Scholarlink

<p>Secure and energy-efficient data transmission is primary requirement for the efficient and secure utilization of bandwidth. The advancement of machine learning opens a new scope for the early prediction of the attacks. In the present work, a Security-aware and Aggregation-aware Energy Efficient Data Transmission algorithm is proposed for Wireless Sensor Networks (WSNs) which integrating machine learning-driven attack prediction with adaptive data aggregation strategies. The proposed method classifies sensor nodes into two risk categories based on the predicted probability of attack: high-risk, and low-risk data transmission. Communication from sensor nodes to cluster heads (CHs) employs an enhanced energy-aware TDMA (EATDMA) mechanism, while data aggregation is selectively applied only at the CH level. Aggregated data is further secured and transmitted to the base station via a bit-mapping assisted (BMA) aggregation-aware protocol. This approach minimizes energy consumption while enhancing resilience to attacks, ensuring both data integrity and operational longevity of the network. The proposed aggregation method utilizes secure data transmission using AES-256 mechanism at application layer.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Security and Aggregation-aware ML-Based MAC Protocol for Energy-Efficient Wireless Sensor Network

  • 期刊论文

  • 发表日期：

    2026年7月1日

查看全文
  • Hari Narayan Shukla,
  • Tushar Chaurasia,
  • Richa Patel,
  • Sonal Laad,
  • Adwan Alownie Alanazi,
  • Akhilesh Panchal

摘要

Secure and energy-efficient data transmission is primary requirement for the efficient and secure utilization of bandwidth. The advancement of machine learning opens a new scope for the early prediction of the attacks. In the present work, a Security-aware and Aggregation-aware Energy Efficient Data Transmission algorithm is proposed for Wireless Sensor Networks (WSNs) which integrating machine learning-driven attack prediction with adaptive data aggregation strategies. The proposed method classifies sensor nodes into two risk categories based on the predicted probability of attack: high-risk, and low-risk data transmission. Communication from sensor nodes to cluster heads (CHs) employs an enhanced energy-aware TDMA (EATDMA) mechanism, while data aggregation is selectively applied only at the CH level. Aggregated data is further secured and transmitted to the base station via a bit-mapping assisted (BMA) aggregation-aware protocol. This approach minimizes energy consumption while enhancing resilience to attacks, ensuring both data integrity and operational longevity of the network. The proposed aggregation method utilizes secure data transmission using AES-256 mechanism at application layer.