Issues in Realization of Cognitive Radio Sensor Networks

Prakash, P. Suman; Lee, Seong-Ro; Noh, Sun-Kok; Choi, Dong-You

doi:10.14257/ijca.2014.7.1.12

Cited by 12 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different types of transmission impairments are very common to occur during the process of spectrum sensing. This includes rigorous fading [ 16 ], issues that arise due to hidden terminals [ 17 ] and shadowing effect [ 18 ]. The impairments cause unfavorable effects on the sensing channel and bring out a drastic loss in the system’s detection performance.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Fusion Based Passive Multistatic Radar Detection

Asif

Kandeepan

2021

Sensors

View full text Add to dashboard Cite

Passive multistatic radars have gained a lot of interest in recent years as they offer many benefits contrary to conventional radars. Here in this research, our aim is detection of target in a passive multistatic radar system. The system contains a single transmitter and multiple spatially distributed receivers comprised of both the surveillance and reference antennas. The system consists of two main parts: 1. Local receiver, and 2. Fusion center. Each local receiver detects the signal, processes it, and passes the information to the fusion center for final detection. To take the advantage of spatial diversity, we apply major fusion techniques consisting of hard fusion and soft fusion for the case of multistatic passive radars. Hard fusion techniques are analyzed for the case of different local radar detectors. In terms of soft fusion, a blind technique called equal gain soft fusion technique with random matrix theory-based local detector is analytically and theoretically analyzed under null hypothesis along with the calculation of detection threshold. Furthermore, six novel random matrix theory-based soft fusion techniques are proposed. All the techniques are blind in nature and hence do not require any knowledge of transmitted signal or channel information. Simulation results illustrate that proposed fusion techniques increase detection performance to a reasonable extent compared to other blind fusion techniques.

show abstract

Section: Introductionmentioning

confidence: 99%

Cooperative Fusion Based Passive Multistatic Radar Detection

Asif

Kandeepan

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…It brings about tremendous challenges in cognitive radio systems and influences the dynamic spectrum access for CR-enable sensor nodes to utilize idle licensed bands opportunistically. By combining the numerous sensor nodes' local sensing results, cooperative spectrum sensing (CSS) can alleviate the inaccuracy of single node's observation and enhance the spectrum sensing performance [4]. However, false reports included in cooperative nodes will cause a serious security threat and may disrupt the cognitive radio system [5].…”

Section: Introductionmentioning

confidence: 99%

Secure Cooperative Spectrum Sensing Strategy Based on Reputation Mechanism for Cognitive Wireless Sensor Networks

Luo

2020

IEEE Access

View full text Add to dashboard Cite

Cooperative spectrum sensing can be regarded as a promising method to resolve the spectrum scarcity owing to achieving spatial diversity gain in cognitive radio sensor networks. However, the spectrum sensing data falsification attack launched by the malicious nodes will result in the wrong decision in the fusion center owing to the falsified observations. It will cause a serious security threat and degrade the decision making process. In this paper, we propose a secure cooperative spectrum sensing strategy based on reputation mechanism for cognitive wireless sensor networks to counter above kind of attack. The beta reputation model is applied to assign reputation value to cognitive sensor nodes according to their historical sensing behavior, and a dynamic trust evaluation scheme of cooperative spectrum sensing is established. In the final decision, the fusion center allocates a reasonable weight value according to the evaluation of the submitted observations to improve the accuracy of the sensing results. Simulation results support that our proposed strategy can weaken the impact of sensing data falsification attacks in cooperative sensing and outperform some traditional methods. INDEX TERMS Cooperative spectrum sensing, reputation mechanism, cognitive wireless sensor networks, cognitive radio.

show abstract

“…By employing cognitive radio (CR) technology, the sensor nodes in cognitive radio sensor networks (CRSNs) can change its parameters according to the interactions with the environment. The available licensed bands will be utilized efficiently, which can alleviate the problem of spectrum scarcity to some extent [1]. As a kind of network with cognitive ability to spectrum resources, CRSN can make more flexible use of the sensing results.…”

Section: Introductionmentioning

confidence: 99%

“…However, during the process of actual spectrum sensing, many kinds of transmission impairments usually occurs, including severe fading [3], shadowing [4], hidden terminal problems [5], et al, which brings about adverse effects on the sensing or reporting channels and causes severe damage of the system's detection performance. More concretely, due to the condition of multi-path fading and shadow effect, the signal attenuation will be too serious for cognitive sensor nodes to distinguish the state of licensed channels under low signal-to-noise ratio (SNR) condition.…”

Section: Introductionmentioning

confidence: 99%

Soft Decision Cooperative Spectrum Sensing With Entropy Weight Method for Cognitive Radio Sensor Networks

Lin

Liu

2020

IEEE Access

View full text Add to dashboard Cite

By fully utilizing the spatial gain and exploiting the multiuser diversity, cooperative spectrum sensing can enhance the sensing accuracy. In the actual wireless environment, the effect of shadowing and fading will result in the different features of signals received by the sensing nodes with different distances from primary user. As a result, some cooperative nodes in deep fading will suffer from serious missed detection, which will affect the final results during the fusing operation. To solve the above problems, a soft decision cooperative spectrum sensing with entropy weight method for cognitive radio sensor networks is presented. Initially, the sensor nodes will be organized into logical groups to obtain energy efficiency and improvement of sensing performance. After receiving the soft sensing information from all member nodes, the cluster heads employs the equal gain soft combination for inter-cluster fusion and then forwards the local decision to the fusion center. During the final decision, the entropy weight method is applied to assign optimal weight value to corresponding cluster local decisions. The simulation results show that the proposed method can outperform some typical clustering scheme for cooperative spectrum sensing in terms of the detection probability and the total error probability.

show abstract

Issues in Realization of Cognitive Radio Sensor Networks

Abstract: Recently, significant growth in the applications of the wireless sensor networks (WSNs)

Cited by 12 publications

References 29 publications

Cooperative Fusion Based Passive Multistatic Radar Detection

Cooperative Fusion Based Passive Multistatic Radar Detection

Secure Cooperative Spectrum Sensing Strategy Based on Reputation Mechanism for Cognitive Wireless Sensor Networks

Soft Decision Cooperative Spectrum Sensing With Entropy Weight Method for Cognitive Radio Sensor Networks

Contact Info

Product

Resources

About