无线传感器网络中自适应数据存取

Adaptive Information Brokerage in Wireless Sensor Networks

数据存取,也称信息中介,是指生产者(传感器节点)将产生的感知数据按照某种策略存放在特定的位置上,而消费者(基站、用户、传感器节点)将查询请求按照对应策略路由到数据存放位置获得感兴趣的数据.利用数据速率和地理位置信息来减少网状拓扑结构传感器网络中数据存取的代价.首先,依据生产者和消费者的关系建模为"一对一"(一个生产者,一个消费者)、"多对一"(多个生产者,一个消费者)、"多对多"(多个生产者,多个消费者)3种模型来对存取代价进行分析.其次,基于上述模型,提出利用数据速率和地理位置来确定数据存放位置的自适应全局最优贪婪算法ODS(optimal data storage)和局部最优近似算法NDS(near-optimal data storage)以及最优数据传输模式.最后,ODS和NDS都依据数据速率、生产者和消费者地理位置、网络拓扑来决定存放位置,并且通过自适应地调整来减少数据存取能量消耗.实验结果表明:NDS不仅能够节省能耗,而且在70%的情况下达到与ODS相同的效果.

Information brokerage in wireless sensor networks involves producers （such as sensor nodes） storing in storage positions a large amount of data that they have collected and consumers （e.g. base stations, users, and nodes） retrieving that information. In this paper, first, the data storage problem is formalized into a one-to-one （one producer and one consumer） model, a many-to-one （m producers and one consumer） model, and a many-to-many （m producers and n consumers） model with the goal of minimizing the total energy consumption. Second, based on the above models, two algorithms are proposed to determine the storage positions based on data rates of producers, query rates of consumers, and transmission scheme of information brokerage. The optimal data storage （ODS） scheme, a greedy algorithm, produces the global optimal data storage positions and the near-optimal data storage （NDS） scheme, an approximate algorithm, can greatly reduce the computational overhead while achieving local optimal positions. Both ODS and NDS are able to adjust the storage positions adaptively to minimize energy consumption that includes the costs of storing and querying the data. Simulation results show that NDS not only provides substantial cost benefits but also performs as effective and efficient as ODS in over 70% of the tested cases.