基于概率的启发式ICN缓存内容放置方法

提出一种基于概率存储的启发式住处中心网络内容缓存方法（PCP）。主要思想是请求消息和数据消息在传输过程中统计必要信息,当数据消息返回时,沿途各缓存节点按照一定概率决策是否在本地缓存该内容。设计缓存概率时综合考虑内容热度和缓存放置收益,即内容热度越高,放置收益越大的内容被缓存的概率越高。实验结果表明,PCP在缓存服务率、缓存命中率、平均访问延迟率等方面,与现有方法相比具有显著优势,同时PCP开销较小。     

Incentive edge caching in software‐defined internet of vehicles: A Stackelberg game approach

In this paper, we investigate an incentive edge caching mechanism for an internet of vehicles (IoV) system based on the paradigm of software‐defined networking (SDN). We start by proposing a distributed SDN‐based IoV architecture. Then, based on this architecture, we focus on the economic side of caching by considering competitive cache‐enablers market composed of one content provider (CP) and multiple mobile network operators (MNOs). Each MNO manages a set of cache‐enabled small base stations (SBS). The CP incites the MNOs to store its popular contents in cache‐enabled SBSs with highest access probability to enhance the satisfaction of its users. By leasing their cache‐enabled SBSs, the MNOs aim to make more monetary profit. We formulate the interaction between the CP and the MNOs, using a Stackelberg game, where the CP acts first as the leader by announcing the popular content quantity that it which to cache and fixing the caching popularity threshold, a minimum access probability under it a content cannot be cached. Then, MNOs act subsequently as followers responding by the content quantity they accept to cache and the corresponding caching price. A noncooperative subgame is formulated to model the competition between the followers on the CP's limited content quantity. We analyze the leader and the follower's optimization problems, and we prove the Stackelberg equilibrium (SE). Simulation results show that our game‐based incentive caching model achieves optimal utilities and outperforms other incentive caching mechanisms with monopoly cache‐enablers whilst enhancing 30% of the user's satisfaction and reducing the caching cost.     

Proactive caching with content‐based pricing for cooperative femtocells in two‐tier heterogeneous networks

With increase in the number of smart wireless devices, the demand for higher data rates also grows which puts immense pressure to the network. A vast majority of this demand comes from video files, and it is observed that only a few popular video files are requested more frequently during any specified time interval. Recent studies have shown that caching provides a better performance as it minimizes the network load by avoiding the fetching of same files multiple times from the server. In this paper, we propose to combine two ideas; proactive caching of files and content‐based pricing in macro‐femto heterogeneous networks. The femtocell access point (FAP) is allowed to manipulate its users' demand through content‐based pricing and serve the users' requests by proactively downloading suitable content into its cache memory which reduces the load of the femtocell. In addition, an incentive mechanism is also proposed which encourages the FAP to help macrocell users under its coverage zone by allowing access to its cached content and thereby reduces the macrocell load. The proposed content‐based pricing and proactive caching scheme for femtocells is modeled as a Stackelberg game among the macrocell base station and the FAP to jointly maximize both of their utilities. Performance analysis of the scheme is presented for a single femtocell scenario and compared with the conventional flat pricing‐based scheme via numerical examples. The results demonstrate significant reduction in network load using our proposed scheme.     

Proactive content caching by exploiting transfer learning for mobile edge computing

To address the vast multimedia traffic volume and requirements of user quality of experience in the next‐generation mobile communication system (5G), it is imperative to develop efficient content caching strategy at mobile network edges, which is deemed as a key technique for 5G. Recent advances in edge/cloud computing and machine learning facilitate efficient content caching for 5G, where mobile edge computing can be exploited to reduce service latency by equipping computation and storage capacity at the edge network. In this paper, we propose a proactive caching mechanism named learning‐based cooperative caching (LECC) strategy based on mobile edge computing architecture to reduce transmission cost while improving user quality of experience for future mobile networks. In LECC, we exploit a transfer learning‐based approach for estimating content popularity and then formulate the proactive caching optimization model. As the optimization problem is NP‐hard, we resort to a greedy algorithm for solving the cache content placement problem. Performance evaluation reveals that LECC can apparently improve content cache hit rate and decrease content delivery latency and transmission cost in comparison with known existing caching strategies.     

Cache‐at‐relay: energy‐efficient content placement for next‐generation wireless relays

Uploading and downloading content have recently become one of the major reasons for the growth of Internet traffic volume. With the increasing popularity of social networking tools and their video upload/download applications, as well as the connectivity enhancements in wireless networks, it has become a second nature for mobile users to access on‐demand content on‐the‐go. Urban hot spots, usually implemented via wireless relays, answer the bandwidth need of those users. On the other hand, the same popular contents are usually acquired by a large number of users at different times, and fetching those from the initial content source each and every time makes inefficient use of network resources. In‐network caching provides a solution to this problem by bringing contents closer to the users. Although in‐network caching has been previously studied from latency and transport energy minimization perspectives, energy‐efficient schemes to prolong user equipment lifetime have not been considered. To address this problem, we propose the cache‐at‐relay (CAR) scheme, which utilizes wireless relays for in‐network caching of popular contents with content access and caching energy minimization objectives. CAR consists of three integer linear programming models, namely, select relay, place content, and place relay, which respectively solve content access energy minimization, joint minimization of content access and caching energy, and joint minimization of content access energy and relay deployment cost problems. We have shown that place relay significantly minimizes the content access energy consumption of user equipments, while place content provides a compromise between the content access and the caching energy budgets of the network. Copyright © 2015 John Wiley & Sons, Ltd.     

基于DCNN-LSTM负荷预测算法的5G基站节能系统研究

伴随着5G网络的大规模快速建设,运营商乃至整体通信行业的能耗压力在同步凸显。通过节能降耗实现行业可持续发展成为当前5G网络发展的新研究方向。以小区物理资源块（physical resource block,PRB）利用率为负荷评估指标,对小区指标进行深度特征提取,提出了一套深度卷积神经网络和长短期记忆（DCNN-LSTM）深度学习算法模型实现PRB利用率未来值预测,进一步结合小区瞬时任务中大小包比例,对各种基站设定动态化的节能策略。并引入网络能耗管理网元,对整体5G接入网络的能耗进行动态化统一管理,在保障无线网络服务质量的基础上,实现了5G基站的智能化节能运作。     

Improving caching resource management: A pricing economic approach using Cournot,Bertrand, and Stackelberg game models

Over-The-Top broadcasts a huge number of medias that mobile network operators have to manage efficiently before to deliver it to their subscribers. We propose an economic pricing approach to address caching resource management issues in the 5G wireless networks and to overcome limitations in terms of throughput, latency, and reliability. Moreover, we consider this approach based on an oligopolistic multi-market deducted from Cournot, Stackelberg, and Bertrand models. For simulation purpose, we consider the routing protocol (Ad-hoc On-Demand Distance Vector [AODV]) commonly used for the wireless network. We use the NS-2 package, and we analyze results in terms of End-to-End delay representing latency, throughput, packet delivery ratio, and normalized network load.     

Energy‐delay‐aware caching strategy in green CCN using markov approximation

One of the basic challenges in content‐centric networking (CCN) is how to optimize the overall energy consumption of content transmission and caching. Furthermore, designing an appropriate caching policy that considers both energy consumption and quality of service (QoS) is a major goal in green CCN. In this paper, the problem of minimizing the total CCN energy consumption while being aware of the end‐to‐end delay is formulated as an integer linear programming model. Since it is an Non‐deterministic Polynomial‐time (NP)‐hard problem, the Markov approximation method for an energy‐delay aware caching strategy (MAEDC) is proposed through a log‐sum‐exp function to find a near‐optimal solution in a distributed manner. The numerical results show that the MAEDC achieves near‐optimal energy consumption with better delay profile compared with the optimal solution. Moreover, due to the possibility of distributed and parallel processing, the proposed method is proper for the online situation where the delay is a crucial issue.     

基于AI深度学习的面向业务5G基站节能系统研究

由于5G业务发展,5G基站数量增多,造成运营商的电费成本急剧增加,节能降耗成为运营商的可持续发展需求。在研究主流5G基站节能模式及多方位5G节能方案的基础上,提出了基于多种AI算法的5G基站节能系统,通过单SIM卡级别的高精度业务识别,在保证5G重要业务等各类型业务稳定运行的基础上,实现了最佳策略的5G基站柔性节能。     

Power‐saving policies for annual energy cost savings in green computing

The concept of green computing is a step toward advanced information technology‐related data computations. There is an urgent need to develop sustainable mobile telecommunication networks subscribed by Telecommunication Regulatory Authority of India (TRAI). This paper aims at deploying major recommendations and directions in computing systems directed by regulating authorities. Green computing–based power‐saving policies include virtualization technique, power‐saving method, and recycling technique‐type solution components that we tried to embed in existing systems. The energy‐efficient green computing reserves the reliability and power of information‐driven technology. This research paper provides a comprehensive approach toward applying green mechanism by spotting the difference between energy consumption of current academic scenario and after embedding power‐saving policies of green computing. Moreover, an account has been maintained about the amount of energy preservation. The extraordinary energy‐ and cost‐saving features of green computing make environment sustainable and lively for future generations.     

A game theoretic approach to balancing energy consumption in heterogeneous wireless sensor networks

Energy balancing is an effective technique in enhancing the lifetime of a wireless sensor network (WSN). Specifically, balancing the energy consumption among sensors can prevent losing some critical sensors prematurely due to energy exhaustion so that the WSN's coverage can be maintained. However, the heterogeneous hostile operating conditions—different transmission distances, varying fading environments, and distinct residual energy levels—have made energy balancing a highly challenging task. A key issue in energy balancing is to maintain a certain level of energy fairness in the whole WSN. To achieve energy fairness, the transmission load should be allocated among sensors such that, regardless of a sensor's working conditions, no sensor node should be unfairly overburdened. In this paper, we model the transmission load assignment in WSN as a game. With our novel utility function that can capture realistic sensors’ behaviors, we have derived the Nash equilibrium (NE) of the energy balancing game. Most importantly, under the NE, while each sensor can maximize its own payoff, the global objective of energy balancing can also be achieved. Moreover, by incorporating a penalty mechanism, the delivery rate and delay constraints imposed by the WSN application can be satisfied. Through extensive simulations, our game theoretic approach is shown to be effective in that adequate energy balancing is achieved and, consequently, network lifetime is significantly enhanced. Copyright © 2012 John Wiley & Sons, Ltd.     

A game theoretic approach for energy‐efficient communications in multi‐hop cognitive radio networks

In multi‐hop cognitive radio networks, it is a challenge to improve the energy efficiency of the radio nodes. To address this challenge, in this paper, we propose a two‐level Stackelberg game model, where the primary users and the secondary users act as the leaders and the followers, respectively. Based on the game model, our proposed scheme not only considers the power allocation problem for secondary users but also takes into account the price of spectrum. First, we give the cognitive radio network model, and show how to set up the game theoretic model in multi‐hop cognitive radio networks. We then analyze this problem and show the existence and uniqueness of the Nash equilibrium point for the game. We also study the impact of the spectrum price of the primary users in the cognitive radio network and study how to select the best price for the primary users to maximize their own profit. Finally, we implement simulations to show the performance of our schemes. Our work gives an insight on how to improve the energy efficiency and allocate spectrum resources in multi‐hop cognitive radio networks. Copyright © 2016 John Wiley & Sons, Ltd.     

Non‐cooperative power control and spectrum allocation in cognitive radio networks: a game theoretic perspective

The invention of cognitive radio (CR) concept aims to overcome the spectral scarcity issues of emerging radio systems by exploiting under‐utilization of licensed spectrum. Determining how to allocate unused frequency bands among CR is one of the most important problems in CR networks. Because different CRs may have different quality‐of‐service requirements, they may have different objectives. In voice communication, high‐speed transmission is the most important factor; hence, voice radios always try to maximize their transmission rate. However, in data communication, the most important factor is the bit error rate. The data radios always try to maximize their signal‐to‐interference‐plus‐noise ratio (SINR). In this paper, two non‐cooperative games named interference minimization game and capacity maximization game, which reflect the target of data radios and voice radios, respectively, are proposed. From the simulations, after these games are applied, the average SINRs of all players at each channel are improved. The average SINR of players in each channel after applying the capacity maximization game is smaller than that after applying the interference minimization game. However, in comparison with that after applying the interference minimization game, the average capacity of players after applying capacity maximization approach is larger. Copyright © 2012 John Wiley & Sons, Ltd.     

GaMe‐PLive: a new game theoretic mechanism for P2P live video streaming

GaMe‐PLive is a game theoretical framework for peer‐to‐peer live video streaming. Prevention of free‐riding and minimization of loss rate in video data transmission are the important objectives of the proposed framework. GaMe‐PLive is also extremely evasive about overhead of extra control messages exchange. At first, a static game with complete information between peers is described, which models the peer's interactive decision process for acceptance/rejection of a video chunk request. All peers repeatedly play this game during video playback periodically. Afterwards, the proposed game is analyzed to obtain a Nash equilibrium, which determines a peer's best strategic response for participation in the video chunk distribution. It will be proved that by applying some simple and feasible conditions, the desired objectives can be reached. The experimental results reveal that the proposed system has been successful in detecting free‐riders with negligible false negative and false positive rate. Also, tolerable loss chunk percentage has been satisfied in all performed tests. Besides, an interesting social norm emerges in GaMe‐PLive: Less participation leads to more missing chunks. GaMe‐PLive will be proven to be quite resistant against cheating peers. The proposed framework displays high performance even if there is not a video server with high upload bandwidth. Copyright © 2015 John Wiley & Sons, Ltd.     

电气自动化的节能设计技术分析

无论是在工业还是人们日常生活当中,节能都成为国家和人们关注的焦点之一。就工业行业来讲,电气是其中的重要的节能领域之一,做好电气节能是所有电气行业的从业者都应该关注的课题。做好电气节能工作,不仅可以保护环境、节约资源,还可以提高工业的生产效益,推动工业的进步。因此,本文简要分析了电气自动化领域的节能设计技术的基本原则,并提出了一些可能的策略和做法。     

节能灯电容器老练工艺的研究

研究了老练工艺对节能灯电容器漏电流的影响，通过不同条件（温度、电压、时间）下进行老练的试验，表明温度是影响漏电流及其稳定性的最主要因素。指出了保证节能灯长寿命、高可靠工作的三个因素：电容器在高温（１０５℃）下漏电流小；常规测试漏电流稳定；损耗小，阻抗频率特性好。     

A learning‐based approach for virtual machine placement in cloud data centers

In recent years, the increasing use of cloud services has led to the growth and importance of developing cloud data centers. One of the challenging issues in the cloud environments is high energy consumption in data centers, which has been ignored in the corporate competition for developing cloud data centers. The most important problems of using large cloud data centers are high energy costs and greenhouse gas emission. So, researchers are now struggling to find an effective approach to decreasing energy consumption in cloud data centers. One of the preferred techniques for reducing energy consumption is the virtual machines (VMs) placement. In this paper, we present a VM allocation algorithm to reduce energy consumption and Service Level Agreement Violation (SLAV). The proposed algorithm is based on best‐fit decreasing algorithm, which uses learning automata theory, correlation coefficient, and ensemble prediction algorithm to make better decisions in VM allocation. The experimental results indicated improvement regarding energy consumption and SLAV, compared with well‐familiar baseline VM allocation algorithms.     

Distributed game theoretic approach for offloading in ad hoc cloud

An ad hoc mobile cloud had been proposed to offload workload to neighboring mobile devices for resource sharing.The issues that whether to offload or not was addressed,how to select the suitable mobile device to offload,and how to assign workload.Game theoretic approach was used to formulate this problem,and then,a distributed scheme was designed to achieve the optimal solution.The experimental results validate the rightness and effectiveness of proposed scheme.     

灵活光网络节能技术的分析与研究

为了减少灵活光网络中的能源消耗,以灵活光网络的关键技术为基础,对网络中能源消耗模型进行分析,从设备层、网络层以及网络全局三个角度对灵活光网络中节能技术进行分析与总结,最后在指出节能工作中多因素限制的条件下,对未来灵活光网络中能源效率的研究进行了展望.     

Congestion‐aware multiaccess edge computing collaboration model for 5G

In 5G cloud computing, the most notable and considered design issues are the energy efficiency and delay. The majority of the recent studies were dedicated to optimizing the delay issue by leveraging the edge computing concept, while other studies directed its efforts towards realizing a green cloud by minimizing the energy consumption in the cloud. Active queue management‐based green cloud model (AGCM) as one of the recent green cloud models reduced the delay and energy consumption while maintaining a reliable throughput. Multiaccess edge computing (MEC) was established as a model for the edge computing concept and achieved remarkable enhancement to the delay issue. In this paper, we present a handoff scenario between the two cloud models, AGCM and MEC, to acquire the potential gain of such collaboration and investigate its impact on the cloud fundamental constraints; energy consumption, delay, and throughput. We examined our proposed model with simulation showing great enhancement for the delay, energy consumption, and throughput over either model when employed separately.