关键词： Wireless communication   Base stations   Network slicing   Simulation   Quality of service   Dynamic scheduling   User experience   Deep deterministic policy gradient   mobile communication   mobile edge computing   network slicing   resource allocation  

摘要： The development of multi-industry compatibility and the coexistence of multiple services and multiple functional communication networks will cause rapid growth in mobile communication system traffic. Users will have increasingly strict requirements for quality of service (QoS), e.g., a high rate, low latency, and low energy consumption. To address these problems, it is helpful to combine network slicing and mobile edge computing (MEC) to provide customized networks while reducing the service processing time. Due to the uncertainty of user requests and the environment, reasonable resource allocation is always particularly challenging. A novel dynamic resource allocation scheme for MEC slice systems, which formulates resource allocation and computation offloading issues as an optimization problem subject to the latency and rate, is proposed. Based on the dynamics of the slice requirements, quantity, and service time, the proposed problem is converted to a Markov decision process (MDP), and a state, action, and reward function are proposed. By exploiting the deep deterministic policy gradient (DDPG) algorithm, the wireless resources and computing resources are configured dynamically according to the requirements of different types of slices to maximize the revenue of the network operator. The simulation results demonstrate the influence of the slice arrival rate and total resources on the allocation policy. Compared with other schemes, the proposed scheme can provide a more effective performance when resources are scarce.

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.

关键词： Data models   Training   Servers   Computational modeling   Internet of Things   Distributed databases   Degradation   Federated learning   mobile edge computing   client selection  

摘要： Federated Learning (FL) has recently attracted considerable attention in internet of things, due to its capability of enabling mobile clients to collaboratively learn a global prediction model without sharing their privacy-sensitive data to the server. Despite its great potential, a main challenge of FL is that the training data are usually non-Independent, Identically Distributed (non-IID) on the clients, which may bring the biases in the model training and cause possible accuracy degradation. To address this issue, this paper aims to propose a novel FL algorithm to alleviate the accuracy degradation caused by non-IID data at clients. Firstly, we observe that the clients with different degrees of non-IID data present heterogeneous weight divergence with the clients owning IID data. Inspired by this, we utilize weight divergence to recognize the non-IID degrees of clients. Then, we propose an efficient FL algorithm, named CSFedAvg, in which the clients with lower degree of non-IID data will be chosen to train the models with higher frequency. Finally, we conduct simulations using publicly-available datasets to train deep neural networks. Simulation results show that the proposed FL algorithm improves the training performance compared with existing FL protocol.

关键词： Roads   Analytical models   Safety   Protocols   Cruise control   Computational modeling   Biological system modeling   Mobile edge computing   vehicular ad-hoc networks   cooperative adaptive cruise control   intelligent transportation systems   smart cities   5G   beyond-5G  

摘要： The growth in the number of connected vehicles in ad-hoc networks or VANETs enables the use of increasingly sophisticated services and applications. One emerging application in this scenario is Platooning, a stable stream of connected autonomous vehicles. This application improves safety, vehicle flow on the road, energy consumption, among other variables. However, vehicles using this technology need to share space with vehicles not connected or with other automation levels. Among the bottlenecks in scenarios like this, Platoons formation and splits stand out, as these maneuvers involve costs and risk. Therefore, our contribution is developing a new strategy for Platoons formation in a mixed universe of vehicles to bring some of these desired benefits. The results show that the proposed solution achieved an accuracy of 89% of the entire amount of platoons in the datasets. Our experiments demonstrate cases with collisions, and the proposed solution provides a solution to a more safe operation on roads. Finally, we conclude that our solution provides more determinism in the traffic conditions, as vehicles achieving stationary speeds with low variation.

关键词： Mobile edge computing   restless multi-armed bandit   index policy   Whittle index  

摘要： Mobile-edge computing (MEC) offloads computational tasks from wireless devices to network edge, and enables real-time information transmission and computing. Most existing work concerns a small-scale synchronous MEC system. In this paper, we focus on a large-scale asynchronous MEC system with random task arrivals, distinct workloads, and diverse deadlines. We formulate the offloading policy design as a restless multi-armed bandit (RMAB) to maximize the total discounted reward over the time horizon. However, the formulated RMAB is related to a PSPACE-hard sequential decision-making problem, which is intractable. To address this issue, by exploiting the Whittle index (WI) theory, we rigorously establish the WI indexability and derive a scalable closed-form solution. Consequently, in our WI policy, each user only needs to calculate its WI and report it to the BS, and the users with the highest indices are selected for task offloading. Furthermore, when the task completion ratio becomes the focus, the shorter slack time less remaining workload (STLW) priority rule is introduced into the WI policy for performance improvement. When the knowledge of user offloading energy consumption is not available prior to the offloading, we develop Bayesian learning-enabled WI policies, including maximum likelihood estimation, Bayesian learning with conjugate prior, and prior-swapping techniques. Simulation results show that the proposed policies significantly outperform the other existing policies.

关键词： Cloud computing   energy efficient resource allocation   mobile edge computing   service rate   user equipment   utility function  

摘要： In mobile edge computing (MEC), one of the important challenges is how much resources of which mobile edge server (MES) should be allocated to which user equipment (UE). The existing resource allocation schemes only consider CPU as the requested resource and assume utility for MESs as either a random variable or dependent on the requested CPU only. This paper presents a novel comprehensive utility function for resource allocation in MEC. The utility function considers the heterogeneous nature of applications that a UE offloads to MES. The proposed utility function considers all important parameters, including CPU, RAM, hard disk space, required time, and distance, to calculate a more realistic utility value for MESs. Moreover, we improve upon some general algorithms, used for resource allocation in MEC and cloud computing, by considering our proposed utility function. We name the improved versions of these resource allocation schemes as comprehensive resource allocation schemes. The UE requests are modeled to represent the amount of resources requested by the UE as well as the time for which the UE has requested these resources. The utility function depends upon the UE requests and the distance between UEs and MES, and serves as a realistic means of comparison between different types of UE requests. Choosing (or selecting) an optimal MES with the optimal amount of resources to be allocated to each UE request is a challenging task. We show that MES resource allocation is sub-optimal if CPU is the only resource considered. By taking into account the other resources, i.e., RAM, disk space, request time, and distance in the utility function, we demonstrate improvement in the resource allocation algorithms in terms of service rate, utility, and MES energy consumption.

关键词： Task analysis   Eavesdropping   Reinforcement learning   Cloud computing   Internet of Things   Mobile handsets   Edge computing   Deep reinforcement learning   Internet of Things   mobile edge computing   task offloading   unmanned aerial vehicles  

摘要： In this work, an intelligent mobile edge computing (MEC) network is studied for Internet of Things (IoT) in the presence of eavesdropping environments, where there are multiple users who can offload their confidential tasks to the computational access point (CAP) for the assistance of computation. One unmanned aerial vehicle (UAV) attacker exists in the system and it can listen to the confidential data transmission from the users to the CAP. We optimize the system design of the intelligent MEC network, by adaptively allocating the offloading ratio and wireless bandwidth, to reduce the linearly weighted cost of the latency as well as energy consumption (EnC). Specifically, starting from the deep reinforcement learning, we devise a deep Q-network (DQN) network to adjust the offloading ratio and transmission bandwidth, which can help calculate the computational tasks and suppress the eavesdropping from the UAV efficiently. We finally provide some simulation results to validate the proposed offloading strategy. In particular, the proposed offloading strategy can achieve a much lower cost compared to the conventional ones, in the terms of latency and EnC.

关键词： Task analysis   Quality of service   Resource management   Optimization   Base stations   Servers   Energy consumption   Mobile edge computing   Internet of Things (IoT)   quality of service (QoS)   service provisioning   multi-path routing   high level petri nets  

摘要： Recent development in modern wireless applications and services, such as augmented reality, image processing, and network gaming requires persistent computing on average commercial wireless devices to perform complex tasks with low latency. The traditional cloud systems are unable to meet those requirements solely. In the said perspective, Mobile Edge Computing (MEC) serves as a proxy between the things (devices) and the cloud, pushing the computations at the edge of the network. The MEC provides an effective solution to fulfill the demands of low-latency applications and services by executing most of the tasks within the proximity of users. The main challenge, however, is that too many simultaneous service requests created by wireless access produce severe interference, resulting in a decreased rate of data transmission. In this paper, we made an attempt to overcome the aforesaid limitation by proposing a user-centric QoS-aware multi-path service provisioning approach. A densely deployed base station MEC environment has overlapping coverage regions. We exploit such regions to distribute the service requests in a way that avoid hotspots and bottlenecks. Our approach is adaptive and can tune to different parameters based on service requirements. We performed several experiments to evaluate the effectiveness of our approach and compared it with the traditional Greedy approach. The results revealed that our approach improves the network state by 26.95% and average waiting time by 35.56% as compared to the Greedy approach. In addition, the QoS violations were also reduced by the fraction of 16.

关键词： Task analysis   Mobile handsets   Delays   Resource management   Cloud computing   Servers   Optimization   Applications offloading   mobile edge computing   system delay   resource allocation  

摘要： Thanks to the development of the technologies in the wireless communications and Internet of Things (IoT), the adoption of mobile devices is growing rapidly. Accordingly, the number of multimedia applications like face recognition and augmented reality generated from various mobile devices is growing at an unprecedented rate. The processing of these multimedia applications needs a lot of computation resources and has to be processed as quickly as possible. However, as these mobile devices have limited computation resources, the undesirable response delay will occur. By offloading the multimedia applications to the edge cloud close to the access point (AP) or the cellular base station (BS), mobile edge computing (MEC) is considered as a prospective approach to improve the quality of service (QoS) and enhance the computing capacity of mobile devices. Multimedia applications offloading in a MEC system are studied in this paper. The objective of the studied problem is to minimize the execution delay of multimedia applications of all mobile devices by allocating both the communication resource and the computing resource in the edge servers. An optimization problem is formulated and an efficient multimedia applications offloading scheme is proposed to get the solution. Simulation results are conducted to verify the proposed application offloading method, which show that there is a significant execution delay reduction.

关键词： Ant colonny algorithme   Intelligence artificial   Migration virtual machines   Mobile edge computing   Routing protocole  

摘要： The whole world is inundated with smaller devices equipped with wireless communication interfaces. At the same time, the amount of data generated by these devices is becoming more important. The smaller size of these devices has the disadvantage of being short of processing and storage resources (memory, processes, energy,…), especially when it needs to process larger amounts of data. In order to overcome this weakness and process massive data, devices must help each other. A low-resource node can delegate the execution of a set of computionly heavy tasks to another machine in the network to process them for it. The machine with sufficient computational resources must also deposit the appropriate environment represented by the adapted virtual machine. Thus, in this paper, in order to migrate the virtual machine to an edge server in a mobile edge computing environment, we have proposed an approach based on artificial intelligence. More specifically, the main idea of this paper is to cut a virtual machine into several small pieces and then send them to an appropriate target node (Edge Server) using the ant colony algorithm. In order to test and prove the effectiveness of our approach, several simulations are made by NS3. The obtained results show that our approach is well adapted to mobile environments. © 2021, Institute of Advanced Engineering and Science. All rights reserved.