1,043 research outputs found

    Cost- and Energy-Aware Multi-Flow Mobile Data Offloading Using Markov Decision Process

    Full text link
    With the rapid increase in demand for mobile data, mobile network operators are trying to expand wireless network capacity by deploying wireless local area network (LAN) hotspots on which they can offload their mobile traffic. However, these network-centric methods usually do not fulfill the interests of mobile users (MUs). Taking into consideration many issues, MUs should be able to decide whether to offload their traffic to a complementary wireless LAN. Our previous work studied single-flow wireless LAN offloading from a MU's perspective by considering delay-tolerance of traffic, monetary cost and energy consumption. In this paper, we study the multi-flow mobile data offloading problem from a MU's perspective in which a MU has multiple applications to download data simultaneously from remote servers, and different applications' data have different deadlines. We formulate the wireless LAN offloading problem as a finite-horizon discrete-time Markov decision process (MDP) and establish an optimal policy by a dynamic programming based algorithm. Since the time complexity of the dynamic programming based offloading algorithm is still high, we propose a low time complexity heuristic offloading algorithm with performance sacrifice. Extensive simulations are conducted to validate our proposed offloading algorithms

    A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

    Get PDF
    The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network

    Computation Offloading and Scheduling in Edge-Fog Cloud Computing

    Get PDF
    Resource allocation and task scheduling in the Cloud environment faces many challenges, such as time delay, energy consumption, and security. Also, executing computation tasks of mobile applications on mobile devices (MDs) requires a lot of resources, so they can offload to the Cloud. But Cloud is far from MDs and has challenges as high delay and power consumption. Edge computing with processing near the Internet of Things (IoT) devices have been able to reduce the delay to some extent, but the problem is distancing itself from the Cloud. The fog computing (FC), with the placement of sensors and Cloud, increase the speed and reduce the energy consumption. Thus, FC is suitable for IoT applications. In this article, we review the resource allocation and task scheduling methods in Cloud, Edge and Fog environments, such as traditional, heuristic, and meta-heuristics. We also categorize the researches related to task offloading in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC), and Mobile Fog Computing (MFC). Our categorization criteria include the issue, proposed strategy, objectives, framework, and test environment.

    Can Unlicensed Bands Be Used by Unlicensed Usage?

    Get PDF
    Since their introduction, unlicensed ISM bands have resulted in a wide range of new wireless devices and services. It is fair to say that the success of ISM was an important factor in the opening of the TV white space for unlicensed access. Further bands (e.g., 3550-3650 MHz) are being studied to support unlicensed access. Expansion of the unlicensed bands may well address one of the principle disadvantages of unlicensed (variable quality of service) which could result in a vibrant new group companies providing innovative services and better prices. However, given that many commercial mobile telephone operators are relying heavily on the unlicensed bands to manage growth in data traffic through the “offloading” strategy, the promise of these bands may be more limited than might otherwise be expected (Musey, 2013).\ud \ud Wireless data traffic has exploded in the past several years due to more capable devices and faster network technologies. While there is some debate on the trajectory of data growth, some notable reports include AT&T, which reported data growth of over 5000% from 2008 to 2010 and Cisco, who predicted that mobile data traffic will grow to 6.3 exabytes per month in average by 2015 (Hu, 2012). Although the data traffic increased dramatically, relatively little new spectrum for mobile operators has come online in the last several years; further, the “flat-rate” pricing strategy has led to declining Average Revenue Per User (ARPU) for the mobile operators. Their challenge, then, is how to satisfy the service demand with acceptable additional expenditures on infrastructure and spectrum utilization.\ud \ud A common response to this challenge has been to offload data traffic onto unlicensed (usually WiFi) networks. This can be accomplished either by establishing infrastructure (WiFi hotspots) or to use existing private networks. This phenomenon leads to two potential risks for spectrum entrants: (1) the use of offloading may overwhelm unlicensed spectrum and leave little access opportunities for newcomers; (2) the intensity of the traffic may increase interference and degrade innovative services.\ud \ud Consequently, opening more unlicensed frequency bands alone may not necessarily lead to more unlicensed usage. In this paper, we will estimate spectrum that left for unlicensed usage and analyze risks for unlicensed users in unlicensed bands in terms of access opportunities and monetary gain. We will further provide recommendations that help foster unlicensed usage in unlicensed bands
    • …
    corecore