一种多源协作网络中轮询节点选择算法

针对放大转发(AF)的多源多中继两跳协作通信网络模型,提出了一种基于轮询分组调度算法的节点选择与用户调度策略.与之前所提出的中继选择选择算法相比,所提出的中继节点轮询分组调度算法,不需要额外的信令开销,复杂度低,并保证中继间的负载均衡.此外,为保证用户传输的公平性,提出一种结合多用户分集的轮询分组调度算法.最后在已选择的中继节点和所调度的源节点用户中引入分布式空时编码.仿真结果表明,该节点选择和用户调度算法能保证所有节点使用的公平性,并且结合分布式空时编码之后,可以提高频谱利用率,从而提高了系统性能.     

带有带宽感知的自适应转发节点集合建立机制

研究了多点中继(MPR)集合建立机制对移动自组织网络(MANET)性能的影响,提出了一种基于可用带宽感知的集合建立方法,其特点是节点在MPR集合计算过程中自适应地为两跳邻居选择可用带宽较大的中继节点,降低节点拥塞程度,而算法的复杂度与原有算法的复杂度相同.仿真结果表明,虽然MPR集合平均元素数量和广播数据包数量略有增加,但是通过文中提出的转发节点集合建立方法,网络中的分组投递率以及端到端延迟性能得到了明显改善,此外该机制还能够根据网络当前状态实时调整,更加适应状态时变的网络特性.     

基于能量收集的认知无线电资源分配研究

本文将能量收集及协作中继技术引入到认知无线电网络中,研究次用户作为中继节点帮助主用户转发数据的协作认知场景,提出了一种基于解码转发模式下的最优能量收集时间及功率分配策略.仿真结果表明:和直接传输相比,不仅降低了主用户的中断概率,增大了次用户的传输速率,而且节省了网络传输能量,延长了网络的寿命周期.     

基于区域分组的城市VANETs的安全信息广播协议

为提高城市车辆自组织网络(VANETs)安全信息广播的有效性,提出了一种基于区域分组的安全信息广播协议(AGBP)。该协议采用正三角形法或正六边形法给中继节点每跳覆盖的区域平均分割,节点按照所在的区域分组。正三角形和正六边形顶点为中继节点的最佳位置,每一组内所有的节点根据自己和中继节点最佳位置的距离计算等待时延,由等待时延决定每一组内的中继节点并转发信息,因此信息可以同时沿着多个方向多条路转发。与传统的沿着一条道路转发信息的广播协议相比,该协议能够避免因分组不当造成信息冗余和碰撞,致使信息的传输效率下降,从而能够降低网络开销、链路时延和转发节点率,提高覆盖率。     

基于可靠性的自由空间光网络路由算法

自由空间光(FSO)网络容易受天气等因素影响造成网络中断。为了提高光网络可靠性,同时均衡网络能耗,延长网络生命周期,首先综合考虑接收光信号强度和中断概率建立可靠性模型,利用可靠性模型对直传和中继链路方式进行对比分析,获取选择直传和中继方式的距离阈值。然后针对网络能耗均衡问题,考虑节点能量进行簇头选举,提出光学中继的能耗均衡路由算法(BEC-ORM)。最后利用Matlab仿真平台求取了BEC-ORM算法选择通信方式的距离阈值,以及最佳的簇头比例。同其他算法对比分析,结果表明该算法能有效提高自由空间光网络的可靠性和能耗均衡性。     

传感器网络移动中继节点部署算法

针对传感器网络节点能量有限性及节点能量消耗不匀性问题,提出一种移动中继节点部署算法。首先假设网络中没有移动中继节点时,对静态节点提出一种最优路由树算法来构建数据传输路径;在此基础上再采用贪婪算法增加移动节点改善网络的拓扑结构提高路由树连通性;接着提出一种高效的分布式迭代算法,使得路由树的拓扑结构收敛于最优位置;最后进行理论分析与仿真实验,结果表明该方法具有一定理论意义与实用价值。     

主用户链路深度衰落下的认知网络中继

针对主用户链路经历深度衰落而发生通信中断的问题,提出了一种认知网络对主用户进行"透明"中继的方案。在不改变主用户通信协议的前提下,该方案首先感知主用户的状态,以判断其是否需要中继服务。当主用户通信发生中断时,认知网络利用从用户的能量检测器选出一个最优的节点解码转发主用户信号。从中断概率角度证明了这种最优单节点中继具有与多节点中继相同的空间分集作用,能够提高主用户平均传输效率,有较大的中继信道容量。通过仿真分析,验证了其分集效果和传输效率的提升。     

无线中继网络系统容量最大化中继选择机制

分析了基于用户频谱效率的中继选择算法的不足,基于最大流最小割定理,给出了系统容量最优化问题模型,分析了两跳中继网络接入链路和中继链路对系统容量的不同影响.基于链路权重因子,提出一种基于系统容量最大化的中继选择算法.对不同中继选择算法下的系统容量差异的理论与仿真分析结果表明,提出的系统容量最大化中继选择算法可以获得更优的系统容量性能,并对网络拓扑和节点个数具有良好的鲁棒性.     

过时信道状态下译码转发中继选择的优化

针对实际通信系统中反馈延迟这一非理想因素,进行了译码转发方式下的中继选择优化研究。针对机会中继选择(ORS)通过选择合适的协作节点参与协作来减少系统功耗和降低成本,但反馈延迟会影响最佳协作中继的选择从而导致协作通信系统性能变差的情况,提出了一种在过时信道下优化机会中继选择方法,以选出最佳中继节点参与协作过程。仿真结果表明,优化机会中继选择降低了译码转发方式下协作通信系统的平均误码率,改善了系统的信道容量,提高整个通信系统性能。     

装甲编队无线紫外光隐秘通信的中继选择研究

针对装甲编队在复杂战场环境下的紫外光端到端通信中断问题,多采用中继辅助方式建立协作通信链路,而中继选择是关键问题之一。为了提高编队之间的通信协同能力,在解码转发协议的前提下,结合门限决策思想,提出了一种基于无线紫外光隐秘通信的装甲编队最佳中继选择算法。该算法结合紫外光非直视通信的优点,根据信噪比门限和信道特性的选择策略,对编队进行最佳中继的选择,并在高斯噪声模型下,分析了其误码率性能。仿真结果表明,在根据不同的信噪比环境和中继数来选取适当的协作门限,可获得最佳中继链路,以及在协作通信链路动态变化时,调整中继的接收和发射状态,能有效提高协作中继链路的通信质量。     

QoS Model of a Router with Feedback Control

The Internet has evolved into a shared, integrated platform of a broad range of applications with different Quality‐of‐Service (QoS) requirements. Routers are an important part of the Internet and play a critical role in assuring QoS. A router is usually placed between two networks to receive data packets from one network and then transmit those data packets to another network if necessary. Data packets are the actual units of data traveling on computer networks. A data packet has two parts: header and data. The data carries messages, such as e‐mail text, from computer applications. The header carries information that is required to control and manage the transmission of the data packet on computer networks. Existing approaches for providing QoS involve prediction or estimation for traffic characterization to determine parameters required of static traffic admission control. However, prediction or estimation inaccuracy in traffic characterization can result in inappropriate parameter settings for static admission control and, in turn, compromise QoS or resource utilization. This study presents a QoS model of a router with feedback control that monitors the state of resource usage and adaptively adjusts parameters of traffic admission control to overcome prediction or estimation inaccuracy and achieve a balance between QoS and resource utilization. The QoS model of a router with feedback control is simulated to test its performance on QoS and resource utilization in both heavy and light traffic conditions. The performance of the QoS model of a router with feedback control is also compared with that of two basic QoS models of a router with static admission control using admission control parameters resulting from over‐ and under‐characterization of traffic, respectively. The simulation results show that the QoS model of a router with feedback control achieves a better balance between QoS and resource utilization than the basic QoS models with over‐ and under‐characterizations of traffic in the heavy traffic condition. This study also shows that the three models of routers demonstrate similar QoS performances and resource utilization in the light traffic condition. Copyright © 2005 John Wiley & Sons, Ltd.     

Advanced spectrum management in wideband code division multiple access systems enabling cognitive radio usage

The authors propose a new advanced spectrum management (ASM) methodology for wideband code division multiple access systems based on the concept of coupling matrix, which is able to capture inter-cell interactions. The proposed methodology takes into account the fact that each cell can be associated to more than one carrier and aims at liberating some carriers in large geographical zones, so that they could eventually be used by, for example, secondary cognitive radio users that exploit the flexible frequency allocation and opportunistic spectrum access. Simulation results show that the proposed methodology increases spectrum efficiency while guaranteeing the requested QoS levels. Moreover, a new metric has been introduced to reflect the capability of the ASM methodology to liberate some carriers in large geographic zones. This metric has been used to compare the different approaches presented here.     

Cooperative wireless networks for better green services

During the last two decades, mobile communication systems (such as GSM, GPRS and 3G networks), wireless broadcasting networks, wireless local area networks (WLAN or WiFi), and wireless sensor networks have been successfully developed and widely deployed through different technological routes for providing a variety of communication services in different application scenarios. While making tremendous contributions to social progress and economic growth, these heterogeneous wireless networks consume a lot of energy in achieving overlapped service coverage, and at the same time, generate strong electromagnetic interference (EMI) and radiation pollution, especially in big cities with high building density and user population. In order to guarantee the overall return on investment (ROI), improve user experience and quality of service (QoS), save energy, reduce EMI and radiation pollution, and enable the sustainable deployment of new profitable applications and services, this paper proposes a cross-network cooperation mechanism to effectively share network resources and infrastructures, and then adaptively control and match multi-network energy distribution characteristics according to actual user/service requirements in different geographic areas. Some idle or lightly-loaded Base Stations (BS or BSs) will be temporally turned off for saving energy and reducing EMI. Initial simulation results show the proposed approach can significantly improve the overall energy efficiency and QoS performance across multiple cooperative wireless networks.     

HELP-WSN-A Novel Adaptive Multi-Tier Hybrid Intelligent Framework for QoS Aware WSN-IoT Networks

Wireless Sensor Network is considered as the intermediate layer in the paradigm of Internet of things (IoT) and its effectiveness depends on the mode of deployment without sacrificing the performance and energy efficiency. WSN provides ubiquitous access to location, the status of different entities of the environment and data acquisition for long term IoT monitoring. Achieving the high performance of the WSN-IoT network remains to be a real challenge since the deployment of these networks in the large area consumes more power which in turn degrades the performance of the networks. So, developing the robust and QoS (quality of services) aware energy-efficient routing protocol for WSN assisted IoT devices needs its brighter light of research to enhance the network lifetime. This paper proposed a Hybrid Energy Efficient Learning Protocol (HELP). The proposed protocol leverages the multi-tier adaptive framework to minimize energy consumption. HELP works in a two-tier mechanism in which it integrates the powerful Extreme Learning Machines for clustering framework and employs the zonal based optimization technique which works on hybrid Whale-dragonfly algorithms to achieve high QoS parameters. The proposed framework uses the sub-area division algorithm to divide the network area into different zones. Extreme learning machines (ELM) which are employed in this framework categories the Zone's Cluster Head (ZCH) based on distance and energy. After categorizing the zone's cluster head, the optimal routing path for an energy-efficient data transfer will be selected based on the new hybrid whale-swarm algorithms. The extensive simulations were carried out using OMNET++-Python user-defined plugins by injecting the dynamic mobility models in networks to make it a more realistic environment. Furthermore, the effectiveness of the proposed HELP is examined against the existing protocols such as LEACH, M-LEACH, SEP, EACRP and SEEP and results show the proposed framework has outperformed other techniques in terms of QoS parameters such as network lifetime, energy, latency.     

Hybrid Swarm Intelligence Based QoS Aware Clustering with Routing Protocol for WSN

Wireless Sensor Networks (WSN) started gaining attention due to its wide application in the fields of data collection and information processing. The recent advancements in multimedia sensors demand the Quality of Service (QoS) be maintained up to certain standards. The restrictions and requirements in QoS management completely depend upon the nature of target application. Some of the major QoS parameters in WSN are energy efficiency, network lifetime, delay and throughput. In this scenario, clustering and routing are considered as the most effective techniques to meet the demands of QoS. Since they are treated as NP (Non-deterministic Polynomial-time) hard problem, Swarm Intelligence (SI) techniques can be implemented. The current research work introduces a new QoS aware Clustering and Routing-based technique using Swarm Intelligence (QoSCRSI) algorithm. The proposed QoSCRSI technique performs two-level clustering and proficient routing. Initially, the fuzzy is hybridized with Glowworm Swarm Optimization (GSO)-based clustering (HFGSOC) technique for optimal selection of Cluster Heads (CHs). Here, Quantum Salp Swarm optimization Algorithm (QSSA)-based routing technique (QSSAR) is utilized to select the possible routes in the network. In order to evaluate the performance of the proposed QoSCRSI technique, the authors conducted extensive simulation analysis with varying node counts. The experimental outcomes, obtained from the proposed QoSCRSI technique, apparently proved that the technique is better compared to other state-of-the-art techniques in terms of energy efficiency, network lifetime, overhead, throughput, and delay.     

Prediction of Cloud Ranking in a Hyperconverged Cloud Ecosystem Using Machine Learning

Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the Internet. The design of reliable and high-quality cloud applications requires a strong Quality of Service QoS parameter metric. In a hyperconverged cloud ecosystem environment, building high-reliability cloud applications is a challenging job. The selection of cloud services is based on the QoS parameters that play essential roles in optimizing and improving cloud rankings. The emergence of cloud computing is significantly reshaping the digital ecosystem, and the numerous services offered by cloud service providers are playing a vital role in this transformation. Hyperconverged software-based unified utilities combine storage virtualization, compute virtualization, and network virtualization. The availability of the latter has also raised the demand for QoS. Due to the diversity of services, the respective quality parameters are also in abundance and need a carefully designed mechanism to compare and identify the critical, common, and impactful parameters. It is also necessary to reconsider the market needs in terms of service requirements and the QoS provided by various CSPs. This research provides a machine learning-based mechanism to monitor the QoS in a hyperconverged environment with three core service parameters: service quality, downtime of servers, and outage of cloud services.     

Quality of service for multimedia traffic using cross-layer design

Quality-of-service (QoS) guarantees are critical for the transmission of multimedia traffic over mobile wireless networks. Currently, wireless networks provide QoS guarantees using the legacy layered protocol architecture where each layer provides a separate, independent solution, with its own optimised adaptation and protection mechanisms. Cross-layer design has been proposed as a methodology to extend that paradigm in wireless links where there is interdependence between the layers and hence opportunity for information sharing. Recently, cross-layer adaptation mechanisms have been proposed which attempt to solve the QoS provisioning problem. However, most of these mechanisms only use the lower (physical and data link) layers and the possibility of using higher protocol layers remains unexplored. As a result, restrictions are placed on the system which introduces functional and efficiency limitations. Here, one such limitation is highlighted, namely the inability to insert more than one class of traffic in a physical layer frame. A physical and application layer cross-layer adaptation mechanism is then proposed, which overcomes this limitation. The performance results of the scheme show that the cross-layer mechanism can be efficiently applied for the purpose of providing QoS guarantees for multimedia traffic.     

Controlled loops for quality-of-service differentiation in delay-sensitive optical burst switching networks

A novel quality-of-service (QoS) differentiation framework for optical burst switching networks is introduced. Two different methods are provided and subsequently validated. Particularly, both methods aim at using controlled loops as deflection routes, which assures an upper bounded number of hops from source to destination. This yields a 2-fold outcome. First, the insufficient offset time problematic of standard deflection routing under just-enough-time is solved. Second, excessively long delays owing to uncontrolled deflections are avoided. In both methods, the number of allowed loops is proposed as a QoS metric, giving support for class differentiation. This applies to a scenario with heterogeneous traffic demands, each one with different latency and burst loss requirements. The benefits of each proposal are quantified and further compared with existent alternatives by simulations.     

Emergency Prioritized and Congestion Handling Protocol for Medical Internet of Things

Medical Internet of Things (MIoTs) is a collection of small and energyefficient wireless sensor devices that monitor the patient’s body. The healthcare networks transmit continuous data monitoring for the patients to survive them independently. There are many improvements in MIoTs, but still, there are critical issues that might affect the Quality of Service (QoS) of a network. Congestion handling is one of the critical factors that directly affect the QoS of the network. The congestion in MIoT can cause more energy consumption, delay, and important data loss. If a patient has an emergency, then the life-critical signals must transmit with minimum latency. During emergencies, the MIoTs have to monitor the patients continuously and transmit data (e.g., ECG, BP, heart rate, etc.) with minimum delay. Therefore, there is an efficient technique required that can transmit emergency data of high-risk patients to the medical staff on time with maximum reliability. The main objective of this research is to monitor and transmit the patient’s real-time data efficiently and to prioritize the emergency data. In this paper, Emergency Prioritized and Congestion Handling Protocol for Medical IoTs (EPCP_MIoT) is proposed that efficiently monitors the patients and overcome the congestion by enabling different monitoring modes. Whereas the emergency data transmissions are prioritized and transmit at SIFS time. The proposed technique is implemented and compared with the previous technique, the comparison results show that the proposed technique outperforms the previous techniques in terms of network throughput, end to end delay, energy consumption, and packet loss ratio.     

A cross-layer adaptive algorithm for multimedia QoS fairness in WLAN environments using neural networks

The authors address the problem of providing fair multimedia quality-of-service (QoS) in IEEE 802.11 distributed co-ordination function-based wireless local area networks in the infrastructure mode where mobile hosts experience heterogeneous channel conditions due to mobility and fading effects. It was observed that unequal link qualities can pose significant unfairness of channel sharing, which may thereby lead to the degradation of multimedia QoS performed in adverse conditions. A cross-layer adaptation scheme that provides fair QoS by online adjusting the multidimensional medium access control layer backoff parameters in accordance with the application-layer QoS requirements as well as the physical-layer channel conditions was proposed. The solution is based on an optimisation approach, which utilises neural networks to learn the cross-layer function. Simulation results demonstrate that the proposed adaptation scheme can tackle heterogeneous channel conditions and random joining (or leaving) of hosts to achieve fair QoS in terms of throughput and packet delay.