3 research outputs found

    An efficient reconfigurable workload balancing scheme for fog computing network using internet of things devices

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    Nowadays a huge amount of data has been communicated using fog nodes spread throughout smarty cities. the communication process is performed using fog nodes which are co-located with cellular base stations (BSs) that can move the computing resources close to internet of things (IoT) devices. In smart cities, a different type of data flow has been communicated through IoT devices. The communication process performs efficiently using the remote cloud. The IoT devices very close to the BS can communicate data without using fog nodes. Due to these phenomena, workload unbalancing occurs in IoT devices communicating in fog computing networks. Hence, it generates communication and computing latency. The task distribution process between the IoT devices is unbalanced. Hence, congestion and loss of information occur in fog computing network. A proposed reconfigurable load balancing algorithm (RLBA) is efficiently balancing the workload by reconfigurable communication channels and deviates the task with respect to the BS locations, IoT devices density and load IoT devices in each fog nodes in a network to minimize the communication and computing latency. As per the performance analysis, the proposed algorithm shows better performance as compared to conventional methods’ average latency ratio, communication latency ratio, computing load and traffic load

    Joint Latency-Energy Minimization for Fog-Assisted Wireless IoT Networks

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    This work aims to present a joint resource allocation method for a fog-assisted network wherein IoT wireless devices simultaneously offload their tasks to a serving fog node. The main contribution is to formulate joint minimization of service latency and energy consumption objectives subject to both radio and computing constraints. Moreover, unlike previous works that set a fixed value to the circuit power dissipated to operate a wireless device, practical models are considered. To derive the Pareto boundary between two conflicting objectives we consider, Tchebyshev theorem is used for each wireless device. The competition among devices is modeled using the cooperative Nash bargaining solution and its unique cooperative Nash equilibrium (NE) is computed based on block coordinate descent algorithm. Numerical results obtained using realistic models are presented to corroborate the effectiveness of the proposed algorithm

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

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    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
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