Current Issue : October - December Volume : 2020 Issue Number : 4 Articles : 6 Articles
In the Internet of things (IoT), network devices and mobile systems should exchange a considerable amount of data with negligible\ndelays. For this purpose, the community has used the software-defined networking (SDN), which has provided high-speed flowbased\ncommunication mechanisms. To satisfy the requirements of SDN in the classification of communicated packets, highthroughput\npacket classification systems are needed. A hardware-based method of Internet packet classification that could be\nsimultaneously high-speed and memory-aware has been proved to be able to fill the gap between the network speed and the\nprocessing speed of the systems on the network in traffics higher than 100 Gbps. The current architectures, however, have not been\nsuccessful in achieving these two goals. This paper proposes the architecture of a processing micro-core for packet classification in\nhigh-speed, flow-based network systems. By using the hashing technique, this classifying micro-core fixes the length of the rules\nfield. As a result, with a combination of SRAM and BRAM memory cells and implementation of two ports on Virtex...................
As real-time and immediate feedback becomes increasingly important in tasks related to mobile information, big data stream\nprocessing systems are increasingly applied to process massive amounts of mobile data. However, when processing a drastically\nfluctuating mobile data stream, the lack of an elastic resource-scheduling strategy limits the elasticity and scalability of data stream\nprocessing systems. To address this problem, this paper builds a flow-network model, a resource allocation model, and a data\nredistribution model as the foundation for proposing Flink with an elastic resource-scheduling strategy (Flink-ER), which consists\nof a capacity detection algorithm, an elastic resource reallocation algorithm, and a data redistribution algorithm. The strategy\nimproves the performance of the platform by dynamically rescaling the cluster and increasing the parallelism of operators based\non the processing load. Theexperimental results show that the throughput of a cluster was promoted under the premise of meeting\nlatency constraints, which verifies the efficiency of the strategy....
In this paper, the L(p, q)-coloring problem of the graph is studied with application to channel allocation of the wireless network. First, by introducing two new logical operators, some necessary and sufficient conditions for solving the L(p, q)-coloring problem are given. Moreover, it is noted that all solutions of the obtained logical equations are corresponding to each coloring scheme. Second, by using the semitensor product, the necessary and sufficient conditions are converted to an algebraic form. Based on this,all coloring schemes can be obtained through searching all column indices of the zero columns. Finally, the obtained result is applied to analyze channel allocation of the wireless network. Furthermore, an illustration example is given to show the effectiveness\nof the obtained results in this paper....
This paper analyzed the development of data mining and the development of the fifth generation (5G) for the Internet of Things\n(IoT) and uses a deep learning method for stock forecasting. In order to solve the problems such as low accuracy and training\ncomplexity caused by complicated data in stock model forecasting, we proposed a forecasting method based on the feature\nselection (FS) and Long Short-Term Memory (LSTM) algorithm to predict the closing price of stock. Considering its future\npotential application, this paper takes 4 stock data from the Shenzhen Component Index as an example and constructs the\nfeature set for prediction based on 17 technical indexes which are commonly used in stock market. The optimal feature set is\ndecided via FS to reduce the dimension of data and the training complexity. The LSTM algorithm is used to forecast closing\nprice of stock. The empirical results show that compared with the LSTM model, the FS-LSTM combination model improves the\naccuracy of prediction and reduces the error between the real value and the forecast value in stock price prediction....
As an indispensable key technology in 5G Internet of Things (IoT), mobile edge computing (MEC) provides a variety of computing and\nservices at the edge of the network for energy-limited and computation-constrained mobile devices (MDs). In this paper, we use the\nmultiaccess characteristics of 5G heterogeneous networks and queuing theory. By considering the heterogeneity of base stations, we\nestablish the waiting and transmission consumption model when tasks are offloaded. Then, the problem of jointly optimizing the energy\nand delay consumption of MDs is proposed. We adopt an optimization scheme based on task assignment probability; moreover, the\ntask assignment algorithm based on quasi-Newton interior point (TA-QNIP) method is developed to solve the optimization issue.\nCompared with the Newton interior point algorithm, the proposed algorithm accelerates the convergence speed and reduces the\ncomplexity of the algorithm and is closer to the objective function optimal solution. The simulation results demonstrate that the\nproposed method can reduce the total consumption of MDs effectively; furthermore, the performance of the algorithm is proved....
The base transceiver stations (BTS) are telecom infrastructures that facilitate wireless communication between the subscriber device\nand the telecom operator networks. They are deployed in suitable places having a lot of freely propagating ambient radio frequency\n(RF) and solar energies. This paper is aimed at converting received ambient environmental energy into usable electricity to power\nthe stations. We proposed a hybrid energy harvesting system that can collect energy from RF and solar energies at the same time.\nThe sources are combined to provide to a significant amount, to contribute to operational expenditures that reduce energy costs,\nand to improve the energy efficiency of the base station sites in rural areas from the most common renewable resources since\nthe base stations are major consumers of cellular networks. The hybrid systems are designed with circuits, simulated, and\ncompared to show their good performance to the base stations. PSIM, PROTEUS, and MATLAB software are used to simulate\nfor evaluating the voltage and the current output of the hybrid systems that meet the power requirements. The design and\nsimulation results show the feasibility of our proposed method with the battery storage that can be deployed not only in real\nbase stations but also for other electrical operated systems....
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