Self-Organizing Sustainable Spectrum Management Methodology in Cognitive Radio Vehicular Adhoc Network (CRAVENET) Environment: A Reinforcement Learning Approach

Ghanshala, Kamal Kumar; Sharma, Sachin; Mohan, Seshadri; Nautiyal, Lata; Mishra, Preeti; Joshi, R. C.

doi:10.1109/icsccc.2018.8703268

Cited by 10 publications

(6 citation statements)

References 13 publications

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“…Such works have attempted to enhance connectivity through heavily mobility-oriented approaches, where the position, speed, and acceleration of vehicles and traffic network topology are substantial elements in their models. Complementarily, we have observed steep advancements in communication technologies; for instance, cognitive radio has become a key enabling technology of dynamic spectrum access to achieve better exploitation of radio spectrum [11,12]. As a result, even though mobility is an important influencing factor to connectivity, urban environments are more comprehensive and contain many more opportunities for devices and vehicles to connect over multiple media, following V2V, V2I, and V2X communication methods.…”

Section: Discussionmentioning

confidence: 99%

MDP-based Vehicular Network Connectivity Model for VCC Management

Saad

Grande

2020

2020 IEEE/ACM 24th International Symposium on Distributed Simulation and Real Time Applications (DS-RT)

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Vehicular Cloud computing is a new paradigm in which vehicles collaboratively exchange data and resources to support services and problem-solving in urban environments. Characteristically, such Clouds undergo severe challenging conditions from the high mobility of vehicles, and by essence, they are rather dynamic and complex.Many works have explored the assembling and management of Vehicular Clouds with designs that heavily focus on mobility. However, a mobility-based strategy relies on vehicles' geographical position, and its feasibility has been questioned in some recent works. Therefore, we present a more relaxed Vehicular Cloud management scheme that relies on connectivity. This work models uncertainty and considers every possible chance a vehicle may be available through accessible communication means, such as vehicle-to-everything (V2X) communications and the vehicle being in the range of road-side units (RSUs) for data transmissions. We propose an markov-decisision process (MDP) model to track vehicles' connection status and estimate their reliability for data transmissions. Also, from analyses, we observed that higher vehicle connectivity presents a trace of repeated connection patterns. We reinforce the connectivity status by validating it through an availability model to distinguish the vehicles which support high availability regardless of their positioning. The availability model thus determines the suitability of the MDP model in a given environment. I like to extend my gigantic thanks to Professor Robson for guidance, teaching, and being available most of the time to answer my stupid questions. Thiago, for helping understand the software we use for this research. My parents for their supports. My friends and colleagues for making this journey bearable. Thank you

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Section: Discussionmentioning

confidence: 99%

MDP-based Vehicular Network Connectivity Model for VCC Management

Saad

Grande

2020

2020 IEEE/ACM 24th International Symposium on Distributed Simulation and Real Time Applications (DS-RT)

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“…In [70], the RL method operates on the centralized selforganizing spectrum optimization in the low latency of vehicle IoT. It was revealed that the devised approach provides better property improvements in spectrum utilization and latency.…”

Section: ) Latency-energy Efficiencymentioning

confidence: 99%

“…To achieve a low latency and high SE communication network, numerous effective optimization strategies for optimizing latency and SE simultaneously for 5G and 6G IoT communication systems has been given in this section [70], [71], [72]. The three papers explore different methodologies to optimize spectrum utilization and minimize latency in various networking contexts.…”

Section: ) Latency-energy Efficiencymentioning

confidence: 99%

“…The three papers explore different methodologies to optimize spectrum utilization and minimize latency in various networking contexts. [70] utilizes a RL-based approach for centralized spectrum optimization in vehicle IoT, yielding improvements in both spectrum utilization and latency, albeit without accounting for dynamic IoT device rates. In contrast, [71] integrates RL, Q-learning, and DNN to address spectrum scarcity in 5G networks, successfully minimizing latency but neglecting considerations for overloaded spectrum resources.…”

Section: ) Latency-energy Efficiencymentioning

confidence: 99%

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Toward Efficient 6G IoT Networks: A Perspective on Resource Optimization Strategies, Challenges, and Future Directions

Liwen,

Qamar,

Liaqat

et al. 2024

IEEE Access

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The next generation (6G) wireless communication technology has super advantages in high transmission rates scenarios. Internet of Things (IoT) has been applied in recent years due to its wide connection. However, effective resource optimization methods must be analyzed to meet the high requirements of key performance indicators in 6G wireless IoT networks. This paper discusses a general investigation of the resource optimization strategies in the 6G IoT system. The study aims to find the main solutions to optimize 6G IoT network performance. First, an overall summary of current research is preferred resource optimization in latency, reliability, Energy Efficiency (EE), Spectrum Efficiency (SE), bandwidth utilization efficiency, rate, and power efficiency. Second, we propose the multi-indicator tradeoff strategies associated with the latest resource optimization approaches and investigate optimal strategies. Furthermore, we show the limitations of the current resource optimization methods and discuss the future works of resource optimization for IoT devices in 6G communication. Our survey aims to help researchers optimize 6G IoT network performance using advanced techniques.

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“…• WSNs [76]- [81] • IoT [82] • Cellular communications [42], [83]- [90] • WiMax [91] • Aeronautical communications [92] • Ad Hoc networks [93] • Satellite communications [94] • Space communications [95] • UAV [96] • Vehicular Ad Hoc networks [97]- [100]10…”

Section: Multimedia Services and Crnsmentioning

confidence: 99%

Multimedia Communication over Cognitive Radio Networks from QoS/QoE Perspective; A Comprehensive Survey

Piran¹

2020

Preprint

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The stringent requirements of wireless multimedia transmission lead to very high radio spectrum solicitation. Although the radio spectrum is considered as a scarce resource, the issue with spectrum availability is not scarcity, but the inefficient utilization. Unique characteristics of cognitive radio (CR) such as flexibility, adaptability, and interoperability, particularly have contributed to it being the optimum technological candidate to alleviate the issue of spectrum scarcity for multimedia communications. However, multimedia communications over CR networks (MCRNs) as a bandwidth-hungry, delay-sensitive, and loss-tolerant service, exposes several severe challenges specially to guarantee quality of service (QoS) and quality of experience (QoE). As a result, to date, different schemes based on source and channel coding, multicast, and distributed streaming, have been examined to improve the QoS/QoE in MCRNs. In this paper, we survey QoS/QoE provisioning schemes in MCRNs. We first discuss the basic concepts of multimedia communication, CRNs, QoS and QoE. Then, we present the advantages of utilizing CR for multimedia services and outline the stringent QoS and QoE requirements in MCRNs. Next, we classify the critical challenges for QoS/QoE provisioning in MCRNs including spectrum sensing, resource allocation management, network fluctuations management, latency management, and energy consumption management. Then, we survey the corresponding feasible solutions for each challenge highlighting performance issues, strengths, and weaknesses. Furthermore, we discuss several important open research problems and provide some avenues for future research.

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Self-Organizing Sustainable Spectrum Management Methodology in Cognitive Radio Vehicular Adhoc Network (CRAVENET) Environment: A Reinforcement Learning Approach

Cited by 10 publications

References 13 publications

MDP-based Vehicular Network Connectivity Model for VCC Management

MDP-based Vehicular Network Connectivity Model for VCC Management

Toward Efficient 6G IoT Networks: A Perspective on Resource Optimization Strategies, Challenges, and Future Directions

Multimedia Communication over Cognitive Radio Networks from QoS/QoE Perspective; A Comprehensive Survey

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