On seamless and high‐bandwidth connectivity for cognitive multi‐unmanned aerial vehicle‐assisted networks

Abstract

Unmanned aerial vehicles (UAVs) can be useful in many different scenarios including disaster management. UAVs can immediately reach the disaster area and collect data that can help relief and rescue activities. Nevertheless, these advantages can be further improved when multiple UAVs coordinate for data collection from different vantage points simultaneously. However, seamless, high‐bandwidth communication between UAVs is required for this coordination. Nonetheless, due to the scarcity of bandwidth, a UAV network operating on an unlicensed ISM band may not be feasible. In this article, we follow a conceptual approach in which UAVs can access the licensed spectrum opportunistically. However, it is highly likely that various UAVs may have different sets of approved channels available and are unable to coordinate. Therefore, UAVs are divided into clusters to establish coordination in this scenario based on the availability of a common channel between different UAVs. Clustering based on a single common channel may still not be practical, due to the sporadic availability of common channels that often causes reclustering. Therefore, we present a multi‐UAV clustering scheme in which clusters of UAVs are established with more common channels to prevent repeated reclustering by having one main channel and the rest as backup channels. However, this may reduce the size of the cluster which can be addressed by limiting the number of backup channels. In this article, we present two variants of the proposed scheme: Multi‐UAV clustering‐I and multi‐UAV clustering‐II. The multi‐UAV clustering‐I scheme attempts to cluster UAVs with as many common channels as possible, whereas the multi‐UAV clustering‐II scheme attempts to limit the number of common channels to two. In addition, we propose a method for choosing the main channel from the different channels, where these channels are rated on the basis of bandwidth capacity and the channel with the highest rank is selected as the main channel. The remaining and the rest of the channels are kept as a backup. Simulation results demonstrate smooth and high‐bandwidth communication among UAVs.

Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

“…The choice between edge or cloud processing in UAV environments depends on latency, amount of data to be processed, available bandwidth, overall cost, and the feasibility to deploy edge servers in remote aerial locations. Using edge computing to locally process the captured sensor data while using cloud for overall SLAM analytics is a preferred approach [60]. (iv) Collaboration and coordination among multiple UAVs: Signal unavailability or interference in an outdoor environment may corrupt the sensor data and induce errors [60].…”

“…Using edge computing to locally process the captured sensor data while using cloud for overall SLAM analytics is a preferred approach [60]. (iv) Collaboration and coordination among multiple UAVs: Signal unavailability or interference in an outdoor environment may corrupt the sensor data and induce errors [60]. Therefore, it maybe required to coordinate sensor data from multiple UAVs for optimum target coverage with low power consumption.…”

“…Enough memory is required to maintain the trajectory of the UAV in a given state [59]. The updated UAV states indicate where the UAV needs to be at that moment in order to arrive at its destination precisely in the shortest time [60].…”

Unmanned aerial vehicles integratedHetNetfor smart dense urban area

“…11 A noteworthy problem faced by this heterogeneous network is during on flying or on-demand available capacity provision. The information can be transmitted efficiently at the rate to ground users and delay implies latency in data transmission refers to capacity.. 12 To provide aerial support to an existing cellular network that can cope more cost-effectively with high traffic situations, small drone cells are used. Although deploying a single UAV is safe for demand, it is very difficult to use the number of UAVs in cooperation with each other due to interference from other air nodes.…”

“…The information can be transmitted efficiently at the rate to ground users and delay implies latency in data transmission refers to capacity. 12 . To provide aerial support to an existing cellular network that can cope more cost‐effectively with high traffic situations, small drone cells are used.…”