We describe CTP, a collection routing protocol for wireless sensor networks. CTP uses three techniques to provide efficient, robust, and reliable routing in highly dynamic network conditions. CTP's link estimator accurately estimates link qualities by using feedback from both the data and control planes, using information from multiple layers through narrow, platform-independent interfaces. Second, CTP uses the Trickle algorithm to time the control traffic, sending few beacons in stable topologies yet quickly adapting to changes. Finally, CTP actively probes the topology with data traffic, quickly discovering and fixing routing failures. Through experiments on 13 different testbeds, encompassing seven platforms, six link layers, and multiple densities and frequencies, and detailed observations of a long-running sensor network application that uses CTP, we study how these three techniques contribute to CTP's overall performance.

We describe grant-to-send, a novel collision avoidance algorithm for wireless mesh networks. Rather than announce packets it intends to send, a node using grant-to-send announces packets it expects to hear others send. We present evidence that inverting collision avoidance in this way greatly improves wireless mesh performance. Evaluating four protocols from 802.11 meshes and 802.15.4 sensor networks, we find that grant-to-send matches or outperforms CSMA and RTS/CTS in all cases. For example, in a 4-hop UDP flow, grant to-send can achieve 96% of the theoretical maximum throughput while maintaining a 99.9% packet delivery ratio. Grant-to-send is also general enough to replace protocol-specific collision avoidance mechanisms common to sensor network protocols. Grant-to-send is simple. For example, incorporating it into 802.11 requires only 11 lines of driver code and no hardware changes. Furthermore, as it reuses existing 802.11 mechanisms, grant-to-send inter-operates with current networks and can be incrementally deployed.

Powertron is a web-based application that visualizes wireless sensor network deployment data. In this particular demo, we use Powertron to show application-level power data collected from more than 250 sensor nodes. In addition, we expose the routing layer of the deployment by providing real-time interactive visual representation of links, routes, and CTP-related statistics. We hope that the community will have feedback on how such a tool can be extended and generalized to fit a variety of wireless sensor network applications.

Network protocols are typically designed and tested individually. In practice, however, applications use multiple protocols concurrently. This discrepancy can lead to failures from unanticipated interactions between protocols.

We describe Neutron, a version of the TinyOS operating system that efficiently recovers from memory safety bugs. Where existing schemes reboot an entire node on an error, Neutron's compiler and runtime extensions divide programs into recovery units and reboot only the faulting unit. The TinyOS kernel itself is a recovery unit: a kernel safety violation appears to applications as the processor being unavailable for 10-20 milliseconds.

Measuring 802.15.4 reception in three testbeds, we find that most intermediate links are bursty: they shift between poor and good delivery. We present a metric to measure this link burstiness and name it β. We find that link burstiness affects protocol performance and that β can predict the effects. We show that measuring β allows us to reason about how long a protocol should pause after encountering a packet failure to reduce its transmission cost. We find that using β as a guide to setting a single constant in a standard sensor network data collection protocol reduces its average transmission cost by 15%. In addition to data from 802.15.4 testbeds, we examine traces from 802.11b networks and find β has a broader relevance in the wireless domain.

Measuring low-level wireless network properties allows researchers to understand how protocols and applications perform in different environments. In this demo, we present SWAT - a software tool that automates gathering and analysis of network measurements. SWAT provides an interface for configuring experimental parameters in a network. It collects raw packet statistics such as the received signal strength and chip error, and provides modules for calculating and visualizing various metrics derived from these statistics.