In order to increase the performance of the IoT (Internet of Things), the fog computing model is proposed. Here, subprocesses of an application process to handle sensor data are performed on fog nodes in addition to s...
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ISBN:
(纸本)9783030290290;9783030290283
In order to increase the performance of the IoT (Internet of Things), the fog computing model is proposed. Here, subprocesses of an application process to handle sensor data are performed on fog nodes in addition to servers. In the TBFC (Tree-Based Fog Computing) model proposed in our previous studies, an application process to handle sensor data is assumed to be a sequence of subprocesses, i.e. linear model. At each level of a TBFC tree, a same subprocess is performed on every node. In this paper, we consider a more general model, GTBFC (General TBFC) model of the IoT where subprocesses of an application process are structured in a tree. Each subprocess in the process tree is performed on fog nodes which are at a same level in the GTBFC tree. Each leaf subprocess is performed on edge nodes which communicate with sensor and actuator devices. We also proposed meg (Minimum Energy in the GTBFC tree) and SMPRG (Selecting Multiple Parents for Recovery in the GTBFC tree) algorithms to select a new parent node for a child node of a faulty node in the GTBFC tree. In the evaluation, we show the energy consumption of nodes in the SMPRG algorithm as 21% and 31% smaller than the meg and RD (Random) algorithms.
Dynamic spectrum allocation (DSA) based on secondary spectrum market is considered a promising technology to improve spectrum utilization efficiency and to relieve the wireless spectrum shortage problem. We propose a ...
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Dynamic spectrum allocation (DSA) based on secondary spectrum market is considered a promising technology to improve spectrum utilization efficiency and to relieve the wireless spectrum shortage problem. We propose a dynamic spectrum allocation algorithm named market equilibrium and game (meg), and construct a complete secondary spectrum market. The market based on the meg algorithm consists of two submarkets: multiple primary services providers (PSPs) and a dynamic spectrum allocation server (DSAS) form the high submarket, while the low submarket is composed of the DSAS and a number of secondary users. In the low submarket, the meg algorithm provides a game type selection strategy. By this strategy, the DSAS can win more payoff's with lower unit spectrum price, which encourages secondary users to use more spectrum. A secondary user can also choose its preferable game type between dynamic game and Nash bargaining flexibly. On the other hand, a bargaining procedure in the high submarket is designed in the meg algorithm to ensure that market equilibrium is quickly reached. A performance analysis shows that the strategy of game type selection is fair and feasible for both the DSAS and the secondary users. Moreover, the bargaining procedure is better than the existing algorithm which adjusts price step by step in the high submarket. Simulation results also demonstrate that the market fluctuation in the low submarket is passed to the high submarket by way of the DSAS. The meg algorithm can effectively satisfy the highly-fluctuating demands from the secondary users. In addition, the meg algorithm can improve the payoffs of all players and increase spectrum utilization efficiency.
The authors investigate dynamic physical resource block (PRB) allocation for the uplink long-term evolution (LTE) system with single carrier-frequency division multiple access (SC-FDMA). Three dynamic PRB allocation a...
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The authors investigate dynamic physical resource block (PRB) allocation for the uplink long-term evolution (LTE) system with single carrier-frequency division multiple access (SC-FDMA). Three dynamic PRB allocation algorithms are proposed, which are referred to as the maximum greedy (MG), mean enhanced greedy (meg) and single mean enhanced greedy (Smeg) algorithms, respectively. Simulation results show that the proposed algorithms significantly outperform the previous two-dimensional (2-D) algorithm in terms of bit error rate (BER) and data rate fairness. The meg algorithm is shown to provide a performance close to the Hungarian algorithm (optimal algorithm to maximise the SE) in terms of spectral efficiency (SE), while requiring a much lower computational complexity. Smeg further reduces the complexity of meg with little performance degradation. Furthermore, the effects of imperfect channel estimation, root mean square (RMS) delay, Doppler spread and channel estimate feedback delay on performance are investigated.
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