Integration of Cognitive Radio Technology with unmanned aerial vehicles: Issues, opportunities, and future research challenges

Saleem, Yasir; Rehmani, Mubashir Husain; Zeadally, Sherali

doi:10.1016/j.jnca.2014.12.002

Cited by 182 publications

(117 citation statements)

References 99 publications

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“…The major advantages of the multi-UAV network over its single-UAV counterpart can be summarized in terms of the networking viewpoint as well as the system viewpoint [6] [7]. Specifically, from the networking viewpoint:…”

Section: B Uav Network: Airborne Ad Hoc Networkmentioning

confidence: 99%

Taking Drones to the Next Level: Cooperative Distributed Unmanned-Aerial-Vehicular Networks for Small and Mini Drones

Wang

Jiang²,

Han

et al. 2017

IEEE Veh. Technol. Mag.

403

185

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Abstract-Unmanned Aerial Vehicles (UAV) have been widely used both in military and in civilian applications. However, the cooperation of small and mini drones in a network is capable of further improving both the performance and the coverage area of UAVs. Naturally, there are numerous new challenges to be solved before the wide-spread introduction of multi-UAV based heterogeneous Flying Ad Hoc Networks (FANET), including the formulation of a stable network structure. Meanwhile, an efficient gateway selection algorithm and management mechanism is required as well. On the other hand, the stability control of the hierarchical UAV network guarantees the efficient collaboration of the drones. In this article, we commence with surveying the FANET structure and its protocol architecture. Then, a variety of distributed gateway selection algorithms and cloudbased stability control mechanisms are addressed, complemented by a range of open challenges.Index Terms-Multi-UAV network system of small and mini drones, FANET network structure, distributed gateway selection algorithms, cloud-based stability control for collaboration and cooperation.

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Section: B Uav Network: Airborne Ad Hoc Networkmentioning

confidence: 99%

Taking Drones to the Next Level: Cooperative Distributed Unmanned-Aerial-Vehicular Networks for Small and Mini Drones

Wang

Jiang²,

Han

et al. 2017

IEEE Veh. Technol. Mag.

403

185

View full text Add to dashboard Cite

show abstract

“…As for the perspectives of our work, we intend to embed our approach into a real UAV platform, such as Parrot drones [39]. In addition, as shown in [40], a promising research direction would be the integration of cognitive radio [41] technology with UAV systems. Another future enhancement would be incorporating an optimization study to determine the optimal number of drones needed as function of the application requirements and the explored region.…”

Section: Resultsmentioning

confidence: 99%

Implementing an emerging mobility model for a fleet of UAVs based on a fuzzy logic inference system

Messous¹,

Sedjelmaci²,

Senouci³

2017

Pervasive and Mobile Computing

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To cite this version:Mohamed-Ayoub Messous, Hichem Sedjelmaci, Sidi-Mohammed Senouci. Implementing an emerging mobility model for a fleet of UAVs based on a fuzzy logic inference system. Pervasive and Mobile Computing, Elsevier, 2017, <10.1016/j.pmcj.2017 Implementing an emerging mobility model for a fleet of UAVs based on a fuzzy logic inference system In this paper, we design and implement a novel generic mobility model, named Alphabased, for a fleet of small interconnected UAVs (Unmanned Aerial Vehicles) that collaborate to explore a geographic area (battle field, research and rescue missions, surveillance applications, etc.). In fact, due to the significant impact of mobility models on the networking performance, the mobility models must realistically capture the UAV's attributes. Hence, we propose to use a combination of energy level, coverage-area and network connectivity for mobility decision-making, in contrast to the literature where only network connectivity and area coverage are investigated. On the one hand, these two metrics are very important, especially for applications where achieving the best area-coverage and maintaining network connectivity represent an essential requirement. On the other hand, energy is another equally noteworthy constraint that should be taken into account. In fact, being a crucial resource for all mobile devices and especially for UAVs, the energy becomes vital to ensure the network lifetime and mission success. As far as we know, Alphabased mobility model is the first to ever consider a combination of these three metrics within the same decision-making criterion. A distributed scheme is adopted, where each UAV determines locally its future movement based on the information it receives from its neighbors. Moreover, a novel fuzzy inference system is implemented in order to compute the values of a followship weighting parameter, named Alpha. This latter is used to choose the most suitable neighboring UAV to follow. To validate the proposed mobility model, rigorous testing has been accomplished, through simulation work. Compared to Randombased and Forces-based mobility models, the Alpha-based mobility model achieves good coverage rate while maintaining connectivity.

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“…The recent development in use of eye in the sky (UAV) is also affected by spectrum scarcity since new devices have been developed which operate within these bands with development focus on cognitive radio technology to address the problem using intrinsic characteristics [31]. Use of EO based data and associated products still faces challenges of data access, availability of high spatial resolution and technological limitations which are further compounded with proliferation of crowdsourced data [32].…”

Section: Challenges Of Using Technology In Crisis and Disaster Managementioning

confidence: 99%

Emerging Trends in Crisis Management: Usability, Earth Observation and Disaster Management

Leonard¹

2018

Crisis Management - Theory and Practice

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Availability of Earth observation (EO) data and dissemination has been revolutionized with technological advancement in computation and use of mobile tools such as phones, tablets and laptops. This is further expanded with availability of affordable data/internet connections which no longer require Ethernet cables to get internet connection. The result is explosion of spatial data on various blogs and personal profiles such as Tweeter, Facebook, Instagram and other internet feeds. This has created explosion of information with a lot of integrity issues on data quality, lack of data source and lack of responsibility and authenticity. The problem is further compounded with advancement in search engines and data harvesting applications which collects similar archived information and makes them available to the user on the internet search engines. There should be a clear link detailing the nature of EO and the kind of relevant information that can be derived from them.

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Integration of Cognitive Radio Technology with unmanned aerial vehicles: Issues, opportunities, and future research challenges

Cited by 182 publications

References 99 publications

Taking Drones to the Next Level: Cooperative Distributed Unmanned-Aerial-Vehicular Networks for Small and Mini Drones

Taking Drones to the Next Level: Cooperative Distributed Unmanned-Aerial-Vehicular Networks for Small and Mini Drones

Implementing an emerging mobility model for a fleet of UAVs based on a fuzzy logic inference system

Emerging Trends in Crisis Management: Usability, Earth Observation and Disaster Management

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