Energy-Efficient Cooperative Spectrum Sensing: A Review

Anjana, S; Nandan, Shivendra

doi:10.1109/icicct.2018.8473014

Cited by 6 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, cooperative sensing has a great impact on resource consumption, such as bandwidth consumption of control channel and transmission energy consumption of sensing reports. The energy consumption usually increases linearly with the number of nodes participating in the cooperation [27]. Therefore, it is more advantageous to select some uncorrelated cognitive sensor nodes to incorporate during the sensing process under certain constraints of sensing accuracy.…”

Section: System Model a Cooperative Spectrum Sensingmentioning

confidence: 99%

Energy-Efficient Cooperative Spectrum Sensing Strategy for Cognitive Wireless Sensor Networks Based on Particle Swarm Optimization

Cao

Pan

2020

IEEE Access

View full text Add to dashboard Cite

Cognitive wireless sensor networks (CWSNs) can use the idle authorized frequency band to solve the problem of spectrum resource shortage in traditional wireless sensor network. By employing spectrum hole in the authorized frequency band, the spectrum sensing technology can degrade the coexistent interference and enhance the performance of whole sensor network. Due to the characteristics of limited battery energy and low processing capacity with sensor nodes, it is necessary to enhance the energy efficiency while improving spectrum sensing performance. In this paper, a cooperative spectrum sensing strategy for CWSNs based on particle swarm optimization is proposed. Firstly, the system throughput and energy consumption are quantitatively analyzed, and the mathematical model related to energy efficiency is established. Secondly, the particle swarm optimization (PSO) algorithm is used to obtain the optimal selected nodes set under the limited conditions of false alarm probability and detection probability. To avoid local optimization in the process of problem solving, Cauchy mutation method is introduced to optimize the parameter selection of fitness function. The experimental results illustrate that our proposed method can improve the throughput of the system while ensuring the sensing performance, and achieve the energy efficiency effectively.

show abstract

Section: System Model a Cooperative Spectrum Sensingmentioning

confidence: 99%

Energy-Efficient Cooperative Spectrum Sensing Strategy for Cognitive Wireless Sensor Networks Based on Particle Swarm Optimization

Cao

Pan

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…RSS varies with the distance between the transmitter and receiver. We employ the Distributed Cooperative Spectrum Sensing method in our scheme [31], which provide sensing probability over 99% theoretically. The result for the first-round spectrum sensing will be given.…”

Section: ) Local Sensing and Cooperative Decisionmentioning

confidence: 99%

PSO-Based Agent Cooperative Spectrum Sensing in Cognitive Radio Networks

Chen

Huang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Secondary users (SUs) can detect the states of primary users (PUs) and access the idle spectrum in an opportunistic way in cognitive radio networks (CRNs). With the spread of wireless communication devices, the mobility of both PUs and SUs is ubiquitous. To obtain more accurate spectrum sensing data, SUs must be located within the transmission range of PUs. With the unknown mobility, it is difficult to guarantee the efficiency of spectrum sensing. Focused on this issue, we propose a new scheme, called Particle Swarm Optimization (PSO) -based agent cooperative spectrum sensing (PSOA) in this paper. In this scheme, we deploy multiple mobile agents spreading over the network, to cooperate in spectrum sensing instead of SUs. All agents will move according to the latest global optimal agents of the corresponding target PUs with the fitness function calculated by modified PSO. With the optimal movement, the distribution of agents can guarantee that most PUs are within the detection coverage of PSOA. The evaluation results show that our scheme can save over 80% of sensing time and over 80% of energy consumption (affected by the agents' number and max velocity) than the active searching scheme. PSOA also guarantees the sensing probability of 80% and higher in our simulation.

show abstract

“…Cooperative spectrum sensing used in CR systems [9]- [13] is also applicable to CVN to improve spectrum sensing efficiency. One of the main technical challenges in dynamic spectrum access (DSA) system is to detect the existence of primary users' transmission, so as to determine the availability of a certain channel [14]- [16]. Cooperation among multiple secondary users, using their spatial diversity, is employed to enhance spectrum sensing performance [17]- [19].…”

Section: Introductionmentioning

confidence: 99%

Privacy Preserving Algorithm for Spectrum Sensing in Cognitive Vehicle Networks

Li,

Hu,

Chen

et al. 2024

Chinese J. Elect.

View full text Add to dashboard Cite

The scarcity of spectrum resources fails to meet the increasing throughput demands of vehicular networks. There is an urgent need to maximize the utilization of spectrum bands in mobile networks. To ascertain the availability of spectrum bands, users should engage in wireless channel sensing and collaboration. However, spectrum sensing data always involves users' privacy, such as their location. This paper first introduces sensing trajectory inference attack in cognitive vehicular networks and then proposes a data confusion-based privacy-preserving algorithm and a cryptonym array-based privacy-preserving aggregation scheme for spectrum sensing in cognitive vehicular networks. Unlike existing methods, the proposed schemes transmit confused data during the aggregation process. This deliberate obfuscation makes it almost impossible to infer users' location from the transmitted data. The analysis demonstrates the resilience of the proposed schemes against sensing trajectory inference attack.

show abstract

Energy-Efficient Cooperative Spectrum Sensing: A Review

Cited by 6 publications

References 6 publications

Energy-Efficient Cooperative Spectrum Sensing Strategy for Cognitive Wireless Sensor Networks Based on Particle Swarm Optimization

Energy-Efficient Cooperative Spectrum Sensing Strategy for Cognitive Wireless Sensor Networks Based on Particle Swarm Optimization

PSO-Based Agent Cooperative Spectrum Sensing in Cognitive Radio Networks

Privacy Preserving Algorithm for Spectrum Sensing in Cognitive Vehicle Networks

Contact Info

Product

Resources

About