A novel approach for energy‐efficient resource allocation in double threshold‐based cognitive radio network

This paper mainly focuses on solving the energy efficiency (EE) maximization problem in double threshold‐based soft decision fusion (SDF) cooperative spectrum sensing (CSS) in the cognitive radio network (CRN). The solution to this objective problem starts with the selection of suitable secondary users (SUs) both for the spectrum sensing and data transmission. Here, energy efficiency is maximized under the constraints of interference to the primary user (PU), an acceptable outage of SUs, the transmission power of the SUs and the probability of false alarm. We propose a novel algorithm called iterative Dinkelbach method (IDM) which jointly optimizes the sensing time and transmission power allocation to the SUs. Further, Lagrangian duality theorem is employed to find the exact power assigned to the SUs. Finally, simulation results are carried out to validate the effectiveness of our proposed scheme by comparing with the other existing schemes. The performance is also analyzed for different system parameters.     

QoE‐ and energy‐efficient resource optimization in OFDMA networks with bidirectional relaying

Because of the surging demands of multimedia services, quality‐of‐experience (QoE) is becoming an important metric to evaluate network quality from users' perspective. In this paper, resource optimisation to achieve optimal tradeoff between QoE and energy consumption in bidirectional orthogonal frequency‐division multiple‐access relaying networks is addressed so as to provide satisfactory multimedia delivery quality and support green communications. We first formulate a QoE‐energy efficiency tradeoff optimisation where QoE requirements and relaying traffic balance are considered and prove that QoE‐energy efficiency is quasiconcave on QoE, which suggests the existence of a unique global optimal tradeoff point. We then propose an optimisation framework to achieve the optimal tradeoff efficiently. With the framework, we develop resource allocation approaches for two specific relaying strategies, that is, two‐phase decode‐and‐forward relaying with dynamic XOR network coding and compute‐and‐forward relaying with physical network coding via structured codes. Numerical results validate theoretical findings and demonstrate the effectiveness of the proposed optimisation solution for achieving the tradeoff between QoE and energy consumption. Copyright © 2015 John Wiley & Sons, Ltd.     

An energy‐saving algorithm for cloud resource management using a Kalman filter

Cloud computing has emerged as a promising technique to provide storage and computing component on‐demand services over a network. In this paper, we present an energy‐saving algorithm using the Kalman filter for cloud resource management to predict the workload and to further achieve high resource availability with low service level agreement. Using the proposed algorithm, one can estimate the potential future workload trend then predict the computing component workload utilizations and further retrench energy consumption and achieve load balancing in a cloud system. Experimental results show that the proposed algorithm achieves more than 92.22% accuracy in the computing component workload prediction, improves 55.11% energy in energy consumption, and has 3.71% in power prediction error rate, respectively. Copyright © 2013 John Wiley & Sons, Ltd.     

Distance and clustering‐based energy‐efficient pseudonyms changing strategy over road network

To improve the fairness, the energy consumption changing pseudonyms needs to be taken into account. Existing works focus on changing velocity‐based pseudonyms changing strategy and short changes interval with limited coverage, but due to similar velocity and short changes, internal attacker guesses easily known communication and location information due to location information of vehicle on tracking, which may expose adversary private information, and frequently, pseudonyms changing occurs due to movement of vehicles' similar velocity and short coverage, which may cause serious attack of vehicle. To overcome this problem, distance and cluster can be performed. In this work, we proposed distance and cluster‐based energy pseudonyms changing method for road network. We proposed distance and energy‐based clustering routing service over road network, the cluster head elected to depend on random number of distance and energy to change pseudonyms of vehicles. An each interval to be establish cluster head vehicle deployed while selects the operation mode and informs the cluster members of the selected mode through beacon signal. The cluster head vehicle node performs the pseudonyms changing based on the predicted distance and energy of the cluster member to use clustering optimization. The data of whole network send to report server through these nodes while near the RSU, and the vehicles in this area will use less energy to change the pseudonyms. The simulation results show that the proposed method enhances pseudonyms changing strategy less consumption and delays sufficient privacy level each vehicle also our method has outperform compare with existing methods than we use Sumo simulation and Matlab tools to verify our proposed method. Our proposed method outperformed in terms of pseudonym changing energy efficiency to careful attention during the cluster formation process, stable and balanced clusters that prolong the network lifetime, increases distances to more CH vehicles connectivity to makes clustering group and changing their pseudonyms in terms of high level privacy and finally, CH nodes use Dijkstra's algorithm use MST among the vehicles nodes depend on existing road networks to follow shortest path selection roads in terms of high connectivity probability of CH and stable structure of the network decreases the topology changes and thus,the clustering overhead is reduced.     

A fault‐tolerant energy‐efficient clustering protocol of a wireless sensor network

Energy efficiency in specific clustering protocols is highly desired in wireless sensor networks. Most existing clustering protocols periodically form clusters and statically assign cluster heads (CHs) and thus are not energy efficient. Every non‐CH node of these protocols sends data to the CH in every time slot of a frame allocated to them using the time division multiple access scheme, which is an energy‐consuming process. Moreover, these protocols do not provide any fault tolerance mechanism. Considering these limitations, we have proposed an efficient fault‐tolerant and energy‐efficient clustering protocol for a wireless sensor network. The performance of the proposed protocol was tested by means of a simulation and compared against the low energy adaptive clustering hierarchy and dynamic static clustering protocols. Simulation results showed that the fault‐tolerant and energy‐efficient clustering protocol has better performance than both the low energy adaptive clustering hierarchy and dynamic static clustering protocols in terms of energy efficiency and reliability. Copyright © 2012 John Wiley & Sons, Ltd.     

Penalty‐aware and cost‐efficient resource management in cloud data centers

Today, data centers are the main source of providing cloud services through a service level agreement (SLA). Most research papers for cloud resource management concentrate on how to reduce host energy consumption and SLA violation (SLAV) to minimize operational cost. However, they do not consider the amount of penalty that cloud provider should pay to users because of SLAV. In this paper, we propose a new penalty‐aware and cost‐efficient method that considers cloud resource management as a cost problem. In this method parameters such as user budget, penalty, and host energy consumption cost play an important role in minimizing operational cost which leads to higher profit for cloud provider. The simulation results with CloudSim show that our proposed method minimizes operational cost compared to the prior resource managements. Copyright © 2016 John Wiley & Sons, Ltd.     

Pricing and power control for energy‐efficient radio resource management in cognitive femtocell networks

Cognitive femtocell has been considered as a promising technique that can improve the capacity and the utilization of spectrum efficiency in wireless networks because of the short transmission distance and low transmit power. In this paper, we study the win–win solution of energy‐efficient radio resource management in cognitive femtocell networks, where the macrocell tries to maximize its revenue by adjusting spectrum utilization price while the femtocells try to maximize their revenues by dynamically adjusting the transmit power. When the spectrum utilization price is given by macrocell, we formulate the power control problem of standalone femtocells as an optimization problem and introduce a low‐complexity iteration algorithm based on gradient‐assisted binary search algorithm to solve it. Besides, non‐cooperative game is used to formulate the power control problem between collocated femtocells in a collocated femtocell set, and then low complexity and widely used gradient‐based iteration algorithm is applied to obtain the Nash‐equilibrium solution. Specially, asymptotic analysis is applied to find the approximate spectrum utilization price in macrocell, which can greatly reduce the computational complexity of the proposed energy‐efficient radio resource management scheme. Finally, extensive simulation results are presented to verify our theoretical analysis and demonstrate the performance of the proposed scheme. Copyright © 2013 John Wiley & Sons, Ltd.     

An overview of network virtualization and cloud network as a service

Cloud computing services delivery and consumption model is based on communication infrastructure (network). The network serves as a linkage between the end‐users consuming cloud services and the providers of data centers providing the cloud services. In addition, in large‐scale cloud data centers, tens of thousands of compute and storage nodes are connected by a data center network to deliver a single‐purpose cloud service. To this end, some questions could be raised, such as the following: How do network architectures affect cloud computing? How will network architecture evolve to support better cloud computing and cloud‐based service delivery? What is the network's role in reliability, performance, scalability, and security of cloud computing? Should the network be a dumb transport pipe or an intelligent stack that is cloud workload aware? This paper focuses on the networking aspect in cloud computing and shall provide insights to these questions. Researchers can use this paper to accelerate their research on devising mechanisms for the following: (i) provisioning cloud network as a service and (ii) engineering network of data centers. Copyright © 2014 John Wiley & Sons, Ltd.     

网络虚拟化环境下基于预测的资源重配置算法

解决虚拟网动态加入、离开导致的底层网络资源占用不均衡问题,提出基于预测的资源重配置算法(FRRA)。FRRA用已知信息预测资源重配置时间间隔,代替已有算法中周期性时间间隔。采取两方面措施保证重配置时机全局最优:将资源划分为关键资源和普通资源并使用不同配置算法;根据资源迁移失败概率,推导重配置请求次数极限值。与算法VNA-II、PMPA实验比较表明,FRRA的重配置花费比VNA-II节省69%,比PMPA节省42%;FRRA的虚拟网请求接收率比VNA-II提高29%,比PMPA提高52%。     

An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

The massive growth of cloud computing has led to huge amounts of energy consumption and carbon emissions by a large number of servers. One of the major aspects of cloud computing is its scheduling of many task requests submitted by users. Minimizing energy consumption while ensuring the user's QoS preferences is very important to achieving profit maximization for the cloud service providers and ensuring the user's service level agreement (SLA). Therefore, in addition to implementing user's tasks, cloud data centers should meet the different criteria in applying the cloud resources by considering the multiple requirements of different users. Mapping of user requests to cloud resources for processing in a distributed environment is a well‐known NP‐hard problem. To resolve this problem, this paper proposes an energy‐efficient task‐scheduling algorithm based on best‐worst (BWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodology. The main objective of this paper is to determine which cloud scheduling solution is more important to select. First, a decision‐making group identify the evaluation criteria. After that, a BWM process is applied to assign the importance weights for each criterion, because the selected criteria have varied importance. Then, TOPSIS uses these weighted criteria as inputs to evaluate and measure the performance of each alternative. The performance of the proposed and existing algorithms is evaluated using several benchmarks in the CloudSim toolkit and statistical testing through ANOVA, where the evaluation metrics include the makespan, energy consumption, and resource utilization.     

Highly efficient optical aggregation network with network functions virtualization

Network functions virtualization enables network edge functions to be relocated from dedicated hardware to distributed pools of commodity servers. Metro aggregation networks provide transport between access gateway nodes and such servers accommodating virtual network functions (VNFs). Networks need to be designed to increase the efficiency of network resource usage and reducing network cost as well as energy consumption. However, independently placing VNFs on a server from a physical network design degrades the efficiency of resource usage and causes an increase in network cost. We can avoid such problems by adequately placing each VNF in consideration of the location of access gateway nodes and a network topology. We thus propose a method for designing an optical aggregation network with VNF placement. We successfully formulate the design method as mixed‐integer linear programming and demonstrate its effectiveness through intensive mathematical experiments. The experiments showed that the proposed method reduced network cost by up to about 18% while performing almost optimally in terms of server load dispersion.     

SPEECH‐MAC: Special purpose energy‐efficient contention‐based hybrid MAC protocol for WSN and Zigbee network

Energy‐efficient Zigbee‐based wireless sensor network (WSN) occupies a major role in emergency‐based applications. The foremost drawback of such applications is maintaining the battery power because frequent changing is not possible in those conditions. In the earlier days, several researches created new model MAC protocols in terms of increase the lifetime of the WSN. But still, there is a research gap particularly in emergency applications. In order to improve the lifetime of such applications, we introduced a novel hybrid MAC protocol, namely, special purpose energy‐efficient contention‐based hybrid MAC (SPEECH‐MAC) protocol. This protocol includes dual hop concept considerably to save the energy. Both the single hop network and the dual hop networks are developed, and the results are analyzed. Prioritization mechanism for SPEECH‐MAC protocol is introduced to analyze the emergency conditions in detail. In summary, according to the simulation, the calculated parameters are total residual energy, end‐to‐end delay, packet drop, packet delivery ratio, and network throughput.     

云计算数据中心的网络虚拟化

本文首先介绍电信运营商的业务转型及IDC传统网络所面临的挑战,然后分析了网络虚拟化的发展趋势,最后探讨了软件定义网络在电信行业的应用。     

An energy‐efficient clustering and multipath routing for mobile wireless sensor network using game theory

Most of the current generation sensor nodes of mobile wireless sensor network (MWSN) are designed to have heterogeneous mobility to adapt itself in the applied environment. Energy optimization in MWSN with heterogeneous mobility is very challenging task. In this paper, a heterogeneous game theoretical clustering algorithm called mobile clustering game theory–1 (MCGT‐1) is proposed for energy optimization in a heterogeneous mobile sensor environment. Energy optimization is achieved through energy‐efficient cluster head election and multipath routing in the network. A heterogeneous clustering game is modelled with varying attributes and located an asymmetric equilibrium condition for a symmetric game with mixed strategies. The real‐time parameters, namely, predicted remaining energy, distance between a base station and nodes, distance between nodes, and mobility speed, were used to calculate the probability to elect the cluster head (CH). The efficient multipath routing is achieved through prior energy prediction strategy. It has mitigated the generation of “hot spots,” reducing its delay and improving the overall residual energy of the network. Simulation results showed that the average lifetime of MCGT‐1 has increased by 6.33 %, 13.1% and 14.2% and the PDR has improved by 4.8%,11.8%, and 17.2% than MCGT, LEACH‐ME and LEACH‐M respectively. The hot spot delay is reduced to 0.063025 seconds, improving the efficiency of the network.     

ABC algorithm‐based trustworthy energy‐efficient MIMO routing protocol

This paper introduces artificial bee algorithm (ABC)‐based energy‐efficient protocol with security for the enhancement of lifetime of wireless sensor network. A trustworthy energy‐efficient routing MIMO (TEEM) technique is used by implementing MIMO technique in TEER protocol to isolate malicious nodes and to reduce the fading effects and interference in the network. The performance of ABC‐based trustworthy energy‐efficient MIMO routing (ABC‐TEEM) protocol is analysed. Alive node performance, residual energy, throughput analysis, and pocket loss rate of ABC‐TEEM protocol for WSN are computed and compared with the performance of existing TEEM protocol for various diversity orders.     

网络虚拟化中动态资源分配算法研究

针对网络虚拟化中资源分配问题,提出一种动态的资源分配算法。算法根据物理节点平均负载差异度,结合当前物理网络容忍的节点负载差异动态地进行虚拟节点迁移,并通过综合影响因子为虚拟节点选择合适的目标宿主,减少虚拟节点迁移对物理链路带宽和虚拟链路时延的影响。仿真实验表明,该算法能使物理节点上的负载分布均衡,同时对时延和带宽的影响较小。     

An energy‐efficient framework for ubiquitous phone access

Energy‐constrained smartphones and operation‐facilitated tablets are popular and necessary in our daily life, and people may prefer to use tablet to access smartphone's features for energy efficiency and easy operation. In this paper, we present uPhone, an energy‐efficient framework for ubiquitous phone access that allows ubiquitous and symmetric access to a smartphone's core features via a third device such as a tablet. More specifically, we design uPhone in two main components of devices and uPhone server, then implement it on a smartphone as a primary device and a tablet as a secondary device, and finally evaluate it in terms of energy savings, latency, and reliability. The evaluation results demonstrate that uPhone achieves up to 44% energy savings compared with simply typing and sending messages, and notifies on a smartphone, incurs little latency overhead that is acceptable by users, and performs roughly equivalent to standard messages with a high‐quality user experience. Copyright © 2016 John Wiley & Sons, Ltd.     

Cognitive radio network with continuous energy‐harvesting

In this paper, the performance of a cooperative cognitive radio (CR) network is investigated under continuous energy harvesting scenario. A CR node harvests energy from both the sources: non‐radio frequency (RF) signal (ambient sources) or from RF signal (primary user signal). It harvests from non‐RF signal during sensing time of its detection cycle, and from both the sources, RF signal and non‐RF signal, during transmission time as per sensing decision. Several novel analytical expressions are developed to indicate the harvested energy, energy reward, energy cost in a detection frame, and throughput. The performance of the CR network is investigated to maximize the throughput considering energy causality constraints and collision constraints. Analytical results are validated through extensive simulation results. Copyright © 2016 John Wiley & Sons, Ltd.