【導(dǎo)讀】有長(zhǎng)期運(yùn)行的復(fù)雜查詢處理技術(shù)且通過(guò)傳感器流對(duì)此處理技術(shù)進(jìn)行評(píng)估。我們考慮使無(wú)線傳感器網(wǎng)絡(luò)的。網(wǎng)絡(luò)內(nèi)評(píng)估意味著對(duì)于算符T的評(píng)估可能會(huì)推至網(wǎng)絡(luò)節(jié)點(diǎn)且同樣意味著對(duì)T. 網(wǎng)絡(luò)節(jié)點(diǎn)的路徑選擇，網(wǎng)絡(luò)節(jié)點(diǎn)需要使用以上量值評(píng)估運(yùn)算符。的持續(xù)網(wǎng)絡(luò)內(nèi)評(píng)估的最大使用期限解決方案。遠(yuǎn)程監(jiān)控是無(wú)線傳感器網(wǎng)絡(luò)最具有吸引力的應(yīng)用之一。補(bǔ)充傳感器電池的能量成本過(guò)高。點(diǎn)上有一組源傳感器節(jié)點(diǎn)，其用于分配查詢結(jié)果給該變量。系統(tǒng)的最大限度壽命-直到傳感器網(wǎng)絡(luò)壽命結(jié)束之前完成其執(zhí)行的預(yù)定任務(wù)。其在網(wǎng)絡(luò)節(jié)點(diǎn)之間傳送數(shù)據(jù)的總量最小化。MLCF問(wèn)題是并行的流量為給定的一組源的目標(biāo)提供數(shù)據(jù)傳輸速率以解決。我們通過(guò)廣泛的實(shí)驗(yàn)評(píng)估表明，為MCP的問(wèn)題提供貪婪啟發(fā)式GREEDYMCP。情況下發(fā)現(xiàn)并證明最佳的解決方案。發(fā)現(xiàn)最接近整數(shù)解來(lái)解決最大生命周期并行流問(wèn)題的方案。我們使用GREEDYMCP和ALGRSMMLCF方法來(lái)最大限度地有效的提高。我們給予必要的準(zhǔn)備工作。我們?cè)诘?節(jié)中表達(dá)描述DAGs中安置的無(wú)線傳感器

　　

【正文】 . The work considered by them is inter style and consists of nodes with ample putational power, which is very different from the energylimited wireless sensor works we consider. In wireless sensor works, the notion of inwork processing was first introduced by Intanagonwiwat et al. [18] to opportunistically eliminate duplicates in the context of directed diffusion. Gehrke and Madden et al.[11,15,24] are among the first to integrate query processing and sensor works so tasking sensor works can be easily done through declarative queries. In the Cougar project [11], a layered architecture of sensor data management is proposed for presenting the sensor work as a distributed database system. In TinyDB [24], a framework Of query processing in WSN is introduced for addressing issues of when, where, and how often data is sampled and which data is delivered in wireless sensor works. Energy efficiency is one of the major factors considered in [11,24], but not with the goal of maximizing the system lifetime. In addition, query operators in [11,24] are modeled from a functionality perspective and often are rather simple operators (aggregation, filter, etc.), while in our work we model the operators from a munication perspective with the consideration of their optimal placement . Ren et al .[27] consider quality aware processing of simple aggregate queries (. pute the average, min, max of measurements of sensors in a rectangular area of interest). A centralized algorithm is proposed to find a subset of sensors whose measurements are collected using reactive routing to the base station to pute a probabilistic answer. Hu et al. [17], expanding upon the work of Olston and Widom[25],are concerned with approximate answers to continuous aggregate queries (sum, mean, count, etc.). They provide a method to allocate a userspecified acceptable tolerance to a query’s answer as tolerance ranges for the sensors. Subsequently, a sensor sends its measurement to the base station if it falls outside its tolerance range. These two works are different from ours in many respects: we consider plex queries with various operators besides min, max, avg, we provide accurate answers to queries, and we seek to optimize the system lifetime directly. Many researchers have advocated the use of datacentric techniques that allow for efficient inwork storage and retrieval of named data using queries [16]. A number of datacentric pushpull query processing techniques have been proposed and examined [6,8,23,28,29,31], which can be categorized to two main approaches: structured and unstructured, which can be represented by the geographic hashbased datacentric storage technique [29] and the bneedle method [23] respectively. Kapadia and Krishnamachari [20] present a parative mathematical analysis of these two approaches based on two types of simple oneshot queries (ALLtype and ANYtype) in singlesink squaregrid sensor works, and later on, Ahn and Krishnamachari [2] find that the scalability of a datacentric sensor works performance depends upon whether or not the increase in energy and storage resources with more nodes is outweighed by the resulting applicationspecific increase in event and query loads. Bonfils et al. [5] consider the placement of operators of a query expression tree to the nodes of a sensor work to minimize the total munication cost of evaluating such a tree. For any pair of parentchild operators in the query tree, the induced munication cost is the product of the length of a shortest path between the nodes, where these operators are placed and the data rate from the child to its parent. They provide a distributed protocol that attempts to refine the placement by continuously walking through neighbor nodes to acmodate the data rate change . The overhead of message exchange generated by walking through neighbor nodes is not considered in [5]. Our ALGRSMMLCF algorithm differs from theirs in that we allow data to be routed over multiple paths rather than as ingle path, and that we seek to optimize the system lifetime rather than the total munication cost. Restricting routing of childparent data to a single path instead of allowing multiple paths can have a detrimental effect on the lifetime. As can been seen in Fig. 10, our approach achieves much better lifetime in all the instances over the best placement when using shortest path routing. Srivastava et al. [30] consider the problem of placing operators onto a hierarchy of work nodes with progressively increasing putational power and work bandwidth, such that the total cost of putation and munication is minimized. We assume a different work model in which sensors are homogeneous and are energy limited, and a different goal of optimizing the system lifetime, which does not necessarily result from minimizing the total cost of putation and munication. Garg and Konemann [14] describe an iterative algorithm with provable approximation ratio for solving the maximum concurrent multimodity flow problem ,Whose LP formulation is different from MLCF . Their objective is to maximize the total work flow under limited capacity of edges, while ours is to maximize work lifetime under limited energy of nodes. In addition, the number of routing paths used in the solution of our ALGRSMMLCF algorithm is bounded, while the algorithm in [14] finds solutions which may use as many routing path as the number of iterations. Having fewer routing paths is important in practice, since the overhead of distributing the relevant routing information to the sensors is kept smaller with fewer paths. Chang and Tassiulas