Reinforcement Learning for UAV Networks

IEEE IoT Journal

This work looks into the problem of a decentralized data offloading within an edge UAV swarm to mitigate the complexities of a single UAV continually generating and processing large application specific data. The mobile edge UAVs considered here are multi-rotor types having constrained energy and processing power, which makes long-term handling of large data volumes impossible for standalone UAVs. The load mitigation is carried out by offloading data from a source UAV to other swarm members with sufficient energy and processing requirements. In this work, we focus on selecting the most optimal multi-hop path through the UAVs concerning available energies and processing resources, which can survive the duration of the data offload between the source and a target UAV. We formulate a Multi-Armed Bandit (MAB) based offload path selection scheme, which selects the most energy and processing optimized multi-hop path between a source and a target UAV. Upon comparison of our scheme against the naive shortest path approach, we observe that our approach results in significant savings of collective network energies, even for long operational durations.

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LSTM for UAV Networks


In this paper, we propose the use of a Long Short-Term Memory (LSTM) based server-side sequence prediction algorithm to ease network data-load caused by rapid polling of multiple sensors onboard aerial robotic platforms, which are wirelessly tethered to a remote server for control and coordination. Our scheme reduces the network access time latencies between these platforms and the remote server hosting the control and scheduling mechanisms. Reduction in the TDMA-based access time is achieved by reducing the actual amount of data transmitted over the network, using partial transmission of actual sensor data over the network and server-side sequence prediction of the voluntarily missed sensor values. Our scheme allows the TDMA control of an increased number of networked platforms without change of infrastructure or the network characteristics.

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Game Theory in UAV Networks

Computer Networks (Elsevier)

This work addresses the challenges of a decentralized and heterogeneous Unmanned Aerial Vehicle (UAV) swarm deployment -- some fitted with multimedia sensors, while others armed with scalar sensors -- in resource-constrained and challenging environments, typically associated with farming. Subsequently, we also address the resulting problem of sensing and processing resource-intensive data aerially within the Edge swarm in the fastest and most efficient manner possible. The heterogeneous nature of the Edge swarm results in under-utilization of the available computation resources due to unequal data generation within its members. To address this, we propose a Nash bargaining- based weighted intra-Edge processing offload scheme to mitigate the problem of heavy processing in some of the swarm members. We do this by distributing the data to be processed to all the swarm members. Real-life hardware tuned simulation of a large UAV swarm shows that by increasing the number of UAVs in the swarm, our scheme achieves better scalability and reduced processing delays for intensive processing tasks. Additionally, in comparison to regular star and mesh topologies, our scheme achieves an increase in collective available network processing speeds by 100% for only 25% of the number of UAVs in a star topology.

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Energy-aware Packet Routing in UAV Networks

IEEE Trans. on Sustainable Computing

In this work, we propose an Energy-aware Collaborative Routing (ECoR) scheme for optimally handling task offloading between source and destination UAVs in a grid-locked UAV swarm. We divide the proposed scheme into two parts – routing path discovery and routing path selection. The scheme selects the most optimal path between a source and destination from a massive set of all possible paths, based on the maximization of residual energy of UAVs along a selected path. This routing path selection ensures balanced energy utilization between members of the UAV swarm and enhances the overall path lifetime without incurring additional delays in doing so. Actual readings from our small-scale UAV swarm testbed are utilized to emulate a largescale scenario and analyze the behavior of our proposed scheme. Upon comparison of the ECoR scheme with broadcast-based routing and the shortest path based routing, we observe better sustainability regarding the longevity of the UAV lifetimes in the swarm, optimized individual UAV, as well as reduced collective path-based energy consumption, all the while having comparable transmission delays to the shortest path based scheme.

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Virtualization of UAV Networks

IEEE Trans. on Vehicular Technology

In this paper, we propose an architecture for UAV virtualization with the primary aim to provide virtualized UAV services to multiple users by envisioning the concept of UAV-asa- Service. In contrast to traditional UAVs, which are resourceconstraint in nature and exhibit shorter flight times, our proposed UAV virtualization overcomes the limitations of short flight time of traditional UAVs, in turn allowing them to provide persistent and ubiquitous services. We achieve the virtualization of a UAV through multiple collaborating real-life UAVs performing various tasks in tandem. In this work, we focus on the placement and selection of UAVs to achieve virtualization. We use a social welfarebased approach to select suitable UAVs, from the available ones, and map the UAV to a virtual one. This work enables the provision of different UAV services to multiple end-users, without actual procurement of the UAVs by the end-users. We compare the results for optimal placement, normal maximum energybased UAV selection, and Atkinson-based selection method. We also compare the virtual model and simple UAV-to-task model to physical UAV usage, task completion ratio, and residual energy of the system. Our proposed model outperforms the traditional model with a task completion efficiency of 94.26%. The residual energy of the system also increases with an increase in the number of tasks.

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UAV Networks in Agriculture

Journal of Network and Computer Applications (Elsevier)

The gain in popularity of unmanned aerial vehicles (UAV), platforms and systems (UAS) can be attributed to its ease of operation, versatility and risk-free piloting. The primary UAV application domain has expanded, from recreational and military ights, to include scientific surveys and agriculture. The popularity of UAVs in scientific data gathering and applications, especially the use of small, multi-rotor UAVs is quite widespread. These multi-rotor UAVs are small, portable, low-cost, highly manoeuvrable, and easy to handle. These features make such UAVs attractive to scientists and researchers worldwide. There has been a sudden spurt of UAV use in niche domains, such as agriculture. Agriculturalists are choosing UAV-based field operations and remote sensing over the time-tested satellite-based ones, especially for local-scale and high spatiotemporal resolution imagery. In this survey, we explore various UAV application areas, types, sensors, research domains, deployment architectures. Comparisons between various UAV types, sensing technologies (UAV, WSN, satellites), UAV architectures and their utility in precision agriculture has been provided. Additionally, crop stress, its types, and detection using various remotely-sensed vegetation indices have been explored for their use in UAV-based remote sensing.

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UAV-based Automated Land Boundary Demarcation

ACM Trans. on IoT

In this work, we propose an autonomous and on-board image-based agricultural land demarcation and path-planning system - IDeAL (IoT-Based Autonomous Aerial Demarcation and Path-planning for Traversing Agricultural Lands) - using our advanced UAV-based aerial IoT platform. Our work successfully addresses the problem of onboard and autonomous path-planning - which conventional UAV-based systems are not capable of - during standalone operations and without preloaded GPS-markers for flight-path way-points. Our aerial system visually identifies non-electronically and singularly tagged agricultural plots and assesses the enclosing boundaries of the identified plot. Subsequently, an on-board path-planning module autonomously generates GPS-waypoints for traversing the identified plot with minimal overlaps and maximal coverage. Our proposed system exhibits an area coverage efficiency of 95.39% , performs pixel-to-GPS coordinate conversion with an accuracy of 90.35%, and has high agricultural potential in applications such as surveying crop-health conditions, spraying pesticide/herbicides, and others. The proposed system has massive applications in scenarios requiring aerial detection, demarcation, geographical tagging and coverage of an area.

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Networked UAV Control and Stabilization


We propose a method for a single ground camera-based visual gesture control of a quadrotor mUAV platform, making its flight more responsive and adaptive to its human controller as compared to a human controller using keypads or joysticks for controls. The proposed camera-based gesture control scheme provides an average accuracy of 100% gestures detected, as compared to accuracies obtained using expensive Kinectbased hardware, or processing intensive CNN-based pose estimation techniques with 97.5% and 83.3% average accuracies, respectively. A fog-based stabilization mechanism is additionally employed, which allows for flight-time stabilization of the mUAV, even in the presence of unbalanced payloads or unbalancing of the mUAV due to minor structural damages. This allows the use of the same mUAV without the need for frequent weight readjustments or mUAV calibration. This approach has been tested in real-time, both indoors as well as outdoors.

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Opportunistic UAV Networks


In this work, we propose a physical approximation scheme – Random Opportunistic and Selective Exploration (ROSE) – for aerial localization of survivors by using a collaborative swarm of IoT-based unmanned aerial vehicles (UAVs). The UAV swarm performs a simultaneous multi-pronged search of a given zone by dividing the search region among the swarm members. This multi-pronged search strategy speeds-up the search, and the division of search areas among the swarm members avoids redundant exploration of an already explored location. As the communication range of the member UAVs is limited, the swarm members communicate opportunistically among themselves to share the information of the visited sites. We formulate the various probabilities associated with opportunistic communication of these aerial IoT nodes and simulate the performance of the approximation algorithm based on these formulations. Simulation results of the proposed approach successfully locate 100% of the ground targets within an acceptable time-frame, and out-performs established searching schemes such as the truncated Levy walk, frontier-based search, and sweep search.

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Blockchains in UAV Networks

IEEE IoT Magazine

Unmanned Aerial Vehicles (UAV) are coming up as a powerful tool in various industrial applications in the context of enabling Industrial IoT. The limited power source and flight time of UAVs along with the need for skilled UAV operators are some of the most daunting challenges to the integration of UAVs in industrial applications such as site inspection, workforce monitoring, logistics, and others. In this paper, we propose the unique paradigm of blockchain-enabled UAV virtualization to provide virtual UAV-as-a-service for industrial applications. The proposed architecture also aims at providing secure and persistent UAV services to the end-users along with a partially decentralized blockchain model to ensure security, privacy, service quality, and transparency. UAV virtualization allows persistent UAV services, globally, without procuring any physical UAVs. The platform offers virtual UAV services on a pay per use basis to the enduser. The UAV owners and virtual UAV service providers gain monetary benefits for their contribution to the UAV services. Finally, we discuss the implications of the proposed platform on the domains of Industrial IoT and the possible challenges in the implementation of this paradigm.