WSN中基于能量感知的最小跳数路由算法

无线传感器网络的拓扑往往由于节点死亡而发生变化。网络拓扑的重新构建加速了剩余传感器节点的死亡,缩短了网络的生存时间。针对无线传感器网络对网络生存时间的苛刻要求,提出了一种基于能量感知的最小跳数路由算法。建立路由时,该算法综合考虑了节点剩余能量和该节点潜在的转发能力。仿真结果显示,该算法在生存时间、存活节点数和吞吐量方面的性能要远优于LEACH算法和HEED算法。     

无线传感器网络中簇首选择算法研究

传统的低功耗自适应集簇分层型协议（LEACH）算法在选择簇首时未能考虑到节点剩余能量对网络寿命的影响,使得簇首分布不够合理。为了克服该问题,在完全分布式成簇算法（HEED）协议的基础上,设计了一种根据节点剩余能量选择簇首的算法。在该算法中,剩余能量越大的节点越有可能成为簇首,进而承担更多数据传输责任,能量消耗更加平均,增强了算法的健壮性。仿真结果证实,提出的算法可以有效提高网络能量的使用效率,减少功耗,延长网络生存时间。     

基于抽样估计的能量异构无线传感器网络分簇算法

提出一种基于抽样估计的能量异构无线传感器网络分簇算法.采取对网络中节点抽样的办法估计出网络中的平均剩余能量,节点根据剩余能量与网络平均能量的比例来进行簇首竞争,使簇首选择更加合理.仿真实验表明:该算法可以更好地实现负载均衡,延长的网络生存时间.     

一种传感器网络跨层能量优先成簇算法

文中提出CLEEC跨层能量优先成簇算法,基于节点剩余能量来选举簇头节点,使网络能量均匀消耗,延长网络的生存时间.模拟实验结果显示,与现有的典型成簇方案相比,新的成簇算法在传感器网络下提供了更长的网络生存时间和更大的网络吞吐量.     

无线传感器网络LEACH算法的改进与仿真分析

对无线传感器网络分簇路由协议中LEACH算法进行了研究,针对算法中簇头节点随机选取、节点能量分布不均等不足之处,提出一种基于节点剩余能量、邻居密度与基站相对距离等因素改进的LEACH算法。改进后的算法通过网络最低能耗计算得出最优簇头数目,在簇头选取阶段,优化簇头的阈值计算公式,使节点剩余能量高、节点邻居数目多、相对基站距离近的节点更容易成为簇头。利用MATLAB软件,对LEACH算法、HEED算法及改进的LEACH算法的性能进行了仿真分析和比较。实验结果表明,改进的LEACH算法的网络整体能耗要低于LEACH算法和HEED算法,并显著地延长了网络的生存时间。     

预测节点剩余能量组合预测的OLSR路由算法

针对传统OLSR路由算法存在的不足,提出了一种节点剩余能量组合预测的OLSR路由算法(MOLSR).首先采用回归移动平均模型对节点的剩余能量线性变化特点进行预测,然后采用神经网络对残差的时间序列建立非线性预测模型,对节点的剩余能量非线性变化特点进行预测,最后将两者的预测结果进行相加,并用于进行OLSR路由算法的路由选择中.仿真结果表明,MOLSR路由算法不仅减小了网络开销,有效防止节点剩余能量过早耗完,而且延长了网络的生存时间,具有一定的实际应用价值.     

k跳受限泛洪的能量平衡组播路由算法

组播路由算法(Energy-Balanced Multicast Routing,EBMR)把无线传感器网络节点的剩余能量作为建立组播路由的重要因子,在不引入过长路径的同时优先选择剩余能量高的节点作为组播数据转发节点,构建组播能量平衡树(EB-Tree),从网络能量均衡消耗的角度来延长了无线传感器网络的生存时间.针对EBMR算法路由开销较大的问题,提出了k跳受限泛洪的能量平衡组播路由算法k-EBMR,控制组播路由报文在k跳范围内传播,并且研究了影响算法性能的关键因子的选取.仿真实验表明,与EBMR算法相比,k-EBMR算法较大程度上降低了路由控制报文的传输,提高了节点能量有效使用性,进一步延长了网络生存时间.     

基于能量均衡的无线传感器网络路由算法

为了提高无线传感器网络的生存时间,针对当前LEACH算法存在的能量不均衡难题,提出一种能量均衡的无线传感器网络节点路由算法。首先将监测区域看成以基站为中心的扇形区域,并将扇形区域分割成不同大小的弧形方块,每个弧形方块中的节点组成一个簇,根据节点剩余能量产生簇头,然后采用单跳和多跳相结合的簇间通信机制,最后采用仿真实验测试算法的性能。结果表明,本文算法有效提高了网络的能量利用率,能够实现节点之间的能耗均衡,使无线传感器的网络生存时间得到延长,适用于对网络生命周期要求较高的应用。     

无线传感器网络路由中的能量预测及算法实现

基于无线传感器网络中路由协议高效合理利用能量的要求,提出一种基于剩余能量预测的地理位置路由(EPGR,energy prediction and geographical routing)算法。算法通过建立传感器网络节点运作模型,及相邻节点剩余能量预测机制,优化路由选择。仿真和分析表明,EPGR算法能够有效地优化数据传输路径,均衡传感器网络节点的能量消耗,延长网络寿命。     

一种基于LEACH的无线传感器网络路由算法及仿真

在低功耗自适应分簇（LEACH,Low Energy Adaptive Clustering Hierarch）算法中,由于每一轮循环都要重新构造簇,距离较远的簇头节点可能会因长距离发送数据而过早耗尽自身能量,能量较低的节点当选为簇头节点时将会加速该节点的死亡,影响整个网络的生命周期。针对LEACH算法分簇机制中存在的不足,提出了一种改进的路由算法。仿真结果表明,改进算法通过考虑节点的剩余能量与固定分簇的方法,有效的改善了网络能量均衡,提高了网络生存时间。     

基于合作博弈的多信道认知无线网络中的频谱共享算法简

采用合作博弈对多信道认知无线网络中的频谱共享问题进行了建模分析,提出了次用户在各信道上的信干噪比乘积作为合作博弈的效用函数。次用户在各信道上保证对主用户的干扰小于一定门限的要求下,通过最大化各自效用函数的乘积来进行功率分配。由于最大化次用户效用函数的乘积问题是非凸的,通过变量替换将其转化为了一个等价的凸优化问题,利用该凸优化问题的对偶分解,提出了一种次用户间的频谱共享算法。仿真结果表明,所提算法在次用户和速率与公平性之间进行了有效折中。     

能量采集型无线传感器网络的高能效路由算法

在能量采集型无线传感器网络中,虽然有能量吸收,但是因能量依然非常珍贵,如何优化路由协议,提高能量利用率,延长网络寿命仍然是值得研究的问题.为求解高能效的路由,提出了一种采用遗传算法的高能效路由算法,建立考虑节点的吸收能量、剩余能量、消耗能量和浪费能量的适应函数,用遗传算法寻找全局最优路径.将该适应函数与3种其他适应函数作对比,其他3种适应函数分别为只考虑路径能耗最小的适应函数,考虑路径能耗与路径上节点的吸收能量、剩余能量的适应函数以及考虑路径能耗与网络中所有节点的浪费能量的适应函数.采用遗传算法解出4种路由,通过仿真分析可知,所提出的路由算法能量利用效率最高.     

Demand‐based charging strategy for wireless rechargeable sensor networks

A wireless power transfer technique can solve the power capacity problem in wireless rechargeable sensor networks (WRSNs). The charging strategy is a widespread research problem. In this paper, we propose a demand‐based charging strategy (DBCS) for WRSNs. We improved the charging programming in four ways: clustering method, selecting to‐be‐charged nodes, charging path, and charging schedule. First, we proposed a multipoint improved K‐means (MIKmeans) clustering algorithm to balance the energy consumption, which can group nodes based on location, residual energy, and historical contribution. Second, the dynamic selection algorithm for charging nodes (DSACN) was proposed to select on‐demand charging nodes. Third, we designed simulated annealing based on performance and efficiency (SABPE) to optimize the charging path for a mobile charging vehicle (MCV) and reduce the charging time. Last, we proposed the DBCS to enhance the efficiency of the MCV. Simulations reveal that the strategy can achieve better performance in terms of reducing the charging path, thus increasing communication effectiveness and residual energy utility.     

Power distribution algorithm based on game theory in the femtocell system

This paper studies the power control problem in femtocell system based on Nash non-cooperative game theory. It designs an utility function taking fem stations' transmit power as variable and relates it to the requirements of macro users' and fem users' signal to interference plus noise ratio (SINR). The utility also takes the impact of fem stations' location into account and improves the fairness of non-cooperative game. On this basis, this paper proposes a distributed power control algorithm and proves the existence and uniqueness of Pareto optimal point. The simulation results show that the algorithm improves the convergence speed and system performance through improving users' SINR.     

Joint Algorithm of Channel Allocation and Power Control in Multi-channel Wireless Sensor Network

In the wireless sensor network, the interference incurred by another transmitter’s transmission may disturb other receivers’ correct receptions of packets, thus, the add of a new transmission must consider its effect on other transmissions. Additionally, in order to reduce the interference and increase QoS, multi-channel technology is introduced into wireless communication, but the energy cost by the channel switch increases with the interval of channels increasing. Based on the above analysis, we consider an energy efficient joint algorithm of channel allocation and power control (JCAPC) for wireless sensor network. In JCAPC, each link firstly establishes its available channel set on which the transmitter of the link can guarantee its transmission successfully and don’t disturb other receivers’ transmissions, and then each link chooses a channel from the available channel set according to the energy cost on anti-interference and channel switch. After that, we formulate power control on each channel as a non-cooperative game with utility function including Signal-to-Interference-and-Noise Ratio (SINR) price. In order to reduce the energy cost of the information exchange during the traditional game, we introduce the thought of game virtual playing, in which each link can decide its own transmission power by imitating the game among links with its once collected information. Consequently, JCAPC can not only increase the transmission efficiency but also reduce the nodes’ energy waste. Moreover, the existence of Nash Equilibrium (NE) is proven based on super-modular game theory, and it’s able to obtain the unique NE by relating this algorithm to myopic best response updates. The introduction of game virtual playing saves the energy cost of network further more by reducing the number of information exchange. Simulation results show that our algorithm can select a channel with good QoS using less energy consumption and provide adequate SINR with less transmit power, which achieves the goal of efficiently reducing energy waste.     

分布式高空气球通信网络功率分配算法

针对高空气球组网通信中干扰严重和电量供应受限的问题,提出了一种基于非合作博弈的分布式功率分配模型.通过在效用函数中增加干扰限制,保证了网络的整体信干噪比,提升了网络的通信质量;通过在效用函数中关注节点的剩余能量,使高空气球的工作时间得到延长.利用数学分析验证了模型中纳什均衡点的存在性和唯一性,并给出了纳什均衡点的求解方法.仿真结果表明,该模型可以延长高空气球的工作时间,增加网络传输的数据总量.     

Non-cooperative power control game for adaptive modulation and coding

Adaptive modulation and coding (AMC) provides the flexibility to match modulation and coding scheme (MCS) to signal to interference plus noise ratio (SINR) of users. To reduce the transmission power and maintain the transmission quality, power control is normally combined with AMC. While the target SINR of power control is fixed, therefore non-cooperative power control game for AMC (NPGA) algorithm was proposed to adapt to the dynamic target SINR changes according to MCS. In NPGA, we formulate system transmission efficiency via utility, where the utility function is constructed based on the modulation and coding efficiency with non-cooperative game theory. It is demonstrated theoretically that NPGA can satisfy the conditions of the supermodular games, and its solution is optimal. The simulation results show that NPGA can improve system transmission efficiency and signal quality with low transmission power, and the convergence performance of NPGA is more fast-effectiveness compared with geometric programming algorithms.     

多业务无线网络中基于最佳等信干比的功率控制

功率控制技术是无线网络的关键技术之一。为改进多业务无线网络中基于非合作博弈的功率控制算法中纳什均衡的帕累托有效性,引入数据终端的最佳等信干比概念,使得系统中所有数据终端都工作在最佳的等信干比下,语音终端工作在语音通信服务质量门限的目标信干比门限之下。推导出一个新的分布式基于最佳等信干比的功率控制算法。仿真表明,该算法明显提高了系统的服务质量,系统中终端均具有相对较高的效用和较低的发射功率,还使得无线网络资源的使用更加合理和公平。     

认知无线网络中一种非合作博弈功率控制算法

针对现有的功率控制算法中存在的干扰问题和功率消耗过大问题,设计出一种新的效用函数,并根据此效用函数提出了一个基于非合作博弈的新的功率控制算法。首先,在效用函数中分别为信干噪比和功率设定了不同的代价因子,并将信道状态概念引入到代价因子里面,使其能够更加合理地控制用户,避免用户过度增加发射功率,同时减小了用户间的干扰;其次,证明了该算法纳什均衡的存在性和唯一性;最后,给出了所提算法的流程图。仿真结果表明,与Nash算法相比,在保证非授权用户服务质量( QoS)前提下,该算法功率消耗明显降低,并且具有较好的抗背景噪声性能;与K-G( Koskie-Gajic)算法相比,该算法保证了所有的用户的信干噪比满足上下限阈值要求,并且提高了系统容量。     

一种基于博弈论的无线传感器网络拓扑控制算法

无线传感器网络对节能有着很高的要求,拓扑控制能够优化网络拓扑,提高无线信道的空间复用率,是提高无线传感器网络能量效率的有效方法.文中提出了-种基于博弈论的无线传感器网络拓扑控制算法,设计了-个与节点度和发射功率有关的收益函数,使拓扑控制博弈存在纳什均衡,网络总收益函数最大,网络的能量效率最高.