Classifications of Routing Protocols in Ad hoc Networks: Comparison
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Wireless Mobile Ad-hoc Networks (WANETs-MANETs) are one of the most in-demand networks in our day and age, being widely used in military fields, disaster environments, autonomous robots, vehicular networks, rural and urban environments, and UAV applications. This is due to the remarkable features of versatility, robustness, and self-configuration with no infrastructure. Such networks' main objective is to deliver reliable data transmission directly to nodes such routers, access points, smartphones, and vehicles with dynamically adjusting the data routes in accordance with the network conditions and GPS information.

However, mobility in ad hoc networks is yet the crucial issue due to the dynamic changes of the network topology; this makes data routing a substantial challenge. To this end, choosing or design the proper routing protocol plays important role in establishing robust, secure and efficient data communication for randomly distributed and unrestricted movement of nodes. In this regard, we will briefly introduce the main types of routing protocols that can be utilized in various types of ad hoc networks such as MANET, VANET or FANET or SANET; and how to evaluate them based on their pros, cons and operation.

  • routing protocols
  • WANET
  • MANET
  • VANET
  • FANET
  • AODV
  • GPSR

1. Introduction

Nowadays, mobile ad hoc networks (MANETs) are becoming a pivotal trend in modern life to keep pace with an accelerated world and rapid technological advances. In this context, the desire for low-cost installation and smarter and more connected devices is dramatically growing. MANETs foster a wide range of vital applications in industries, military operations, emergency situations, Intelligent Transport Systems (ITSs), intelligent health care systems, etc. [1][2]. Accordingly, MANETs are classified into various categories, such as Vehicular Ad hoc Networks (VANETs) for smart cities, Internet of Vehicles (IoV) for smart automobiles, Flying Ad hoc Networks (FANETs) for the communication of Unmanned Aerial Vehicles (UAVs), and the Sea Ad hoc Network (SANET) to communicate with Autonomous Underwater Vehicles (AUVs), vessels and boats [3][4][5]. Some types of wireless ad hoc networks (WANETs) and their communication applications are shown in Figure 1. This classification can be a helpful guideline for designers and researchers to understand the specific requirements, challenges, and design considerations associated with each network, in addition to the specialized routing protocols, algorithms, and system architectures [6][7].
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Figure 1.
Examples of wireless ad hoc networks (WANETs) communication applications.
Although MANET appears simple and versatile in various applications, the design of an efficient routing protocol for data communications in such networks is still a challenging task. Since the topology of the network changes rapidly and nodes can join or leave the network at any moment, this leads to a remarkable problem in choosing the relevant forwarding node and routing the packets. To this end, the vital issue is to design efficient routing protocols that are mobility-aware to find the optimal routes between communicating nodes and ensure better routing with less overhead and high network reliability [8][9][10]. Typically, the traditional topology-based routing protocols are mainly classified into three categories: reactive (on-demand), proactive (table-driven) and hybrid. These routing schemes are widely developed to cope with various challenges like high node mobility and dynamic network topology, transmission power or energy restrictions [11][12]. Specifically, such schemes allow the nodes to set an entire route before forwarding data packets, causing high control overhead due to the dynamic topology of MANETs. For example, reactive routing protocols, such as AODV (on-demand routing), do not adapt well in high-mobility environments [13].
On the other hand, geographic routing protocols have been shown more interest due to their location awareness via using a Global Positioning System (GPS) device or running localization algorithms. In effect, these approaches employ non-flooding-based route discovery and offer high scalability for large-scale networks depending on the nodes’ position information. This means that nodes must know their own positions before broadcasting their beacons’ “Hello” messages. Also, such routing protocols do not necessitate the setup of a route management process or link maintenance [14][15][16]. Instead, the routing decision requires only the position information, i.e., the nodes need only maintain one-hop neighbor information for routing decision; this makes them more robust and efficient protocols for dynamic networks. However, such routing mechanisms have some drawbacks because the position information of each node may change due to network conditions such as GPS devices that may not work well in some locations like tunnels because of the absence of satellite signals. Besides that, a high mobility of nodes may change the network density from sparse to dense or vice versa. In consequence, this will increase the links’ breakages of one-hop neighbor nodes and degrade the accuracy of neighbors’ awareness [17][18].
Notably, owing to the rapid topology change in the network, geo-routing protocols require broadcasting the proactive beacon packets (hello packets) periodically or non-periodically to discover neighboring nodes and maintain the correctness of routing selection. However, a high beacon rate (i.e., frequency of beacons) means excessive beacon overhead, which will raise the packets’ collisions and transmission delay in the network [14]. Beacons are very small messages; they include parameters such as the position, velocity, and direction of mobility of the nodes. Such key information is utilized to build the routing decisions by estimating various network measures such as geographical distance, expected transmission count (ETX), defined as the neighbor link reliability, signal strength, stability, and link lifetime (link duration) [8][15][17][18][19]. In sparse networks, for example, broadcasting beacons with high transmission power is essential to expand the awareness area or communicate with distant nodes in mobile wireless environments. Nevertheless, the increase in the beacon transmission power or the frequency of beacons (beacon rate) may negatively affect the routing performance due to the limitations of the wireless link’s bandwidth and network resources. Therefore, the trade-off between the beacon rate overhead and the accuracy of routing information (precision) is still an open challenge in ad hoc networks [20][21].

2. Classifications of Routing Protocols

The routing of wireless ad hoc networks is still challenging owing to the absence of a centralized authority and the unpredictability of network topology. Besides that, the characteristics of self-organization, mobility, medium type and node deployment present further difficulties in the design of an ad hoc network routing protocol [22][23]. Therefore, routing protocols for ad hoc networks are categorized into various classes; the two most well-known are (a) topology-based and (b) geographic-based routing protocols.
In fact, topology-based routing protocols, like the Ad hoc On-Demand Distance Vector (AODV), are also known as traditional routing protocols because they store link information in the routing table to forward packets from source to destination nodes. On the other hand, geographic-based routing algorithms, like GPSR, are designed to cope with some of the constraints of topology-based routing by using extra strategies, where the sender utilizes a location service to figure out the destination’s position. Furthermore, the information is obtained through a beaconing procedure. There are also other routing protocol classifications such as hybrid-, hierarchical-, multicast-, Geo-cast-, and cluster-based routing protocols. Table 1 briefly summarizes the main routing categories [24][25][26][27][28].
Table 1.
Routing categories in Mobile Ad hoc Networks.
Routing Protocol Categories Description Advantages Drawbacks
Topology-based Reactive: AODV, DSR, ACOR, DYMO On-demand routing; routes are founded when required by establishing route requests across the network.
  • Adaptable to high dynamic topologies
  • Support multicasting
  • High latency.
  • Excessive flooding.
  • Not reliable shortest path due to nodes mobility.
Proactive: STAR, DSDV, OLSR The routes kept updating in a table even though there is no demand for a route (table-driven)
  • Loop-free
  • Established route up to date
  • High overhead.
  • Low efficiency in high density.
  • High rapid networks.
Hybrid: DVRP, ZRP, HSLS Combine Reactive and Proactive routing algorithms.
  • Efficient in rapid networks
  • Low latency
  • Low energy consumption
  • Low overhead
  • Scalability
  • High traffic in high-density networks.
  • Progress complexity.
Geographic-based GPSR, LAR, MORA, VADD, The path establishment is based on the node’s location information from GPS equipment.
  • Efficient in rapid networks
  • Low energy consumption
  • Low overhead
  • Low latency
  • Scalability
  • Perimeter mode fails to determine the most effective route.
  • Not self-learning.
Hierarchical-based HSR, HDVG The nodes are organized into hierarchal groups.
  • Low overhead
  • Low congestion
  • High reliability
  • Consistent energy dissipation
  • Complexity in maintenance of the cluster head.
Multicast-based CBM, MDR, SMORT, AOMDV A node establishes route to a single or multiple destinations simultaneously.
  • Low Processing Cost
  • Low bandwidth requirement
  • Low reliability.
  • High overhead.
  • Route based on subscription information.
Geo-cast-based DGR, IVG Send messages to a single or multiple nodes based on location (combine geographic and multicast routing).
  • Low overhead
  • Low storage load
  • Multicast routing and position information may not be available at the required location.
Cluster based GBDRP, PBSM, LEACH The protocol divides network nodes into a number of overlapping or disparate clusters and assigns cluster heads to keep track of cluster membership.
  • Low energy consumption
  • Low storage
  • High scalability
  • Low traffic
  • Complexity.
  • Lack of selecting optimal cluster head.
  • High latency.
  • No mobility and direction.

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