Energy aware endurance framework for mission critical aerial networks

Özçevik, Yusuf; Canberk, Berk

doi:10.1016/j.adhoc.2019.101992

Cited by 5 publications

(2 citation statements)

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“…Bozkaya and Canberk 15 proposed a software-defined networking (SDN)-based solution for ABS deployment and a path planning strategy to serve the covered users and decrease the energy consumption of ABSs. Özçevik and Canberk 16 provided an energy-aware endurance framework by modeling Aerial Pickup and Delivery Problem for ABS flight planning strategy. However, not only the mobility of ABSs but also the mobility of UEs should be included to the problem domain in aerial networks, and the problem should be evaluated differently from 2D solutions.…”

Section: Related Workmentioning

confidence: 99%

Energy‐aware mobility for aerial networks: A reinforcement learning approach

et al. 2021

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With recent advancements in aerial networks, aerial base stations (ABSs) have become a promising mobile network technology to enhance the coverage and capacity of the cellular networks. ABS deployment can assist cellular networks to support network infrastructure or minimize the disruptions caused by unexpected and temporary situations. However, with 3D ABS placement, the continuity of the service has increased the challenge of providing satisfactory Quality of Service (QoS). The limited battery capacity of ABSs and continuous movement of users result in frequent interruptions. Although aerial networks provide quick and effective coverage, ABS deployment is challenging due to the user mobility, increased interference, handover delay, and handover failure. In addition, once an ABS is deployed, an intelligent management must be applied. In this paper, we model user mobility pattern and formulate energyaware ABS deployment problem with a goal of minimizing energy consumption and handover delay. To this end, the contributions of this paper are threefold: (i) analysis of reinforcement learning (RL)-based state action reward state action (SARSA) algorithm to deploy ABSs with an energy consumption model, (ii) predicting the user next-place with a hidden Markov model (HMM), and (iii) managing the dynamic movement of ABSs with a handover procedure. Our model is validated by comprehensive simulation, and the results indicate superiority of the proposed model on deploying multiple ABSs to provide the communication coverage.

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Section: Related Workmentioning

confidence: 99%

Energy‐aware mobility for aerial networks: A reinforcement learning approach

et al. 2021

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“…Another possible method is to use a fleet-based radar system where every drone transmits to one another [8] Due to the current limitations that battery life induces for drones, Hassija, et al, propose a charging station network system for drones [9]. Aerial base stations which could prolong the operation time of drones [10]. Routing algorithms can even be implemented to ensure the drone flight paths are optimized [11] Relatively, a fairly popular algorithm in drone technology that practitioners like to base their routing programs is the traveling salesman problem.…”

Section: Introductionmentioning

confidence: 99%

Design of an Aerial Drone Delivery System

Africa¹

2020

IJETER

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Considering the weight of the traffic in the Philippines, the delivery of products via land vehicles has long since been an inconvenience to consumers due to extensive delivery times. Due to the increasing number of people using automobiles on the road every year, traffic congestion becomes more prevalent in both rural and urban areas, especially during rush hours. To compensate for this, an alternative form of courier service via unmanned aerial vehicles (UAVs) or specific drones is proposed. Drone delivery is performed such that drones would transfer parcels from headquarters to the assigned delivery location within a maximum distance of 12 kilometers. The approximated travel time of the drones is calculated assuming perfect weather conditions, no wind turbulence, and no object collision. The estimation is made considering the average speed of the drone and the input distance from headquarters to the delivery location. A Matlab program is made that computes for the delivery time of the drone given an input between 0 to 12 kilometers. Relatively, tests show that input distances lower than 0 or greater than 12 kilometers results in an error message.

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ARMS‐EGR—Adaptive ranking and mobile sink‐enabled energy‐efficient geographic routing protocol in flying ad hoc networks

Agrawal

Kapoor

2022

Int J Communication

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Unmanned aerial vehicles (UAVs) have become widespread because of their involvement in a variety of applications. The task of designing the energyefficient routing between UAVs has been considered a matter of great interest due to the inherent challenges of controlling the dynamics exhibited by UAVs.Energy limitations are considered the main limitations of UAVs. This research paper proposes a novel routing protocol, adaptive ranking and mobile sink (MS)-enabled energy-efficient geographic routing (ARMS-EGR) for flying ad hoc networks. In ARMS-EGR, the whole network is partitioned into cells. The cell contains cell members (CM) and cell heads (CHs). The CH works as a cluster head. Additionally, two MSs have been used to collect data captured by CM. Multihop communication on the network leads to an increase in traffic and consumes the energy of the UAVs located near the base station (BS). MSs are used for power distribution and load balancing across the network. Adaptive ranking of forwarder UAVs and CHs is performed during intracell and intercell multihop communication, respectively, using adaptive ranking. A cell with one-hop communication can directly send packets to the MS, but the ARMS-EGR routing protocol has been proposed for multihop communication. The proposed approach is simulated in NS-2.35 software. The results show that end-to-end latency and power consumption during packet transmission are greatly minimized. ARMS-EGR also demonstrates improvements in message success rates, number of alive nodes, and packet delivery ratio, making ARMS-EGR particularly suitable for flying ad hoc networks (FANETs).adaptive ranking, energy-efficient routing, flying ad hoc network, mobility management model, unmanned aerial vehicle | INTRODUCTIONThe flying ad hoc network (FANET) includes a set of unmanned aerial vehicles (UAVs). UAVs coordinate and communicate with each other to complete all tasks. UAVs, also known as drones, come in a variety of sizes and specifications. UAVs can be deployed quickly when needed. 1,2 FANETs have grown significantly from single large UAVs in the past to

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Energy aware endurance framework for mission critical aerial networks

Cited by 5 publications

References 54 publications

Energy‐aware mobility for aerial networks: A reinforcement learning approach

Energy‐aware mobility for aerial networks: A reinforcement learning approach

Design of an Aerial Drone Delivery System

ARMS‐EGR—Adaptive ranking and mobile sink‐enabled energy‐efficient geographic routing protocol in flying ad hoc networks

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