Group Handover for Drone Base Stations

Aydın, Yücel; Kurt, Güneş Karabulut; Ozdemır, Enver; Yanıkömeroğlu, Halim

doi:10.1109/jiot.2021.3068297

Cited by 20 publications

(10 citation statements)

References 21 publications

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“…In our proposed method, the network drones are responsible for the handover process. The number of network drones depends on the threshold value as defined in the group authentication methods at [6], [12], [13]. In order to perform the group authentication as in [6], [12], [13], the number of group members must be equal or greater than the threshold value.…”

Section: B Terrestrial Bs Handovermentioning

confidence: 99%

“…• The guard drones perform group authentication as in [6], [12], [13]. • If the authentication is valid, the pre-defined guard drone perform the key agreement step [6], [12], [13] with the new drone. • The group key is encrypted by the agreed key and sent to the new drone.…”

Section: A Authentication Of New Drones By Drone Swarmmentioning

confidence: 99%

“…The number of network drones depends on the threshold value as defined in the group authentication methods at [6], [12], [13]. In order to perform the group authentication as in [6], [12], [13], the number of group members must be equal or greater than the threshold value. The network drones and BS create a group and perform group authentication.…”

Section: B Terrestrial Bs Handovermentioning

confidence: 99%

“…• The network drones share their public key pairs with the t-BS as shown in Figure 5. • The t-BS performs the group authentication as in [6], [12], [13].…”

Section: B Terrestrial Bs Handovermentioning

confidence: 99%

See 3 more Smart Citations

Authentication and Handover Challenges and Methods for Drone Swarms

Aydin,

Kurt,

Ozdemir

et al. 2022

Preprint

Self Cite

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Drones are begin used for various purposes such as border security, surveillance, cargo delivery, visual shows and it is not possible to overcome such intensive tasks with a single drone. In order to expedite performing such tasks, drone swarms are employed. The number of drones in a swarm can be high depending on the assigned duty. The current solution to authenticate a single drone using a 5G new radio (NR) network requires the execution of two steps. The first step covers the authentication between a drone and the 5G core network, and the second step is the authentication between the drone and the drone control station. It is not feasible to authenticate each drone in a swarm with the current solution without causing a significant latency.Authentication keys between a base station (BS) and a user equipment (UE) must be shared with the new BS while performing handover. The drone swarms are heavily mobile and require several handovers from BS to a new BS. Sharing authentication keys for each drone as explained in 5G NR is not scalable for the drone swarms. Also, the drones can be used as a UE or a radio access node on board unmanned aerial vehicle (UxNB). A UxNB may provide service to a drone swarm in a rural area or emergency. The number of handovers may increase and the process of sharing authentication keys between UxNB to new UxNB may be vulnerable to eavesdropping due to the wireless connectivity.In this work, we present a method where the time and the number of the communication for the authentication of a new drone joining the swarm are less than 5G NR. In addition, groupbased handover solutions for the scenarios in which the base stations are terrestrial or mobile are proposed to overcome the scalability and latency issues in the 5G NR.

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Section: B Terrestrial Bs Handovermentioning

confidence: 99%

Section: A Authentication Of New Drones By Drone Swarmmentioning

confidence: 99%

Section: B Terrestrial Bs Handovermentioning

confidence: 99%

“…• The network drones share their public key pairs with the t-BS as shown in Figure 5. • The t-BS performs the group authentication as in [6], [12], [13].…”

Section: B Terrestrial Bs Handovermentioning

confidence: 99%

See 2 more Smart Citations

Authentication and Handover Challenges and Methods for Drone Swarms

Aydin,

Kurt,

Ozdemir

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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“…For instance, in the city planning, several specific areas are chosen as the economic or educational centers. Those geographical centers usually have higher population density and communication demand compared with other areas [35]. And the movement trajectory of targets in those area tend to limit in a specific region.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Ultra-Dense Networks with Traffic Hotspots: A Unified Handover Analysis

Zhang¹,

Zhou²,

Li³

et al. 2022

Preprint

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With the ever-growing communication demands and the unceasing miniaturization of mobile devices, the Internet of Things is expanding the amount of mobile terminals to an enormous level. To deal with such numbers of communication data, plenty of base stations (BSs) need to be deployed. However, denser deployments of heterogeneous networks (HetNets) lead to more frequent handovers, which could increase network burden and degrade the users experience, especially in traffic hotspot areas. In this paper, we develop a unified framework to investigate the handover performance of wireless networks with traffic hotspots. Using the stochastic geometry, we derive the theoretical expressions of average distances and handover metrics in HetNets, where the correlations between users and BSs in hotspots are captured. Specifically, the distributions of macro cells are modeled as independent Poisson point processes (PPPs), and the two tiers of small cells outside and inside the hotspots are modeled as PPP and Poisson cluster process (PCP) separately. A modified random waypoint (MRWP) model is also proposed to eliminate the density wave phenomenon in traditional models and to increase the accuracy of handover decision. By combining the PCP and MRWP model, the distributions of distances from a typical terminal to the BSs in different tiers are derived. Afterwards, we derive the expressions of average distances from a typical terminal to different BSs, and reveal that the handover rate, handover failure rate, and ping-pong rate are deduced as the functions of BS density, scattering variance of clustered small cell, user velocity, and threshold of triggered time. Simulation results verify the accuracy of the proposed analytical model and closed-form theoretical expressions.

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FRAT: a fuzzy rule based adaptive technique for intelligent placement of UAV-mounted base station

Mandloi

Arya

2023

Wireless Netw

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Group Handover for Drone Base Stations

Cited by 20 publications

References 21 publications

Authentication and Handover Challenges and Methods for Drone Swarms

Authentication and Handover Challenges and Methods for Drone Swarms

Heterogeneous Ultra-Dense Networks with Traffic Hotspots: A Unified Handover Analysis

FRAT: a fuzzy rule based adaptive technique for intelligent placement of UAV-mounted base station

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