Data Processing Method Of Dynamic Environmental Monitoring For Wireless Sensor Network

Wireless sensor networks is integrated with technologies such as sensor networks,embedded computing, distributed information processing, and wireless communicationtechnologies. It can not only collaborately monitor, sense and collect information of avariety of environmental monitoring object, but also process and transmit the data.Wireless sensor networks is a new interdisciplinary research field with broad applicationprospects which is causing a high degree of attention both in academia and industry.However, a number of open problems within the research scope of wireless sensornetworks are still at the exploratory stage, such as energy management, data managementand data security, QoS guarantee, opportunistic routing and other issues. Only after solvingthese technical issues, wireless sensor networks can really play a potentially huge role. Thewireless sensor networks is often deployed in dynamic environment with dynamic changesof network structure, data sources of uncertaint as well as other complex factors. How toeffectively carry out data processing becomes a challenging task. The dissertationinvestigates data processing methods of wireless sensor networks from the point of viewfor dynamic environmental monitoring. The main research work can be summarized as thefollowing five aspects.The key technology, its application and the state of art are summarized. The concept,characteristics and prospects of wireless sensor networks for dynamic environmentalmonitoring are investigated in detail. Besides, the main challenges of wireless sensornetwork data processing for dynamic environmental monitoring are analyzed.Estimation of missing data for dynamic environmental monitoring are presented.Under the condition of dynamic environmental monitoring, the lack of perceived databrings great difficulties to effective applications of wireless sensor network. It reduces boththe availability and utilization ratio of the sensing dataset. Moreover, such problemindirectly reduces the overall efficiency of wireless sensor networks. It turns out that themonitoring data collected by physically adjacent sensor nodes are usually have somesimilarities or functional relations. Taking such characteristics into consideration, a highlyaccurate and stable algorithm for estimating the missing data based on the spatial andtemporal natural neighbor (STNNI) is proposed.A novel localization algorithm that is range-free for dynamic environmentalmonitoring is presented. In most wireless sensor network applications, the perception databecomes valuable only when associated with location information. Therefore, it isnecessary to address how to efficiently position a wireless sensor network node. Based onthe analysis of wireless sensor network self-positioning systems and algorithms, arange-free positioning method is proposed. To position a node, a gradient field of thebeacon node as the origin of hops is created in the first step where the distance in hops from the node to the beacon node can be obtained. Then, the real distance from the node toall the beacon nodes can be approximated by the approximate distances between the nodeand the nearest neighbor beacon node, as well as the real distances between beacon nodes.By applying the positioning method, the estimation of the average distance between thenode to be positioned and the beacon nodes can be optimized. The accumulative error isminimized, and the positioning accuracy is improved. The algorithm is so concise andeasy-to-implement that no extra hardware is needed. So it is fairly practical in realapplications.Some Event detection methods for dynamic environmental monitoring are introducted.Event detection is one of the main tasks of wireless sensor networks. As wireless sensornetworks are generally deployed in severe environments, soft failure may cause the nodesto provide erroneous data, which reduces the monitoring accuracy, and results in thefalse-alarm problem. To cope with the problem, a distributed fault-tolerant event boundarydetection algorithm based on neighborhood statistics (NNB-DEBD) is proposed. Whenapplying NNB-DEBD, the soft failure of a node can be fast detected by exchanging thesensing data with its neighbor only once. When the monitoring value of a normal nodetriggers the event condition, neighborhood statistics is applied to determine if it is on theevent boundary whose border width can be adjusted according to the user requirements.The communication volume required by the algorithm at runtime is low. In addition, thealgorithm has low computational complexity, latency and a good scalability for large-scalenetworks. To depict the specifics of the spatial event detection, a spatial event model isdeveloped. Based on the model, the composite operators and their semantics of spatialevents are defined. The closing property of the composite operators under the definition isproved. Composite event detection model is established on the basis of colored Petri netswhich can be simplified by utilizing the event public expression. The problem ofconflicting transitions can be solved by applying their priority changes. A detectionalgorithm based on the model is proposed. And an experimental simulation of the detectionis adopted to verify the feasibility of the model and algorithm.Data aggregation method for dynamic environmental monitoring is presented. To savethe energy and prolong the network lifetime is very important to wireless sensor networksin a dynamic environment. Such requirement asks the information processing algorithmswithin the network be highly adaptable and robust. Aggregation mechanism within thenetwork is an effective and energy-efficient data aggregation strategy, which can take fulladvantage of the sensor node’s own processing capability. When transmitting the raw datato the base station, the data can be aggregated at the intermediate nodes. The energyconsumed by the network can be significantly reduced through a reasonable trade-offbetween accuracy and energy consumption. An approximate aggregate minimum spanningtree algorithm GLB-MST for in-network aggregation mechanism is proposed, which has a low computing complexity and good energy saving performance in practice.