Existing systems in Data processing in sensor networks are limited in two fundamental ways. Those limitations are lack of data independent and poor integration with the higher layers of the data processing chain. In this paper they presented new technique for data processing in sensor networks called SwissQM. It support adaptability, multiple user and applications, high quality turn-around, extensibility and optimizes used of support when comparing to other data processing techniques in sensor networks.
SwissQM borrowed many ideas mainly from TinyDB. TinyDB is the declarative database abstraction layer under TinyOS and it provides in network query processing and aggregation. But comparing SwissQM along with TinyDB, SwissQM is intended as the next generation query processing in sensor networks. It provides richer and more flexible functionality at the senor network level and powerful adaptable layer to the outside world. Also it provides data independent, query language independent and optimized perform in wide range when comparing with existing systems such as TinyDB, Giotto a runtime environment for embedded systems. Rather than other systems, SwissQM is based on a special machine that runs optimized byte code rather than queries. Also it provides generic high-level declarative programming model and impose no data model. Comparing SwissQM with other data processing techniques, SwissQM used new technique to query processing. SwissQM translate the query for byte code. Therefore the rage of expressions supported by SwissQM is not limited. Also it supports multi-query on two layers. First it merges different user queries into virtual query and then it performs multi-programming in the query machine.
What are the key contributions of the paper?
Going through this paper we can figure out SwissQM’s design considerations has become the key contributions of this paper. SwissQM has been designed to fulfill several key requirements which had not fulfilled by other data processing techniques in sensor networks. Those are
- Separation of sensors and external interface
- Dynamic, multi-user, multi-programming environment
- Optimized use of sensors
These novel design principles are helpful to SwissQM become the next generation data processing in sensor networks. They categorize SwissQM sensor network for four categories and shown the performances of this new design approach. SwissQM sensor networks consist with the sensor network gateway, the query machine, query machine byte code and query programs.
Separation between gateway and sensor nodes and the implementation of a virtual machine at the sensor nodes, rather than the query processor are the main key design decisions in SwissQM. These two key designs gives the Turing-completeness, independent of query language used, independent of the user model and the extensibility of SwissQM.
Rather than presenting the features, this paper mentioned few examples of SwissQM queries and explained those queries preciously. They used well known compiler technique called “templating” to translate virtual queries to SwissQM queries. Also they clearly illustrate init, delivery and reception which are the main three QM program sections by using different examples.
Memory is the utmost crucial factor when considering the sensor networks. SwissQM has fifty nine instructions set and they showed the complete instruction set need 33kB flash memory and 3kB SRAM memory. This is a big advantage when comparing with TinyDB because it takes 65kB of flash memory and 3kB of SRAM memory.
Message size also becomes the crucial factor in ad-hoc sensor networks. Different radio platforms provide different massage sizes. SwissQM has addressed the solution for this problem. In SwissQM they were opted message size for 36 bytes. But TinyDB uses 49 bytes. This is big step in ad-hoc sensor networking data processing.
SwissQM has used three-tier architecture for visualizing the sensor network and permit multi-query optimization for more efficient use of sensor networks. It used query merging, sub expression matching and window processing optimization for multi-query optimization. By using this three-tier architecture the optimized virtual queries are transformed into network queries and those networks queries can be easily understood by the sensor nodes.
 R. Mueller, G. Alonso and D. Kossmann, "SwissQM: Next generation data processing in sensor networks," in Third Biennial Conference on Innovative Data Systems Research, Asilomar, CA, USA, January 7-10, 2007