Logging and tracing are important methods to gain insight into the behavior of sensor network applications. Existing generic solutions are often limited to nodes with a direct serial connection and do not provide the required efficiency for network-wide logging. Instead, this is often realized by application-specific subsystems developed for custom logging statements. In this paper, we present TinyLTS - a generic and efficient Logging and Tracing System for TinyOS. TinyLTS consists of a compiler extension that separates dynamic from static information at compile time, a declarative solution for inserting logging statements, an extensible framework for flexible storing and transmitting of logging data and a frontend for recombining dynamic and static information. Our system provides concise yet expressive programming abstractions for the developer combined with efficiency comparable to custom solutions.

Cycle-accurate emulation of sensor networks allows a detailed analysis of platform target code for development and evaluation. However, the high overhead incurred by providing the necessary fidelity limits the size of the emulated networks considerably. The use of multiple cores provided by modern hardware can significantly improve the speed of emulation but requires synchronization algorithms to preserve causality. Based on the well-known multithreaded event-driven emulator Avrora, we investigate a number of synchronization methods including an algorithm that does not require any locks to improve the performance. We show that both the speed and the scalability can be significantly improved without sacrificing correctness. Additionally, we evaluate the impact of modern CPU technologies such as simultaneous multithreading on emulation performance.

Boundary recognition is an important and challenging issue in wireless sensor networks when no coordinates or distances are available. The distinction between inner and boundary nodes of the network can provide valuable knowledge to a broad spectrum of algorithms. This article tackles the challenge of providing a scalable and range-free solution for boundary recognition that does not require a high node density. We explain the challenges of accurately defining the boundary of a wireless sensor network with and without node positions and provide a new definition of network boundary in the discrete domain. Our solution for boundary recognition approximates the boundary of the sensor network by determining the majority of inner nodes using geometric constructions, which guarantee that for a given d , a node lies inside of the construction for a d -quasi unit disk graph model of the wireless sensor network. Moreover, such geometric constructions make it possible to compute a guaranteed distance from a node to the boundary. We present a fully distributed algorithm for boundary recognition based on these concepts and perform a detailed complexity analysis. We provide a thorough evaluation of our approach and show that it is applicable to dense as well as sparse deployments.

Boundary recognition is an important and challenging issue in wireless sensor networks when no coordinates or distances are available. The distinction between inner and boundary nodes of the network can provide valuable knowledge to a broad spectrum of algorithms. This paper tackles the challenge of providing a scalable and range-free solution for boundary recognition that does not require a high node density. Our solution approximates the boundary of the sensor network by determining the inner nodes using geometric constructions that guarantee that, for a given d, a node lies inside of the construction for a d-quasi unit disk graph model of the wireless sensor network. Moreover, such geometric constructions make it possible to compute a guaranteed distance from a node to the boundary. We provide a thorough evaluation of our approach and show that it is applicable to dense as well as sparse deployments.