Data Dissemination in IoT Using a Cognitive UAV

Almasoud, Abdullah M.; Kamal, Ahmed E.

doi:10.1109/tccn.2019.2922263

Cited by 43 publications

(30 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To unlock the potential of UAV-enabled communication systems, joint UAV's trajectory and transmit power design has been studied under different scenarios [29]- [33]. However, these works assumed perfect channel estimations, which depends on the availability of accurate estimation for the locations of ground nodes.…”

Section: A Background and Motivationmentioning

confidence: 99%

“…By jointly optimizing the base station/UAV transmit power and the UAV's trajectory, the achievable rate of the ground user can be maximized. In [33], the authors deployed a cognitive UAV to convey data to a set of Internetof-Things devices. Although the above literature discussed the application of cognitive UAVs in various scenarios, they did not address the robust UAV's trajectory and transmit power design issues in UAV-enabled CR networks when the locations of ground nodes cannot be accurately estimated.…”

Section: ) Traditionally Terrestrial Cr Networkmentioning

confidence: 99%

“…The numbers of decision variables m is on the order of (K + L)N , e.g., m = O((K + L)N ). Then, the total complexity of Algorithm 2 under the probabilistic location error model is given by (33), as shown at the bottom of this page. Note that the proposed Algorithm 2 also has a polynomial time computational complexity which is suitable for practical implementation.…”

Section: B Outage-constrained Robust Asrm Problemmentioning

confidence: 99%

See 2 more Smart Citations

Robust Trajectory and Transmit Power Optimization for Secure UAV-Enabled Cognitive Radio Networks

Zhou

et al. 2020

IEEE Trans. Commun.

View full text Add to dashboard Cite

Cognitive radio is a promising technology to improve spectral efficiency. However, the secure performance of a secondary network achieved by using physical layer security techniques is limited by its transmit power and channel fading. In order to tackle this issue, a cognitive unmanned aerial vehicle (UAV) communication network is studied by exploiting the high flexibility of a UAV and the possibility of establishing line-ofsight links. The average secrecy rate of the secondary network is maximized by robustly optimizing the UAV's trajectory and transmit power. Our problem formulation takes into account two practical inaccurate location estimation cases, namely, the worst case and the outage-constrained case. In order to solve those challenging non-convex problems, an iterative algorithm based on S-Procedure is proposed for the worst case while an iterative algorithm based on Bernstein-type inequalities is proposed for the outage-constrained case. The proposed algorithms can obtain effective suboptimal solutions of the corresponding problems. Our simulation results demonstrate that the algorithm under the outage-constrained case can achieve a higher average secrecy rate with a low computational complexity compared to that of the algorithm under the worst case. Moreover, the proposed schemes can improve the secure communication performance significantly compared to other benchmark schemes.

show abstract

Section: A Background and Motivationmentioning

confidence: 99%

Section: ) Traditionally Terrestrial Cr Networkmentioning

confidence: 99%

Section: B Outage-constrained Robust Asrm Problemmentioning

confidence: 99%

See 1 more Smart Citation

Robust Trajectory and Transmit Power Optimization for Secure UAV-Enabled Cognitive Radio Networks

Zhou

et al. 2020

IEEE Trans. Commun.

View full text Add to dashboard Cite

show abstract

“…The authors in [ 67 ] proposed a data-dissemination mechanism for a UIoT network using a cognitive UAV. Their main objective was to maximize the number of bits received by the IoT devices from the UAV.…”

Section: Mac Protocols For Uiotmentioning

confidence: 99%

“…In SSU [ 67 ], the optimal transmission strategy is employed to maximize the number of bits received by the IoT from the cognitive UAV. The UAV keeps the number of slots interfering with the primary user (PU) below a certain threshold, to protect the PU from harmful interference.…”

Section: Comparison Of Mac Protocols For Uiotmentioning

confidence: 99%

Medium Access Control Protocols for the Internet of Things Based on Unmanned Aerial Vehicles: A Comparative Survey

Khisa

Moh

2020

Sensors

View full text Add to dashboard Cite

The Internet of Things (IoT), which consists of a large number of small low-cost devices, has become a leading solution for smart cities, smart agriculture, smart buildings, smart grids, e-healthcare, etc. Integrating unmanned aerial vehicles (UAVs) with IoT can result in an airborne UAV-based IoT (UIoT) system and facilitate various value-added services from sky to ground. In addition to wireless sensors, various kinds of IoT devices are connected in UIoT, making the network more heterogeneous. In a UIoT system, for achieving high throughput in an energy-efficient manner, it is crucial to design an efficient medium access control (MAC) protocol because the MAC layer is responsible for coordinating access among the IoT devices in the shared wireless medium. Thus, various MAC protocols with different objectives have been reported for UIoT. However, to the best of the authors’ knowledge, no survey had been performed so far that dedicatedly covers MAC protocols for UIoT. Hence, in this study, state-of-the-art MAC protocols for UIoT are investigated. First, the communication architecture and important design considerations of MAC protocols for UIoT are examined. Subsequently, different MAC protocols for UIoT are classified, reviewed, and discussed with regard to the main ideas, innovative features, advantages, limitations, application domains, and potential future improvements. The reviewed MAC protocols are qualitatively compared with regard to various operational characteristics and system parameters. Additionally, important open research issues and challenges with recommended solutions are summarized and discussed.

show abstract

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

Hasan

Ejaz

Zghaibeh

et al. 2020

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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.

show abstract

Data Dissemination in IoT Using a Cognitive UAV

Cited by 43 publications

References 20 publications

Robust Trajectory and Transmit Power Optimization for Secure UAV-Enabled Cognitive Radio Networks

Robust Trajectory and Transmit Power Optimization for Secure UAV-Enabled Cognitive Radio Networks

Medium Access Control Protocols for the Internet of Things Based on Unmanned Aerial Vehicles: A Comparative Survey

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

Contact Info

Product

Resources

About