Owing to the explosive expansion of wireless communication and networking technologies, cost-effective unmanned aerial vehicles (UAVs) have recently emerged and soon they will occupy the major part of our sky. UAVs can be exploited to efficiently accomplish complex missions when cooperatively organized as an ad hoc network, thus creating the well-known flying ad hoc networks (FANETs). The establishment of such networks is not feasible without deploying an efficient networking model allowing a reliable exchange of information between UAVs. FANET inherits common features and characteristics from mobile ad hoc networks (MANETs) and their sub-classes, such as vehicular ad hoc networks (VANETs) and wireless sensor networks (WSNs). Unfortunately, UAVs are often deployed in the sky adopting a mobility model dictated by the nature of missions that they are expected to handle, and therefore, differentiate themselves from any traditional networks. Moreover, several flying constraints and the highly dynamic topology of FANETs make the design of routing protocols a complicated task. In this paper, a comprehensive survey is presented covering the architecture, the constraints, the mobility models, the routing techniques, and the simulation tools dedicated to FANETs. A classification, descriptions, and comparative studies of an important number of existing routing protocols dedicated to FANETs are detailed. Furthermore, the paper depicts future challenge perspectives, helping scientific researchers to discover some themes that have been addressed only ostensibly in the literature and need more investigation. The novelty of this survey is its uniqueness to provide a complete analysis of the major FANET routing protocols and to critically compare them according to different constraints based on crucial parameters, thus better presenting the state of the art of this specific area of research.

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Routing in Flying Ad Hoc Networks: Survey, Constraints, and Future Challenge Perspectives

We provide a comparative analysis of various routing strategies that affect the end-to-end performance in wireless mesh networks. We first improve well-known link quality metrics and routing algorithms to enhance performance in wireless mesh environments. We then investigate the route optimality, i.e., whether the best end-to-end route with respect to a given link quality metric is established, and its impact on the network performance. Network topologies, number of concurrent flows, and interference types are varied in our evaluation and we find that a non-optimal route is often established because of the routing protocol's misbehavior, inaccurate link metric design, interflow interference, and their interplay. Through extensive simulation analysis, we present insights on how to design wireless link metrics and routing algorithms to enhance the network capacity and provide reliable connectivity.

Comparative analysis of link quality metrics and routing protocols for optimal route construction in wireless mesh networks

For next generation wireless networks, wireless mesh networks have become the basic technology. WMNs consist of mesh router and mesh client. WMNs provide broadband internet access, wireless local area network coverage and network connectivity for both mobile and stationary nodes. Routing are used in mesh networks in order deliver packets from source to destination efficiently. Mainly 3 types of routing protocols are used in WMNs. Proactive, reactive and hybrid protocol. In which hybrid protocol are the best protocols when compared to others. This paper presents a survey of wireless mesh networks.

Wireless mesh network: A survey

The networking infrastructure of wireless mesh networks (WMNs) is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic—i.e., neural networks (NNs). This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission). The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance.

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A Neural Networks-Based Hybrid Routing Protocol for Wireless Mesh Networks

Unmanned aerial vehicles (UAVs) are becoming important communication infrastructures. One major challenge of communications with UAV networks is the routing protocol design. Due to the inherent characteristics (e.g., dynamic network topology and limited UAV device capabilities), it is difficult to directly apply existing routing protocols that utilize network topology information and routing path explorations. In this article, two novel routing protocols are designed based on random network coding (RNC) for a swarm UAV network, where UAVs operate cooperatively as a swarm, enabling efficient routing process. The first routing protocol utilizes the unique feature of RNC: Original packets can be decoded as long as an UAV accumulates sufficient generations. This property can be used to effectively expedite the underlying routing process. The second routing protocol further improves the efficiency where each forwarding UAV only needs to create a new generation rather than decoding original packets. Accordingly, the duration of each hop can be significantly reduced. Extensive simulations have been conducted to evaluate the performance of the designed routing protocols. The simulation results demonstrate that our designed routing protocols can effectively enhance the performance on both average transmission delay and delay violation probabilities compared to benchmark methods.

Random Network Coding Enabled Routing Protocol in Unmanned Aerial Vehicle Networks

IEEE 802.11s defines a new mesh data frame format and an extensibility framework for routing. The default routing protocol Hybrid Wireless Mesh Protocol (HWMP) is described. HWMP is based on Ad hoc On-demand Distance Vector Routing (AODV) and has a configurable extension for proactive routing. It uses MAC address with layer 2 routing and uses Radio-Aware as routing metric. We implemented HWMP on OPNET and evaluated its performance. The experimental results showed that HWMP has the lower average latency and the higher data transmission throughput compared with AODV.

Hybrid Routing Protocol for Wireless Mesh Network

The fifth generation (5G) of cellular networks is significantly more complex than its predecessors due to several factors, such as increased cell density, differentiated service requirements, and coexistence with legacy networks. As a result, traditional operation and management (O&M) solutions, which heavily rely on human intervention, are no longer feasible to support such complex networks at reasonable operating expense (OPEX). Over the past few years, the telecommunication industry has come to the realization that leveraging artificial intelligence (AI) technology to enable a fully automated network O&M is a must to lowering OPEX and enhancing network key performance indicators (KPIs) for 5G, Beyond 5G (B5G), and the sixth generation (6G) of cellular networks. There have been numerous research efforts from both industry and academia to develop AI-powered network automation solutions. Many telecommunication operators and vendors have already adopted AI technology to automate some repetitive operational tasks and reduce reliance on personnel experience, such as cell planning, network deployment simplification, fault detection, and KPI optimization. While there has been notable progress for certain network O&M applications, the development of network automation solutions still faces several unique technical challenges that arise from telecommunication fields, including overwhelming network complexity, massive and diverse proprietary data, lack of industry-wide standards for radio access network (RAN) interfaces, and scarcity of labeled datasets. 

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Kai Yang

Papers

Hybrid Routing Protocol for Wireless Mesh Network

Guest Editorial: AI-Powered Telco Network Automation: 5G Evolution and 6G