Energy-Aware 3D Aerial Small-Cell Deployment over Next Generation Cellular Networks

Chou, Shih-Fan; Yu, Ya-Ju; Pang, Ai‐Chun; Lin, Tsung-Yi

doi:10.1109/vtcspring.2018.8417735

Cited by 11 publications

(4 citation statements)

References 17 publications

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“…Therefore, the authors of [58] proposed a practical swarm optimization technique to enable the drones' 3D deployment to provide a large coverage area, maintain connectivity, and satisfy users' QoS requirements and drone capacity. Also, 3D deployment for swarm drones was introduced for maximizing the available lifetime of drones and the total throughput of all users [59].…”

Section: Drones For Public Safetymentioning

confidence: 99%

Collaboration of Drone and Internet of Public Safety Things in Smart Cities: An Overview of QoS and Network Performance Optimization

Alsamhi

Ansari

et al. 2019

Drones

104

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This paper studies the network performance of collaboration between the Internet of public safety things (IoPST) and drones. Drones play a vital role in delivering timely and essential wireless communication services for the recovery of services right after a disaster by increasing surge capacity for the purposes of public safety, exploring areas that are difficult to reach, and providing an area of rapid coverage and connectivity. To provide such critical facilities in the case of disasters and for the purposes of public safety, collaboration between drones and IoPST is able to support public safety requirements such as real-time analytics, real-time monitoring, and enhanced decision-making to help smart cities meet their public safety requirements. Therefore, the deployment of drone-based wireless communication can save people and ecosystems by helping public safety organizations face threats and manage crises in an efficient manner. The contribution of this work lies in improving the level of public safety in smart cities through collaborating between smart wearable devices and drone technology. Thus, the collaboration between drones and IoPST devices establishes a public safety network that shows satisfying results in terms of enhancing efficiency and information accuracy.

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Section: Drones For Public Safetymentioning

confidence: 99%

Collaboration of Drone and Internet of Public Safety Things in Smart Cities: An Overview of QoS and Network Performance Optimization

Alsamhi

Ansari

et al. 2019

Drones

104

View full text Add to dashboard Cite

show abstract

“…Determining the optimal placement for a drone can be done using a PSO algorithm as well, as discussed in [28], where a coverage area can be maximized while still considering the drone capacity in the scope of public safety and disaster management. Considering the lifetime of drones to maximize the total throughput of the receivers was also discussed in [29] when deploying and positioning a swarm of drones.…”

Section: Related Workmentioning

confidence: 99%

Three Dimensional UAV Positioning for Dynamic UAV-to-Car Communications

Hadiwardoyo

Calafate

Cano

et al. 2020

Sensors

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In areas with limited infrastructure, Unmanned Aerial Vehicles (UAVs) can come in handy as relays for car-to-car communications. Since UAVs are able to fully explore a three-dimensional environment while flying, communications that involve them can be affected by the irregularity of the terrains, that in turn can cause path loss by acting as obstacles. Accounting for this phenomenon, we propose a UAV positioning technique that relies on optimization algorithms to improve the support for vehicular communications. Simulation results show that the best position of the UAV can be timely determined considering the dynamic movement of the cars. Our technique takes into account the current flight altitude, the position of the cars on the ground, and the existing flight restrictions.

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“…In general, we can classify them roughly as (a) those that minimize the number of UAVs deployed [ 20 , 21 , 22 , 23 ] (i.e., minimize deployment cost); (b) those maximize energy efficiency or battery lifetime [ 24 , 25 , 26 ] (i.e., UAV endurance [ 27 ]). Energy-aware UAV optimal deployment problems typically considering the power consumption due to either radio communication [ 25 , 26 ] or mechanical energy [ 24 ] using energy consumption models [ 28 , 29 ]. The reader is encouraged to read the surveys [ 2 , 19 , 30 ] for a more detailed list of works and references that address the problem of 3D placement with emphasis in energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service

Mayor

Estepa

et al. 2020

Sensors

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This paper formulates a new problem for the optimal placement of Unmanned Aerial Vehicles (UAVs) geared towards wireless coverage provision for Voice over WiFi (VoWiFi) service to a set of ground users confined in an open area. Our objective function is constrained by coverage and by VoIP speech quality and minimizes the ratio between the number of UAVs deployed and energy efficiency in UAVs, hence providing the layout that requires fewer UAVs per hour of service. Solutions provide the number and position of UAVs to be deployed, and are found using well-known heuristic search methods such as genetic algorithms (used for the initial deployment of UAVs), or particle swarm optimization (used for the periodical update of the positions). We examine two communication services: (a) one bidirectional VoWiFi channel per user; (b) single broadcast VoWiFi channel for announcements. For these services, we study the results obtained for an increasing number of users confined in a small area of 100 m2 as well as in a large area of 10,000 m2. Results show that the drone turnover rate is related to both users’ sparsity and the number of users served by each UAV. For the unicast service, the ratio of UAVs per hour of service tends to increase with user sparsity and the power of radio communication represents 14–16% of the total UAV energy consumption depending on ground user density. In large areas, solutions tend to locate UAVs at higher altitudes seeking increased coverage, which increases energy consumption due to hovering. However, in the VoWiFi broadcast communication service, the traffic is scarce, and solutions are mostly constrained only by coverage. This results in fewer UAVs deployed, less total power consumption (between 20% and 75%), and less sensitivity to the number of served users.

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Energy-Aware 3D Aerial Small-Cell Deployment over Next Generation Cellular Networks

Cited by 11 publications

References 17 publications

Collaboration of Drone and Internet of Public Safety Things in Smart Cities: An Overview of QoS and Network Performance Optimization

Collaboration of Drone and Internet of Public Safety Things in Smart Cities: An Overview of QoS and Network Performance Optimization

Three Dimensional UAV Positioning for Dynamic UAV-to-Car Communications

Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service

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