关键词： Mobile edge computing   Computation offloading   Resource competition   Dynamic programming   Energy-efficient  

摘要： In mobile edge computing systems, the energy consumption and execution delay can be reduced dramatically by mobile edge computation offloading (MECO) . However, due to the limited computing capacity of edge cloud, an energy-efficient offloading strategy plays a significant role. In this paper, the offloading decision problem for multi-device edge computing systems based on time-division multiple access is studied. The scheduling of offloading devices at the edge cloud is considered when modelling the edge computing system. Then, the offloading decision problem is formulated as an energy consumption minimization problem with the constraint of latency tolerance. It is a mixed integer programming problem of NP-hardness. To address the problem, a Dynamic Programming-based Energy Saving Offloading (DPESO) algorithm is designed to obtain the offloading strategy including the offloading option, offloading sequence and transmission power. First, the MECO with infinite edge cloud capacity is solved by device classification and transmission power decision. Then, we sort and adjust the offloading devices to meet the latency tolerance for the MECO with finite edge cloud capacity. Finally, simulation results demonstrate that the DPESO algorithm achieves better energy efficiency than the baseline strategies and has good scalability.

关键词： Mobile edge computing  

摘要： Today a lot of data sensed from the environment by the Internet of things applications. These data need to process with the lowest delay. Mobile devices (MDs) as ubiquitous tools are end devices in the network. These devices with limited resources cannot process all computations locally. Mobile edge computing (MEC) is a good architecture for processing computations in the network's edge. It solves the cloud challenges such as delay, energy, and cost. If MDs could not process the computations, they will offload tasks to the edge or cloud. Research shows that ignoring context information of application, requests, sensors, resources, and network tools cause to not complete the offloading method. In this article, we consider Bayesian learning automata (BLA) with considering context-aware offloading in MEC with multiuser. BLA learns all states and actions in the network and helps us to improve the offloading algorithm. The contexts are collected using autonomous management as the monitor-analysis-plan-execution loop in all offloading processes. The simulation results indicate that our method is superior to local computing and offload without considering context-aware algorithms in some metrics such as energy consumption, execution cost, network usage, delay, and fairness. © 2020 John Wiley & Sons Ltd

关键词： Mobile Edge Computing   energy-harvesting   load balancing   energy consumption   queue redundancy  

摘要： With the development of cloud computing, the Mobile Edge Computing has emerged and attracted widespread attentions. In this paper, we focus on the load balancing in MEC with energy harvesting. We first introduce the load balancing in MEC as a problem of minimizing both the energy consumption and queue redundancy. Thereafter, we adapt such a optimization problem to the Lyapunov algorithm and solve this optimization problem. Finally, extensive simulation results validate that the obtained strategy improves the capabilities of MEC systems.

关键词： Mobile edge computing   device-to-device cooperation   human mobility   task scheduling   latency minimization  

摘要： Mobile edge computing (MEC) has emerged as a new paradigm to assist low latency services by enabling computation offloading at the network edge. Nevertheless, human mobility can significantly impact the offloading decision and performance in MEC networks. In this context, we propose device-to-device (D2D) cooperation based MEC to expedite the task execution of mobile user by leveraging proximity-aware task offloading. However, user mobility in such distributed architecture results in dynamic offloading decision that instigates mobility-aware task scheduling in our proposed framework. We jointly formulate task assignment and power allocation to minimize the total task execution latency by taking account of user mobility, distributed resources, tasks properties, and energy constraint of the user device. We first propose Genetic Algorithm (GA)-based evolutionary scheme to solve our formulated mixed-integer non-linear programming (MINLP) problem. Then we propose a heuristic named mobility-aware task scheduling (MATS) to obtain effective task assignment with low complexity. The extensive evaluation under realistic human mobility trajectories provides useful insights into the performance of our schemes and demonstrates that, both GA and MATS achieve better latency than other baseline schemes while satisfying the energy constraint of mobile device.

关键词： Task analysis   Mobile handsets   Edge computing   Energy consumption   Resource management   Servers   Computational modeling   Unmanned aerial vehicles (UAVs)   mobile edge computing   tasks assignment   block successive upper-bound minimization (BSUM)  

摘要： Unmanned aerial vehicles (UAVs) have been deployed to enhance the network capacity and provide services to mobile users with or without infrastructure coverage. At the same time, we have observed the exponential growth in Internet of Things (IoTs) devices and applications. However, as IoT devices have limited computation capacity and battery lifetime, it is challenging to process data locally on the devices. To this end, in this letter, a UAV-aided mobile edge computing system is proposed. The problem to jointly minimize the energy consumption at the IoT devices and the UAVs during task execution is studied by optimizing the task offloading decision, resource allocation mechanism and UAV's trajectory while considering the communication and computation latency requirements. A non-convex structure of the formulated problem is revealed and shown to be challenging to solve. To address this challenge, a block successive upper-bound minimization (BSUM) algorithm is introduced. Finally, simulation results are provided to show the efficiency of our proposed algorithm.

关键词： Mobile edge computing   latency   energy consumption   opportunistic selection  

摘要： In this paper, we investigate a mobile edge computing (MEC) network with two computational access points (CAPs), where the source is equipped with multiple antennas and it has some computational tasks to be accomplished by the CAPs through Nakagami-m distributed wireless links. Since the MEC network involves both communication and computation, we first define the outage probability by taking into account the joint impact of latency and energy consumption. From this new definition, we then employ receiver antenna selection (RAS) or maximal ratio combining (MRC) at the receiver, and apply selection combining (SC) or switch-and-stay combining (SSC) protocol to choose a CAP to accomplish the computational task from the source. For both protocols along with the RAS and MRC, we further analyze the network performance by deriving new and easy-to-use analytical expressions for the outage probability over Nakagami-m fading channels, and study the impact of the network parameters on the outage performance. Furthermore, we provide the asymptotic outage probability in the low regime of noise power, from which we obtain some important insights on the system design. Finally, simulations and numerical results are demonstrated to verify the effectiveness of the proposed approach. It is shown that the number of transmit antenna and Nakagami parameter can help reduce the latency and energy consumption effectively, and the SSC protocol can achieve the same performance as the SC protocol with proper switching thresholds of latency and energy consumption.

关键词： Future internet architecture   Mobile edge computing   Cloud computing   Analytics   Adaptation  

摘要： Information-Centric Networking (ICN) enables in-network data management and communication between multiple parties by replicating data and activating interactions between decoupled senders and receivers. Existing data management and offloading schemes in ICNs primarily use the transport layer hence it becomes inefficient to actively develop and update the ICN standards because of continuously evolving heterogeneous future internet architectures such as mobile edge cloud computing (MECC) architectures. In this paper, we present an adaptive execution model for mobile data stream mining (MDSM) applications in MECC environments to enable device-centric adaptive data management and offloading. We designed the proposed execution model considering multiple factors of complexity such as volume and velocity of continuously streaming data, the selection of data fusion and data preprocessing methods, the choice of learning models, learning rates, learning modes, mobility, limited computational and memory resources in mobile devices, the high coupling between application components, and dependency over Internet connections. We integrated the proposed execution model with multiple MDSM applications mapping to a real-word use-case for activity detection using MECC as a future network architecture. We thoroughly evaluated the proposed execution model in terms of battery power consumption, memory utilization, makespan, accuracy, and the amount of data reduced during in-network communication. The comparison showed that our proposed adaptive execution model outperformed the static and dynamic execution models which were deployed in the same ICN architecture. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Task analysis   Servers   Protocols   Uplink   Downlink   Energy consumption   Time-frequency analysis   Mobile edge computing   task offloading   energy efficient   resource allocation   convex optimization  

摘要： This paper investigates an asynchronous offloading protocol for mobile edge computing (MEC) systems where the computational tasks of mobile users are partially offloaded to a MEC server at a base station (BS). In the proposed asynchronous approach, upload, computation, and download of the tasks are performed in an asymmetric and non-orthogonal manner. A joint optimization problem of the transmit power, the offloading size, and the time-frequency resources is addressed to minimize the energy consumption of the overall system. By applying convex optimization techniques, the optimal solution for the asynchronous MEC offloading problem can be obtained. Numerical results demonstrate the effectiveness of the proposed asynchronous protocol over the conventional synchronous approach.

关键词： Task analysis   Servers   Cloud computing   Smart homes   Edge computing   Energy consumption   Internet of Things   Deep learning   integer linear programming   mobile edge computing   smart home   task offloading  

摘要： Mobile edge computing which provides computing capabilities at the edge of the radio access network can help smart home reduce response time. However, the limited computing capacity of edge servers is the bottlenecks for the development of edge computing. We integrate cloud computing and edge computing in the Internet of Things to expand the capabilities. Nevertheless, the cost of leasing computing resources has been seldom considered. In this paper, we study the joint transmission power and resource allocation to minimize the users' overhead which is measured by the sum of energy consumption and cost leasing servers. We formulate the problem as a Mixed Integer Linear Programming which is NP-hard and present the Branch-and-Bound to solve it. Due to high complexity, a learning method is proposed to accelerate the algorithm. The branching policy can be learned to reduce the time-cost of the Branch-and-Bound algorithm. Simulation results show our approach can improve the Branch-and-Bound efficiency and performs closely to the traditional branching scheme.

关键词： GLOMONET   security   smartcard   ECC   timestamp   AVISPA   span   NS2   4G   5G   mobile edge computing  

摘要： With the advent of new 5G technology there is a need to develop security architecture. Two factor authentication schemes are developed to address the security features such as user anonymity and privacy preservation during roaming scenario in GLObal Mobility NETwork. The entire communication during roaming is carried over insecure channel and owing to this, security concern is raised. The main objective of the proposed protocol is to secure the channel and to overcome all active and passive security attacks. The proposed protocol addresses the active and passive security attacks that exist in 5G cellular networks which are formally verified using AVISPA tool. The proposed protocol is simulated using NS2.35 simulator and the performance metrics such as throughput, end to end delivery and packet delivery ratio are computed. The protocol is efficient in terms of computational and communication cost. The proposed scheme is robust and practically implementable.