硬实时系统中基于任务同步及节能的动态调度算法

提出基于任务同步及节能的动态实时调度算法HDSA(hybrid dynamic scheduling algorithm),以有效地解决任务同步及节能的难题.HDSA 结合RM及EDF算法,在满足任务实时可调度性及任务同步的限制条件下,采用DVFS节省能耗.HDSA包含静态算法及动态算法两部分.静态算法在静态条件下,求出任务的静态速度.动态调度算法在实际运行中,固定临界区的运行速度,并充分回收、利用任务运行时的空闲执行时间,调节处理器的速度,以有效降低能耗并满足实时可调度性.同时避免高优先权任务被阻塞时,临界区继承高优先权任务的速度时所造成的处理器电压开关的频繁切换,因而能有效地降低实时任务调度的成本.实验测试表明,HDSA在调度性能上明显优于目前所知的有效算法.     

一种基于动态聚合方法的网格任务调度算法

网格中的资源都是动态的,传统的静态任务调度算法不能适应网格的动态特性.通过对资源在未来一段时间内的状态进行预测,可以提高调度算法的性能.文中提出了一种用动态聚合进行调度的算法.首先对处理器的负载进行取样,然后根据网格任务的执行时间,对处理器的取样值进行动态聚合,再利用AR（p）模型进行预测,最后利用预测到的值作为参数对网格任务进行调度,把网格任务分配给每个处理器,使得每个处理器完成子任务的时间都相同,从而使得整个任务的执行时间最短.实验表明,这种算法能很好地适应处理器负载高度变化的情况.     

WSN中基于云计算的动态频率分配研究

随着无线传感器网络的快速发展,频谱需求急速增长,频谱资源日渐紧缺,无线传感器网络频率的高效分配已成为一个亟需解决的难题。为了提高频谱的利用效率和应对无线传感网络中多节点频率信息动态配置问题,提出了一种基于云计算的WSN动态频率分配系统。该动态频率分配系统在无线传感器网络的无线通信框架上,利用云资源调度、分布式数据管理、自适应分析决策支持等技术,实现对无线传感器网络中频率资源的动态分配。     

动态电压调整多处理器实时系统任务调度

动态电压调整DVS（Dynamic Voltage Scaling）是根据处理器电压（速度）降低之后，能量消耗平方级的减少这一原理提出的。文章通过DVS机制在多处理器实时系统中进行任务调度．通过对任务调度中的静态能量管理进行分析，在此基础上提出了一种新的基于DVS的适用于多处理器实时系统中的调度算法。这种新的调度算法是通过对贪婪法调度进行研究，发现其不足．并以此为基础进行改进。结合了动态电压调整的多处理器实时系统任务调度的能量消耗比普通的任务调度能量消耗有了很大的改善。     

无线传感器网络的动态电压调节算法研究

对于能量有限的无线传感器网络,研究如何高效地利用有限能量具有重要意义.根据无线传感器网络多跳路由和拓扑易变的特点,提出一种基于任务驱动的含反馈的动态电压调节算法FB-DVS.该算法根据节点的任务集实时地调节节点的工作电压和频率,并通过反馈环节来修正误差,在保证任务实时性的前提下降低节点能耗.通过对仿真结果分析表明,改进的算法能有效地减少节点的能量消耗,延长无线传感器网络的生命周期.     

基于嵌入式处理器的系统级低功耗管理研究

针对嵌入式系统低功耗设计问题,分析了动态功率管理DPM和动态电压/频率调节DVFS两种嵌入式功耗管理策略,并提出了系统级低功耗控制框架.讨论了基于嵌入式处理器i.MX1硬件平台实现系统级功耗控制方案,并给出了具体的设计方法.实际应用表明,该设计方案可有效降低系统能耗.     

1553B网络BC的SPN建模与分析

针对1553B网络中BC节点采用不同的实时任务调度算法将影响轮询总线表实时任务实施次数,进而影响到总线网络的吞吐量和数据传输时延,为研究实时任务调度算法对1553B总线网络通信的影响,建立了BC节点采用静态调度算法下的任务集SPN模型,模型分析结果表明固定优先级高的任务使轮询总线表任务不能实时执行,容易被阻塞,影响了总线网络的通信效果,因此BC节点适宜采用动态调度算法。     

基于无线传感器网络能耗系统的研究

节能已经成为无线传感器网络研究的核心部分。该文研究了无线传感器网络拓扑结构的邻近节点数对网络能耗的影响,主要采用动态电压调节技术(DVS)来降低无线传感器网络中节点的能耗。动态电压调节主要通过减少门等效电容、供电电压以及降低转换因子、时钟频率来达到降低动态能耗的目的,其中,降低供电电压节能效果最佳。与其他方法相比,动态电压调节降低能耗更加明显、效率更高。通过在CC2430节点芯片上测试验证,通过改变其分频比,得出了功耗和频率的近似线性关系。     

基于改进微粒群算法的WSN节点调度

为了降低无线传感器网络监测区域节点能耗和延长网络生命周期,设计了一种基于改进微粒群算法的节点调度方法.首先,以网络覆盖率和休眠工作节点数为目标建立了系统的数学模型,然后设计了粒子的编码方式、适应度函数以及自适应动态惯性权重,并定义了使用改进的微粒群算法对传感器网络节点调度的具体算法.仿真实验表明,该方法能正确地实现无线传感器网络监测区域的节点调度,在迭代次数较少时,就能以较少的节点获得较高的网络覆盖率,且与其他方法相比,具有收敛速度快和全局寻优能力强的优点.     

基于Nash均衡的网格多调度节点的任务调度算法

 目前网格任务调度算法主要是针对1×n型即单调度节点多资源的网格环境,而针对m×n型的网格环境研究较少.论文用M/M/1排队系统对m×n型网格环境建模,然后以每个调度节点调度任务的平均完成时间为优化目标,提出了m×n型网格环境任务调度的Nash均衡问题,并利用粒子群算法求得该Nash均衡解.通过仿真验证了该算法在单位时间内平均完成的任务数,网络平均负载,以及系统的平均负载上均优于基于均匀调度策略的调度算法.     

无线传感器网络节点太阳能电源系统设计

对于无线传感器网络节点而言,电源是系统的关键部分之一。在此提出一种收集环境中太阳能为传感器节点供能的电源系统。该系统采用了高效安全的充电控制技术,独特的电池电压监测电路,以及低功耗的DC—DC转换电路。通过实验验证,基于此太阳能电源的传感器节点功耗动态调整节性能好,生存周期显著增加。该系统可应用于各种户外监测的节点,如环境监测,精细农业,森林防火等。     

A distributed reinforcement learning based sensor node scheduling algorithm for coverage and connectivity maintenance in wireless sensor network

The fundamental challenge for randomly deployed resource-constrained wireless sensor network is to enhance the network lifetime without compromising its performance metrics such as coverage rate and network connectivity. One way is to schedule the activities of sensor nodes and form scheduling rounds autonomously in such a way that each spatial point is covered by at least one sensor node and there must be at least one communication path from the sensor nodes to base station. This autonomous activity scheduling of the sensor nodes can be efficiently done with Reinforcement Learning (RL), a technique of machine learning because it does not require prior environment modeling. In this paper, a Nash Q-Learning based node scheduling algorithm for coverage and connectivity maintenance (CCM-RL) is proposed where each node autonomously learns its optimal action (active/hibernate/sleep/customize the sensing range) to maximize the coverage rate and maintain network connectivity. The learning algorithm resides inside each sensor node. The main objective of this algorithm is to enable the sensor nodes to learn their optimal action so that the total number of activated nodes in each scheduling round becomes minimum and preserves the criteria of coverage rate and network connectivity. The comparison of CCM-RL protocol with other protocols proves its accuracy and reliability. The simulative comparison shows that CCM-RL performs better in terms of an average number of active sensor nodes in one scheduling round, coverage rate, and energy consumption.

     

Maximizing Lifetime of Target Coverage in Wireless Sensor Networks Using Learning Automata

In wireless sensor networks, when each target is covered by multiple sensors, we can schedule sensor nodes to monitor deployed targets in order to improve lifetime of network. In this paper, we propose an efficient scheduling method based on learning automata, in which each node is equipped with a learning automaton, which helps the node to select its proper state (active or sleep), at any given time. To study the performance of the proposed method, computer simulations are conducted. Results of these simulations show that the proposed scheduling method can better prolong the lifetime of the network in comparison to similar existing methods.     

Sleep scheduling with expected common coverage in wireless sensor networks

Sleep scheduling, which is putting some sensor nodes into sleep mode without harming network functionality, is a common method to reduce energy consumption in dense wireless sensor networks. This paper proposes a distributed and energy efficient sleep scheduling and routing scheme that can be used to extend the lifetime of a sensor network while maintaining a user defined coverage and connectivity. The scheme can activate and deactivate the three basic units of a sensor node (sensing, processing, and communication units) independently. The paper also provides a probabilistic method to estimate how much the sensing area of a node is covered by other active nodes in its neighborhood. The method is utilized by the proposed scheduling and routing scheme to reduce the control message overhead while deciding the next modes (full-active, semi-active, inactive/sleeping) of sensor nodes. We evaluated our estimation method and scheduling scheme via simulation experiments and compared our scheme also with another scheme. The results validate our probabilistic method for coverage estimation and show that our sleep scheduling and routing scheme can significantly increase the network lifetime while keeping the message complexity low and preserving both connectivity and coverage.     

Elastic DVS Management in Processors With Discrete Voltage/Frequency Modes

Applying classical dynamic voltage scaling (DVS) techniques to real-time systems running on processors with discrete voltage/frequency modes causes a waste of computational resources. In fact, whenever the ideal speed level computed by the DVS algorithm is not available in the system, to guarantee the feasibility of the task set, the processor speed must be set to the nearest level greater than the optimal one, thus underutilizing the system. Whenever the task set allows a certain degree of flexibility in specifying timing constraints, rate adaptation techniques can be adopted to balance performance (which is a function of task rates) versus energy consumption (which is a function of the processor speed). In this paper, we propose a new method that combines discrete DVS management with elastic scheduling to fully exploit the available computational resources. Depending on the application requirements, the algorithm can be set to improve performance or reduce energy consumption, so enhancing the flexibility of the system. A reclaiming mechanism is also used to take advantage of early completions. To make the proposed approach usable in real-world applications, the task model is enhanced to consider some of the real CPU characteristics, such as discrete voltage/frequency levels, switching overhead, task execution times nonlinear with the frequency, and tasks with different power consumption. Implementation issues and experimental results for the proposed algorithm are also discussed     

无线传感器网络自适应目标跟踪节点调度算法

在无线传感器网络中,设计合理的节点调度算法是提高网络感知能力、降低系统能耗的关键。在分析节点能耗模型的基础上,针对移动目标跟踪型网络应用,提出一种高能效的无线传感器网络自适应节点调度算法ANSTT。该算法根据节点对移动目标的感知能力,以及节点的相对剩余能量水平,自动调整节点工作模式。仿真实验表明,ANSTT算法在维持低感知延时、高目标感知率的同时,可有效降低系统能耗,延长网络寿命。     

Energy-aware wireless systems with adaptive power-fidelity tradeoffs

Wireless networked embedded systems, such as multimedia terminals, sensor nodes, etc., present a rich domain for making energy/performance/quality tradeoffs based on application needs, network conditions, etc. Energy awareness in these systems is the ability to perform tradeoffs between available battery energy and application quality requirements. In this paper, we show how operating system directed dynamic voltage scaling and dynamic power management can provide for such a capability. We propose a real-time scheduling algorithm that uses runtime feedback about application behavior to provide adaptive power-fidelity tradeoffs. We demonstrate our approach in the context of a static priority-based preemptive task scheduler. Simulation results show that the proposed algorithm results in significant energy savings compared to state-of-the-art dynamic voltage scaling schemes with minimal loss in system fidelity. We have implemented our scheduling algorithm into the eCos real-time operating system running on an Intel XScale-based variable voltage platform. Experimental results obtained using this platform confirm the effectiveness of our technique     

A multiprocessor-oriented power-conscious scheduling algorithm for periodic tasks

With the rapid development of advanced technology in VLSI circuit designs, many processors could provide dynamic voltage scaling (DVS) to save power consumption when the supply voltage is allowed to be lower. In this paper, we propose a multiprocessor-oriented power-conscious scheduling algorithm for the real-time periodic tasks with task migration constrained scheme. We classify periodic tasks into fixed tasks and migration tasks, and limit the number of migration tasks and the number of destination processors which execute migration tasks. The proposed algorithm is made up of two steps. Firstly, choosing a processor to sort all of the periodic tasks in a non-increasing order according to task utilization, afterwards, allocating them to other processors. Secondly, scheduling the migration tasks with a virtual execution windows policy, and then scheduling the fixed tasks with EDF algorithm. The experiment results show that compared with arbitrary task migration policy and no task migration allowed policy, the power consumption in multiprocessor real-time periodic tasks scheduling is lowered significantly with the proposed algorithm.     

QoS provisioning in wireless video sensor networks: a dynamic power management framework

Recent technological advances in microelectronics and nano-systems technologies have made it feasible to equip wireless sensor nodes with small low-cost cameras to capture and transmit video. Wireless video sensor networks are gaining popularity due to numerous potential applications such as video surveillance, environmental and habitat monitoring, and so on. However, due to the limited battery available in wireless video sensor nodes, provisioning of QoS in such a network is a challenging task. We provide a survey on the major issues related to QoS provisioning in wireless video sensor networks and possible solution approaches. A dynamic power management framework is proposed for a wireless video sensor node to improve energy saving performance so that the lifetime of the sensor node can be increased. This framework considers the video traffic arrival process in the sensor node, the sleep and wakeup processes in the camera and wireless transceiver electronics, the queue status, and the wireless channel condition. Performance analysis results show that the proposed mechanism can achieve considerable energy saving in a sensor node while providing a target level of QoS performance.