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.

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.

ECoR: Energy-Aware Collaborative Routing for Task Offload in Sustainable UAV Swarms

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 large-scale 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.

Dynamic Leader Selection in a Master-Slave Architecture-Based Micro UAV Swarm

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 large-scale 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.

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.

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.

In this paper, we propose an architecture for UAV virtualization with the primary aim of providing virtualized UAV services to multiple users by envisioning the concept of UAV-as-a-Service. In contrast to traditional UAVs, which are resource-constraint 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 reallife 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 welfare-based 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 energy-based 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.

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 AerialBlocks for blockchain-enabled UAV virtualization to provide virtual UAV-as-a-service for industrial applications. AerialBlocks 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 end-user. 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.

In this paper, we propose a delay-aware Q-learning-based routing algorithm - XiA - for sending data from users to nearest Access Points (APs) through Unmanned Aerial Vehicle (UAV) swarms communicating using 6G technology. These UAVs assist the ground networks in overcoming communication voids while maneuvering through different demographics. However, the communication links in the THz band have limited transmission range, causing the UAVs to frequently disconnect from the swarm. We overcome such issues by waiting until the UAV comes in contact with others in case of non-time-sensitive data. In the case of time-sensitive data, the UAVs send the data to the APs through Low Earth Orbit (LEO) satellites. To empower XiA to adapt to the changing environments and expensive delays in LEO, we model the rewards by accounting for spreading and absorption in the 6G channels, and Doppler effect and pointing error in the satellite channel. We show our bias for the parameters through extensive simulations and prove that the Q-model in XiA achieves convergence under all conditions.