传感器网络的上层 fulltutorialhandout.pptVIP

HiPC 2003 Tutorial System Support for Sensor Networks Speakers: Sharad Mehrotra, Univ. of California, Irvine Nalini Venkatasubramanian, Univ. of California, Irvine Rajesh Gupta, Univ. of California, San Diego Acknowledgements (for Slides) Nesime Tatbul Kevin Hoeschele, Anurag Shakti Maskey (AURORA team) Jennifer Widom, Rajeev Motwani (STREAM) Sam Madden (TinyDB) Anantha Chandrakasan (MIT uAMPS TEAM) Qi Han, Iosif Lazaridis, Xingbo Yu (QUASAR team) Srini Seshan (Irisnet) Slides for tutorial available at /~quasar/tutorial/hipc.ppt Various Sensor Applications Taxonomy of Applications (1) Data Access needs of applications Historical data Analysis to better understand the physical world Current data Monitoring and control to optimize the processes that drive the physical world Future data Forecasting trend in data for decision making Taxonomy of Applications (2) Predictability of Data access Fixed data access needs of applications known a-priori Unpredictable (ad-hoc) Data access needs of applications not known at any instance of time Predictable (continuous) Data access needs of applications can be predicted for some time in the future with high probability Application Landscape Basic architecture of sensor nodes Sensor Properties – Different Capabilities Storage Built-in memory Sensing Computing Micro-processor or micro-controller Communication Short range radio for wireless communication Sensor Properties – Resource Constraints Lower transmission distances ( 10m) Lower bit rates (typically kbps) Limited battery capacity Sensor Devices today MIT uAMPS 59Mhz to 206 Mhz processor 2 radios , capable of transmitting at 1Mbps 4KB RAM Berkeley Mica motes 8bit, 4Mhz processor 40kbit CSMA radio 4KB RAM, TinyOS based Sensor OS Concepts Constrained Scheduling Event-based(?) Constrained Storage Model frame per component, shared stack, no heap Very lean multithreading Efficient Layering Sensor Network Properties Controversies with sensor networks How is this di