带有卸载时延感知的边缘云增强FiWi网络节能机制

边缘云增强光无线融合网络中,存在传统节能机制与卸载业务不匹配的问题。该文提出一种带有负载转移的光网络单元卸载协同休眠机制。通过分析当前光网络单元负载,结合无线域多跳传输时延和目标光网络单元的报告帧发送时刻,进而确定休眠和目的光网络单元完成负载转移。然后光网络单元协同考虑边缘服务器的回传数据到达时刻和无线域控制帧的发送时刻,选取最合适的休眠时长以减少控制开销。仿真结果表明,所提机制在有效降低网络能耗的同时能保证卸载业务的时延性能。     

带有上行数据帧聚合的光无线融合接入网络节能机制

光无线融合接入网存在光网络单元利用率低,数据传输过程中控制开销较大的问题。该文提出一种带有上行数据帧聚合的节能机制,建立M/G/1模型分析数据帧在无线域节点及光域节点的队列时延,结合不同优先级业务的最大容忍时延,推导各优先级聚合帧在不同网络状态下的最佳长度,进而根据所得到的最佳帧长对光域节点进行休眠调度,在保障业务时延的前提下,尽可能地延长节点休眠时间长度,提高网络能量效率。仿真结果表明,所提方法在有效降低整个网络能耗的同时能够保证业务的时延性能。     

基于任务相关性的无线传感器网络任务调度算法

针对现有无线传感器网络中的静态调度算法(SCP算法)在调度具有相关性任务时的缺陷,提出了一种调度簇树并加以研究,在此基础上提出了一种新的基于任务复制的聚簇调度算法(ICS算法).通过与SCP算法进行比较,ICS算法可以在不增加任务的完成时间的同时,减少所需节点的个数,进而减少重复任务的计算和不同节点间的通信开销,以达到降低网络能耗的目的.     

无线传感网中一种基于即时信息的TDMA方案

媒体访问控制(MAC)协议对无线传感网的性能具有重要影响。根据无线传感网在网络性能方面的要求,针对现有无线传感网协议在节点能耗和时延方面的不足,提出了一种IM-TDMA方案,根据节点流量的变化,动态地调节帧长,提高信道利用率;同时采用计数器管理及续传优先的调度方式,简化了调度复杂度,降低了节点能耗。仿真结果表明:IM-TDMA方案能有效地节约能耗、降低时延,可运用于实际无线传感网的MAC协议方案中。     

基于无线传感器网络的业务流调度算法

实时性要求是无线传感器网络调度算法性能评价的重要内容。对于实时性要求很高的应用场合,调度算法的首要标准是降低数据包的传输时延。针对无线传感器网络的业务流调度问题,结合传统的加权循环调度算法WRR,着眼于解决无线传感器网络中业务流突发引起的时延性能下降问题,提出了新的调度算法-WSWRR。新算法合理分配传感器节点的数据感知和传输时间,使节点在不需要工作时转入低功耗的休眠模式,且能在满足应用要求服务质量的前提下,高效利用节点能量,延长整个传感器网络的生命周期。通过仿真实验,验证了新算法在调度突发数据包时性能得到了很好的改善,且没有增加网络的整体能耗,证明了WSWRR算法的有效性。     

分布式信源编码的压缩感知技术在无线传感器网络中的应用

文章提出了分布式压缩感知理论,利用传感器节点的数据相关性,把单个信号的压缩采样扩展到信号群的压缩采样,可以实现无线传感器网络的数据重构,减少节点的通信开销,降低整个网络的能耗。     

EPON中节能动态带宽分配算法

结合光网络单元(ONU)休眠控制和动态带宽分配机制,提出一种节能的双向DBA算法。该算法根据上/下行业务量为每个ONU分配合适的上/下行授权带宽,保证每个ONU在上/下行方向都具有较低的传输时延,同时尽可能增加ONU的休眠时间来降低网络能耗。仿真结果表明,与已有算法相比,该算法具有较低的上/下行平均时延,并能减少ONU的能耗。     

分簇无线传感器网络动态时隙分配MAC协议

为降低大规模无线传感器网络的平均能耗,提出了一种基于动态分配的调度型无线传感器网络MAC协议（SDC-MAC）。该协议簇间使用FDMA方式分配无线信道,簇内通过TDMA方式给各个节点分配可变长的时隙。随着簇结构的变化,簇头通过时隙分配通知,对簇内节点的时隙分配进行动态调整,簇成员节点则根据控制信息进行休眠和唤醒。仿真结果显示,该算法有效地降低了网络的平均能耗,当网络流量高时还可降低平均数据包时延。     

基于云雾混合计算的车联网联合资源分配算法

针对车联网业务的低时延、低功耗需求及海量设备计算卸载引起的网络拥塞问题,该文提出一种在云雾混合网络架构下的联合计算卸载、计算资源和无线资源分配算法(JODRAA)。首先,该算法考虑将云计算与雾计算结合,以最大时延作为约束,建立最小化系统能耗和资源成本的资源优化模型。其次,将原问题转化为标准二次约束二次规划(QCQP)问题,并设计一种低复杂度的联合卸载决策和计算资源分配算法。进一步,针对海量设备计算卸载引起的网络拥塞问题,建立卸载用户接入请求队列的上溢概率估计模型,提出一种基于在线测量的雾节点时频资源配置算法。最后,借助分式规划理论和拉格朗日对偶分解方法得到迭代的带宽和功率分配策略。仿真结果表明,该文算法可以在满足时延需求的前提下,最小化系统能耗和资源成本。     

无线传感器网络能量高效混合MAC算法

针对无线传感器网络MAC协议中存在的能耗问题,提出了能量高效的无线传感器网络混合MAC(EEH-MAC)算法,采用基于TDMA机制的时槽系数动态调整簇内节点的时槽大小来降低数据的传输时延;同时,对部分不需要数据传输的节点不分配时槽来减少能耗;按簇内节点剩余能量系数形成时槽分配顺序来减少状态转换的能耗;在簇头之间采用CSMA/CA机制的随机分配策略进行通信.仿真结果表明,EEH-MAC协议能有效减少能耗并延长网络生命周期.     

V2X offloading and resource allocation under SDN and MEC architecture

To address the serious problem of delay and energy consumption increase and service quality degradation caused by complex network status and huge amounts of computing data in the scenario of vehicle-to-everything (V2X),a vehicular network architecture combining mobile edge computing (MEC) and software defined network (SDN) was constructed.MEC sinks cloud serviced to the edge of the wireless network to compensate for the delay fluctuation caused by remote cloud computing.The SDN controller could sense network information from a global perspective,flexibly schedule resources,and control offload traffic.To further reduce the system overhead,a joint task offloading and resource allocation scheme was proposed.By modeling the MEC-based V2X offloading and resource allocation,the optimal offloading decision,communication and computing resource allocation scheme were derived.Considering the NP-hard attribute of the problem,Agglomerative Clustering was used to select the initial offloading node,and Q-learning was used for resource allocation.The offloading decision was modeled as an exact potential game,and the existence of Nash equilibrium was proved by the potential function structure.The simulation results show that,as compared to other mechanisms,the proposed mechanism can effectively reduce the system overhead.     

DDPG-based intelligent rechargeable fog computation offloading for IoT

In view of the existing computation offloading research on fog computing network scenarios, most scenarios focus on reducing energy consumption and delay and lack the joint consideration of smart device rechargeability. This paper proposes a deep deterministic policy gradient-based intelligent rechargeable fog computation offloading mechanism that is combined with simultaneous wireless information and power transfer technology. Specifically, an optimization problem that minimizes the total energy consumption for completing all tasks in a multiuser scenario is formulated, and the joint optimization of the task offloading ratio, uplink channel bandwidth, power split ratio and computing resource allocation is fully considered. Based on the above nonconvex optimization problem with a continuous action space, a communication, computation and energy harvesting co-aware intelligent computation offloading algorithm is developed. It can achieve the optimal energy consumption and delay, and similar to a double deep Q-network, an inverting gradient updating-based dual actor-critic neural network design can improve the convergence and stability of the training process. Finally, the simulation results validate that the proposed mechanism can converge quickly and can effectively reduce the energy consumption with the lowest task delay.

     

移动低占空比传感网中基于分段拟合压缩的数据容灾存储算法

数据存储是无线传感器网络中数据管理的基础操作.在移动低占空比传感网中,由于节点的移动性,每个节点需要频繁更新邻居节点集合,使得节点能量消耗过大;同时,节点大部分时间处于睡眠状态,仅在少部分时间内苏醒工作,造成数据备份的通信延迟过大.提出一种快速的低能耗数据保存机制.首先,源节点基于连续时间序列对感知数据进行分段线性拟合压缩;接着,节点根据预估故障概率和存储空间大小,计算出合理的压缩数据备份数量.在此基础上,设计一种动态自适应传输协议.实验仿真表明,与已有存储算法比较,该机制具有更低的传输能耗和通信延迟.     

无线自组传感器网络中基于能量的超宽带多路径路由算法

The Energy based Ultra-Wideband Multipath Routing (EUMR) algorithm for Ad hoc sensor network is proposed. It utilizes the function of UWB positioning to reduce the network communication delay and route overhead. Furthermore, the algorithm considers energy consumption, the residual energy and node hops of communication paths to make energy consumption more balanced and extend the network lifetime. Then routing which is stable, energy-saving and low-delay is realized. Simulation results show that the algorithm has better performance on saving energy, route overhead, stability and extending network lifetime.     

Joint offloading strategy based on quantum particle swarm optimization for MEC-enabled vehicular networks

With the development of the mobile communication technology, a wide variety of envisioned intelligent transportation systems have emerged and put forward more stringent requirements for vehicular communications. Most of computation-intensive and power-hungry applications result in a large amount of energy consumption and computation costs, which bring great challenges to the on-board system. It is necessary to exploit traffic offloading and scheduling in vehicular networks to ensure the Quality of Experience (QoE). In this paper, a joint offloading strategy based on quantum particle swarm optimization for the Mobile Edge Computing (MEC) enabled vehicular networks is presented. To minimize the delay cost and energy consumption, a task execution optimization model is formulated to assign the task to the available service nodes, which includes the service vehicles and the nearby Road Side Units (RSUs). For the task offloading process via Vehicle to Vehicle (V2V) communication, a vehicle selection algorithm is introduced to obtain an optimal offloading decision sequence. Next, an improved quantum particle swarm optimization algorithm for joint offloading is proposed to optimize the task delay and energy consumption. To maintain the diversity of the population, the crossover operator is introduced to exchange information among individuals. Besides, the crossover probability is defined to improve the search ability and convergence speed of the algorithm. Meanwhile, an adaptive shrinkage expansion factor is designed to improve the local search accuracy in the later iterations. Simulation results show that the proposed joint offloading strategy can effectively reduce the system overhead and the task completion delay under different system parameters.     

无线照明系统休眠策略的研究与实现

为解决传统无线控制系统在空闲时仍处于完全功耗状态,造成能源利用率低的问题,提出一种基于休眠唤醒策略的节能机制。首先,网络采用有利于节点休眠的网状拓扑结构,通过节点配置,保证网络通信在节点休眠期间的可靠性;其次,休眠节点按照其功能要求,分别采用了事件驱动和定时唤醒的机制,在满足使用要求的情况下,最大限度地减低功耗;最后,根据相关数据对上述休眠机制进行分析研究,验证其有效性和可用性。     

车联网中一种基于软件定义网络与移动边缘计算的卸载策略

在新兴的车联网络中,汽车终端请求卸载的任务对网络带宽、卸载时延等有着更加严苛的需求,而新型通信网络研究中移动边缘计算(MEC)的提出更好地解决了这一挑战。该文着重解决的是汽车终端进行任务卸载时卸载对象的匹配问题。文中引入了软件定义车载网络(SDN-V)对全局变量统一调度,实现了资源控制管理、设备信息采集以及任务信息分析。基于用户任务的差异化性质,定义了重要度的模型,在此基础上,通过设计任务卸载优先级机制算法,实现任务优先级划分。针对多目标优化模型,采用乘子法对非凸优化模型进行求解。仿真结果表明,与其他卸载策略相比,该文所提卸载机制对时延和能耗优化效果明显,能够最大程度地保证用户的效益。     

Research and Improvement of Wireless Sensor Network Secure Data Aggregation Protocol Based on SMART

The privacy-preserving of information is one of the most important problems to be solved in wireless sensor network (WSN). Privacy-preserving data aggregation is an effective way to protect security of data in WSNs. In order to deal with the problem of energy consumption of the SMART algorithm, we present a new dynamic slicing D-SMART algorithm which based on the importance degree of data. The proposed algorithm can decrease the communication overhead and energy consumption effectively while provide good performance in preserving privacy by the reasonable slicing based on the importance degree of the collected raw data. Simulation results show that the proposed D-SMART algorithm improve the aggregation accuracy, enhance the privacy-preserving, reduce the communication overhead to some extent, decrease the energy consumption of sensor node and prolong the network lifetime indirectly.