基于最大生存周期的无线传感器网络能量模型研究

在分析了无线传感器网络的应用和特性的基础上,从节点能量计算模型、节点的能量消耗模型和状态转换模型3个方面论述了无线传感器网络的系统能量模型,通过引入Flag标志和长期睡眠状态机制来防止网络中的某些节点因为过早耗尽能量而死亡,从而实现无线传感器络中节点的能量均衡和网络生存周期的最大化,对无线传感器网络的应用和研究有着深远的意义。     

紫外光无线传感器网络节能的研究与仿真

为了减少无线传感器网络节点的能量消耗,采用紫外光作为无线传感器网络的信息载体,研究了紫外光传感器节点的能量模型。理论分析了单跳节能和多跳节能,得出了计算最优跳数的数学表达式,并对单跳通信、多跳通信和最优跳通信的平均能量消耗进行了计算机仿真,仿真结果与理论分析一致;对于多跳通信带来的能量消耗不均匀的问题,利用移动sink节点来解决,通过仿真对比了sink节点不同移动速率对网络平均能量消耗、丢包率和端到端时延的影响。结果表明,借助移动sink节点可以降低网络的平均能量消耗,但要根据场景选择合适的移动速率。     

无线传感器网络中蚁群路由算法的改进

在无线传感器节点能量有限的条件下,如何使网络寿命最大化是无线传感器网络研究的重点,均衡网络能量消耗是延长网络寿命的一个有效方法。在蚁群算法的基础上引入了模糊理论的概念,提出了一种ACO for Fuzzy Theory算法,根据节点剩余能量、通信距离、邻居节点数目和信息素等因素采用模糊综合评判法进行下一跳节点的选择。仿真实验表明,与基于能量有效蚁群算法(EEABR)进行比较,相同条件下AFT算法有效地减少了网络平均能量消耗,增强了网络节点的存活率。     

基于传输功率和数据包尺寸的优化网络寿命的研究

在无线传感网络中,数据包长度和传输功率等级成为影响能量效率和网络寿命的关键因素。为此,提出基于传输功率和数据包长度的联合优化网络寿命算法,记为TP-PS-INL。首先,依据Mica2传感节点的能量消耗特性和无线信道模型,构建链路层模型,然后再利用链路层模型,建立混合整数规划MIP模型,进而联合优化传输功率和数据包长度,最大化网络寿命。数值仿真分析表明,MIP模型能较准确地反映数据包长度和传输功率对网络寿命的影响,并且TP-PS-INL算法能够最大化网络寿命。     

一种节能和时延保证的无线传感器QoS路由算法

无线传感器网络中实时数据（如视频、语音、图像）的传输对时延和抖动比较敏感,文章在充分考虑无线传感器节点能量受限的基础上,针对实时业务对时延的要求提出一种路由算法,该算法在路径创建和维护的过程中考虑时延和节点剩余能量来选择下一跳节点,在路由更新阶段对失效的节点进行标记,能够简化重新建立有效路由的步骤,从而减少路由建立时间,保证了传输的可靠性。仿真结果表明该路由算法既延长了无线传感器网络的生命周期,又能更好地满足实时数据的传输。     

基于ERPMT改进启发式方法的WSN寿命最大化算法

针对传感器节点的电池容量限制导致无线传感网络寿命低的问题,基于容量最大化(CMAX)、线上最大化寿命(OML)两种启发式方法以及高效路由能量管理技术(ERPMT),提出了基于ERPMT改进启发式方法的无线传感网络寿命最大化算法。首先,通过启发式方法初始化每个传感器节点,将节点能量划分为传感器节点起源数据和其它节点数据延迟;然后利用加入的一种优先度量延迟一跳节点的能量消耗;最后,根据路径平均能量为每个路由分配一个优先级,并通过ERPMT实现最终的无线传感网络优化。针对不同分布类型网络寿命的实验验证了本文算法的有效性及可靠性,实验结果表明,相比较为先进的启发式方法CMAX及OML,本文算法明显增大了无线传感网络的覆盖范围,并且大大地延长了网络的寿命。     

无线传感器网络中终端节点的休眠算法

针对无线传感器网络中节点能量有限等特点,提高能量利用效率和延长网络寿命是无线传感器网络设计的目标。由于终端节点只负责数据采集,不需要转发其它节点的数据,因此可以减少终端节点的空闲侦听时间,从而进入休眠状态。仿真结果验证了所提方案的能量消耗低于传统非休眠方案的能量消耗,该休眠算法能有效延长网络的生存时间。     

无线供电混合多址接入网络的最优能效资源分配研究

在能量受限的通信系统中，能量效率是衡量系统性能的关键指标.本文研究由一个基站和多个分簇用户组成的无线供电混合非正交多址接入系统.在该网络中，基站通过无线能量传输方式给用户供能，用户则利用收集到的能量向基站传输各自的信息.为降低基站的接收解码复杂度，用户采用分簇的方式进行信息传输：簇间用户的信息传输采用时分多址方式，而簇内用户的信息传输采用非正交多址方式.通过联合分配能量传输与信息传输的时间长度以及控制基站和用户的发射功率来实现网络能量效率的最大化.由于涉及的优化问题是非凸的，本文先通过寻找问题最优解的结构，然后根据分式规划理论，提出了一种新的迭代资源分配算法来求解该问题.仿真结果表明，与"吞吐量最大化策略"和 "固定时间分配策略"两种基准策略相比，所提出的算法显著提高了网络的能量效率.     

一种基于无交集节点分组的无线传感器网络覆盖算法

无线传感器网络是一种无线自组织网络,它由大量能量有限的传感器节点组成.能量消耗和网络覆盖是无线传感器网络的两个核心问题,网络覆盖决定了无线传感器网络对物理世界的监测能力,能量消耗则决定了无线传感器网络的生存时间.本文研究了一种改进的基于无交集节点分组算法,针对随机选取节点实现无交集节点分组方式获得的分组个数少且节点通信...     

无线传感器网络能耗分析与节能策略研究

能量供应与消耗目前影响无线传感器网络生存时间的主要因素。介绍了无线传感器网络节点组成,从硬件消耗和软件消耗两方面分析了影响能量消耗的主要因素,阐述了降低能耗、延长网络生存时间的的5种基本策略,并对无线传感器网络的能量供应方式的发展作了分析预测。     

An ant colony optimization based routing algorithm for extending network lifetime in wireless sensor networks

Reducing the energy consumption of network nodes is one of the most important problems for routing in wireless sensor networks because of the battery limitation in each sensor. This paper presents a new ant colony optimization based routing algorithm that uses special parameters in its competency function for reducing energy consumption of network nodes. In this new proposed algorithm called life time aware routing algorithm for wireless sensor networks (LTAWSN), a new pheromone update operator was designed to integrate energy consumption and hops into routing choice. Finally, with the results of the multiple simulations we were able to show that LTAWSN, in comparison with the previous ant colony based routing algorithm, energy aware ant colony routing algorithms for the routing of wireless sensor networks, ant colony optimization-based location-aware routing algorithm for wireless sensor networks and traditional ant colony algorithm, increase the efficiency of the system, obtains more balanced transmission among the nodes and reduce the energy consumption of the routing and extends the network lifetime.     

大规模输电线路状态监测传感器网络的周期性低功耗通信技术方案

为了实现输电线路监测的功耗低、寿命长、绿色发展的目的，提出大规模输电线路状态监测传感器网络周期性低功耗通信技术方案。依据网络中传感器网络组网特征以及节点运行状态转换特点，设置睡眠定时器，以周期性运行方式使传感器网络通信节点在初始化、睡眠、激活状态间转换，通信节点在输电线路状态监测数据无传递需求时进入睡眠状态，节省通信功耗；传感器网络汇聚（sink）节点利用梯度创建上行路由，通过源路由的方式创建下行路由，以跳数和剩余能量为依据进行上、下行路由数据分组传递，降低节点功耗，延长通信运行时间。实验显示，大规模输电线路状态监测传感器网络应用该技术方案后，通信功耗明显降低，运行时间明显延长，且不会影响监测传感器网络的数据传输性能，延长了监测传感器网络的使用寿命。     

Monitoring power transmission lines using a wireless sensor network

Power transmission is the bulk transfer of electrical energy from power plants to sub‐stations. A wireless sensor network is a promising technology for transmission line monitoring due to its low cost, easy installation, large‐scale coverage, and fault tolerance characteristics. A wireless sensor network is application‐specific; therefore, we investigate the new features and requirements of the wireless sensor network used in transmission line monitoring. Then, we propose an efficient wireless sensor network framework, which includes a clustering algorithm to simplify network management and to balance the network's energy consumption and a hybrid media access control (MAC) (H‐MAC) protocol to handle traffic variability. The framework takes advantage of the features of network topology and traffic pattern to optimize the protocols' performance on real time and energy efficiency. The results indicate that the H‐MAC shows a significant improvement in the network's reliability, real‐time performance, and energy efficiency, and the cluster hierarchy can balance the network's energy consumption. Furthermore, the cluster hierarchy also prolongs the network's lifetime. Copyright © 2014 John Wiley & Sons, Ltd.     

Traffic-Aware Density-Based Sleep Scheduling and Energy Modeling for Two Dimensional Gaussian Distributed Wireless Sensor Network

Sleep scheduling of sensors in network domain is considered to be the most fundamental way of achieving higher life expectancy of wireless sensor networks. In this paper we have proposed density-based sleep scheduling strategy with traffic awareness in Gaussian distributed sensor network for minimizing energy consumption. In uniform distributed sensor network, it has been found that nodes in the nearest belt around the sink consume more energy. The reason behind is that the nodes near the sink involve more packet relaying load than the distant nodes. Consequently, the energy of these sensors get exhausted rapidly, thereby creating connectivity breaks known as energy hole. For this purpose, Gaussian distribution is used by densely deploying nodes around the sink which well-balances the relaying load. In addition, we have developed the analytical model for computing the energy consumption and coverage analysis in the sensor network. The performance of our sleep scheduling method is evaluated with respect to the Randomized Scheduling and Linear Distance-based Scheduling protocols. The simulation results of our proposed work show commendable improvement in network lifetime.     

Power Efficient Video Multipath Transmission over Wireless Multimedia Sensor Networks

This paper proposes a power efficient multipath video packet scheduling scheme for minimum video distortion transmission (optimised Video QoS) over wireless multimedia sensor networks. The transmission of video packets over multiple paths in a wireless sensor network improves the aggregate data rate of the network and minimizes the traffic load handled by each node. However, due to the lossy behavior of the wireless channel the aggregate transmission rate cannot always support the requested video source data rate. In such cases a packet scheduling algorithm is applied that can selectively drop combinations of video packets prior to transmission to adapt the source requirements to the channel capacity. The scheduling algorithm selects the less important video packets to drop using a recursive distortion prediction model. This model predicts accurately the resulting video distortion in case of isolated errors, burst of errors and errors separated by a lag. Two scheduling algorithms are proposed in this paper. The Baseline scheme is a simplified scheduler that can only decide upon which packet can be dropped prior to transmission based on the packet’s impact on the video distortion. This algorithm is compared against the Power aware packet scheduling that is an extension of the Baseline capable of estimating the power that will be consumed by each node in every available path depending on its traffic load, during the transmission. The proposed Power aware packet scheduling is able to identify the available paths connecting the video source to the receiver and schedule the packet transmission among the selected paths according to the perceived video QoS (Peak Signal to Noise Ratio—PSNR) and the energy efficiency of the participating wireless video sensor nodes, by dropping packets if necessary based on the distortion prediction model. The simulation results indicate that the proposed Power aware video packet scheduling can achieve energy efficiency in the wireless multimedia sensor network by minimizing the power dissipation across all nodes, while the perceived video quality is kept to very high levels even at extreme network conditions (many sensor nodes dropped due to power consumption and high background noise in the channel).     

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.     

Extending the Lifetime of Wireless Sensor Networks Through Adaptive Sleep

In recent years, the use of wireless sensor networks for industrial applications has rapidly increased. However, energy consumption still remains one of the main limitations of this technology. As communication typically accounts for the major power consumption, the activity of the transceiver should be minimized, in order to prolong the network lifetime. To this end, this paper proposes an Adaptive Staggered sLEEp Protocol (ASLEEP) for efficient power management in wireless sensor networks targeted to periodic data acquisition. This protocol dynamically adjusts the sleep schedules of nodes to match the network demands, even in time-varying operating conditions. In addition, it does not require any a priori knowledge of the network topology or traffic pattern. ASLEEP has been extensively studied with simulation. The results obtained show that, under stationary conditions, the protocol effectively reduces the energy consumption of sensor nodes (by dynamically adjusting their duty-cycle to current needs) thus increasing significantly the network lifetime. With respect to similar nonadaptive solutions, it also reduces the average message latency and may increase the delivery ratio. Under time-varying conditions, the protocol is able to adapt the duty-cycle of single nodes to the new operating conditions, while keeping a consistent sleep schedule among sensor nodes. The results presented here are also confirmed by an experimental evaluation in a real testbed.      

海水养殖监测系统无线传感器网络协议研究

文中将无线传感器网络应用于海水养殖监测系统,根据海上养殖监测的特点,提出了一种适合海水养殖的无线传感器网络协议。该协议采用最小跳数异步分簇路由算法构建无线传感器网络,协调簇内能量平衡。对于正常数据和异常数据,采用自适应的数据传送机制进行数据传输,降低数据传输量,实现网络能量优化。仿真结果表明,在异常数据率较小的情况下,自适应的数据传输中节点产生的平均数据量和平均冗余数据量比普通的事件驱动型数据传输要小很多,降低了能耗,提高了传输效率。在信道传输质量较差的情况下,自适应的数据传送机制的传输效率更具优势。     

Optimizing sensor networks in the energy-latency-density design space

In wireless sensor networks, energy efficiency is crucial to achieving satisfactory network lifetime. To reduce the energy consumption significantly, a node should turn off its radio most of the time, except when it has to participate in data forwarding. We propose a new technique, called sparse topology and energy management (STEM), which efficiently wakes up nodes from a deep sleep state without the need for an ultra low-power radio. The designer can trade the energy efficiency of this sleep state for the latency associated with waking up the node. In addition, we integrate STEM with approaches that also leverage excess network density. We show that our hybrid wakeup scheme results in energy savings of over two orders of magnitude compared to sensor networks without topology management. Furthermore, the network designer is offered full flexibility in exploiting the energy-latency-density design space by selecting the appropriate parameter settings of our protocol.     

Optimization of listening time of S-MAC for wireless sensor networks

In wireless sensor networks, sensor nodes are usually battery-operated computing and sensing devices, hence operations are limited by the initially equipped batteries, which are hard to be recharged or replaced. In this sense, saving energy consumption becomes significant. As most energy waste is from the always-on wireless interface, S-MAC is suggested to reduce energy consumption by introducing periodic listen/sleep scheme. However, when designing the listening time, S-MAC fails to consider the traffic distribution factor. In this article, an optimization for this scheme is proposed based on the distribution model. Evaluations show that the optimized S-MAC achieves considerable improvement in energy and latency.