摘要 :
The widespread acceptability of mobile devices in present times have caused their applications to be increasingly rich in terms of the functionalities they provide to the end users. Such applications might be very prevalent among ...
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The widespread acceptability of mobile devices in present times have caused their applications to be increasingly rich in terms of the functionalities they provide to the end users. Such applications might be very prevalent among users but the execution results in dissipating many of the device end resources. Mobile cloud computing (MCC) has a solution to this problem by offloading certain parts of the application to cloud. At the first place, one might find computation offloading quite promising in terms of saving device end resources but eventually may result in being the other way around if performed in a static manner. Frequent changes in device end resources and computing environment variables may lead to a reduction in the efficiency of offloading techniques and even cause a drop in the quality of service for applications involving the use of real-time information. In order to overcome this problem, the authors propose an adaptive computation offloading framework for data stream applications wherein applications are partitioned dynamically followed by being offloaded depending upon the device end parameters, network conditions, and cloud resources. The article also talks about the proposed algorithm that depicts the workflow of the offloading model. The proposed model is simulated using the CloudSim simulator. In the end, the authors illustrate the working of the proposed system along with the simulated results.
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Mobile Edge Computing (MEC) has been regarded as a key technology of the future communication systems in the industry due to its capability to satisfy a wide range of requirements of the emerging wireless terminals (virtual realit...
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Mobile Edge Computing (MEC) has been regarded as a key technology of the future communication systems in the industry due to its capability to satisfy a wide range of requirements of the emerging wireless terminals (virtual reality devices, augmented reality, and Intelligent Vehicles), such as high data rate, low latency, and huge computation. Besides, difficulties in the lack of resources in the licensed band have prompted researches on mobile data offloading. Owing to the cheap and effective characteristics of WiFi AP, it is utilized to offload some devices from small base stations (SBS) in this paper. Furthermore, a multi-Long Short Term Memory (LSTM) based deep-learning model is constructed to predict the real-time traffic of SBS, which may help us perform the offloading process accurately. According to the prediction results, an mobile data offloading strategy based on cross entropy (CE) method has been proposed. The presented results based on actual dataset provide strong proofs of the applicability of the prediction and offloading scheme we proposed. (C) 2019 Elsevier B.V. All rights reserved.
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Mobile sensing emerges as an important application for mobile networks. Smartphones equipped with sensors are used to monitor a diverse range of human activities. One key and challenging procedure of the mobile sensing application...
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Mobile sensing emerges as an important application for mobile networks. Smartphones equipped with sensors are used to monitor a diverse range of human activities. One key and challenging procedure of the mobile sensing applications is data gathering, where the sensed data from distributed mobile nodes are captured and uploaded to the cloud or base station for further processing. Yet the mobile sensing application, which usually periodically generates some sensed data, would definitely deteriorate the 3G quality because the network cannot cope with the high demand; and users would be charged at high prices by using the 3G channel, which makes the mobile sensing application infeasible. In this paper, we proposed a hybrid data gathering and offloading algorithm DGO for the mobile sensing applications. Besides the direct uploading through 3G or Wifi offloading, the sensed data could also be forwarded to other peer nodes through short range communications. Nodes collect meta-data such as remaining energy, contact regularity, and expected contact duration to calculate the upload/offload utility and upload priority for data segments. Based on these utility factors, each data segment could decide its own approach at a specific time for uploading. Experimental studies show that DGO is efficient in data gathering and data offloading in mobile sensing applications. Given the low accessibility of Wifi APs, DGO still gains about more than 30 % of data offloading compared with existing algorithms without much extra transmission overhead or delay.
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摘要 :
To cope with recent exponential increases in demand for mobile data, wireless Internet service providers (ISPs) are increasingly changing their pricing plans and deploying Wi-Fi hotspots to offload their mobile traffic. However, t...
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To cope with recent exponential increases in demand for mobile data, wireless Internet service providers (ISPs) are increasingly changing their pricing plans and deploying Wi-Fi hotspots to offload their mobile traffic. However, these ISP-centric approaches for traffic management do not always match the interests of mobile users. Users face a complex, multi-dimensional tradeoff between cost, throughput, and delay in making their offloading decisions: while they may save money and receive a higher throughput by waiting for Wi-Fi access, they may not wait for Wi-Fi if they are sensitive to delay. To navigate this tradeoff, we develop Adaptive bandwidth Management through USer-Empowerment (AMUSE), a functional prototype of a practical, cost-aware Wi-Fi offloading system that takes into account a user's throughput-delay tradeoffs and cellular budget constraint. Based on predicted future usage and Wi-Fi availability, AMUSE decides which applications to offload to what times of the day. Since nearly all traffic flows from mobile devices are TCP flows, we introduce a new receiver-side bandwidth allocation mechanism to practically enforce the assigned rate of each TCP application. Thus, AMUSE users can optimize their bandwidth rates according to their own cost-throughput-delay tradeoff without relying on support from different apps’ content servers. Through a measurement study of 20 smartphone users’ traffic usage traces, we observe that though users already offload a large amount of some application types, our framework can offload a significant additional portion of users’ cellular traffic. We implement AMUSE on Windows 7 tablets and evaluate its effectiveness with 3G and Wi-Fi usage data obtained from a trial with 37 mobile users. Our results show that AMUSE improves user utility; when compared with AMUSE, other offloading algorithms yield 14 and 27 percent lower user utilities for light and heavy users, respectively. Intelligently managing u- ers’ competing interests for cost, throughput, and delay can therefore improve their offloading decisions.
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With rapid increases in demand for mobile data, mobile data offloading by deploying wireless local area network (LAN) access points (APs) has been an attractive approach to alleviate mobile network operators' (MNOs') cellular netw...
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With rapid increases in demand for mobile data, mobile data offloading by deploying wireless local area network (LAN) access points (APs) has been an attractive approach to alleviate mobile network operators' (MNOs') cellular network congestion. MNOs ought to reap the profit from the huge mobile data demand. However, it is reported that one of the largest Chinese MNO, China Mobile, has suffered from profit falls even for huge mobile traffic increase. The reason is that traditional methods to deploy wireless LAN APs in a heuristic manner do not consider MNO's profit. This poses a question: How to deploy wireless LAN APs to maximize MNO's profit? Existing works concentrated either on cost reduction effect of wireless LAN data offloading from MNOs's perspective, or on maximization of mobile users (MUs) fulfillment from MUs' perspective. In this paper, we formulate a MNO's profit maximization problem by considering optimal wireless LAN APs deployment, to answer the aforementioned question.
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In this letter, for offloading traffic to Wireless Local Area Network (WLAN) with transport layer mobility where WLAN service is intermittently available, we propose a novel scheme to freeze and melt the timeout handling procedure...
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In this letter, for offloading traffic to Wireless Local Area Network (WLAN) with transport layer mobility where WLAN service is intermittently available, we propose a novel scheme to freeze and melt the timeout handling procedure of SCTP. Simulation results show that the proposed scheme significantly improves the performance in terms of file transfer completion time.
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Mobile edge computing can augment the capabilities of mobile terminals (MTs) by enabling computing and caching functionalities for base stations (BSs). Because BSs have only limited computation and storage resources compared with ...
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Mobile edge computing can augment the capabilities of mobile terminals (MTs) by enabling computing and caching functionalities for base stations (BSs). Because BSs have only limited computation and storage resources compared with cloud servers, they need to efficiently manage the computation offloading and data caching for MTs. In this paper, a novel scheme for efficient computation offloading and data caching assisted by mobile-edge-computing-enabled BSs (MEC-BSs) is proposed. To maximize the MT benefits in terms of reduced time and energy consumption, our scheme determines the probability that each MT offloads each type of its tasks to the MEC-BS and indicates whether the cloud data for each type of MT task is cached at the MEC-BS. A balance factor is used to flexibly adjust the tendency of the optimization between the minimization of time and energy consumption. Based on the stable probabilistic characteristics of MT tasks and the MEC-BS service, the optimization algorithm of our scheme can be executed independently and concurrently without deteriorating the system performance. The simulation results demonstrate that our scheme can largely improve the system performance and that it always outperforms other reference schemes in scenarios with multiple criteria.
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Faced with the tremendous increase in the amount of data traffic and associated congestion, mobile network operators are moving towards Heterogeneous networks (HetNets), in the process of expanding network capacity. Offloading dat...
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Faced with the tremendous increase in the amount of data traffic and associated congestion, mobile network operators are moving towards Heterogeneous networks (HetNets), in the process of expanding network capacity. Offloading data traffic onto Wi-Fi in order to avoid congestion in the backbone is an important step in the evolution of HetNets. On-the-spot and delayed offloading have been widely studied in the literature. This paper proposes an offloading algorithm which has low computational complexity. The proposed algorithm offloads data based on a balking function which is dependent on present network condition. Using extensive simulations, the authors demonstrate that the proposed algorithm achieves reduction in mean transmission delay without sacrificing much on the offloading efficiency. This technique is more efficient and applicable to real-time traffic, like live streaming video and audio, which has short and stringent delay requirements or deadlines.
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To cope with explosive vehicular traffic and ever-increasing application demands in the vehicular cellular network, opportunistic vehicular networks are used to disseminate mobile data by high-capacity device-to-device communicati...
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To cope with explosive vehicular traffic and ever-increasing application demands in the vehicular cellular network, opportunistic vehicular networks are used to disseminate mobile data by high-capacity device-to-device communication, which offloads significant traffic from the cellular network. In the current opportunistic vehicular data transmission, coding-based schemes are proposed to address the challenge of opportunistic contact. However, whether coding techniques can be beneficial in the context of vehicular mobile data offloading is still an open question. In this paper, we establish a mathematical framework to study the problem of coding-based mobile data offloading under realistic network assumptions, where 1) mobile data items are heterogeneous in terms of size; 2) mobile users have different interests to different data; and 3) the storage of offloading participants is limited. We formulate the problem as a users' interest satisfaction maximization problem with multiple linear constraints of limited storage. Then, we propose an efficient scheme to solve the problem, by providing a solution that decides when the coding should be used and how to allocate the network resources in terms of contact rate and offloading helpers' storage. Finally, we show the effectiveness of our algorithm through extensive simulations using two real vehicular traces.
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The unprecedented growth of mobile data traffic challenges the performance and economic viability of today’s cellular networks and calls for novel network architectures and communication solutions. Mobile data offloading through ...
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The unprecedented growth of mobile data traffic challenges the performance and economic viability of today’s cellular networks and calls for novel network architectures and communication solutions. Mobile data offloading through third-party Wi-Fi or femtocell access points (APs) can significantly alleviate the cellular congestion and enhance user quality of service (QoS), without requiring costly and time-consuming infrastructure investments. This solution has substantial benefits both for the mobile network operators (MNOs) and the mobile users, but comes with unique technical and economic challenges that must be jointly addressed. In this paper, we consider a market where MNOs lease APs that are already deployed by residential users for the offloading purpose. We assume that each MNO can employ multiple APs, and each AP can concurrently serve traffic from multiple MNOs. We design an iterative double-auction mechanism that ensures the efficient operation of the market by maximizing the differences between the MNOs’ offloading benefits and APs’ offloading costs. The proposed scheme takes into account the particular characteristics of the wireless network, such as the coupling of MNOs’ offloading decisions and APs’ capacity constraints. Additionally, it does not require full information about the MNOs and APs and creates nonnegative revenue for the market broker.
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