Quantum scheduling optimization for UAV-enabled IoT networks

Vista, Francesco; Iacovelli, Giovanni; Grieco, Luigi Alfredo

doi:10.1145/3488658.3493783

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…A recent research attempted to solve the spectrum resource allocation problem as a discrete Markov decision process (MDP) using Quantum Reinforcement Learning [24]. Apart from Metaverse, a plethora of research studies have been conducted using QA on traditional cellular networks, such as, next-generation quantumenabled multiple-input multiple-output (MIMO) processing [25], routing [26], scheduling/optimization for Internet of Things (IoT) networks [27] [28], resource optimization for Network Function Virtualization (NFV) [29], and so forth. All the quantum inspired optimization research struggle to simulate large-scale NP-hard problems due to the limited availability of freely available qubits (quantum resources) in today's world [29].…”

Section: Volume IIImentioning

confidence: 99%

Warm and Cold Start Quantum Annealing for Metaverse Resource Optimization

Emu,

Choudhury,

Salomaa

2024

IEEE Open J. Commun. Soc.

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Metaverse refers to the intersection of parallel virtual worlds with their physical counterparts by allowing users to interact with virtual people, objects, and environments. Resource allocation in various aspects of Metaverse domains, called as MetaSlices hereinafter, is a crucial optimization research problem. To serve this purpose, we consider a MetaSlice framework with the notion of sharing resources among common functions and enable placing time-sensitive services at the edge of multi-tier architecture in proximity to users. Unfortunately, the classical Integer Linear Programming is inappropriate for such heavily constrained optimization problem due to the extensive running time and memory. Hence, we model a novel Quadratic Unconstrained Binary Optimization (QUBO) formulation to simultaneously optimize resources and secure Quality of Service for MetaSlices as a paradigm shift towards quantum computing. Furthermore, we propose to employ two hybrid classical-quantum strategies, Warm Start and Cold Start Quantum Annealing to optimize resource under bandwidth uncertainty, offer ultra-low running time, and increase service acceptance rate/scalability in a resource-hungry and dynamic Metaverse system.

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Section: Volume IIImentioning

confidence: 99%

Warm and Cold Start Quantum Annealing for Metaverse Resource Optimization

Emu,

Choudhury,

Salomaa

2024

IEEE Open J. Commun. Soc.

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“…Quantum computing is a rapidly‐emerging technology that can speed up computation and provide real‐time solutions for problems too complex in classical systems using the laws of quantum mechanics (Vista et al, 2021). Recently, Quantum communication is being used as a promising technology to ensure robust security of information exchange in UAV‐assisted 5G and 6G mobile networks, while supporting many applications.…”

Section: Future Research Directions For Fanetsmentioning

confidence: 99%

“…In addition, Quantum Evolutionary Algorithms are used to allocate tasks among a network of heterogeneous resource‐constrained UAVs in a remote and highly dynamic environment, where the energy and resources available at the UAVs are limited (Mousavi et al, 2019). Quantum Annealing is also used to solve a binary optimization problem to derive an optimal scheduling plan in a UAV‐enabled IoT network (Vista et al, 2021). However, when it comes to using quantum communications, an open issue arises, which is signal degradation due to weather conditions (Vista et al, 2021).…”

Section: Future Research Directions For Fanetsmentioning

confidence: 99%

“…Quantum Annealing is also used to solve a binary optimization problem to derive an optimal scheduling plan in a UAV‐enabled IoT network (Vista et al, 2021). However, when it comes to using quantum communications, an open issue arises, which is signal degradation due to weather conditions (Vista et al, 2021). The attenuation of a signal transmitted through the UAV‐assisted 5G or 6G quantum‐based mobile networks, significantly and directly degrades the performance of quantum communication.…”

Section: Future Research Directions For Fanetsmentioning

confidence: 99%

“…Quantum computing is a rapidly-emerging technology that can speed up computation and provide real-time solutions for problems too complex in classical systems using the laws of quantum mechanics (Vista et al, 2021) the UAVs are limited (Mousavi et al, 2019). Quantum Annealing is also used to solve a binary optimization problem to derive an optimal scheduling plan in a UAV-enabled IoT network (Vista et al, 2021).…”

Section: Quantum Communicationmentioning

confidence: 99%

See 2 more Smart Citations

A systematic literature review of flying ad hoc networks: State‐of‐the‐art, challenges, and perspectives

Pasandideh

Costa

Kunst

et al. 2023

Journal of Field Robotics

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Unmanned aerial vehicles (UAVs), also known as drones, communicate, collaborate, and form flying ad hoc networks (FANETs) to perform many different missions, ranging from delivery tasks to agriculture applications. Recently, FANETs have been integrated with different technologies, such as artificial intelligence (AI), virtual reality, and Internet of Things. Such new avenues for the use of UAVs directly impact the research on FANETs and cause some major challenges, such as security and physical layer issues, resource management, and UAV positioning issues that need to be addressed. Several researchers have been working for the last few years to propose AI and machine learning (ML)‐based solutions for different use cases in UAV‐based networks. They present the limitations of the existing research work and highlight some possible future works on FANETs. However, exhibiting the trends in the UAV research papers in a quantitative manner is still required to motivate researchers to rethink the research on FANETs. Therefore, this study covers more than 170 scientific publications extracted from five trusted academic databases published from 2013 to 2021 to provide a thorough overview of the main research and development statistics in the area of FANETs, the open challenges existing in this area and the ML‐based solutions to solve these challenges. In addition, the investigation of emerging technologies integrated with FANETs, as well as the simulation tools employed for evaluating FANETs' performance are discussed. Moreover, the future research directions in the area of FANETs are considered within a prospective vision discussion.

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