DPTR: Distributed priority tree-based routing protocol for FANETs

Abstract

Collaborative Flying Ad Hoc Networks (FANETs) are mutually operating ad hoc systems comprising ground ad hoc and aerial ad hoc networks as their coordinating units. Coordination amongst different ad hoc networks and their hybridization elongate the application of ad hoc networks. These networks can be utilized for both military and civilian applications. However, despite possessing a lot of advantages, network partitioning and relaying are the major issues that arise when two or more networks coordinate with each other. There subsist a plethora of approaches, which highlighted the issues cognate to network partitioning as well as routing, but these fixated on such quandaries within the same network. Even the existing routing protocols counterfeit these problems, but with a scope inhibited to a single network and do not resolve these issues for two different operating networks. In this paper, the problem of network partitioning is considered between the aerial ad hoc network and the ground ad hoc network, and a felicitous routing protocol is proposed that can handle transmission in this mutually coordinated system. The proposed protocol resolves both the issues related to topology formation as well as routing between simultaneously operating nodes of two different ad hoc formations. The proposed protocol is derived over distributed Red–Black (R-B) tree, which forms a priority network that allows selection of an appropriate node and a channel for relaying, and is termed as Distributed Priority Tree-Based Routing Protocol (DPTR). The protocol operability is demonstrated using network simulations in comparison with the existing state-of-the-art routing protocols. The results suggest significant gains in channel utilization, packet delivery ratio, end to end delay, overheads, probability of connectivity and network throughput.

Introduction

Multi-Unmanned Aerial Vehicles (UAVs) guided networks are one of the major applications of Flying Ad Hoc Networks (FANETs) [1]. These are formed by collaboration between the aerial ad hoc and the ground ad hoc networks with one of the networks acting as a coordinating unit. These networks form a special class of distributed networks. Network operating in distributed mode has varied applications in areas such as surveillance, territorial-security, disaster management and navigation [2]. Unmanned aerial network operating in coordination with other networks increases maneuverability and provides in-depth navigation that increases the scalability of network and enhances performance in terms of coverage and range [3], [4].

Networking with UAVs has emerged as a new area of research in recent years [5], [6]. Recent surveys have shown that networking using UAVs can increase the applications and will lower the risks involved due to manned aerial vehicles [7]. Unmanned aerial vehicles have the capability to perform maneuverabilities autonomously. These can perform complex tasks based on certain computations that are fixed as on-board processors over these vehicles. Ground coordinated flying ad hoc networks are the set of simultaneously operating two ad hoc systems; one operating on the ground and another in the air. These can be treated as a single ad hoc formation to perform data transmission between them.

Single UAV systems that are controlled by operators have already been implemented by various military agencies. However, controlling them from a distance without actually knowing the dynamics at a certain time instance lowers the applications and performance of these systems. The solution to such problem can be obtained using Multi-UAVs in a cooperative mode so as to overcome the limitations of the single UAV system [8], [9]. Many frameworks exist in the literature, which focuses on the formation of aerial swarms or aerial cooperative units for relaying between UAVs [10]. However, despite their varied applications, even these frameworks rely either on the UAV’s local frame of reference or some navigation system such as Global Positioning System (GPS), Assisted-GPS or Inertial Navigation System (INS). Also, these multi-flying aerial vehicles neither use an on-ground guidance system nor do they form a mutual ad hoc network with ground nodes, thus, causing a limitation to such ad hoc formations. Recent research has focused on multi-UAV guided systems that comprise multiple ad hoc networks. These networks have reduced the geographical boundaries and have formulated a hybrid ad hoc network. Such networks can also be termed as Mutual Ad Hoc Networks.

Mutual ad hoc networks increase the applicability and operability of such dynamic networks. These ad hoc networks operating in coordination with each other enhance the operations of the multi-UAV system and overpower the limitations of a single UAV system. However, data transmission and forwarding is a complex task in such networks as parameters like mobility, location, difference in processing power, delays along with network partitioning cause hindrance in network operations. Despite frameworks that unite these two ad hoc networks, there is a requirement of certain protocols that will provide routing facility within such networks to efficiently transfer data between two simultaneously operating ad hoc units.

Network partitioning¹ leads to different pieces of a network which may inhibit a particular piece from participating in the transmission. A network may become isolated if the partitions remain unidentified as there will be no information regarding the nodes that no longer receives any information. Network partitioning can also result when two differently operating networks are combined together as in the case of mutual ad hoc formations. Such networks are much tedious to manage as these require an effective strategy for managing topology as well as addressing of the nodes. Lack of metadata and need for more tabular information further elongate the issues of transmission in partitioned networks. Routing becomes tedious and excessive overheads are induced in the network causing a high end to end delay while transmitting information over the network. Such scenarios can be seen in patrolling services (surveillance)– which use both ground and aerial nodes in combination with each other, disaster scenarios- where the connecting nodes between the two networks may fail and lead to partitioning, capacity and coverage enhancement of networks- which use UAVs in combination with ground terminals for better services, etc. Thus, it is of utmost importance to understand the risks and management implications associated with the aerial-ground ad hoc formations along with their possible solutions for enhancing their utilization.

The existing solutions to network partitioning rely on the identification of each partition and then use a separate address schema for resolving it [11]. Message ferrying is also one of the efficient solutions, which uses proactive routing for transmitting data in wireless partitioned networks [12]. However, such solutions are more applicable to stationary networks and need a combination of routing schemes for their applicability to general wireless networks. Akkaya et al. [13] and Wang and Wu [14] focused on distributed recovery via controlled mobility during high partitioning in mobile and sensor networks, respectively. Repairing of network partitioning can also be considered, but these are limited to a single network and cannot be considered for distributed networks [15], [16]. Further, genetic algorithms and game theory can also be used for supporting transmissions in highly partitioned networks [17], [18]. All these solutions are efficient subject to their domain, but their scope for handling aerial nodes is limited as these lack parameter configurations for supporting aerial-ground coordination. Further, most of them rely only on resolving partitions and do not ensure relaying between the partitions. Thus, it is necessary to develop a generic solution for aerial-ground coordination, which can handle their dynamic behaviour and can support transmissions without failing.

In this paper, the problem of routing between aerial and ground ad hoc networks is addressed along with a solution for topological formations while operating these two networks together. A solution that solves the problem of network partitioning along with the provision of routing is proposed. The protocol is termed as Distributed Priority Tree-based Routing (DPTR) Protocol as it uses the properties of an R-B tree and formulates distributed routing trees by adding-up certain rules in the formation of these trees, thus, providing a solution that allows routing to be performed over simultaneously operating distributed ad hoc networks. The rules are helpful in building the network and also resolve the network isolations. These rules are further extended for the inclusion of protocol functioning schema followed by addressing schema and path formations. The proposed protocol is evaluated via simulations conducted using Network Simulator (NS-2) and Matlab in three parts. The first part evaluates the performance for aerial ad hoc formations, the second part evaluates it for ground ad hoc formations and the third part analyzes the performance for mutual ad hoc formations. The evaluations are presented for Packet Delivery Ratio (PDR), end to end delays, channel utilization, network throughput, network connectivity time and the probability of connectivity. Initially, the results are presented for the standalone formation of mutual ad hoc network between the aerial and the ground nodes and these are studied in comparison with the existing state-of-the-art routing protocols. A comparative table is also presented and discussed for evaluation of the proposed protocol with the existing solutions, which focus on the issues of network partitioning as well as routing for FANETs.

Remaining paper is structured as follows: Historical perspective and related work are discussed in Section 2. Proposed routing scheme is explained in Section 3. Section 4 provides the detailed working of the proposed routing protocol. Simulations and results-analysis are provided in Section 5 and Section 6, respectively. Finally, Section 7 concludes the paper with its distinguished features and highlights.