Effective resource allocation is a crucial aspect in improving the performance of visible light communication (VLC)/hybrid WiFi networks. This study presents a dynamic resource allocation algorithm for multiuser scenarios. A fuzzy logic technique is employed to select the network resources with higher scores for communication. In this sense, to ensure fairness in the hybrid VLC-WiFi network, an enhanced proportional fairness (PF) algorithm is utilized. The algorithm considers the access delay and the distance to the access point as users move, striking a reasonable balance between fair allocation of user resources and maximizing system resource utilization. Therefore, to enhance the user experience in real-time scheduling, a compensation factor is introduced to compensate users with higher latency, increasing their priority. A thorough analysis is provided for the throughput, latency, utility value, and fairness models of the VLC-WiFi heterogeneous network system. The algorithm proposed in this paper is simulated and compared with four traditional algorithms, namely the Round-Robin algorithm, maximum carrier to interference ratio (Max C/I) algorithm, modified largest weighted delay first algorithm, and PF algorithm, under different metrics. Simulation results show that the proposed method is effective, and the improved algorithm yields better fairness and higher throughput compared to the conventional algorithm. As the number of frames increases, the fairness index of the proposed algorithm increases the fastest and gradually converges to 1. The throughput of this method is significantly greater than that of previous algorithms, reaching a stable rate of roughly 1.3 Mbps. The suggested algorithm is approximately 150% higher than the throughput achieved by conventional PF algorithms. The algorithm improves user satisfaction in different regions, and the utility value remains around 0.975. The packet loss rate is approximately 75% lower than traditional typical algorithms, and the average latency is approximately 60% lower. Simulation results show that the proposed algorithm has good application prospects in heterogeneous networks.

An improved SLM algorithm is proposed to improve the PAPR reduction capability of OFDM-based UWOC. In order to reduce data correlation, chaotic sequences are employed. Simulation findings demonstrate the effectiveness of the algorithm.

An improved bat algorithm is proposed to increase the energy efficiency of macro/femtocell heterogeneous networks based on OFDMA. Simulation results indicate that the algorithm enhances the optimization ability and convergence speed in later stages.

One of the key features of cloud computing is on demand resource provision. Unfortunately, different cloud service providers often have different standards, which makes the job of choosing the suitable resource to be a very difficult one for the common users. Then the technology of service broker came out which is designed to choose the appropriate services among different providers to meet the user's requests. Besides get the final computing results, there are many other conditions often be taken into account, like energy consumption and response time. Maximizing the energy efficiency is not only good for the environment protection but also could benefit the user and provider financially. This paper proposed an energy-aware service brokering strategy, which aimed at finding a suitable trade-off between energy consumption and user satisfaction. The simulation results have shown that the proposed strategy can effectively reduce the level of energy consumption and meanwhile maintain the user satisfaction at a reasonably good level.