1. A Survey of Wireless Sensor Network Data Collection Schemes by Brett Wilson

2. The Problem How do you reliably and efficiently collect specific sensor data from a Wireless Sensor Network (WSN)? How do you reliably and efficiently collect specific sensor data from a Wireless Sensor Network (WSN)? –Limited processing and storage at each node –Inherently “lossy” network links –Extreme power consumption constraints

3. Three Approaches TinyDiffusion TinyDiffusion –UCLA In use collecting environmental data at James Reserve In use collecting environmental data at James Reserve API provided with which developers can build applications API provided with which developers can build applications TinyDB TinyDB –UC Berkeley Complete, ready to use “out of the box” Complete, ready to use “out of the box” Major goal is to abstract away all the technicalities Major goal is to abstract away all the technicalities –User submits simple “SQL-like” queries Cougar Cougar –Cornell Research project formulating concepts and testing ideas Research project formulating concepts and testing ideas Working system not yet built – only tested in simulations Working system not yet built – only tested in simulations

4. Architecture Overview TinyDiffusion TinyDiffusion –Data requests/responses consist of sets of attributes of the form key, op, value (photo GT 123) Nodes express interest in data through “subscriptions” Nodes express interest in data through “subscriptions” –Flood interest msgs throughout network –All nodes save information about received interests in a cache Nodes express availability of data by “publishing” attributes Nodes express availability of data by “publishing” attributes –e.g. Photo IS 125 –When an interest msg is received, a node attempts to match it to its published attributes –If data that meets the subscription is found, a response msg is sent back along the path from which the interest was received  Uses interest cache for routing –Filters can intercept data msgs and perform aggregation –No underlying network layer (it IS the network)

5. Architecture Overview TinyDB TinyDB –User queries network using connected PC Requests are made using SQL-like declarative language (what I want, not how to get it) Requests are made using SQL-like declarative language (what I want, not how to get it)  SELECT roomno, AVG(light), AVG(volume) FROM sensors GROUP BY roomno HAVING AVG(light) < l AND AVG(volume) < v EPOCH DURATION 5min Query Optimizer processes request and generates optimal data retrieval plan Query Optimizer processes request and generates optimal data retrieval plan –Uses known capabilities of nodes and network topology –Optimizes communications and in-network computation Query Plan distributed to only those nodes identified in the optimization Query Plan distributed to only those nodes identified in the optimization Uses underlying network layer to form “network tree” Uses underlying network layer to form “network tree” –Network layer required to provide specific interface to TinyDB

6. Architecture Overview Cougar Cougar –Research into using declarative languages to extract data from sensor networks No working application yet developed No working application yet developed Some ideas have been tested using simulation Some ideas have been tested using simulation –TinyDB implements many of the concepts being explored in the Cougar project –Many opportunities for further research Query processing/optimization Query processing/optimization Metadata management Metadata management Integrating use of filters Integrating use of filters Distributed triggers Distributed triggers

7. Advantages/Disadvantages TinyDiffusion TinyDiffusion –Highly efficient, small memory footprint –Only API provided, no “out of box” functionality Significant development effort required to assemble an application Significant development effort required to assemble an application TinyDB TinyDB –Ready to use in standard mica mote networks –User enters simple SQL-like queries into base station PC –High degree of optimization possible –Requires modification of underlying network layer or development of a “wrapper” around layer to provide required functionality Cougar Cougar –Exploratory research into declarative language data extraction