【正文】 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 level (Pmin). However, initially all nodes either from the same or different groups use a fixed transmission power level for munication and form a connected work 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 performed using Mica mote [21] with RF frequency 866 MHz and given in Table 1, 0 is considered as the minimum (Pmin) and 3 as the maximum (Pmax) transmission power level for municating among nodes and we consider this value throughout our paper. Before proceeding to the next section of the paper, we define few technical terms that are used in our protocol. Fig. 1. Randomly deployed sensor nodes with connectivity holes among different group of nodes. Table 1. Energy consumption for different power levels and corresponding munication distances, obtained from our experimental result Power levels 0 1 2 3 Output power (dBm) ?13 ?7 ?1 5 Power levels 0 1 2 3 Range (m) 177。 Distributed algorithm。 節(jié)點將自己的 ID 添加到 PGID 字 段聲明本身為父 網關和其他參數，如 SID， LHC、 GHC和 NEL 也根據定義添加到相應 構造包 的字段里。 數據包的 PGPL 字段中給出的上游組可連接節(jié)點的有效功率級別。然而，在最壞的情況 PTx(ij) = 3 可用作可能的有效傳輸功率級。 因此，值得在這里提到的節(jié)點數數可能使用最大動力 (Pmax = 3) 作為有效的傳輸功率與上游組節(jié)點通信。 計算的 {Dij} 值后 , 一個節(jié)點再次使用稱為 PTx(ij) 的方程 (3)，來估計有效發(fā)射功率之間最接近的發(fā)送 (i) 及 自身(j)。 (6) 應注意一個組的節(jié)點可能是另一個節(jié)點的已收到幾個通知數據包。為網絡中的全數字節(jié)點考慮 N (5) 接收通知數據包的節(jié)點集，獲取通知數據包后并使用式 (3)，讓 {dij}作為發(fā)送方 Si 和接收方 Rj 的估計距離， i = 1, 2, …, m。在接到通知包，可以用式 (3) 估計