Data Analysis and Management Techniques in Wireless Sensor Networks

Harvesting the benefits of a sensor-rich world presents many data analysis and management challenges. Recent advances in research and industry aim to address these challenges. Modern sensors and information technologies make it possible to continuously collect sensor data, which is typically obtained as real-time and real valued numerical data. Examples include vehicles driving around in cities or a power plant generating electricity, which can be equipped with numerous sensors that produce data from moment to moment. Though the data gathering systems are becoming relatively mature, a lot of innovative research needs to be done on knowledge discovery from these huge repositories of data. The data management techniques and analysis methods are required to process the increasing volumes of historical and live streaming data sources simultaneously. Analysts need improved techniques are needed to reduce an analyst’s decision response time and to enable more intelligent and immediate situation awareness. Faster analysis of disparate information sources may be achieved by providing a system that allows analysts to pose integrated queries on diverse data sources without losing data provenance. This paper proposed to develop abstractions that make it easy for users and application developers to continuously apply statistical modeling tools to streaming sensor data. Such statistical models can be used for data cleaning, prediction, interpolation, anomaly detection and for inferring hidden variables from the data, thus addressing many of the challenges in analysis and managing sensor data. Current archive data and streaming data querying techniques are insufficient by themselves to harmonize sensor inputs from large volumes of data. These two distinct architectures (push versus pull) have yet to be combined to meet the demands of a data-centric world. The input of sensor streaming data from multiple sensor types further complicates the problem.