Cognitive Data Offloading in Mobile Edge Computing for Internet of Things

Apostolopoulos, Pavlos Athanasios; Tsiropoulou, Eirini Eleni; Papavassiliou, Symeon

doi:10.1109/access.2020.2981837

Cited by 53 publications

(31 citation statements)

References 42 publications

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“…Table 1. Typical values for simulation parameters [36,37]. Parameter denoting the processing capability of the server, centers the sigmoid on to realistic values of offloading data 6.1.…”

Section: Numerical Resultsmentioning

confidence: 99%

Data Offloading in UAV-Assisted Multi-Access Edge Computing Systems: A Resource-Based Pricing and User Risk-Awareness Approach

Mitsis

Tsiropoulou

Papavassiliou

2020

Sensors

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Unmanned Aerial Vehicle (UAV)-assisted Multi-access Edge Computing (MEC) systems have emerged recently as a flexible and dynamic computing environment, providing task offloading service to the users. In order for such a paradigm to be viable, the operator of a UAV-mounted MEC server should enjoy some form of profit by offering its computing capabilities to the end users. To deal with this issue in this paper, we apply a usage-based pricing policy for allowing the exploitation of the servers’ computing resources. The proposed pricing mechanism implicitly introduces a more social behavior to the users with respect to competing for the UAV-mounted MEC servers’ computation resources. In order to properly model the users’ risk-aware behavior within the overall data offloading decision-making process the principles of Prospect Theory are adopted, while the exploitation of the available computation resources is considered based on the theory of the Tragedy of the Commons. Initially, the user’s prospect-theoretic utility function is formulated by quantifying the user’s risk seeking and loss aversion behavior, while taking into account the pricing mechanism. Accordingly, the users’ pricing and risk-aware data offloading problem is formulated as a distributed maximization problem of each user’s expected prospect-theoretic utility function and addressed as a non-cooperative game among the users. The existence of a Pure Nash Equilibrium (PNE) for the formulated non-cooperative game is shown based on the theory of submodular games. An iterative and distributed algorithm is introduced which converges to the PNE, following the learning rule of the best response dynamics. The performance evaluation of the proposed approach is achieved via modeling and simulation, and detailed numerical results are presented highlighting its key operation features and benefits.

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“…Table 1. Typical values for simulation parameters [36,37]. Parameter denoting the processing capability of the server, centers the sigmoid on to realistic values of offloading data 6.1.…”

Section: Numerical Resultsmentioning

confidence: 99%

Data Offloading in UAV-Assisted Multi-Access Edge Computing Systems: A Resource-Based Pricing and User Risk-Awareness Approach

Mitsis

Tsiropoulou

Papavassiliou

2020

Sensors

Self Cite

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“…Authors in [3] maximized the perceived satisfaction, by offloading portion of computing tasks to the different MEC servers. In [25], deep learning and edge computing were combined and used for efficient processing of huge amount of data with distinct accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…However, mobile devices are resourceconstrained in energy, which reduces the quality of service [1]. MEC can enhance the computing power of mobile devices [2] and [3]. It allows mobile devices to offload their intensive computing to edge devices within coverage [4].…”

Section: Introductionmentioning

confidence: 99%

Computation Scheduling of Multi-Access Edge Networks Based on the Artificial Fish Swarm Algorithm

Yang

Zhang

et al. 2021

IEEE Access

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With the increasing requirements for computing in modern society, Multi-access Edge Computing (MEC) has received widespread attention for meeting low-latency. In MEC network, mobile devices can offload computing-intensive tasks to edge servers for computing. Wireless Power Transmission (WPT) provides initial energy for mobile devices, and the tasks of mobile devices consume energy when they are locally calculated or completely offloaded. The combination of the two technologies forms the Wireless Powered Mobile Edge Computing (WP-MEC) network. In this article, considering the impact of WPT transmission time τ0, we study the offloading and scheduling of tasks for multiple mobile devices in the WP-MEC network, which is an NP-hard problem. We formulate this scheduling problem to minimize the time delay under the constraint of WPT transmission energy. We regard our problem studied in this paper as a multidimensional knapsack problem (MKP). The difference is that the knapsack capacity in MKP is limited, while in our problem, the knapsack that one item can choose is limited. Therefore, we improve the Artificial Fish Swarm Algorithm (AFSA) and propose Computation Scheduling Based on the Artificial Fish Swarm Algorithm (CS-AFSA) to find the optimal scheduling. We encode a scheduling scheme as an artificial fish and regard the delay corresponding to the scheduling as the optimization object. The optimal artificial fish can be gradually approached and determined through the swarm, follow and prey behavior of artificial fish. The optimal artificial fish is the optimal scheduling scheme. More importantly, based on the original behavior of AFSA, we also improve the scheme that does not meet the WPT energy constraint, including the modification of infeasible artificial fish and insufficient artificial fish. Besides, we also consider how to find the best WPT transmission time τ0. Finally, we perform data simulation on the proposed algorithm.INDEX TERMS Artificial Fish Swarm Algorithm, WP-MEC network, WPT transmission time.

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“…Mobile devices offload data to the MEC servers using a wireless medium. The capability of data offloading to the MEC servers is investigated in [140]. The cognitive IoT devices' gain and lose averse fashion is considered for resource management.…”

Section: J Latest Hybrid Algorithmsmentioning

confidence: 99%

Towards Mobile Edge Computing: Taxonomy, Challenges, Applications and Future Realms

et al. 2020

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The realm of cloud computing has revolutionized access to cloud resources and their utilization and applications over the Internet. However, deploying cloud computing for delay critical applications and reducing the delay in access to the resources are challenging. The Mobile Edge Computing (MEC) paradigm is one of the effective solutions, which brings the cloud computing services to the proximity of the edge network and leverages the available resources. This paper presents a survey of the latest and state-of-the-art algorithms, techniques, and concepts of MEC. The proposed work is unique in that the most novel algorithms are considered, which are not considered by the existing surveys. Moreover, the chosen novel literature of the existing researchers is classified in terms of performance metrics by describing the realms of promising performance and the regions where the margin of improvement exists for future investigation for the future researchers. This also eases the choice of a particular algorithm for a particular application. As compared to the existing surveys, the bibliometric overview is provided, which is further helpful for the researchers, engineers, and scientists for a thorough insight, application selection, and future consideration for improvement. In addition, applications related to the MEC platform are presented. Open research challenges, future directions, and lessons learned in area of the MEC are provided for further future investigation.

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Cognitive Data Offloading in Mobile Edge Computing for Internet of Things

Cited by 53 publications

References 42 publications

Data Offloading in UAV-Assisted Multi-Access Edge Computing Systems: A Resource-Based Pricing and User Risk-Awareness Approach

Data Offloading in UAV-Assisted Multi-Access Edge Computing Systems: A Resource-Based Pricing and User Risk-Awareness Approach

Computation Scheduling of Multi-Access Edge Networks Based on the Artificial Fish Swarm Algorithm

Towards Mobile Edge Computing: Taxonomy, Challenges, Applications and Future Realms

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