感知無線電是一項頗具希望解決物聯網資源缺乏問題之技術。然而在感知無線電物聯網中，當可用資源改變時，現存資源分配算法通常會在不使用歷史資訊的情況下重新啟動最佳化；此外，它們主要著重於一個或兩個目標函數，其中一些更僅針對純功率控制或純頻譜分配，限制了感知無線電物聯網的潛力。本論文研究了一種動態多目標方法應用於感知無線電物聯網之能源使用與頻譜分配，其中可用頻譜通道隨時間改變，且多個目標函數包含在內。我們制定了一個以能源效率，公平性和頻譜利用率為目標之動態多目標最佳化問題，並採用了柏拉圖最適性。我們提出一種由混合初始化方法和可行解生成機制所組成之動態資源分配算法，以解決動態多目標最佳化問題。為了動態調整資源，我們將歷史近似柏拉圖最佳解以質心和副本表示，並用於預測新的最佳解。與類似之多目標資源分配算法相比，所提出之方法可以達到更好的收斂水平和收斂速度。與傳統之單目標方法相比，它亦能於目標間取得出色的平衡。

Cognitive radio (CR) is a promising technology to address resource scarcity in Internet of Things (IoT). In CR­based IoT, however, existing resource allocation algorithms normally restart optimization processes without utilizing historical information when the availability of resources changes; in addition, they mostly focus on one or two objectives and some of them only address pure power control or pure spectrum allocation, limiting the potential of CR­based IoT. This paper thus investigates a dynamic multiobjective approach to power and spectrum allocation in a CR­based IoT environment in which available spectrum channels vary over time and multiple objectives are involved. A dynamic multiobjective optimization problem (MOP) with energy efficiency, fairness, and spectrum utilization as objectives is formulated and Pareto optimality is adopted. A dynamic resource allocation algorithm consisting of a hybrid initialization method and feasible point generation is proposed to solve the dynamic MOP. To dynamically adjust resources, historical approximate Pareto optimal solutions are represented by a center and a manifold and used to predict new optima. The proposed approach can yield a better convergence level and convergence rate than those attained by comparable multiobjective resource allocation algorithms; it also achieves an excellent balance between the objectives as compared with conventional single­objective approaches.

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