Unmanned aerial vehicles (UAVs) have gained popularity for diverse applications and services in both the military and civilian domains. For cooperation and collaboration among UAVs, they can be wirelessly interconnected in an ad hoc manner, resulting in a UAV network. UAV networks have unique features and characteristics that are different from mobile ad hoc networks and vehicular ad hoc networks. The dynamic behavior of rapid mobility and topology changes in UAV networks makes the design of a routing protocol quite challenging. In this paper, we review the routing protocols for UAV networks, in which the topology-based, position-based, hierarchical, deterministic, stochastic, and social-network-based routing protocols are extensively surveyed. The routing protocols are then compared qualitatively in terms of their major features, characteristics, and performance. Open issues and research challenges are also discussed in the perspective of design and implementation.

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Routing Protocols for Unmanned Aerial Vehicle Networks: A Survey

The use of unmanned aerial vehicles (UAVs) is attracting considerable interest in academic research, commercial, and military applications. Multi-UAV systems organized in an ad hoc fashion called a flying ad hoc network (FANET) can cooperatively and collaboratively accomplish complex missions more efficiently compared to single large UAV systems. However, the unique features of FANETs such as high mobility, low node density, and high frequency of topology changes introduce challenges to the communication design, especially routing. Thus, the routing requirements of FANETs surpass those of MANETs or VANETs. In this paper, we present UAV classification, communication and application architectures, and an exhaustive survey of the existing routing protocols for flying ad hoc networks. Furthermore, we highlight the key features, strengths and weaknesses, and different mobility models used for the performance evaluation of the existing FANET routing protocols. More importantly, a proposed taxonomy and a review on the existing FANET routing protocols are presented. Finally, we highlight the existing challenges and open research issues.

Routing in Flying Ad Hoc Networks: A Comprehensive Survey

A review of wireless communication using high-altitude platforms for extended coverage and capacity

Internet of things IoT is playing a remarkable role in the advancement of many fields such as healthcare, smart grids, supply chain management, etc. It also eases people’s daily lives and enhances their interaction with each other as well as with their surroundings and the environment in a broader scope. IoT performs this role utilizing devices and sensors of different shapes and sizes ranging from small embedded sensors and wearable devices all the way to automated systems. However, IoT networks are growing in size, complexity, and number of connected devices. As a result, many challenges and problems arise such as security, authenticity, reliability, and scalability. Based on that and taking into account the anticipated evolution of the IoT, it is extremely vital not only to maintain but to increase confidence in and reliance on IoT systems by tackling the aforementioned issues. The emergence of blockchain opened the door to solve some challenges related to IoT networks. Blockchain characteristics such as security, transparency, reliability, and traceability make it the perfect candidate to improve IoT systems, solve their problems, and support their future expansion. This paper demonstrates the major challenges facing IoT systems and blockchain’s proposed role in solving them. It also evaluates the position of current researches in the field of merging blockchain with IoT networks and the latest implementation stages. Additionally, it discusses the issues related to the IoT-blockchain integration itself. Finally, this research proposes an architectural design to integrate IoT with blockchain in two layers using dew and cloudlet computing. Our aim is to benefit from blockchain features and services to guarantee a decentralized data storage and processing and address security and anonymity challenges and achieve transparency and efficient authentication service.

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A Survey on the Integration of Blockchain With IoT to Enhance Performance and Eliminate Challenges

This paper reviews the current development of artificial intelligence (AI) techniques for the application area of robot communication. The research of the control and operation of multiple robots collaboratively toward a common goal is fast growing. Communication among members of a robot team and even including humans is becoming essential in many real-world applications. The survey focuses on the AI techniques for robot communication to enhance the communication capability of the multi-robot team, making more complex activities, taking an appreciated decision, taking coordinated action, and performing their tasks efficiently. We present a comprehensive review of the intelligent solutions for robot communication which have been proposed in the literature in recent years. This survey contributes to a better understanding of the AI techniques for enhancing robot communication and sheds new lights on future research direction in the subject area.

Survey on artificial intelligence based techniques for emerging robotic communication

Unmanned Aerial Vehicles (UAVs), as a recently emerging technology, enabled a new breed of unprecedented applications in different domains. This technology's ongoing trend is departing from large remotely-controlled drones to networks of small autonomous drones to collectively complete intricate tasks time and cost-effectively. An important challenge is developing efficient sensing, communication, and control algorithms that can accommodate the requirements of highly dynamic UAV networks with heterogeneous mobility levels. Recently, the use of Artificial Intelligence (AI) in learning-based networking has gained momentum to harness the learning power of cognizant nodes to make more intelligent networking decisions. An important example of this trend is developing learning-powered routing protocols, where machine learning methods are used to model and predict topology evolution, channel status, traffic mobility, and environmental factors for enhanced routing. 
This paper reviews AI-enabled routing protocols designed primarily for aerial networks, with an emphasis on accommodating highly-dynamic network topology. To this end, we review the basics of UAV technology, different approaches to swarm formation, and commonly-used mobility models, along with their impact on networking paradigms. We proceed with reviewing conventional and AI-enabled routing protocols, including topology-predictive and self-adaptive learning-based routing algorithms. We also discuss tools, simulation environments, remote experimentation platforms, and public datasets that can be used for developing and testing AI-enabled networking protocols for UAV networks. We conclude by presenting future trends, and the remaining challenges in AI-based UAV Networking, for different aspects of routing, connectivity, topology control, security and privacy, energy efficiency, and spectrum sharing.

A Review of AI-enabled Routing Protocols for UAV Networks: Trends, Challenges, and Future Outlook.

The emerging Flying Ad-Hoc Networks (FANETs) provide efficient infrastructure solutions for a wide range of military and commercial applications. These networks are typically composed of high-speed Unmanned Aerial Vehicles (UAVs) and form dynamic network topologies. As such, conventional routing algorithms do not efficiently accommodate these continued topology changes and exhibit poor delay performances. In this paper, we introduce an optimal routing algorithm for UAV networks with queued communication systems based on Dijkstra's shortest path algorithm as a primary step towards developing a fully predictive communication platform. The core idea is to incorporate the anticipated locations of intermediate nodes during a transmission session into the path selection criterion. We further evaluate the impact of measurement uncertainties on choosing the optimal path and on the resulting end-to-end delay. The simulation results confirm the superior delay performance of the proposed algorithm compared to the conventional routing algorithms. The enhancement is more significant, when the network size is larger, the relative node velocities are higher, and the average waiting times in transmit buffers are longer.

Predictive routing for dynamic UAV networks

Blockchain is an emerging technology that uses distributed ledgers for transparent, reliable, and traceable information exchange among network nodes. Blockchain and its 3rd generation Tangle-based implementations quickly extend their territory beyond crypto-currency to a broad range of applications using fee-less transactions over the Internet of things (IoT). However, this technology suffers from sluggishness in consensus-based validation of information that restricts its applicability to time-sensitive applications such as smart health. In this letter, we propose an optimized policy for sampling rate by IoT sensors that utilize blockchain and Tangle technologies for their transmission with the goal of minimizing the age of information (AoI) experienced by the end-users, considering both processing and networking resource constraints. Simulation results confirm the efficacy of the proposed algorithm compared to the current fixed-rate update policy. Further, a closed-form solution is obtained for the optimal sampling rate in a network of homogeneous IoT nodes as a benchmark system.

Optimizing the Age of Information for Blockchain Technology With Applications to IoT Sensors

Current networking protocols deem inefficient in accommodating the two key challenges of Unmanned Aerial Vehicle (UAV) networks, namely the network connectivity loss and energy limitations. One approach to solve these issues is using learning-based routing protocols to make close-to-optimal local decisions by the network nodes, and Q-routing is a bold example of such protocols. However, the performance of the current implementations of Q-routing algorithms is not yet satisfactory, mainly due to the lack of adaptability to continued topology changes. In this paper, we propose a full-echo Q-routing algorithm with a self-adaptive learning rate that utilizes Simulated Annealing (SA) optimization to control the exploration rate of the algorithm through the temperature decline rate, which in turn is regulated by the experienced variation rate of the Q-values. Our results show that our method adapts to the network dynamicity without the need for manual re-initialization at transition points (abrupt network topology changes). Our method exhibits a reduction in the energy consumption ranging from 7% up to 82%, as well as a 2.6 fold gain in successful packet delivery rate, compared to the state of the art Q-routing protocols.

Fully-Echoed Q-Routing With Simulated Annealing Inference for Flying Adhoc Networks

Unmanned aerial vehicles (UAVs), commonly known as drones, are becoming increasingly popular for various applications. Freely flying drones create highly dynamic environments, where conventional routing algorithms which rely on stationary network contact graphs fail to perform efficiently. Also, link establishment through exploring optimal paths using hello messages (as is used in AODV algorithm) deems extremely inefficient and costly for rapidly changing network topologies. In this paper, we present a distance-based greedy routing algorithm for UAV networks solely based on UAVs' local observations of their surrounding subnetwork. Thereby, neither a central decision maker nor a time consuming route setup and maintenance mechanism is required. To evaluate the proposed method, we derive an analytical bound for the expected number of hops that a packet traverses. Also, we find the expected end-to-end distance traveled by each packet as well as the probability of successful delivery. The simulation results verify the accuracy of the developed analytical expressions and show considerable improvement compared to centralized shortest path routing algorithms.

Arnau Rovira-Sugranes

Papers

A Review of AI-enabled Routing Protocols for UAV Networks: Trends, Challenges, and Future Outlook.

Predictive routing for dynamic UAV networks

Optimizing the Age of Information for Blockchain Technology With Applications to IoT Sensors

Fully-Echoed Q-Routing With Simulated Annealing Inference for Flying Adhoc Networks

On Greedy Routing in Dynamic UAV Networks