Energy aware resource allocation and complexity reduction approach for cognitive radio networks using game theory

With the development of edge computing, edge storage solutions are attracting widespread attention. When facing the requirements of lower latency and faster access speed from end devices, edge storage solutions are considered to be an alternative to the cloud. However, edges are usually owned by small organizations which have limited operations and maintenance capabilities. This makes these edge devices can be easily disabled by external attacks or internal hardware failures. Besides, the heterogeneity of the edge devices will also make it difficult to price the edge resources uniformly. To tackle these problems, we propose SmartStore: an auction mechanism based on blockchain to allocate edge resources. Considering centralized solutions have access bottlenecks and trust issues, we built SmartStore on the smart contract. With Bayesian game theory, SmartStore can analyze how data owners (DO) and edges price the resources can maximize their benefits. From an economic perspective, both DO and edges can make full use of edge heterogeneous resources with SmartStore. Besides, a two‐stage submission strategy is proposed to complete the sealed auction. Furthermore, considering the reliability of edge storage, we propose a cluster‐based block distribution algorithm for SmartStore's intelligent edge recommendation process. SmartStore ensures the reliability of edge storage while maximizing the benefits and resource utilization of both parties. Finally, we conduct specific experiments on the proposed auction smart contract through “Ethereum” and the experimental results of implementation show the effectiveness and efficiency of our SmartStore.

SmartStore: A blockchain and clustering based intelligent edge storage system with fairness and resilience

Congestion centric
 multi‐objective
 reptile search algorithm‐based clustering and routing in cognitive radio sensor network

Enhanced cooperative behavior and fair spectrum allocation for intelligent IoT devices in cognitive radio networks

Nowadays, wireless communication system is facing the problems of spectrum resource shortage. Cognitive radio technology allows cognitive users to use the spectrums authorized to primary users to improve the spectrum utilization. In this paper, a cognitive network model based on hybrid overlay–underlay spectrum access mode is established. To solve the resource allocation problem, a multi-agent resource allocation algorithm based on graph convolution reinforcement learning which combines deep Q network (DQN) and graph attention network is proposed. DQN is used for action selection and graph attention network is used to obtain the information about neighbours, so as to achieve local cooperation. The proposed algorithm can adaptively optimize cognitive network throughput, spectrum efficiency, or power efficiency by controlling the transmission power and channel selection of cognitive users. To improve the information interaction efficiency, the agent's states are divided into two categories, whether it needs to interact with neighbours or not, which shortens training time and improves convergence speed. Simulation results show that the proposed algorithm can effectively improve the power efficiency of cognitive networks. Compared with Q-learning, DQN and exiting graph convolutional reinforcement learning algorithm, the proposed algorithm has faster convergence speed and higher stability, and obtains higher network power efficiency. 

Graph convolutional reinforcement learning for resource allocation in hybrid overlay–underlay cognitive radio network with network slicing

The explosive growth of mobile devices and the rapid increase in wireless services necessitate an improvement in communication techniques. This technology can boost spectrum utilization as well as meet the massive connectivity requirement to draw attention from academia and industry. Cognitive Radio Network (CRN) is an evident optimal solution in alleviating the challenges of spectrum unused potentially in wireless networks. The Secondary Users are allowed intelligently to access the sections of spectrum unused (spectrum holes) by the authorized Primary Users (PUs). The objective of this paper is to analyze channel throughput, delay, carrier aggregations, energy efficiency, and security challenges and provide research ideas to enhance the overall performance of the CRN. This review paper completely investigated the potential management of spectrum techniques, essentially in spectrum sensing, spectrum allocation, spectrum sharing, and spectrum handoff in CRN. In addition, we study the energy efficient routing techniques like network optimization frameworks in cooperative networks, protocols and different types of attack issues, approaches for handling the secure communication in CRNs. The state of art review, which deeply examines the Cognitive Radio Network providing an idea of important technology and challenges are addressed. The open research challenges related to spectrum management, energy efficient routing and security are discussed.

A Comprehensive Review on Spectrum Management, Security and Energy-Efficient Cognitive Radio Networks

The demand of real-time wireless communication is increasing drastically where users demand for better Quality of Service (QoS) for various applications. Recently, the cognitive radio network has gained attraction in the field of communication due to its efficient nature of spectrum sensing and sharing. However, the interference between primary and secondary user is a tedious task which can degrade the performance. In order to address this issue, a multiple-access schemes are introduced and Non-Orthogonal Multiple Access (NOMA) is considered as most promising technique for addressing dynamic resource allocation, channel state information and eavesdropping. In this paper, we have focused on downlink communication for Cognitive Radio (CR)-NOMA network for resource allocation and secrecy. The simulation result obtained ensures higher throughput compared to the existing techniques.

Improving the Resource Allocation and Secrecy in Cognitive-NOMA Network

With the advancement of Cognitive Radio Network (CRN), localization of primary users progress the improvement of network performance with respect to power adoption and reliability. Moreover, CRN localization is essential because storage energy of CR devices is limited. By considering these issues, we propose an Energy Efficient Cooperative Localization approach $(E^{2}- CL)$ in CRN, which is used for placing Primary User (PU) position with the help of a mobile aided CR. Moreover, communication among CRs cooperation that leads to demand of more energy, a mobile CR is allowed to manage the overall positional accuracy and wake up minimum number of CR to collaborate CR manager. We evaluated the simulation results for the proposed Energy Efficient Cooperative Localization $(E^{2}\,- CL)$ approach which achieves better performance compared to existing Cooperative Localization (CL) with respect to energy efficiency.

E 2 -CL:Energy Efficient Cooperative Localization in Cognitive Radio Network

Rapid growth of mobile wireless communication system has increased for numerous real-time applications. This causes heavy usage of radio spectrum in computing services. However, the system provides high speed communication is unable to serve the required performance with wrong use of resources and spectrum. In order to overcome this problem, Cognitive Radio Network (CRN) architecture is recommended in real time communication systems. CRN based system experiences lack of resource allocation, spectrum sensing and consumption of power. In this paper, we recommend game theory approach for allocation of resources, sharing spectrum cooperatively and ensures maxumized payoff. The extensive simulation study results in optimizing the backlog performance and reduces the consumption of energy.

K N Shyleshchandra Gudihatti

Papers

Energy aware resource allocation and complexity reduction approach for cognitive radio networks using game theory

A Comprehensive Review on Spectrum Management, Security and Energy-Efficient Cognitive Radio Networks

Improving the Resource Allocation and Secrecy in Cognitive-NOMA Network

E 2 -CL:Energy Efficient Cooperative Localization in Cognitive Radio Network

EAARG(Energy Aware Allocation of Resources using Game Theory) Approach for Multi-hop Cognitive Radio Network