Rate adaptation in networks of wireless sensors

Unrestricted In this dissertation we explore rate-adaptation in networks of wireless sensors. We investigate how the concept of rate adaptation can be used to build sensing systems that adapt to the network and environment dynamics. Specifically, we design a rate-adaptive transport protocol that can reliably and efficiently deliver sensor data on an architecture that respects the technological constraints of the embedded sensors. We also design a rate-adaptive sensing system that adapts to the environment and the behavior of the user to provide energy-efficient location sensing.; We first present the Tenet architecture that respects the technological constraints of the embedded sensors. Tenet constrains multinode fusion to the master tier while allowing motes to process locally-generated sensor data. We show through extensive evaluation and real-world deployments that Tenet architecture simplifies application development and allows mote-tier software to be reused without significant loss of performance. We also show that tiered architecture scales network capacity and allows reliable delivery of high rate data.; We next present RCRT, a rate-controlled reliable transport protocol suitable for resource constrained sensor nodes in wireless sensor networks. RCRT uses end-to-end explicit loss recovery, but places all the congestion detection and rate adaptation functionality in the sinks. We show that, because sinks make rate allocation decisions, they are able to achieve greater efficiency and flexibility since they have a more comprehensive view of network behavior. We evaluate RCRT extensively on a real wireless sensor network testbed and show that RCRT achieves a significantly better rate than that achieved by IFRC and WRCP, two recently proposed interference-aware distributed rate-control protocols. We also present results from a 3-month-long real world deployment of RCRT in an imaging application and show that RCRT works well in real long-term deployments.; Finally, we present RAPS, a rate-adaptive positioning system for smartphone applications. It is based on the observation that GPS is generally less accurate in urban areas, so it suffices to turn on GPS only as often as necessary to achieve this accuracy. RAPS uses a collection of techniques and sensors to cleverly determine whether and when to turn on GPS. We evaluate RAPS through real-world experiments using a prototype implementation on a modern smartphone and show that it can increase phone lifetimes by more than a factor of 3.8 over an approach where GPS is always on.