Making Wireless Sensor Networks Easier to Implement
Sun has come out with its own hardware tools for implementing wireless sensor networks. The SPOT is a device that senses temperature, light, and motion, and comes in a package the size of a stack of cards. As I blogged about in last week’s post WSNs are hard to program and implement because most use TinyOS which requires low-level programming knowledge. As one engineer put it, ”if you want to modify a small part of the network's function . . . you have to start over.” Sun proposes using Java which is used in many cellphones. It’s smart about battery usage and easier to program than TinyOS. If you’re interested in getting one check out this page.
Another approach is to combine existing software technologies with wireless sensor networks to make them easier to use. In this paper the author, Frank Lewis, applies supervisory control concepts to the wireless sensor network challenge. He breaks the problem down into three steps: sensing, communication, and computing. As WSNs grow, he predicts fault tolerance, effective deployment, mobility , and data management will become important aspects to the success of a WSN and therefore proposes the use of a supervisory control paradigm since it provides these features.
The author uses the concept of a “mission” to represent a sequence of tasks to be performed by the WSN. Missions are coordinated by “high-level planners.” A typical supervisory control system consists of data acquisition, online analysis, logging and storage, offline analysis, display/sharing/reporting, and a supervisory control function. To achieve efficiency in data collection and management, the author applied a “dead-band” which collects data only when something significant happens. A supervisory system comes with many of the tools that WSNs use including network status, trending, and reporting.
It’s an interesting approach to use supervisory control to manage a WSN. While the data management, and visualization is readily accomplished, there remains the challenge of resource management (battery power, memory footprint, etc) that makes for a more robust WSN implementation. The paper does not go into any detail on these points. Also, a supervisory control fundamentally controls the network from a central location, while WSNs seek decentralized control letting the nodes communicate with each other and share data without a centralized network architecture.
I can see how supervisory control-based WSNs would solve specific industrial applications by removing the wires and making implementation easier. But in applications requiring true mesh-network functionality, there may be limitations.