FANET: Smart city mobility off to a flying start with self‐organized drone‐based networks

Ṣiddiqi, Muḥammad Zubair; Draz, Umar; Ali, Amjad; Iqbal, Muddesar; Alruwaili, Madallah; Alhwaiti, Yousef; Alanazi, Saad

doi:10.1049/cmu2.12291

Cited by 13 publications

(9 citation statements)

References 35 publications

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“…In recent years, three main strategies have been used to achieve drone swarms’ persistent surveillance [ 8 , 11 , 12 , 13 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ]: (i) on-duty UAV replacement scheme based on recharging stations [ 13 , 19 , 25 ]; (ii) energy-efficiency path planning [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ]; and (iii) novel structures of UAV teaming [ 8 , 11 , 18 , 22 ]. UAV distribution’s hierarchical architecture helps plan efficient and adaptive surveillance missions when the surveillance map changes due to weather or invisible factors.…”

Section: System Model and Problem Statementmentioning

confidence: 99%

“…With the popularity of FANET technology, it has received much research interest in numerous practical applications [ 10 , 11 , 12 , 13 ]. To realize remote command and situational awareness, the authors in [ 10 ] constructed a cooperative monitoring network consisting of multiple UAVs and ground stations.…”

Section: Introductionmentioning

confidence: 99%

“…In the process of urban’s digital and intelligent development, FANET technology is envisaged to play an important part. Siddiqi et al [ 13 ] designed an enhanced Ant Colony Optimization (ACO) technique for traffic detection in remote urban areas, which improves the network life and the number of received packets compared to comparison algorithms.…”

Section: Introductionmentioning

confidence: 99%

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A Low Complexity Persistent Reconnaissance Algorithm for FANET

Guo

Tang

Qin

2022

Sensors

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In recent years, with the rapid progress of unmanned aerial vehicle (UAV) technology, UAV-based systems have been widely used in both civilian and military applications. Researchers have proposed various network architectures and routing protocols to address the network connectivity problems associated with the high mobility of UAVs, and have achieved considerable results in a flying ad hoc network (FANET). Although scholars have noted various threats to UAVs in practical applications, such as local magnetic field variation, acoustic interference, and radio signal hijacking, few studies have taken into account the dynamic nature of these threat factors. Moreover, the UAVs’ high mobility combined with dynamic threats makes it more challenging to ensure connectivity while adapting to ever-changing scenarios. In this context, this paper introduces the concept of threat probability density function (threat PDF) and proposes a particle swarm optimization (PSO)-based threat avoidance and reconnaissance FANET construction algorithm (TARFC), which enables UAVs to dynamically adapt to avoid high-risk areas while maintaining FANET connectivity. Inspired by the graph editing distance, the total edit distance (TED) is defined to describe the alterations of the FANET and threat factors over time. Based on TED, a dynamic threat avoidance and continuous reconnaissance FANET operation algorithm (TA&CRFO) is proposed to realize semi-distributed control of the network. Simulation results show that both TARFC and TA&CRFO are effective in maintaining network connectivity and avoiding threats in dynamic scenarios. The average threat value of UAVs using TARFC and TA&CRFO is reduced by 3.99~27.51% and 3.07~26.63%, respectively, compared with the PSO algorithm. In addition, with limited distributed moderation, the complexity of the TA&CRFO algorithm is only 20.08% of that of TARFC.

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Section: System Model and Problem Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Low Complexity Persistent Reconnaissance Algorithm for FANET

Guo

Tang

Qin

2022

Sensors

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“…The World Health Organization recently reported 27.4 deaths per 100,000 populations due to road traffic accidents (RTAs). 1 Accidents related to road traffic and congestion on route remain a significant global problem. Additionally, the long path between two points consumes more time and energy for travel.…”

Section: Introductionmentioning

confidence: 99%

An enhanced distance and residual energy‐based congestion aware ant colony optimization routing for vehicular ad hoc networks

Ramamoorthy

Thangavelu²

2022

Int J Communication

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Summary Due to the ad hoc nature of vehicular ad hoc networks (VANETs), routing in VANETs is challenging. Using an energy‐efficient shortest path without congestion (EESPWC) increases routing efficiency by satisfying specific parameters (high throughput, high packet delivery ratio, low overhead, low end‐to‐end delay, low packet loss ratio, and low energy consumption). In this work, enhanced distance and residual energy‐based congestion aware ant colony optimization (EDR‐CAACO) for VANETs is proposed to find an EESPWC. The proposed EESPWC produces high throughput and high packet delivery ratio with low overhead and low delay. In EDR‐CAACO, the roulette wheel selects the EESPWC based on the link's fitness values. The fitness value is computed based on the pheromone level of the link. The enhanced ACO (ant colony optimization) of the proposed model relies on the distance, residual, and congestion levels of the vehicles to estimate pheromone levels. For path selection, the roulette wheel relies on links that have the highest fitness value. The combination of high fitness value links provides an energy‐efficient non‐congested shortest route. The simulation results prove that the overall performance of EDR‐CAACO is better than Improved Distance Based Ant Colony Optimization Routing (IDBACOR), Fuzzy‐based Ant colony optimization (F‐ANT), ant colony optimization routing algorithm (ARA), and AntNet in terms of throughput, routing overhead, packet delivery ratio, end to end delay, and packet loss rate.

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“…As the scale of smart cities extremely expands, computing facilities face enormous challenges including rapidly growing computing demands and inhibited cooling and energy cost, which limits the construction and development of sustainable cities and society [1,2]. Carbon footprint monitoring is required to provide significant basis for the energy utilization and computing resource allocation of computing facilities.…”

Section: Introductionmentioning

confidence: 99%

Priority‐aware intelligent device access management for carbon footprint monitoring in sustainable cites and society

Wang

Liao

et al. 2022

IET Communications

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Carbon footprint monitoring provides significant basis for computing facilities to improve the computing efficiency and reduce the energy cost, which can enable low/zero carbon computing and facilitate the construction of sustainable cites. Massive sensing devices access to 5G base station to transmit collected carbon emission data, which requires intelligent access management. Fast uplink grant possesses the advantages of reducing signalling overhead and access conflicts while facing the problems of incomplete information and difficulty in guaranteeing priority constraint. This paper examines, the maximum access queuing delay minimization problem is formulated under the long‐term service priority constraint and the short‐term access management constraint. First, Lyapunov optimization is leveraged to decouple the long‐term service priority constraint and short‐term access management optimization, and decompose the long‐term stochastic optimization problem into a series of short‐term deterministic problems. Then, a priority‐aware deep Q‐network (DQN)‐based fast uplink grant access management (PDAC) algorithm is proposed to achieve intelligent access management with differentiated service priority requirements. PDAC utilizes DQN to handle non‐convex high‐dimensional optimization problem with service priority constraint to achieve intelligent access management and priority awareness. Simulation results demonstrate that PDAC outperforms the existing algorithms in access queuing delay, buffer queue backlog, and priority deficit fluctuation.

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FANET: Smart city mobility off to a flying start with self‐organized drone‐based networks

Cited by 13 publications

References 35 publications

A Low Complexity Persistent Reconnaissance Algorithm for FANET

A Low Complexity Persistent Reconnaissance Algorithm for FANET

An enhanced distance and residual energy‐based congestion aware ant colony optimization routing for vehicular ad hoc networks

Priority‐aware intelligent device access management for carbon footprint monitoring in sustainable cites and society

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