Sympathy: A Debugging System for Sensor Networks

Sensor networks—networks of small, resource-constrained wireless devices embedded in a dynamic physical environment—have led to new algorithm, protocol, and operating system designs [1], [2]. Interactions between sensor hardware, protocols, and environmental characteristics are impossible to predict, so sensor network application design is an iterative process between debugging and deployment [3]. Current debugging techniques fall short for systems which contain bugs characteristic of both distributed and embedded systems. Such bugs can be difficult to track because they are often multicausal, non-repeatible, timing-sensitive and have ephemeral triggers such as race conditions, decisions based on asynchronous changes in distributed state, or interactions with the physical environment. Furthermore, it is a challenge to extract debugging information from a running system without introducing the probing effect (alteration of normal behavior due to instrumentation) or draining excessive energy. This paper presents a preliminary design and evaluation of Sympathy, a debugging tool for pre-deployment sensor networks and motivated by Ruan and Pai’s DeBox system [4]. Sympathy consists of mechanisms for collecting system performance metrics with minimal memory overhead; mechanisms for recognizing events based on these metrics; and a system for collecting events and their spatio-temporal context. Sympathy introduces the idea of correlating seemingly unrelated events, and providing context for these events, in order to track down bugs and find their root causes. Using Sympathy we have begun to distill out the important metrics, events and generic correlators that help find bugs quickly, and to transmit this data in ways that minimize energy consumption and probing effects. This process is ongoing. Our current contribution, then, is a tool that can be used for pre-deployment debugging, and for analysis on the role of a debugging tool in the entire design process. Eventually, Sympathy will be part of a system that can aid in debugging sensor networks both preand post-deployment. Below we present a useful case study that demonstrates our current contributions by showing how Sympathy was used to debug a failure in tiny diffusion. In related work, [5] and [6] address the data collection aspects of post-deployment debugging, but focus on the mechanism to gather statistics instead of their content. Our work is complementary, since Sympathy is so far mostly concerned with content: discovering the most useful metrics to collect. Simulations and visualization tools are also helpful, but do not capture historical context or aid in determining the cause of a failure. While log files can provide context to a failure, they often contain excessive data which can obfuscate important events. Sympathy distinguishes itself from passive data logging approaches by proactively collecting and highlighting potentially relevant events and their context in order to aid in isolating their causes.