Abstract

The wireless sensor network community approached networking abstractions as an open question, allowing answers to emerge with time and experience. The Trickle algorithm has become a basic mechanism used in numerous protocols and systems. Trickle brings nodes to eventual consistency quickly and efficiently while remaining remarkably robust to variations in network density, topology, and dynamics. Instead of flooding a network with packets, Trickle uses a “polite gossip ” policy to control send rates so each node hears just enough packets to stay consistent. This simple mechanism enables Trickle to scale to thousand-fold changes in network density, reach consistency in seconds, and require only a few bytes of state yet impose a maintenance cost of a few sends an hour. Originally designed for disseminating new code, experience has shown Trickle to have much broader applicability, including route maintenance and neighbor discovery. This paper provides an overview of the research challenges wireless sensor networks face, describes the Trickle algorithm, and outlines several ways it is used today. 1 Wireless Sensor Networks Although embedded sensing applications are extremely diverse, ranging from habitat and structural monitoring to vehicle tracking and shooter localization, the software and hardware architectures used by these systems are surprisingly similar. The typical architecture is embodied by the mote platforms, such as those shown in Figure 1. A microcontroller provides processing, program ROM, and data RAM, as well as analog-to-digital converters for sensor inputs, digital interfaces for connecting to other devices, and control outputs. Additional flash storage holds program images and data logs. A low power CMOS radio provides a simple link layer. Support circuitry allows the system to enter a low-power sleep state, wake quickly, and respond to important events. Four fundamental constraints shape wireless embedded system and network design: power supply, limited memory, the need for unattended operation, and the lossy and transient behavior of wireless communication. A typical power Figure 1: EPIC, KMote, and Telos motes. Each has an

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