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类型基于拓扑结构的分布式无线传感器网络的功率控制 文献翻译.doc

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    1、基于拓扑结构的分布式无线传感器网络的功率控制摘要无线传感器网络由大量的传感器节点电池供电限制在一定区域内的随机部署的几个应用。由于传感器能量资源的有限他们中的每一个都应该减少能源消耗延长网络的生命周期。在这篇文章中一种分布式算法的基础上提出了无线传感器网络的构建一种高效率能源树结构而无需定位信息的节点。节点的能量守恒是由传输功率控制完成的。除此之外维护的网络拓扑结构由于能源短缺的节点也提出了协议。仿真结果表明我们的分布式协议可以达到类似集中算法的理想水平的能量守恒,可以延长网络的生命周期比其他没有任何功率控制的分布式算法。关键词:无线网络传感器,分布式算法,功率控制,拓扑结构引言近年来无线网络

    2、技术在硬件和软件的发展使小尺寸、低功耗、低成本、多功能传感器节点1的基础上由传感、数据处理及无线通信组件组成。这些低能量节点的电池部署在数百到成千上万的无线传感器网络。在无线传感器网络系统、音视频信号处理系统使用更高的发射功率和转发数据包相似的路径是种主要消费传感器的能量。除此之外补充能量的电池更换和充电几百节点上的传感器网络应用的大部分地区特别是在严酷的环境是非常困难的有时不可行。因此节能2,3,4的传感器节点是一个关键问题如传感器网络的生命周期的完全取决于耐久性的电池。传感器节点一般都是自组织建立了无线传感器网络监察活动的目标和报告的事件或信息多跳中的基站。有四种主要的报告模式的传感器网络

    3、:事件驱动、队列驱动、期刊、查询和混合的报告。在事件驱动模型中节点报告接收器同时报告遥感一些事件例如火灾或水灾而敲响了警钟。定期报告中节点模型的数据收集和可聚合所需资料成为集然后定期的发送到上游。资料相结合的方法称为数据融合5,6,7和8从而降低了数量的传输数据。这样的例子也可应用在这里如报告的温湿度的地方。所以,集合到一个单一的类似数据包的数据融合的遥感数据的传送到接收器的多级跳环境中,从而保存能源也是在传感器网络中的重要研究问题。 在9的基础上对传感器节点的每个单元的电源消耗比较进行分析,它观察耗能接收的电源和空闲状态几乎相同,CPU 的功耗是很低。在10的基础上,在作者建议中的理想的发射

    4、功率评估通过节点互动与信号衰减节点的无线传感器网络 MAC 协议的传输功率控制。计算理想的发射功率的反复改进和存储当前的理想发射功率,为每个相邻的节点。在11作者介绍了拓扑控制的无线传感器网络,于一体的有效子网和短跃点方法来达到节能降耗的两级策略。分析是在非对称无线链接并不罕见,具有不同的最大传输范围,在异构无线设备的网络拓扑控制的问题。详细分析了在12。因为节点是异构的他们有不同的最大传输功率和广播范围需要可调整的功率控制的分布式天线。作者在13中的采取一套主动节点和节点的传播范围,建议尽量减少总功率消耗的无线传感器网络的最小电源配置方法。扑控制的问题。详细分析了在12。因为节点是异构的他们

    5、有不同的最大传输功率和广播范围需要可调整的功率控制的分布式天线。作者在13中的采取一套主动节点和节点的传播范围,建议尽量减少总功率消耗的无线传感器网络的最小电源配置方法。在14作者提出了一个分析路由协议的范围的可变传动方案。从他们的分析研究表明该算法可以提高可变传动范围全面的网络性能。LEACH 15为基础的算法,这些算法是让一些节点使用较高的发射功率帮助邻居传输数据到了 BS。然而 LEACH 需要全球的传感器网络的知识,并且假定每个节点接近 BS。在16中两个局部拓扑结构的控制算法并提出了异构多跳无线网络的非均匀传输范围。虽然这个协议保护网络的连接和谈论如何控制的拓扑结构它不谈网络拓扑结构

    6、和能耗的密度较大的问题如无线传感器网络节点。17是跨省电种技术特设的无线网络无显著降低能耗的能力或连接的网络。这是一个分布式的随机算法为了节省功率最大对电池进行关闭。但是它使用固定传输功率范围该算法适用于低密度等 IEEE 802.11 无线通信网络的节点。在18提出了构建集中算法进行了静态的无线网络的拓扑结构。根据这一算法的基础上初步每个节点有它自己的组成部分。然后它通过合并交互连通到一个整体上。毕竟部件连接环和优化的后处理解除功耗的网络。虽然该算法18是专为无线网络拓扑结构的优化它是一个集中并不能改变发射功率动态。分布式算法在无线传感器网络的传输功率控制提出了19。他们指派一个任意选择的传

    7、输功率级传感器节点在可能分裂的网络中。同样他们提出了全球性的解决方案与不同的传输功率算法 拓扑结构创造了一个连接的网络和设定不同的传输范围为所有的节点。所以他们的工作能耗的节点可能更多因为在无线传感器网络中的节点是相邻的。在无线传感器网络中、通信是能量消耗的主要因素20。然而传输功率调节控制网络拓扑结构可以延长寿命及提高无线传感器网络的能力。另外而非控制发射功率水平总是使用一个固定的高功率水平网络的节点的节点将迅速减少死亡网络的生存时间。收集数据感觉到最重要的信息可能包含一些要求提供一种连接网络拓扑结构是非常必要的无线传感器网络。因此在我们的工作中我们提出如何控制发射功率水平的每个节点的网络来

    8、节约能源。我们提出一个分布式算法调整传输功率级别的节点动态和构建一棵树和一个中间功率电平拓扑结构之间的最大和最小在不同的群体达到一种连接网络的节点。本算法在一种无线传感器网络中没有位置信息建立连接节点分布式的拓扑结构。接下来的文章是有组织的。第二节提出了本协议的系统模型。第三节我们提出了分布式控制协议。第四节是性能分析和仿真结果,第五节为结论。2 系统模型让我们考虑一种单一的多跳的无线传感器网络传感器和部署在某些随机地理区域这样小的连通性存在不同组的节点如图 1。它认为水槽内通信范围至少一个节点的网络。在这个网络的连通性孔由于不可抗拒的自然物质间隙小另一批节点之间的差距部署或由于同一地区的节点

    9、因为他们不能与最小传输功率(P min)连接。然而所有的节点要么来自同一或不同的组织使用固定传输功率电平交流和形成一个连接的网络没有任何权力的控制。这个固定传输功率电平可以被假定为最高(P max)或最小值和最大值的权力之间的水平。根据我们的实验用云母尘和 RF 频率 866,表 1 兆赫兹,0 被视为最低(P min)和 3 为最高(P max)传输功率电平之间的交流,我们认为这种价值节点在我们的文中。下一章我们定义了一些术语用于此协议。图 1.传感器节点部署和连通性随机之间的不同的组节点对于不同的电力能源消费水平和相应的交流,得到了来自距离的实验结果功率电平 0 1 2 3输出功率(dbm

    10、)-13 -7 -1 5范围(m) 2.10.2 3.40.2 5.90.2 10.20.2当前消耗(mA)9.5 10.8 15.8 25.4定义上游和下游的组:让G1,G2,G3,作为套组节点分布在某一地区。如果两个组 Gi 和 Gj,ij,以致控制数据包转发的 Gi 的任何节点到 Gj,然后Gi 被称作为上游组于 Gj,Gj 是下游组对应 Gi。例如在图 1 中的组 G1 包含接收器节点被认为是上游组组 G2、G3、G4,相对于控件作为数据包是最初广播从组包含到其他组网络的接收器。相对于 G1下游组 G2、G3、G4。同样,如果控制数据包 G2 从广播到这些组,在这种情况下 G3、 G4

    11、 被视为下游组的 G2,G2 可以组 G3、G4 的一个上游组。本地跃点计数 LHC:这是表示的控制数据包遍历本地一的组内时它发送到另一个节点的跃点数的计数器。LHC 的控制数据包的值初始化为 0 和的数据包在同一组内的每个后续跳跃1 递增。一般来说 LHC=LHC+1。如果节点 A 将数据包转发到 B,并且 B 然后将同一个数据包转发到价值的 LHC 在控制数据包中的 A=0,B =1 和 C=2。组跃点计数(GHC):这是表示的控制数据包传递到其他传输从一个组时的跃点数的计数器。如果包传递给另一组,GHC 的值是唯一的特定组中的所有节点,它会增加 1。GHC 的值初始化为 0,在一般的 G

    12、HC=GHC+1,后续跳跃的数据包从一个组到其他组。父网关 ID(PGID):使连接与一个上游组的节点的组中的所有节点的节点称为父网关和其 ID 称为为 PGID。节点的每个组中存在只有一个父网关。子网关:连接到父网关下游组的节点称为子网关。组中存在至少一个子网关。在某些的情况下如果一组都包含唯一的节点的单个节点被视为为该组的父和子网关。节点能级(NEL ):当前节点的能量级别称为 NEL。 例如在广播一控制数据包,如果一个节点的能量级别是 X,NEL 在控制数据包中作为 X 的单位。父网关功率级(PGPL):任何组的父网关的发射功率水平它可以与子网关的上游组织连接被称为父网关功率级(PGPL

    13、) 。自从接收器始终是父网关的组里,它 PGPL 分配为 0。然而对于其它组中的父网关 Pmin PGPL P max 按我们的假设这值可能在 1 和 3 之间。源 ID( SID):如果 A 和 B 是相同或者不同组中的两个不同传感器节点,将数据包 A 发送到 B,A 节点的 ID 是源节点,A 也是 B 的源节点。3 分布式的功率控制协议在本节中,我们将提出我们基于拓扑结构协议的功率控制,这是一种动态的拓扑结构。我们假设在网络中的每个节点具有一个唯一的 ID,他们每个人都知道在拓扑结构之前邻居的 ID。根据我们协议的每个系统模型,由于每个组的节点之间存在的连接孔,我们假设网络可能会断开连接

    14、,如果他们使用低传输功率级与另一个节点的一组之间,并且可能会消耗更多的精力,如果他们使用最大传输功率级进行通信。此外,在我们假设传输电源网络中的所有节点级别后部署可能是最大或最小值和最大值之间。因此,我们的协议,在树拓扑构造节点使用最小传动功率级的每个组之间(P min=0) ,整个网络的树拓扑连接在不同组节点中形成并使用有效功率级别(PT x) ,这里(P min=0) PTx (P max=3) 。这个分布式协议的不同阶段将在下面进行描述。施工阶段一旦所有的节点都部署在网络中,就通过广播最小发射功率的构造数据包启动施工阶段(P min=0)以与邻居直接连接如图Power control b

    15、ased topology construction for the distributed wireless sensor networks AbstractWireless sensor network consists of large number of sensor nodes with limited battery power which are randomly deployed over certain area for several applications. Due to limited energy resource of sensors each of them s

    16、hould minimize the energy consumption to prolong the network lifetime. In this paper a distributed algorithm for the multi-hop wireless sensor network is proposed to construct a novel energy efficient tree topology without having location information of the nodes. Energy conservation of the nodes is

    17、 accomplished by controlling transmission power of the nodes. Besides maintenance of the network topology due to energy scarcity of the gateway nodes is also proposed in the protocol. Simulation results show that our distributed protocol can achieve energy conservation up to an optimum level similar

    18、 to the centralized algorithm that we have considered and can extend the network lifetime as compared to other distributed algorithms without any power control. Keywords: Wireless sensor network;Distributed algorithm;Power control;Topology construction1. IntroductionRecent advances in hardware and s

    19、oftware for the wireless network technologies have enabled the development of small sized, low-power, low-cost and multi-functional sensor 1, which consists of sensing, data processing and wireless communicating components. These nodes are operated with very low powered batteries and deployed hundre

    20、ds to thousands in the wireless sensor network (WSN).In wireless sensor network, signal processing, communication activities using higher transmission power and forwarding of similar data packets along the multi-hop paths are main consumers of sensor energy. Besides, replenishing energy by replacing

    21、 and recharging batteries on hundreds of nodes in most of the sensor network applications, particularly in harsh terrains is very difficult and sometimes infeasible too. Here, energy conservation 2, 3 and 4 of the sensor nodes is a critical issue in WSN, as the network lifetime totally depends on th

    22、e durability of the battery.Sensor nodes are generally self organized to build the wireless sensor network, monitor the activities of the target and report the event or information to the sink or the base station (BS) in a multi-hop fashion. There are four main reporting models of the sensor network

    23、: event driven, query driven, periodical and mixed reporting. In event driven model, nodes report the sink, while sensing some events such as fire or flood alarm. In periodical reporting model, nodes collect the sensed data and may aggregate the required information into a set and then send them to

    24、the upstream periodically. The method of combining data is called data fusion 5, 6, 7 and 8,which reduces the amount of transmitted data. Some of the examples of such application may be cited here, like the reporting of transmitted data. Some of the examples of such applications may be cited here, l

    25、ike the reporting of temperature or humidity readings of a locality. So, collection of sensed data, fusing similar data to a single packet, route them in a multi-hop environment to the sink and there by to save energy are also important research issues in sensor network.In 9, the power consumption c

    26、omparison of each unit of sensor node is analyzed and it is observed that the energy consumption of the received power and idle state are almost same and the power consumption of CPU is very low. In 10,the authors propose the transmission power by the nodes through node interaction and signal attenu

    27、ation. The proposed algorithm calculates the ideal transmission power by repeated refinements and stores the current ideal transmission power for each neighboring nodes. In 11,authors present a two-level strategy for topology control in wireless sensor networks, which integrates the active sub netwo

    28、rk and short hop methods to achieve the energy saving. The problem of topology control in a network of heterogeneous wireless devices with different maximum transmission ranges, where asymmetric wireless links are not uncommon, is analyzed in 12.Since, nodes are heterogeneous, they have different ma

    29、ximum transmission power and radio ranges, which requires Omni-directional antenna with adjustable transmission power. Taking a set of active nodes and transmission ranges of the nodes, authors in 13propose the minimum power configuration approach to minimized the total power consumption of WSN.In 1

    30、4,authors have proposed an analysis of the routing protocol based on the variable transmission range scheme. From their analysis, it is observed that the variable transmission range scheme can improve the overall network performance. The LEACH15based algorithm let some nodes to be the cluster leader

    31、 and uses the higher transmission power to help the neighbor transmitting data to the BS. However, LEACH needs the global knowledge of the sensor network and assumes each node in the radio proximity of the BS. So, it may not be suitable in multi-hop sensor networks. In16, two non-uniform transmissio

    32、n ranges are proposed. Though the protocols preserve network connectivity and talk how to control the topology, it does not talk about the construction of network topology and the energy consumption issues for higher density of nodes such as WSN. Span17 is a power saving technique for multi-hop ad h

    33、oc wireless networks, which reduces energy consumption without significantly diminishing the capacity or connectivity of the network. It is a distributed, randomized algorithm to turn off and on the battery in order to save power to the maximum. But, it uses fixed transmission power range and the al

    34、gorithm is applicable for the low density wireless nodes such as IEEE 802.11 networks.In18, the authors present a centralized greedy algorithm to construct an optimized topology for a static wireless network. According to this algorithm, initially each node has its own component. Then, it works inte

    35、ractively by merging the connected components until there is just one. After all components are connected, a post-processing removes the loop and optimizes the power consumption of the network. Although this algorithm 18is meant for an optimized topology of wireless network, it is a centralized one

    36、and can not change the transmission power dynamically. The distributed algorithms for the transmission power control in WSN is proposed in 19. They assign an arbitrarily chosen transmission power level to all sensor nodes, which may split the network. Also, they propose the global solution with dive

    37、rse transmission power algorithm that creates a connected network and set different transmission ranges for all the nodes, even if the topology construction is over. So, in their work the energy consumption of the nodes may be more, as the nodes in WSN are close to each other.In WSN, communication i

    38、s the main factor of the energy consumption 20. However, transmission power adjustment to control the topology can extend the network lifetime and enhance the capability of the sensor network. Moreover, without controlling the transmission power level and always using a fixed higher power level for

    39、all nodes of the network will make the nodes die quickly and minimize the network life time. Since, the collected sensed data may contain some important information as required by the sink, providing a connected topology for the multi-hop network is highly essential for the wireless sensor network.

    40、Hence, in our work we propose how to control the transmission power level of each nodes of the network to save energy. We propose a distributed algorithm that adjust the transmission power levels of the nodes dynamically and construct a single tree topology with an intermediate power level between t

    41、he minimum and maximum, among different group of nodes to achieve a connected network. Our algorithm works in a multi-hop wireless sensor network without taking location information of the nodes and constructs the connected topology distributive.The rest of the paper is organized as follows. System

    42、model of our protocol is presented in Section 2. Our distributed power control protocol is described in Section 3. Performance analysis and simulation results are presented in Section 4 and conclusion is drawn in Section 5 of the paper.2 System modelLet us consider a multi-hop, homogeneous wireless

    43、sensor network, in which sensor nodes are randomly and densely deployed over certain geographical area such that small connectivity holes exist among different group of nodes, as shown in Fig.1. It is also assumed that the sink is within communication range of at least one node of the network. The c

    44、onnectivity holes in the network may occur due to small physical gaps among different group of nodes at the time of deployment or due to gap among the nodes of the same region, as they are unable to be connected with minimum transmission power level(Pmin). However, initially all nodes either from th

    45、e same or different groups use a fixed transmission power level for communication and form a connected network without any power control. This fixed transmission power level could be assumed as the maximum(Pmax) or in between the minimum and maximum power levels. As per our experimental results perf

    46、ormed using Mica mote 21 with RF frequency 866MHZ and given in Table 1,0 is considered as the minimum(Pmin)and 3 as the maximum(Pmax)transmission power level for communicating among nodes and we consider this value throughout our paper. Before proceeding to the next section of the paper, we define f

    47、ew technical terms that are used in our protocol.Fig. 1.Randomly deployed sensor nodes with connectivity holes among different group of nodes.Table 1Energy consumption for different power levels and corresponding communication distance, obtained from our experimental resultPower levels 0 1 2 3Output

    48、 power(dBm )-13 -7 -1 5Range(m ) 2.10.2 3.40.2 5.90.2 10.20.2Current consumption(mA)9.5 10.8 15.8 25.42.1 DefinitionsUpstream and Downstream Groups: Let G1,G2,G3,be the set of group of nodes distributed over certain area. If two groups Gi and Gj, for ij , such that a control packets is forwarded fro

    49、m any node of Gi to Gj, then Gi is know as the upstream group with respect to Gj and Gj is the downstream group with respect to Gi.For example ,in Fig.1, group G1 that contains the sink node is considered as the upstream group with respect to the group G2,G3andG4,as the control packet is initially broadcast from the group containing the sink to other groups of the network. G2,G3 and G4 are the downstream groups with respect to G1. Similarly, G2 can be an upstream groups and in that cas

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