Wireless sensor networks necessitate a programming model different from those used to develop desktop applications. Typically, resources in terms of power and memory are constrained. C is the most common programming language used to develop applications on very small embedded sensor devices. We claim that C does not provide efficient mechanisms to address the implicit asynchronous nature of sensor sampling. C applications for these devices suffer from a disruption in their control flow. In this paper, we present FlowTalk, a new object-oriented programming language aimed at making software development for wireless embedded sensor devices easier. FlowTalk is an object-oriented programming language in which dynamicity (e.g., object creation) has been traded for a reduction in memory consumption. The event model that traditionally comes from using sensors is adapted in FlowTalk with controlled disruption, a light-weight continuation mechanism. The essence of our model is to turn asynchronous long-latency operations into synchronous and blocking method calls. FlowTalk is built for TinyOS and can be used to develop applications that can fit in 4 KB of memory for a large number of wireless sensor devices.

Large scale simulation studies, such as routing in communication systems, rely on complex networks. However, the specific network structure of the application domain is often neglected in analysing network behaviour with the consequence of limited ability to conduct a sensitivity analyses. Network evolution models have been developed to account for structural properties of social graphs, such as the Internet, and enable a model-based analysis on different scales. Despite some analytical results with respect to queueing metrics, little work has been done to frame network evolution models in the context of queuing systems. This paper addresses this issue discussing social and random network evolution models and their application to queueing systems. When the stability criterion holds, analytical solutions using Jackson's Theorem and Little's Law exist and provide valuable insights into a number of queueing performance metrics. Specifically, a sensitivity analysis of the mean total waiting time versus the utilisation is carried out which shows a significant difference between the social and random network models. Understanding the impact of the structural features of complex networks is important in the design of simulation studies and operating real world systems. This is especially true when adaptive algorithms are employed and insights of their performance compared to analytical scenarios are required.

Tools for the generation of code from model descriptions are valuable in helping developers maintain consistency between a model and its implementation. In this paper, we describe a new method for generating Java implementation code from UML diagrams. Our method accepts UML diagrams specified at a higher-level than current tools, and places fewer constraints on the supported UML constructs, like multiple generalizations and association classes. Unlike current tools, it generates implementation code that shields system implementers from the low-level details of how associations and other UML constructs are mapped to Java. Furthermore, it supports the modular design of systems according to concerns[2, 10] by being able to generate code from a set of related UML diagrams. While our discussion is focused on the special problem of generating Java implementation code, the issues discussed in this paper are applicable more generally to object-oriented implementation languages.

In this paper we discuss some preliminary results on using Widrow's Adaptive Noise Cancelling (ANC) algorithm to reduce the background noise present in a fighter pilot's speech. With a dominant noise source present and with the pilot wearing an oxygen facemask, we demonstrate that good (>10 dB) cancellation of the additive noise and little speech distortion can be achieved by having the reference microphone attached to the outside of the facemask and by updating the filter coefficients only during silence intervals.