Simulation is widely used for developing, evaluating and analyzing sensor network applications, especially when deploying a large scale sensor network remains expensive and labor intensive. However, due to its computation intensive nature, existent simulation tools have to make trade-offs between fidelity and scalability and thus offer limited capabilities as design and analysis tools. In this paper, we introduce DiSenS (DIstributed SENsor network Simulation) -- a highly scalable distributed simulation system for sensor networks. DiSenS does not only faithfully emulates an extensive set of sensor hardware and supports extensible radio/power models, so that sensor network applications can be simulated transparently with high fidelity, but also employs distributed-memory parallel cluster system to attack the complex simulation problem. Combining an efficient distributed synchronization protocol and a sophisticated node partitioning algorithm (based on existent research), DiSenS achieves greater scalability than even many discrete event simulators. On a small to medium size cluster (16-64 nodes), DiSenS is able to simulate hundreds of motes in realtime speed and scale to thousands in sub-realtime speed. To our knowledge, DiSenS is the first full-system sensor network simulator with such scalability.

Software development for sensor network is made difficult by resource constrained sensor devices, distributed system complexity, communication unreliability, and high labor cost. Simulation, as a useful tool, provides an affordable way to study algorithmic problems with flexibility and controllability. However, in exchange for speed simulation often trades detail that ultimately limits its utility. In this paper, we propose a new development paradigm, simulation-based augmented reality, in which simulation is used to enhance development on physical hardware by seamlessly integrating a running simulated network with a physical deployment in a way that is transparent to each. The advantages of such an augmented network include the ability to study a large sensor network with limited hardware and the convenience of studying a part of the physical network with simulation's debugging, profiling and tracing capabilities. We implement the augmented reality system based on a sensor network simulator with high fidelity and high scalability. Key to the design are "super" sensor nodes which are half virtual and half physical that interconnect simulation and physical network with fine-grained traffic forwarding and accurate time synchronization. Our results detail the overhead associated with integrating live and simulated networks and the timing accuracy between virtual and physical parts of the network. We also discuss various application scenarios for our system.

Sensor network computing can be characterized as resource-constrained distributed computing using unreliable, low bandwidth communication. This combination of characteristics poses significant software development and maintenance challenges. Effective and efficient debugging tools for sensor network are thus critical. Existent development tools, such as TOSSIM, EmStar, ATEMU and Avrora, provide useful debugging support, but not with the fidelity, scale and functionality that we believe are sufficient to meet the needs of the next generation of applications.In this paper, we propose a debugger, called S2DB, based on a distributed full system sensor network simulator with high fidelity and scalable performance, DiSenS. By exploiting the potential of DiSenS as a scalable full system simulator, S2DB extends conventional debugging methods by adding novel device level, program source level, group level, and network level debugging abstractions. The performance evaluation shows that all these debugging features introduce overhead that is generally less than 10% into the simulator and thus making S2DB an efficient and effective debugging tool for sensor networks.

Mobile, battery-powered devices such as personal digital assistants and web-enabled mobile phones have successfully emerged as new access points to the world's digital infrastructure. However, the growing gap between device capabilities and battery technology requires novel techniques that extend battery life. Key to the success of such techniques, is our ability to accurately predict the power consumption of a program.In this paper, we investigate the degree to which battery dissipation induced by program execution can be measured by applica-tion-level software tools and predicted by a compiler and runtime system. We present a novel technique with which we can accurately estimate whole-program power-consumption for an arbitrary program by composing battery dissipation rates of benchmarks. We empirically evaluate our technique using an iPAQ hand-held device and a number of MiBench and other programs.

Feature based design is the main characteristic of current prevailing parametric solid modeling systems. By means of enabling users to build their own feature libraries, user defined features (UDF) provided as an extensional design method can overcome the shortcoming of inconvenience that exists in the traditional feature based modeling system. Traditional UDF representation is implemented either by procedural definition, which represents the features implicitly (J.J. Shah, 1991), or by an explicit representation or enumerative way (M.J. Pratt, 1989; M. Ranta and M. Mantyla, 1993). The paper proposes an innovative approach to the representation of UDFs by extending the common procedural UDFs' data structure with boundary features. Then a unified feature attachment interface is described. Thus, a unified representation of UDFs is introduced.