Adaptive Communication Protocols in Flying Ad Hoc Network
12 Jan 2018-IEEE Communications Magazine (Institute of Electrical and Electronics Engineers (IEEE))-Vol. 56, Iss: 1, pp 136-142
TL;DR: The proposed adaptive hybrid communication protocols including a novel position-prediction-based directional MAC protocol (PPMAC) and a self-learning routing protocol based on reinforcement learning (RLSRP) have the potential to provide an intelligent and highly autonomous communication solution for FANETs.
Abstract: The flying ad hoc network (FANET) is a new paradigm of wireless communication that governs the autonomous movement of UAVs and supports UAV-to-UAV communication. A FANET can provide an effective real-time communication solution for the multiple UAV systems considering each flying UAV as a router. However, existing mobile ad hoc protocols cannot meet the needs of FANETs due to high-speed mobility and frequent topology change. In addition, the complicated flight environment and varied flight tasks lead to the traditional built-in-rules protocols no longer meeting the demands of autonomy. Hence, we have proposed adaptive hybrid communication protocols including a novel position-prediction-based directional MAC protocol (PPMAC) and a self-learning routing protocol based on reinforcement learning (RLSRP). The performance results show that the proposed PPMAC overcomes the directional deafness problem with directional antennas, and RLSRP provides an automatically evolving and more effective routing scheme. Our proposed hybrid adaptive communication protocols have the potential to provide an intelligent and highly autonomous communication solution for FANETs, and indicate the main research orientation of FANET protocols.
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TL;DR: This survey presents a detailed survey on wireless evolution towards 6G networks, characterized by ubiquitous 3D coverage, introduction of pervasive AI and enhanced network protocol stack, and related potential technologies that are helpful in forming sustainable and socially seamless networks.
Abstract: While 5G is being commercialized worldwide, research institutions around the world have started to look beyond 5G and 6G is expected to evolve into green networks, which deliver high Quality of Service and energy efficiency. To meet the demands of future applications, significant improvements need to be made in mobile network architecture. We envision 6G undergoing unprecedented breakthrough and integrating traditional terrestrial mobile networks with emerging space, aerial and underwater networks to provide anytime anywhere network access. This paper presents a detailed survey on wireless evolution towards 6G networks. In this survey, the prime focus is on the new architectural changes associated with 6G networks, characterized by ubiquitous 3D coverage, introduction of pervasive AI and enhanced network protocol stack. Along with this, we discuss related potential technologies that are helpful in forming sustainable and socially seamless networks, encompassing terahertz and visible light communication, new communication paradigm, blockchain and symbiotic radio. Our work aims to provide enlightening guidance for subsequent research of green 6G.
324 citations
Cites background from "Adaptive Communication Protocols in..."
...The authors in [32] proposed adaptive hybrid communication protocols which outperform existing protocols....
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TL;DR: This article provides a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security.
Abstract: Unmanned aerial vehicles (UAVs) will be an integral part of the next generation wireless communication networks. Their adoption in various communication-based applications is expected to improve coverage and spectral efficiency, as compared to traditional ground-based solutions. However, this new degree of freedom that will be included in the network will also add new challenges. In this context, the machine-learning (ML) framework is expected to provide solutions for the various problems that have already been identified when UAVs are used for communication purposes. In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security.
186 citations
Cites methods from "Adaptive Communication Protocols in..."
...In Reference [84], the system model consists of several flying UAVs, which are equipped with a GPS and an identical switched beam antenna array....
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TL;DR: A comprehensive survey is presented covering the architecture, the constraints, the mobility models, the routing techniques, and the simulation tools dedicated to FANETs, better presenting the state of the art of this specific area of research.
Abstract: 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.
171 citations
Additional excerpts
...the data delivery in each application [8]....
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TL;DR: A comprehensive review of the literature on reinforcement learning-based routing protocols is provided, structured in a way that shows how network characteristics and requirements were gradually considered over time.
Abstract: Reinforcement learning (RL), which is a class of machine learning, provides a framework by which a system can learn from its previous interactions with its environment to efficiently select its actions in the future. RL has been used in a number of application fields, including game playing, robotics and control, networks, and telecommunications, for building autonomous systems that improve themselves with experience. It is commonly accepted that RL is suitable for solving optimization problems related to distributed systems in general and to routing in networks in particular. RL also has reasonable overhead—in terms of control packets, memory and computation—compared to other optimization techniques used to solve the same problems. Since the mid-1990s, over 60 protocols have been proposed, with major or minor contributions in the field of optimal route selection to convey packets in different types of communication networks under various user QoS requirements. This paper provides a comprehensive review of the literature on the topic. The review is structured in a way that shows how network characteristics and requirements were gradually considered over time. Classification criteria are proposed to present and qualitatively compare existing RL-based routing protocols.
126 citations
Cites background or methods from "Adaptive Communication Protocols in..."
...SomeRL-based routing algorithms only include generic reward functions with generic arguments [35], [36], [61], [93]....
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...The amount of control packet needed depends on the period of Hello packets as in [32], [35], [42], [47], [53], [54], [55], [63], [64], [65], [66], [72], [91], [93], [96]....
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...RLSRP (RL based Self-Routing Protocol) – [93] proposed RLSRP to address rapid topology changes in FANETs composed of flying nodes, such as drones....
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TL;DR: The routing protocols, mobility and trajectory optimization models that have been used in FANet to solve communication and collaboration issues between UAVs are exposed, the security challenges that need to be overcome are outlined and FANET networking open issues are discussed.
123 citations
References
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01 May 2013
TL;DR: In this paper, Flying Ad-Hoc Networks (FANETs) are surveyed which is an ad hoc network connecting the UAVs, and the main FANET design challenges are introduced.
Abstract: One of the most important design problems for multi-UAV (Unmanned Air Vehicle) systems is the communication which is crucial for cooperation and collaboration between the UAVs. If all UAVs are directly connected to an infrastructure, such as a ground base or a satellite, the communication between UAVs can be realized through the in-frastructure. However, this infrastructure based communication architecture restricts the capabilities of the multi-UAV systems. Ad-hoc networking between UAVs can solve the problems arising from a fully infrastructure based UAV networks. In this paper, Flying Ad-Hoc Networks (FANETs) are surveyed which is an ad hoc network connecting the UAVs. The differences between FANETs, MANETs (Mobile Ad-hoc Networks) and VANETs (Vehicle Ad-Hoc Networks) are clarified first, and then the main FANET design challenges are introduced. Along with the existing FANET protocols, open research issues are also discussed.
1,072 citations
TL;DR: The paper shows how an UAS can automatically obtain information in real-time of the evolution of the fire front shape and potentially other parameters related to the fire propagation by means of on-board infrared or visual cameras.
Abstract: The paper presents an Unmanned Aircraft System (UAS), consisting of several aerial vehicles and a central station, for forest fire monitoring. Fire monitoring is defined as the computation in real-time of the evolution of the fire front shape and potentially other parameters related to the fire propagation, and is very important for forest fire fighting. The paper shows how an UAS can automatically obtain this information by means of on-board infrared or visual cameras. Moreover, it is shown how multiple aerial vehicles can collaborate in this application, allowing to cover bigger areas or to obtain complementary views of a fire. The paper presents results obtained in experiments considering actual controlled forest fires in quasi-operational conditions, involving a fleet of three vehicles, two autonomous helicopters and one blimp.
364 citations
"Adaptive Communication Protocols in..." refers background in this paper
...[10] proposed the RARP routing protocol to increase the robustness and reliability of the established routing path and improve the communication performance....
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TL;DR: The challenges with using Uavs as relay nodes in an ad-hoc manner are identified, network models of UAVs are introduced, and open research issues with analyzing opportunities and future work are depicted.
Abstract: In recent years, the capabilities and roles of Unmanned Aerial Vehicles (UAVs) have rapidly evolved, and their usage in military and civilian areas is extremely popular as a result of the advances in technology of robotic systems such as processors, sensors, communications, and networking technologies. While this technology is progressing, development and maintenance costs of UAVs are decreasing relatively. The focus is changing from use of one large UAV to use of multiple UAVs, which are integrated into teams that can coordinate to achieve high-level goals. This level of coordination requires new networking models that can be set up on highly mobile nodes such as UAVs in the fleet. Such networking models allow any two nodes to communicate directly if they are in the communication range, or indirectly through a number of relay nodes such as UAVs. Setting up an ad-hoc network between flying UAVs is a challenging issue, and requirements can differ from traditional networks, Mobile Ad-hoc Networks (MANETs) and Vehicular Ad-hoc Networks (VANETs) in terms of node mobility, connectivity, message routing, service quality, application areas, etc. This paper identifies the challenges with using UAVs as relay nodes in an ad-hoc manner, introduces network models of UAVs, and depicts open research issues with analyzing opportunities and future work.
318 citations
Proceedings Article•
27 Nov 1995
TL;DR: Simulation results show that PQ-routed is superior to Q-routing in terms of both learning speed and adaptability.
Abstract: In this paper, we propose a memory-based Q-Iearning algorithm called predictive Q-routing (PQ-routing) for adaptive traffic control. We attempt to address two problems encountered in Q-routing (Boyan & Littman, 1994), namely, the inability to fine-tune routing policies under low network load and the inability to learn new optimal policies under decreasing load conditions. Unlike other memory-based reinforcement learning algorithms in which memory is used to keep past experiences to increase learning speed, PQ-routing keeps the best experiences learned and reuses them by predicting the traffic trend. The effectiveness of PQ-routing has been verified under various network topologies and traffic conditions. Simulation results show that PQ-routing is superior to Q-routing in terms of both learning speed and adaptability.
168 citations
TL;DR: This work proposes a combined omnidirectional and directional transmission scheme, together with dynamic angle adjustment, which features hybrid use of unicasting and geocasting routing using location and trajectory information for flying ad hoc networks.
Abstract: Ever-increasing demands for portable and flexible communications have led to rapid growth in networking between unmanned aerial vehicles often referred to as flying ad-hoc networks (FANETs). Existing mobile ad hoc routing protocols are not suitable for FANETs due to high-speed mobility, environmental conditions, and terrain structures. In order to overcome such obstacles, we propose a combined omnidirectional and directional transmission scheme, together with dynamic angle adjustment. Our proposed scheme features hybrid use of unicasting and geocasting routing using location and trajectory information. The prediction of intermediate node location using 3-D estimation and directional transmission toward the predicted location, enabling a longer transmission range, allows keeping track of a changing topology, which ensures the robustness of our protocol. In addition, the reduction in path re-establishment and service disruption time to increase the path lifetime and successful packet transmissions ensures the reliability of our proposed strategy. Simulation results verify that our proposed scheme could significantly increase the performance of flying ad hoc networks.
108 citations