-Wireless ad hoc sensor networks (WASNs) are in need of the study of useful applications that will help the researchers view them as distributed physically coupled systems, a collective that estimates the physical environment, and not just energylimited ad hoc networks. We develop this perspective using a large and interesting class of WASN applications called aggregation applications. In particular, we consider the challenging periodic aggregation problem where the WASN provides the user with periodic estimates of the environment, as opposed to simpler and previously studied snapshot aggregation problems. In periodic aggregation our approach allows the spatial-temporal correlation among values sensed at the various nodes to be exploited towards energy-efficient estimation of the aggregated value of interest. Our approach also creates a system level energy vs. accuracy knob whereby the more the estimation error that the user can tolerate, the less is the energy consumed. We present a distributed estimation algorithm that can be applied to explore the energy-accuracy subspace for a sub-class of periodic aggregation problems, and present extensive simulation results that validate our approach. The resulting algorithm, apart from being more flexible in the energy-accuracy subspace and more robust, can also bring considerable energy savings for a typical accuracy requirement (five -fold decrease in energy consumption for 5% estimation error) compared to repeated snapshot aggregations.

-Wireless ad hoc sensor networks have emerged as one of the key growth areas for wireless networking and computing technologies. So far these networks/systems have been designed with static and custom architectures for specific tasks, thus providing inflexible operation and interaction capabilities. Our vision is to create sensor networks that are open to multiple transient users with dynamic needs. Working towards this vision, we propose a framework to define and support lightweight and mobile control scripts that allow the computation, communication, and sensing resources at the sensor nodes to be efficiently harnessed in an application-specific fashion. The replication/migration of such scripts in several sensor nodes allows the dynamic deployment of distributed algorithms into the network. Our framework, SensorWare, defines, creates, dynamically deploys, and supports such scripts. Our implementation of SensorWare occupies less than 180Kbytes of code memory and thus easily fits into several sensor node platforms. Extensive delay measurements on our iPAQ-based prototype sensor node platform reveal the small overhead of SensorWare to the algorithms (less than 0.3msec in most high-level operations). In return the programmer of the sensor network receives compactness of code, abstraction services for all of the node's modules, and in-built multi-user support.

No abstract

No abstract