关键词： Maritime video surveillance   Maritime network   Mobile edge computing   Binary search algorithm   Multi-user offloading  

摘要： Video surveillance has broad application prospects in maritime rescue, ship transportation and other fields. Mobile edge computing can effectively guarantee low-latency and highly reliable data transmission in maritime video surveillance. This paper comprehensively formulates the edge computing offloading schemes in the middle-sea scenario. The middle-sea scenario has sufficient edge computing nodes as in the offshore scenario and delay constraints due to limited network connectivity as in the far-sea scenario. Taking into account these characteristics, a single-user single-hop unicast offloading model is established and extends to a multi-user model. In addition, for the sufficient and limited edge computing nodes, the multi-user model is further divided into the multi-user single-hop unicast situation 1 and 2 models. We split the mixed-integer nonlinear programming problem and approximate the optimal transmission power by the binary search method. We use the offloading decision allocation algorithm based on alternating selection, offloading decision allocation algorithm based on multi-objective alternating selection, and offloading decision allocation algorithm based on node redistribution to optimize the offloading decisions of the above models. Subsequently, we analyze the simulation results from algorithm comparisons, changes in the number of subtasks, data, and oceanic user equipments. We verify the effectiveness of the proposed schemes and algorithms in saving delay.

关键词： Mobile Edge Computing   Online Education   Video Caching Strategy  

摘要： With the rapid development of mobile internet technology, mobile network data traffic presents an explosive growth trend. Especially, the proportion of mobile video business has become a large proportion in mobile internet business. Mobile video business is considered as a typical business in the 5G network, such as in online education. The growth of video traffic poses a great challenge to mobile network. In order to provide users with better quality of experience (QoE), it requires mobile network to provide higher data transmission rate and lower network delay. This paper adopts a combined optimization to minimize total cost and maximize QoE simultaneously. The optimization problem is solved by ant colony algorithm. The effectiveness is verified on experiment.

关键词： Mobile edge computing  

摘要： This paper studies one typical mobile edge computing (MEC) system, where a single user has some intensively calculating tasks to be computed by M edge nodes (ENs) with much more powerful calculating capability. In particular, unmanned aerial vehicle (UAV) can act as the ENs due to its flexibility and high mobility in the deployment. For this system, we propose several EN selection criteria to improve the system whole performance of computation and communication. Specifically, criterion I selects the best EN based on maximizing the received signal-to-noise ratio (SNR) at the EN, criterion II performs the selection according to the most powerful calculating capability, while criterion III chooses one EN randomly. For each EN selection criterion, we perform the system performance evaluation by analyzing outage probability (OP) through deriving some analytical expressions. From these expressions, we can obtain some meaningful insights regarding how to design the MEC *** finally perform some simulation results to demonstrate the effectiveness of the proposed MEC network. In particular, criterion I can exploit the full diversity order equal to M. © 2022. Jun Liu et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.

关键词： Edge federation   5G network   mobile edge computing   resource allocation   Stackelberg game   utility maximization  

摘要： Mobile Edge Computing (MEC) plays key role for providing fast-response and high interactivity in the emerging 5G network. Edge service providers (ESP) are responsible for serving edge users or IoT devices running latency-critical applications. An MEC server provides faster response to ESPs but has limited computation resources, hence, it can be overloaded due to extensive resource demand. Thus, federation of multiple MEC servers offers an opportunity for dynamic resource allocation in a distributed manner. The federation objective is to maximize the usage of underutilized edge resources and reduction of service provision time simultaneously. As all the ESPs and MECs act autonomously, it is quite impossible for all the individuals to achieve optimal behavior simultaneously. In this paper, we develop a Stackelberg Game (SBG) based dynamic resource allocation method to reach the expected performance. The SBG analyzes the pricing of MECs and the resource-purchasing problem of ESPs. We also develop a many-to-many matching algorithm for resource sharing among the MECs and a one-to-many matching algorithm for that between an MEC server and ESPs. The results from an extensive performance evaluation demonstrate effectiveness of the proposed system in increasing utilities for MECs and ESPs, reducing the turnaround time of application tasks, and ensuring fair resource distribution compared to state-of-the-art works.

关键词： mobile edge computing   MEC platform   human resource system   human resource management  

摘要： The purpose is to process large data, classify unclear information in the human resource management system, and improve the human resource management efficiency for enterprises. Here, a new system is proposed for human resource analysis and management based on Mobile Edge Computing (MEC) technology. First, the MEC technology is analysed. Then, the MEC technology is integrated into the human resource management system through network protocol according to the distributed computing function of MEC technology and human resources management requirements. Subsequently, the B/S and C/S data models are combined. Finally, the new human resource analysis and management model is proposed through the combination of B/S and C/S data models based on MEC. The proposed system can manage human resources and business operations locally to save transportation costs and increase system calculation speed through a series of analyses and research, and thus improving system performance.

关键词： Task analysis   Servers   Computational modeling   Delays   Cloud computing   Simulation   Optimization   Computation offloading   edge-cloud computing   mobile edge computing   optimization   Internet of Things   unmanned aerial vehicles   linear programming  

摘要： The emergence of delay-sensitive and computationally-intensive mobile applications and services pose a significant challenge for Unmanned Aerial Vehicles (UAVs) devices due to the scarcity in their resources such as computational power and battery lifetime. Mobile cloud computing has been introduced as a promising solution to overcome these limitations through task offloading. However, high-latency and security issues are considered the main challenges of this paradigm. Subsequently, the edge-cloud computing paradigm has been introduced and widely used to help to mitigate these issues. Nevertheless, the current task offloading models permit UAVs to execute their intensive tasks at the connected edge server, which leads to excessive loads due to the large number of UAVs and thereby increases the delay. Therefore, in this paper, we propose a delay-optimal task offloading approach for multi-tier edge-cloud computing in a multi-user environment. The problem is formulated as an optimization model using Integer Linear Programming (ILP) techniques to minimize the total service time of UAVs. Simulation results demonstrate that the proposed approach not only saves the service time by 33.5% and 55% for edge and cloud execution policies respectively, but also scales well for a large number of UAVs.

关键词： Mobile Edge Computing   Internet of Things   Service Function Chain   Column   Column Generation   Network Function Virtualization  

摘要： The dynamic network state and the mobility of the terminals make the service function chain (SFC) orchestration mechanisms based on static and deterministic assumptions hard to be applied in SDN/NFV mobile edge computing networks. Designing dynamic and online SFC orchestration mechanism can greatly improve the execution efficiency of compute-intensive and resource-hungry applications in mobile edge computing networks. In order to increase the overall profit of service provider and reduce the resource cost, the system running time is divided into a sequence of time slots and a dynamic orchestration scheme based on an improved column generation algorithm is proposed in each slot. Firstly, the SFC dynamic orchestration problem is formulated as an integer linear programming (ILP) model based on layered graph. Then, in order to reduce the computation costs, a column generation model is used to simplify the ILP model. Finally, a two-stage heuristic algorithm based on greedy strategy is proposed. Four metrics are defined and the performance of the proposed algorithm is evaluated based on simulation. The results show that our proposal significantly provides more than 30% reduction of run time and about 12% improvement in service deployment success ratio compared to the Viterbi algorithm based mechanism.

关键词： Mobile edge computing  

摘要： With the rapid development of mobile communication technology, there is a growing demand for high-quality point of interest(POI) recommendation. The POIs visited by users only account for a very small proportion. Thus traditional POI recommendation method is vulnerable to data sparsity and lacks a clear and effective explanation for POI ranking result. The POI selection made by the user is influenced by various contextual attributes. The challenge lies in representing accurately and aggregating multiple contextual information efficiently. We transform the POI recommendation into a contextual multi-attribute decision problem based on the neutrosophic set (NS) which is suitable for representing fuzzy decision information. We establish a unified framework of contextual information. Firstly, we propose a contextual multi-attribute NS transformation model of POI, including the NS model for single-dimensional attributes and the NS model for multi-dimensional attributes. And then through the aggregation of multi attribute NS, the POI that best conforms to user’s preferences is recommended. Finally, the experimental results based on the Yelp dataset show that the proposed strategy performs better than the typical POI recommendation method in NDCG, accuracy, and recall rate. © 2021, CC BY.

关键词： Mobile edge computing  

摘要： Federated Learning (FL) is a decentralized machine learning architecture, which leverages a large number of remote devices to learn a joint model with distributed training data. However, the system-heterogeneity is one major challenge in a FL network to achieve robust distributed learning performance, which comes from two aspects: i) device-heterogeneity due to the diverse computational capacity among devices;ii) data-heterogeneity due to the non-identically distributed data across the network. Prior studies addressing the heterogeneous FL issue, e.g., FedProx, lack formalization and it remains an open problem. This work first formalizes the system-heterogeneous FL problem and proposes a new algorithm, called FedLGA, to address this problem by bridging the divergence of local model updates via gradient approximation. To achieve this, FedLGA provides an alternated Hessian estimation method, which only requires extra linear complexity on the aggregator. Theoretically, we show that with a device-heterogeneous ratio ρ, FedLGA achieves convergence rates on non-i.i.d. distributed FL training data for the nonconvex optimization problems with O ((1+p)/√ENT + 1/T) and O ((1+p)/√E//TK + 1/T) for full and partial device participation respectively, where E is the number of local learning epoch, T is the number of total communication round, N is the total device number and K is the number of selected device in one communication round under partially participation scheme. The results of comprehensive experiments on multiple datasets show that FedLGA outperforms current FL methods against the system-heterogeneity. Copyright © 2021, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： In this paper, we propose a novel algorithm for energy-efficient, low-latency dynamic mobile edge computing (MEC), in the context of beyond 5G networks endowed with Reconfigurable Intelligent Surfaces (RISs). In our setting, new computing requests are continuously generated by a set of devices and are handled through a dynamic queueing system. Building on stochastic optimization tools, we devise a dynamic learning algorithm that jointly optimizes the allocation of radio resources (i.e., power, transmission rates, sleep mode and duty cycle), computation resources (i.e., CPU cycles), and RIS reflectivity parameters (i.e., phase shifts), while guaranteeing a target performance in terms of average end-to-end (E2E) delay. The proposed strategy is dynamic, since it performs a low-complexity optimization on a per-slot basis while dealing with time-varying radio channels and task arrivals, whose statistics are unknown. The presence and optimization of RISs helps boosting the performance of dynamic MEC, thanks to the capability to shape and adapt the wireless propagation environment. Numerical results assess the performance in terms of service delay, learning, and adaptation capabilities of the proposed strategy for RIS-empowered MEC. Copyright © 2021, The Authors. All rights reserved.