Coordinated Static and Mobile Sensing for Environmental Monitoring

Distributed embedded sensor networks are now being successfully deployed in environmental monitoring of natural phenomena as well as for applications in commerce and physical security. While substantial progress in sensor network performance has appeared, new challenges have also emerged as these systems have been deployed in the natural environment. First, in order to achieve minimum sensing fidelity performance, the rapid spatiotemporal variation of environmental phenomena requires impractical deployment densities. The presence of obstacles in the environment introduces sensing uncertainty and degrades the performance of sensor fusion systems in particular for the many new applications of image sensing. The physical obstacles encountered by sensing may be circumvented by a new mobile sensing method or Networked Infomechanical Systems (NIMS). NIMS integrates distributed, embedded sensing and computing systems with infrastructure-supported mobility. NIMS now includes coordinated mobility methods that exploits adaptive articulation of sensor perspective and location as well as management of sensor population to provide the greatest certainty in sensor fusion results. The architecture, applications, and implementation of NIMS will be discussed here. In addition, results of environmentally-adaptive sampling, and direct measurement of sensing uncertainty will be described

Virtual high-resolution for sensor networks

The resolution at which a sensor network collects data is a crucial parameter of performance since it governs the range of applications that are feasible to be developed using that network. A higher resolution, in most situations, enables more applications and improves the reliability of existing ones. In this paper we discuss a system architecture that uses controlled motion to provide virtual high-resolution in a network of cameras. Several orders of magnitude advantage in resolution may be achieved, depending on tolerable tradeoffs. We discuss several system design choices in the context of our prototype camera network implementation that realizes the proposed architecture. We also mention how some of our techniques may apply to sensors other than cameras. Real world data is collected using our prototype system and used for the evaluation of our proposed methods

Adaptive delay estimation for low jitter audio over Internet

Real time voice applications typically produce uniformly spaced voice packets and faithful reconstruction demands that these be played out at the same intervals. Best effort packet networks, however, produce variable delays on different packets and the receiver is required to buffer the received packets before playout. Excessive buffering delays deteriorate the system performance for interactive audio and so intelligent algorithms that keep this delay minimum while maintaining an acceptable packet loss have to be employed. In this research, we develop a new "α-adaptive" algorithm which offers considerable reduction in delays compared to existing algorithms, especially for low packet losses. A generic jitter control procedure is also proposed which may be used with any buffering algorithm to enhance its jitter performance without significantly affecting the delay loss tradeoff. Further, an existing algorithm based on Normalized Least Mean Squares filter is discussed and modifications are proposed for its practical implementation. All suggestions are supported by simulations on Internet delay traces.© IEE