Explosive Auction Traffic

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Also known as the Henry R. Lawrence Memorial Bridge and the Canton Bridge, the structure was more than three-thousand feet long. Work is underway to finish the new bridge this year that includes opening to four-lane traffic a multi-use path.


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View Slideshow 1 of 4. Close-up view of the explosion from a boat, courtesy of Lake Barkley Resort. Taylor Inman. View Slideshow 2 of 4. Boats were out to watch the explosion as early as a.

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Jiang et al. The historical QoE data under different primary channels are collected by the secondary users and delivered to a cognitive radio base station, which will allocate available channel resources to the secondary users based on their QoE expectations and maintain a priority service queue. In [ 12 ], Zhang et al.

Their framework desirably supports a variety of design requirements, including 1 dynamic design for timely reflecting fluctuation of supply-demand relations, 2 joint design for supporting the heterogeneous user demands, and 3 truthful design for discouraging bidders from cheating behaviors. Their theoretical analysis shows that the worst-case performance of their mechanism can be well-bounded [ 12 ]. Feng et al. They have introduced a reverse auction framework to model the interactions between the platform and the smartphones and have proposed a mechanism called TRAC which consists of two main components.

The first component is a near-optimal approximate algorithm for determining the winning bids with polynomial-time computation complexity. The second component is a critical payment scheme which, despite the approximation of determining winning bids, guarantees that submitted bids of smartphones reflect their real costs of performing sensing tasks [ 13 ]. In [ 14 ], Rahimi et al. In this paper, they introduced MAPCloud, a hybrid, tiered cloud architecture consisting of local and public clouds, and showed how it can be leveraged to increase both performance and scalability of mobile applications.

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They modeled the mobile application as a workflow of tasks and aim to optimally decompose the set of tasks to execute on the mobile client and two-tier cloud architecture considering multiple quality-of-service QoS factors such as power, price, and delay. Their results indicated that MAPCloud can provide improved scalability and efficiency in comparison to only using public cloud [ 14 ]. In the paper [ 15 ], authors have proposed a novel framework to model mobile applications as a location-time workflows of tasks and showed that an optimal mapping of location-time workflows to tiered mobile cloud resources is an NP-hard problem.

In addition, they proposed an efficient heuristic algorithm that can perform well and scale well to a large number of users while ensuring high application QoS. The paper [ 16 ] discussed the current state of the art in the merger of cloud computing and smartphone technologies that is called as mobile cloud computing MCC.

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In addition, this research would identify research gaps covering critical aspects of how MCC can be realized and effectively utilized at scale. These include improved resource allocation in the MCC environment through efficient task distribution and offloading, security, and privacy [ 16 ]. Xu et al. Specifically, they unveiled the causes of virtual machine performance overhead by illustrating representative scenarios, discussed the performance modeling methods with a particular focus on their accuracy and cost, and compared the overhead mitigation techniques by identifying their effectiveness and implementation complexity.

Finally, they presented future research challenges pertinent to the modeling methods and mitigation techniques of virtual machine performance overhead in the IaaS cloud [ 17 ]. Demestichas et al. They found the QoS levels that can be offered and the networks that can support the demand at the selected QoS levels. In addition, they included a first approach to the definition, mathematical formulation, and solution of a version of the SCD-CRE problem.

Yan et al. Furthermore, they discussed unsolved issues, specified research challenges and indicated future research trends by proposing a research model for holistic trust management in IoT. In addition, they explored the literature towards trust worthy IoT in order to point out a number of open issues and challenges and suggested future research trends related to trust management.

Finally, they presented a further research model in order to achieve comprehensive trust management in IoT [ 19 ]. To deal with the problem of cellular traffic overload, some methods have been studied to efficiently conduct offloading. Zhuo et al. To minimize the incentive cost given an offloading target, users with high delay tolerance and large offloading potential should be prioritized for traffic offloading. Gao et al. First, we design a game model of mobile data offloading system and then explain the proposed algorithm in detail.

Each IoTM has a traffic that wants to offload their cellular traffic. Based on this topology, we develop two game models—VCG mechanism and Rubinstein game—to manage the data offloading in IoT systems. In this work, this function is developed based on the Rubinstein bargaining game, which is explained in detail in the Subsection 3.

The main issue between the MNO and APOs is to maximize the profit by cooperation and fairly divide the surplus profit.


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  • Game-based data offloading scheme for IoT system traffic congestion problems | SpringerLink.
  • To satisfy this goal, we propose a new fair division model based on the Rubinstein-Stahl bargaining approach. Through several rounds of offers and counteroffers, they finally come to an agreement. On the other hand, the major goal of the MNO is to fairly share this surplus profit getting from the bandwidth sharing.

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    To address this division problem involving two players, Rubinstein bargaining game can provide a unique solution to fairly share a given benefit [ 9 ]. At the same time, each APO checks its own channel condition. Step 6: According to Eqs. From the simulation results in Figs. Based on the combination of VCG mechanism and Rubinstein bargaining approach, the proposed scheme can constantly monitor the IoT system conditions and appropriately balance the system performance, whereas the other schemes [ 8 , 20 ] cannot offer such an attractive system performance.

    Data offloading is a promising technique to alleviate network traffic congestion and enhance service QoS. In this paper, we develop a new data offloading algorithm based on the game model.

    The numerical results have shown the effectiveness of our proposed scheme and confirmed the feasibility of interactive game approach. For the future work, our game-based data offloading mechanism can be applied to many directions in various research areas and sheds light on different decision problems. For example, it would be useful to study the non-cooperative game model under complete information.

    Skip to main content Skip to sections. Advertisement Hide. Download PDF. Game-based data offloading scheme for IoT system traffic congestion problems.

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    Open Access. First Online: 16 July The main contributions of our work are as follows: 1. We consider an effective incentive model for the user-initiated data offloading. In our model, IoTMs initiate the offloading process while offering necessary incentives in order to access to APs. Usually, a huge amount of cellular data traffic has been generated by IoTMs, which exceeds the capacity of cellular network.

    It deteriorates the network quality. Therefore, the MNO should offload the part of cellular traffic to other coexisting networks. This technique is a desirable and promising approach to solve the cellular network overload problem. On the other hand, APOs can get a surplus profit through providing their remaining bandwidth. Under dynamic IoT changing situations, our game-based approach can toward an effective system performance Fig.

    Open image in new window. With a discounted price, IoTMs enjoy an additional profit for data transmission in a time slot. All the players try to maximize their payoff. Finally, we develop a new a social welfare function to represent a total system efficiency; it can be estimated through an effective data offloading technique.

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