.e. when needed.

• TORA (Temporally Ordered Routing Algorithm): TORA, a unicast routing algorithm, aims to provide routes to all nodes in the network.

• Hybrid Routing Protocols: ZRP and HARP are examples of hybrid routing protocols.

• ZRP (Zone-based Hierarchical Link State Routing): ZRP employs a hybrid routing strategy, combining proactive and reactive algorithms. Each node defines a zone around itself, encompassing nearby nodes, and uses proactive and reactive methods to forward packets within and outside the zone.

• HARP (Hybrid Adhoc Routing Protocol): HARP is a unicast routing protocol used for route discovery between source and destination nodes. It operates in two phases, Interzone and Interzone, employing reactive and proactive algorithms within these phases (Aravindhan & Dhas, 2019).

4.2.2. Position based routing

GPCR is an example of position-based routing. It forwards the packet along the road according to the vehicle movement. All packets are given priority to be forwarded to a junction node to determine the next hop.GPCR can not wholly solve the local maximum problem that, forward, the node may reach the situation where its distance to distance is closer than its neighbours' distance to the destination. Another routing protocol is GPSR. It is a responsive and efficient protocol for mobile wireless networks. It uses graph-theoretic notation for the shortest path and transitive reachability to determine the routes. It uses a greedy mechanism to forward the data packets to the nodes near the destination (Devangavi & Gupta, 2017).

4.2.3. Map based routing

GSR is a Map-based routing that forwards the data packets according to the forwarding path. The path is calculated based on the vehicle location and placement map. The geographic stateless VANET routing protocol is map-based, which combines the node locations with a digital map and uses an improved forwarding algorithm to address unreliable wireless channel issues. The simulation shows that GeoSVR can provide a higher packet delivery ratio than AODV and GPSR's (Ding et al., 2016).

4.2.4. Path based routing

VADD(Vehicle-Assisted-data-delivery) is an example of Path-based routing. This protocol uses a carry and forward mechanism. Here, three protocols forward the packet: location first Probe, Direction first Probe, and Hybrid-ADD to select the optimal path. The experimental result shows that Hybrid VADD performs better than DSR and GPSR regarding packet delivery ratio, data packet delay, and traffic overhead (B. Ji et al., 2020).

4.3. Scenarios routing

Another protocol is the Vehicular routing protocol, which needs the exchange of road information. This protocol uses ant colony optimisation. Another routing protocol, DTN (delay tolerant routing protocol), deals with the occasional lack of connectivity and uses the carry-forward mechanism (D. Lin et al., 2016). Another routing protocol is DAR(Disrupted adaptive routing), which reduces network transmission compared to traditional routing protocol and improves the delay ratio and average packet delay. There are three types of routing scenarios.

1. 1D scenarios Routing

2. 2D scenarios Routing

3. 3D scenarios Routing

4.3.1. 1D scenarios routing

It is the most straightforward routing protocol in IoV. 1D means that It is in the same line and moving in the same or precisely the opposite direction. Examples of 1D scenarios are highways and stretches of road without intersections. Some other geo cast and broadcast routing protocols examples are IVG, BROADCOMM, and DV-CAST (X. Ji et al., 2016).

4.3.2. 2D scenarios routing

Existing routing protocols such as position-based and map-based GPSR and GSR are examples of 2D scenarios. Other position-based and map-based protocols are DRG, GeoSVR, RBVT, and STAR, examples of 2D routing scenarios (Mershad, 2020).

4.3.3. 3D scenarios routing

Here, the author proposed TDR for VANET. This protocol establishes a routed hop-by-hop and transmits the data packet to find the optimal neighbour node. Compared to GPSR, TDR has the highest delivery ratio and lowest end-to-end delay in 3D scenarios (Rayeni & Hafid, 2018).

4.4. Network types

According to the network types, It is classified into homogeneous and heterogeneous networks. Routing is possible in both homogeneous and heterogeneous networks.

4.4.1. Routing in homogeneous network

All data packets are transmitted through the shortest route via wireless technology. Here, the routing algorithm performance is analysed and compared with other protocols. AODV, DSR, and DSDV are homogeneous network protocols (H. Ghafoor & Koo, 2017).

4.4.2. Routing in heterogeneous network

Clustering is a vital strategy for routing in a diverse network environment. This approach organises network nodes into clusters, each subsequently appointing a leader. Following this organisation, the source node makes the critical decision regarding which vehicle will assume the role of a proxy across a broad spectrum of radio access technologies (Fan et al., 2023). Solving the routing challenge within a heterogeneous network involves addressing four key aspects: (1) Timing handoff, (2) Gateway node in the VANET, (3) Wireless technology where the data packet is transmitted, (4) IP addressing and management of the vehicles (Venkatramana et al., 2017)

5. Detailed description of routing protocols in IoV

Various parameters play a crucial role in addressing the scalability challenges of the Internet of Vehicles (IoV). These parameters encompass Packet Delivery Ratio, Jitter, End-to-End Delay, Routing Cost, and Efficiency. IoV uses an intelligent transport system to improve traffic and reduce accidents. IoV is a heterogeneous Vehicular network. IoV is a six-layered architecture describing each layer's functionalities and representation (Bujari et al., 2018). Here, we present the SDC-IoV(software-defined cognitive Internet of vehicles). In this case, two methods are used, known as RL(Reinforcement Learning)and SDN(Software defined network) technology, to achieve the cognitive procedure for IoV. The optimal routing policy is used for IoV (Bujari, 2016). We present two schemas known as global timeout schema and anti-packet data dissemination schema. The SIR model is to characterise the global timeout schema. For epidemic routing, It also provides performance evaluation and protocol design guidelines. Routing Protocols are classified according to the Protocol design pattern.

1. Position-Based Routing Protocol known as Geographic Routing Protocol

2. Traffic Awareness And Link control protocol known as Geographic Routing protocol

3. Distance weighed back pressure dynamic protocol known as Geographic Protocol

4. Mobile switched-grid-based sustainable routing protocol compared with DSR and GPSR.

5. Stable link-based zone routing protocol known as Hybrid protocol

6. Efficient Hierarchical Clustering Protocol

7. SDN-Based Routing Protocol

5.1. Geographic routing protocol

Geographical Routing is another valuable concept for IoV and IoT (Ronzani, 2018). In this context, vehicle positioning is determined using the RAVP (Routing Algorithm Based on Vehicle Position) algorithm. This approach relies on two pivotal principles: vehicle position analysis and trajectory prediction (Sun et al., 2015). The primary challenge stems from the dynamic nature of vehicles, where their movement influences their character and position. This effect is further influenced by two key factors: the probability matrices for vehicle movement positions and the vehicle position association matrix (L. Xiao et al., 2023). Subsequently, the next-hop forwarding node handles vehicle routing. The study outcomes indicated that the RAVP algorithm outperformed the other three routing algorithms (Singh et al., 2019).

The authors present TLRP (Traffic awareness and Link control protocol) for urban Internet Vehicles. The routing path, real-time traffic, and link information are calculated here. The way with the lowest weight can be considered the routing candidate. The simulation results demonstrate the superior performance of the proposed protocol compared to MM-GPSR and E-GYTAR (Data et al., 2019). Our approach employs two distinct data transmission strategies based on the source and destination nodes. It leverages packet forwarding between these nodes based on their positions to determine the optimal routing path, achieving minimal transmission delay and maximal packet forwarding ratio (Zhang, Hu, Liang, Li & Gupta, 2023).

IoV (Internet of Vehicles) is a pivotal technology aimed at realising an Intelligent Transport System (ITS) (C. Wang et al., 2018). IoV encompasses various applications, including safety enhancements, traffic flow optimisation, and vehicular networks dedicated to road safety applications. Making accurate decisions is a critical challenge within the IoV framework (Hu et al., 2023; Zhang, Hu, Liang, Li & Pathan, 2023).

The DBDR (Distance Weighted Back Pressure Dynamic Routing) protocol focuses on prioritising vehicles near the destination, with its primary objectives being efficient packet forwarding and Internet Gateway (IGW) selection. The protocol employs Lyapunov drift theory to ensure network stability and capacity (P.-Y. Chen et al., 2018). In our practical road scenario, we implemented the DBDR protocol using NS2 VANET MOBISIM.

DBDR represents a dynamic routing protocol, while DSDV (Destination Sequenced Distance Vector Routing) and GPSR (Greedy Perimeter Stateless Routing) are classic routing protocols designed for Mobile ad-hoc networks (MANETs). Additionally, an SDN-based IoV protocol (L.-L. Wang et al., 2020) facilitates data packet forwarding both vehicle-to-vehicle and vehicle-to-everything communication scenarios. This protocol divides roads into distinct road segments, each assigned a unique ID, and possesses the unique capability to relay control messages.

1. Process of packet forwarding from source to destination and set of rules for it by the controller in SD-IoV.

2. path handling for path calculation at SDN controller

3. path failure notification handling at EC and SDN controller

SD-IoV protocol is used for the evaluation of the road-aware approach. In this approach, We use the SUMO simulator. SUMO generates traffic on the roads, and MININET-WIII tests the protocol. This technology is beneficial for innovative city applications (J. Cheng et al., 2020). It overcomes the shortcomings of the traditional approach and uses this technology that transmits the data and traffic shaping in the different vehicular scenarios. Here, the QRA routing algorithm is used, which forwards the data packet toward the most reliable and connected path to the destination. For finding the coordinate route, the SINR (signal-to-noise ratio )constraint is used, which has a high probability of connectivity and it uses three algorithms (F. Abbas & Fan, 2018).

The multiple objective functions are used to find out the closest destination. This approach is evaluated using simulation. The QRA algorithm helps the central controller to find the optimal path (Cooper et al., 2016). IoV is gradually changing the existing research into the smart city, smartphone, smart health, smart transport, and smart industry. IoV slowly develops from the vehicular network. A protocol stack considers the management, operational, and security planes (Vasudev & Das, 2019). A researcher who is interested in future vehicular communication they are benefiting from the different functionalities. As IoV is a part of ITS, different functions like speed, road condition, traffic flow, and other protocols are used to solve the other routing problems. The author used the Markov method for mobility model intersection and analysis here. For urban transportation, a novel routing protocol is used (Kayarga & Kumar, 2021).

Here, we proposed MSGR(Mobile switched grid-based sustainable routing protocol), which compares with DSR and GPSR protocol. The simulation is done with Matlab. The simulation result shows that MSGR performs better than DSR and GPSR. Another software-defined networking approach is RSIR(Reinforcement learning and software-defined intelligent routing), which is used to find the optimal root (Talat et al., 2019). RSIR compares with Dijkstra's algorithm by taking different edge weights. Compared to other algorithms, RSIR produces the shortest paths to avoid traffic concentration and congestion. The other variation of Dijkstra's algorithm produces a more significant path, including the low-capacity link. In the future, RSIR can be evaluated as DRL(Deep Reinforcement learning). Here, two communication channels are used, known as IVC and RVC. The main goal of this communication is to group the vehicle according to the moving direction. ROMSGP(Received on the most stable path) can remove the most stable root. In ROMSGP, the link expiration time is calculated for each vehicle path. The path with the most considerable LET(Link expiration time) is considered a stable path (Han et al., 2020). ROMSGP protocol compared with DSR(Dynamic source routing) and ABR(Area border routing). The performance evaluation shows that ROMSGP has high stability and throughput compared to DSR and ABR. So, the ROMSGP protocol provides good stability and maintains the increased throughput in IVC and RVC communication. The colouring vehicle concept is introduced to identify the MSMD traffic flow. ACO is used for route selection that considers the length of the path, the traffic density of the path, and travel time (Qureshi et al., 2020). Here, the connected device acts as an ant and takes its routing decision decentralised. Connected vehicles switch from multiple sources to multiple destinations using the IoV environment. Here, simulation is done by the Net log platform to evaluate the ACO-based routing method's performance. The ACO-based routing method is used to enhance traffic efficiency (M. T. Abbas et al., 2020).

5.2. Hybrid routing protocol

SL-ZRP, short for Stable-link-based Zone Routing Protocol, aims to identify stable communication links among vehicles, treating them as interactive nodes within the network. This routing protocol facilitates vehicle interactions, enhancing the network's Quality of Service (QoS), as highlighted by K. Z. Ghafoor et al. (2018). SL-ZRP represents an evolution of the ZRP protocol, designed specifically to maintain link stability in the context of Internet of Vehicles (IoV) applications. Empirical evidence suggests that SL-ZRP outperforms the traditional ZRP protocol.

In addition to SL-ZRP, there are two other crucial routing protocols in the IoV ecosystem. The first is IARP (Intra Zone Routing Protocol), which manages and updates the routing table. The second, IERP (Inter-zone Routing Protocol), focuses on establishing routes for nodes beyond the zone radius, as described by W. Xu et al. (2017).

Integrating the Network simulator NS3 and the traffic simulator SUMO is essential in various vehicle and road traffic scenarios to simulate realistic mobility patterns. These tools help replicate real-world network conditions. SL-ZRP relies on three key parameters for operation: node speed, node priority, and delay. Its performance assessment encompasses critical network metrics, such as low latency, packet delivery ratio, network overhead, and scalability, as elucidated by Wu et al. (2014).

5.3. Clustering protocol

Efficient Hierarchical Clustering Protocol (EHCP) is another vital component of IoV networks. EHCP calculates a cost metric for internet connectivity among vehicles and utilises this metric to select suitable cluster heads. EHCP operates through four stages: registration, neighbourhood detection, cluster head selection, and maintenance. Research conducted by Casas-Velasco et al. (2020) suggests that EHCP outperforms existing methods in the context of IoV.

For a visual representation of the various routing protocols within IoV, please refer to Figure 4.

Figure 4. Taxonomy of routing protocol in IoV

A Wireless Media Sensor Network (WMSN) employs a grid routing protocol to facilitate operations. Within the context of the Internet of Vehicles (IoV) sensing layer, WMSN plays a crucial role in ensuring the delivery of guaranteed Quality of Service (QoS). The IoV's sensing layer faces the intricate challenge of collecting substantial information to address road traffic complexities, as discussed by Taleb et al. (2007).

Maintaining a high-quality service becomes a formidable task since WMSN is a dynamically evolving heterogeneous network. To address this challenge, the Quality of service-aware grid Computing Protocol (QAGR) is introduced and compared to traditional grid-based clustering routing protocols, as proposed by T. Jiang et al. (2018). In the realm of heterogeneous WMSN, the routing protocol assumes the responsibility of ensuring reliability and minimising delays.

One of the most pressing challenges within the IoV domain revolves around devising efficient routing mechanisms and medium access control protocols. These are essential to ensure timely and dependable dissemination of safety-related and other application messages across the IoV. The primary objective of IoV routing protocols is to establish efficient routes between network nodes. However, this is a formidable task due to the constantly changing vehicle topology, as noted by Nguyen and Jung (2021).

In this context, we delve into the current issues associated with IoV routing protocols, specifically focusing on their relevance to road safety communications. The field of IoV routing presents numerous emerging research areas, particularly in addressing concerns such as security and QoS. Challenges in geocast routing, which are yet to be fully understood, persist within the IoV domain, as highlighted by Qureshi et al. (2017).

IoV represents an ongoing challenge, especially concerning routing optimisation. In the context of IoV network operations, data packets are disseminated to all nodes within a specified area, commonly called flooding. While IoV networks present their fair share of challenges, they also offer opportunities to deploy wireless communication technologies, benefiting society and enhancing daily life, as underscored by Gasmi et al. (2020).

The Internet of Vehicles (IoV) encompasses various functionalities, including an Intelligent Transport System, Vehicle Automatic Control, and Intelligent Road Information Service. Its primary objective is to mitigate traffic accidents and congestion while offering additional information services. Software-Defined Networking (SDN) stands out as the prevailing approach in network management.

The ROMER (Resilient, Opportunistic Mesh Routing for Wireless Mesh Network) routing protocol leverages Roadside Units (RSUs) to facilitate message routing within Vehicular Ad Hoc Networks (VANETs) (Ning et al., 2017). This protocol amalgamates two widely-used routing methods: Geographical and Carry Forward.

Another notable routing protocol in the IoV domain is SURFER, which harnesses SDN architecture and blockchain systems to ensure secure and efficient packet routing. SURFER boasts two implementations: one employing SDN within the RSU network and another utilising SDN across the entire IoV landscape. Notably, SURFER excels in terms of efficiency compared to other network parameters.

5.4. SDN-based routing protocol

SURFER-1 and SURFER-2 represent two distinct SDN-based routing protocols designed for IoV. IoV is subdivided into clusters of RSUs, each RSU cluster housing an SDN cluster controller. These SDN cluster controllers are linked to the central SDN controller (Sherazi et al., 2019).

To enhance the security and reliability of routing and data management within IoV, we propose a blockchain model incorporating the High-Performance Blockchain Consensus Algorithm (HPBC). This model is instrumental in creating and upholding the routing and data blockchain.

These two protocols, SURFER-1 and SURFER-2, are subject to a comparative analysis against QRA and SD-IoV.

The emergence of the concept of IoV has translated into tangible real-world applications. IoV represents an evolved iteration of an ad-hoc network primarily designed for autonomous driving. It encompasses a multitude of connected vehicles, presenting numerous opportunities and challenges that are, as of yet, not entirely clear (J. Cheng et al., 2020).

The author of this study delves into various IoV technologies and explores potential solutions. Within the realm of Intelligent Transportation Systems (ITS), VANET holds significant importance, serving as an intelligent transportation network to enhance road safety. In this context, we develop into the cross-layer design approach, classification, and performance parameters of Quality of Service (QoS) routing protocols. Notably, three distinct routing protocols are employed: proactive, reactive, and hybrid (Hasrouny et al., 2017).

Numerous routing protocols depend on Mobility models, emphasising the Random Way Point (RWP) and Random Direction (RD) models. These models illustrate the simulation of mobile nodes' movements, encompassing alterations in both velocity and location over time.

In the case of the RWP model, each node randomly selects a point within a predefined wrap-around square area as its destination and proceeds to travel at a constant speed. In contrast, the RD model involves nodes moving in a specified direction within a designated wrap-around area. These mobility models serve as pivotal tools for replicating the intricate movement patterns of mobile nodes across a wide array of networking scenarios.

This paper explores various routing methods, including cluster-based, position-based, broadcast, geocast, and performance-oriented routing protocols. The paper also addresses various routing challenges. The Internet of Vehicles (IoV) is vital to Intelligent Transportation Systems (ITS). IoV primarily focuses on enhancing road safety, optimising traffic flow, providing infotainment services, and strengthening vehicular networks. It empowers drivers to make informed decisions for safer driving (Dutta et al., 2020).

In pursuit of a consistent, secure, and safe driving environment, researchers examine traffic congestion and routing issues in urban and highway settings. Different protocols exhibit varying limitations concerning extensive networks and routing overhead. To overcome these limitations, researchers have proposed bio-inspired, big data, genetic algorithm, and machine learning approaches (Kelarestaghi et al., 2019). This paper delves into the bio-inspired technique, IoV, extensive data analysis, genetic algorithms, machine learning, and their effectiveness.

Among the diverse array of Bio-inspired Swarm Intelligence techniques, IoV technology holds the most promising potential. Nevertheless, it faces challenges such as backup failure, rapid adaptability, robustness in the face of failures, ease of design, and network scalability. Hybridizing bio-inspired algorithms with machine learning can address these challenges effectively, resulting in superior outcomes (D. Wang et al., 2019).

Security and privacy concerns represent pivotal Internet of Vehicles (IoV) considerations. In IoV, security requirements must encompass authentication, non-repudiation, data integrity, availability, and confidentiality to ensure a robust safeguarding framework. Moreover, the distinctive communication characteristics and diverse applications within IoV introduce complex security and privacy challenges, rendering this environment particularly intricate. Below, we outline the security challenges stemming from the unique characteristics of IoV.

1. Scalability

2. Mobility

3. Time Constraints

4. Data Dissemenation

5. Privacy

The researchers discussed the development of IoT, cloud computing, big data, and software-defined network technology. Car accidents occur when the vehicles or RSU are under attack or control. It collapses and produces incorrect results, which is the worst case (Alam et al., 2015). We discuss the consensus problem in SD-IoV. So SD-IoV is built with infrastructure, i.e. RSU. RSU has a black hole capacity and powerful computing capacity. The author proposed two protocols, i.e. CPR and CPV. The CPR(Consensus problem for RSU) is designed for the RSU, and the CPV (Consensus problem for vehicles) is intended for the vehicles. It can also increase ITS application where higher reliability is required (Kadhim & Seno, 2019). Figure 5 depicts SDN-enabled routing in IoV.SDN enables routing to decouple the data layer and forwarding layer, which can be known as a logical centralised controller to sense the global status information and control the whole network. It contains loosely coupled data and control planes, supporting centralised network state control and achieving the network application business. It has a flexible software programming capability to solve problems like resource control, business Innovation, network environment sensing, and other issues. The SDN Controller platform typically runs on a server and uses the protocol to switch where to send the data packets.SDN controllers direct the traffic according to a network operator's forwarding policies, thereby minimising manual configurations for individual network devices. The basic principle of SDN is to automate network supply, management, and control through centralised policy.

Figure 5. SDN enabled routing in IoV.

6. Summary of the routing protocol

Types of routing, routing protocol, Information used, Delay sensitivity (DS), Dealy Tolerant (DT) Different scenarios and Network types are described in the above table. The major works about routing and routing protocol in IoV are presented in Table 2.

Table 2. Types of routing protocol comparison

Routing Protocols	Types of Routing	Information Used	DS/DT	Different Scenarios	Network Types
DSDV	Unicast	Topology	DS	1D/2D	Homogeneous
DSR	Unicast	Topology	DS	1D/2D	Homogeneous
AODV	Unicast	Topology	DS	1D/2D	Homogeneous
GPSR	Unicast	Position	DT	2D	Homogeneous
GPCR	Unicast	Position	DT	2D	Homogeneous
IVG	Geocast	Position	DS	1D	Homogeneous
DRG	Geocast	Position	DS	2D	Homogeneous
BROAD COMM	Broadcast	N/A	DS	1D	Homogeneous
Flooding UMB	Broadcast	Map	DS	2D	Homogeneous
DV-CAST	Broadcast	Local Information	DT	1D	Homogeneous
IGRP	Unicast	Position	DT	2D	Homogeneous
GSR	Unicast	Map	DT	2D	Homogeneous
STAR	Unicast	Map	DT	2D	Homogeneous
ANT Colony	Unicast	Map	DS	2D	Homogeneous
TDR	Unicast	Position Based	DS	3D	Homogeneous

6.1. Lesson learned from researcher

In this paper, we have followed 77 papers, including 14 survey papers, which give a new idea regarding the routing mechanism. We discuss various routing protocols and their routing technique and compare them based on different strategies. The protocols are reached by taking the different parameters such as Routing type, Route discovery, Network structure, Transmission strategies, simulation Scenario, and Routing table. After the comparison, we observed that the proactive routing protocol works best in highway scenarios and reactive protocols in urban scenarios (Zhao et al., 2019).

7. Challenges and future aspects of IoV

The landscape of the Internet of Vehicles (IoV) presents a multitude of challenges and opportunities. The core objective of IoV systems is to enhance the efficiency, safety, and inclusivity of vehicle maintenance processes (Kakarla et al., 2011). To establish a controllable and well-managed IoV ecosystem, it is imperative to devise intelligent models and innovative solutions. These novel solutions play a pivotal role in enhancing communication processes within IoV, especially within its heterogeneous open system.

Multimode communication introduces many challenges, encompassing issues like managing multiple users, ensuring network security, conducting comprehensive big data analysis, and addressing scalability concerns. To optimise resource utilisation, IoV networks are interconnected with the Internet and various short and large-scale communication networks, necessitating stability, scalability, and robustness considerations.

IoV networks also contend with various other challenges, including the rapid movement of vehicle nodes, dynamic network topologies, unpredictable network behaviour, substantial environmental changes, and the efficient utilisation of bandwidth. Another layer of complexity arises from human behaviour within the IoV, as drivers navigate a vast virtual platform enabled by cooperative technologies.

Accurate location information presents yet another challenge, with reliance on GPS for location data, subject to signal degradation in challenging environments such as under bridges, within tunnels, and complex urban areas (C.-F. Cheng et al., 2021).

Another essential ability of the IoV network is communication ability and joint optimisation. In most cases, the driver does not follow the system requirement. Service sustainability is another challenge for the IoV network model (Tripp-Barba et al., 2019). The main future goal of IoV is to make a fully automated environment in the Internet of Things field. The different applications of IoV are autonomous vehicle tracking systems, traffic, and parking management, which includes innovative traffic lifts and adaptable lighting systems (Ardakani, 2018). Parking space equipped with sensors and beacons and Real-time information from CCTV cameras. Some other aspects of IoV technology are described here.

1. It develops unified concepts for a suitable scope of the IoV network nationwide.

2. It standardises protocols and establishes benchmarks for developing IoV software and hardware.

3. It constructs a navigation system that prioritises security, precision, and regulatory compliance.

4. It ensures the pervasive presence of all network components.

5. It masters the secure and reliable management of large datasets.

6. It surmounts technical challenges related to cloud platform integration, data mining, and analysis.

7. It facilitates the creation of new location-based service applications and tailors man-to-machine terminals specifically for IoV applications.

In the forthcoming years, the Internet of Vehicles (IoV) system is poised to make substantial contributions across diverse fields, including social and economic development. It aims to establish a secure, robust, and scalable infrastructure while transforming traditional transportation systems to align with modern requirements (Kumar et al., 2018).

The creation of innovative models and architectural solutions is paramount to managing vast volumes of data efficiently. Energy conservation is a pivotal future focus, particularly in devising methods to reduce fuel consumption through novel traffic control approaches. IoV's potential to mitigate traffic congestion translates into a reduced fuel footprint. As a result, emerging devices entering the market leverage these advantages to promote their products.

With each passing day, network complexity escalates, accompanied by a surging demand for seamless communication. Consequently, the need arises for developing more intelligent models to reduce the proliferation of smart devices. The IoV network is a prominent future direction within the communication industry (Kaur et al., 2015).

8. Conclusion

Typically, an Intranet begins as a small local area network. As the TCP/IP model evolved, it gave rise to the Internet, a globally utilised network infrastructure serving various intelligent applications. The realm of smart industries benefits from applications like real-time road condition updates emergent vehicle preemption systems, and general information services. In response to these needs, the IoV network employs three primary protocols: Position-based, Map-based, and Path-based. IoV's mobile nodes exhibit dynamic behaviour, posing challenges in maintaining stable routes. These challenges find solutions in a homogeneous network, such as VANET.

The IoV network encompasses dimensions spanning 1D, 2D, and 3D scenarios. The first two dimensions correspond to Plane-based routing, which, in real-world applications, proves less effective due to the absence of the third dimension. IoV networks are further categorised into homogeneous and heterogeneous types, and exploring hybrid approaches combining these network types represents a promising avenue in the future of IoT. Both homogeneous and heterogeneous networks are validated to foster effective vehicle utilisation in the future development of the IoV network.

This paper's key contributions lie in presenting a comprehensive layered architecture, addressing communication aspects, delving into data analysis methods, and highlighting recent challenges. These details are invaluable for new researchers and industries venturing into the IoV field.

The IoV encompasses the traditional VANET and a vast and diverse heterogeneous network structure. Researchers continuously work to bridge the gap between small-scale IoV networks and large heterogeneous network structures, aiming to realise the IoV's full potential. Future endeavors will explore enhanced connectivity for drone-enabled IoV systems. This paper has shed light on the existing IoV system, particularly its efficacy in data routing.

Disclosure statement

No potential conflict of interest was reported by the author(s).