Saturday, July 7, 2012

Sensor Network Programming Challenges

Traditional programming technologies rely on operating systems to provide abstraction for processing, I/O, networking, and user interaction hardware. When applying such a model to programming networked embedded systems, such as sensor networks, the application programmers need to explicitly deal with message passing, event synchronization, interrupt handing, and sensor reading. As a result, an application is typically implemented as a finite state machine (FSM) that covers all extreme cases: unreliable communication channels, long delays, irregular arrival of messages, simultaneous events, and so on. In a target tracking application implemented on a Linux operating system and with directed diffusion routing, roughly 40% of the code implements the FSM and the glue logic of interfacing computation and communication.

For resource-constrained embedded systems with real-time requirements, several mechanisms are used in embedded operating systems to reduce code size, improve response time, and reduce energy consumption. Microkernel technologies modularize the operating system so that only the necessary parts are deployed with the application. Real-time scheduling allocates resources to more urgent tasks so that they can be finished early. Event-driven execution allows the system to fall into low-power sleep mode when no interesting events need to be processed. At the extreme, embedded operating systems tend to expose more hardware controls to the programmers, who now have to directly face device drivers and scheduling algorithms, and optimize code at the assembly level. Although these techniques may work well for small, stand-alone embedded systems, they do not scale up for the programming of sensor networks for two reasons.

● Sensor networks are large-scale distributed systems, where global properties are derivable from program execution in a massive number of distributed nodes. Distributed algorithms themselves are hard to implement, especially when infrastructure support is limited due to the ad hoc formation of the system and constrained power, memory, and bandwidth resources.

● As sensor nodes deeply embed into the physical world, a sensor network should be able to respond to multiple concurrent stimuli at the speed of changes of the physical phenomena of interest.

In the rest of the chapter, we give several examples of sensor network software design platforms. We discuss them in terms of both design methodologies and design platforms . A design methodology implies a conceptual model for programmers, with associated techniques for problem decomposition for the software designers. For example, does the programmer think in terms of events, message passing, and synchronization, or does he/she focus more on information architecture and data semantics? A design platform supports a design methodology by providing design-time (precompile time) language constructs and restrictions, and run-time (postcompile time) execution services.

There is no single universal design methodology for all applications. Depending on the specifi c tasks of a sensor network and the way the sensor nodes are organized, certain methodologies and platforms may be better choices than others. For example, if the network is used for monitoring a small set of phenomena and the sensor nodes are organized in a simple star topology, then a client – server software model would be sufficient. If the network is used for monitoring a large area from a single access point (i.e., the base station), and if user queries can be decoupled into aggregations of sensor readings from a subset of sensor nodes, then a tree structure that is rooted at the base station is a better choice. However, if the phenomena to be monitored are moving targets, then neither the simple client – server model nor the tree organization is optimal. More sophisticated design methodologies and platforms are required.

Source of Information : Elsevier Wireless Networking Complete 2010
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