Reputation-Based Self-Differential Sequential Mechanism for Collaborative Spectrum Sensing Against Byzantine Attack in Cognitive Wireless Sensor Networks

IF 2.2 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Sensors Letters Pub Date : 2024-09-04 DOI:10.1109/LSENS.2024.3454708
Shengfei Xiao;Jun Wu;Peiyang Lin;Lei Qiao;Zhaoyang Qiu;Mingkun Su
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Abstract

In order to meet the increasing frequency demand for sensors and their related applications, cognitive radio (CR) technology has been integrated into wireless sensor networks, detecting available spectrum resources through collaborative spectrum sensing (CSS) among multiple sensors and avoiding harmful interference to the primary user. However, some malicious sensor nodes (MSNs) may also take advantage of collaborative opportunities to launch Byzantine attack, reducing the performance and efficiency of CSS. In order to suppress the negative impact of MSNs, this letter proposes a reputation-based self-differential sequential mechanism (R-SDSM) to defend against Byzantine attack. First, sensor nodes with high reputation value are prioritized to participate in CSS and complete the data fusion with more appropriate weight allocation. Furthermore, a self-differential sequential mechanism is proposed to reduce the reporting decisions required for the fusion center. Finally, numerical simulation results demonstrate that in contrast to other data fusion rules, the proposed R-SDSM provides higher detection accuracy and fewer reporting decisions.
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认知式无线传感器网络中基于声誉的自差分序列机制--用于协作式频谱传感以对抗拜占庭攻击
为了满足传感器及其相关应用日益增长的频率需求,认知无线电(CR)技术已被集成到无线传感器网络中,通过多个传感器之间的协作频谱感知(CSS)检测可用频谱资源,避免对主用户造成有害干扰。然而,一些恶意传感器节点(MSN)也可能利用协作机会发起拜占庭攻击,降低 CSS 的性能和效率。为了抑制 MSN 的负面影响,本文提出了一种基于声誉的自差异序列机制(R-SDSM)来防御拜占庭攻击。首先,声誉值高的传感器节点优先参与 CSS,并以更合适的权重分配完成数据融合。此外,还提出了一种自差序机制,以减少融合中心所需的报告决策。最后,数值模拟结果表明,与其他数据融合规则相比,所提出的 R-SDSM 具有更高的检测精度和更少的报告决策。
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来源期刊
IEEE Sensors Letters
IEEE Sensors Letters Engineering-Electrical and Electronic Engineering
CiteScore
3.50
自引率
7.10%
发文量
194
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