计算机专业外文翻译-----基于拓扑结构的分布式无线传感器网络的功率控制(编辑修改稿)

计算机专业外文翻译-----基于拓扑结构的分布式无线传感器网络的功率控制(编辑修改稿)内容摘要：

by to save energy are also important research issues in sensor work. In [9], the power consumption parison 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 control in MAC protocols for wireless sensor work to assess the ideal transmission power by the nodes through node interaction and signal attenuation. 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 twolevel strategy for topology control in wireless sensor works, which integrates the active subwork and short hop methods to achieve the energy saving. The problem of topology control in a work of heterogeneous wireless devices with different maximum transmission ranges, where asymmetric wireless links are not unmon, is analyzed in [12]. Since, nodes are heterogeneous, they have different maximum transmission power and radio ranges, which requires omnidirectional antenna with adjustable transmission power. Taking a set of active nodes and transmission ranges of the nodes, authors in [13] propose the minimum power configuration approach to minimize the total power consumption of WSN. In [14], 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 work performance. The LEACH [15] based algorithm let some nodes to be the cluster leader and uses the higher transmission power to help the neighbor transmitting data to the BS. However, LEACH needs the global knowledge of the sensor work and assumes each node in the radio proximity of the BS. So, it may not be suitable in multihop sensor works. In [16], two localized topology control algorithms for the heterogeneous wireless multihop works with nonuniform transmission ranges are proposed. Though the protocols preserve work connectivity and talk how to control the topology, it does not talk about the construction of work topology and the energy consumption issues for higher density of nodes such as WSN. Span [17] is a power saving technique for multihop ad hoc wireless works, which reduces energy consumption without significantly diminishing the capacity or connectivity of the work. 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 algorithm is applicable for the low density wireless nodes such as IEEE works. In [18], the authors present a centralized greedy algorithm to construct an optimized topology for a static wireless work. According to this algorithm, initially each node has its own ponent. Then, it works interactively by merging the connected ponents until there is just one. After all ponents are connected, a postprocessing removes the loop and optimizes the power consumption of the work. Although this algorithm [18] is meant for an optimized topology of wireless work, it is a centralized one and cannot 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 work. Also, they propose the global solution with diverse transmission power algorithm that creates a connected work 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, munication is the main factor of the energy consumption [20]. However, transmission power adjustment to control the topology can extend the work lifetime and enhance the capability of the sensor work. Moreover, without controlling the transmission power level and always using a fixed higher power level for all nodes of the work will make the nodes die quickly and minimize the work life time. Since, the collected sensed data may contain some important information as required by the sink, providing a connected topology for the multihop work is highly essential for the wireless sensor work. Hence, in our work we propose how to control the transmission power level of each nodes of the work to save energy. We propose a distributed algorithm that adjusts the transmission power levels of the nodes dynamically and constructs a single tree topology with an intermediate power level between the minimum and maximum, among different group of nodes to achieve a connected work. Our algorithm works in a multihop wireless sensor work without taking location information of the nodes and constructs the connected topology distributively. The rest of the paper is organized as follows. System 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 model Let us consider a multihop, homogeneous wireless sensor work, 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 munication range of at least one node of the work. The connectivity holes in the work 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 lev。