A Radio Link Quality Model and Simulation Framework for Improving the Design of Embedded Wireless Systems

Despite the increasing application of embedded wireless systems, developers face numerous challenges during the design phase of the application life cycle. One of the critical challenges is ensuring performance reliability with respect to radio link quality. Specifically, embedded links experience exaggerated link quality variation, which results in undesirable wireless performance characteristics. Unfortunately, the resulting post-deployment behaviors often necessitate network redeployment. Another challenge is recovering from faults that commonly occur in embedded wireless systems, including node failure and state corruption. Self-stabilizing algorithms can provide recovery in the presence of such faults. These algorithms guarantee the eventual satisfaction of a given state legitimacy predicate regardless of the initial state of the network. Their practical behavior is often different from theoretical analyses. Unfortunately, there is little tool support for facilitating the experimental analysis of self-stabilizing systems. We present two contributions to support the design phase of embedded wireless system development. First, we provide two empirical models that predict radio-link quality within specific deployment environments. These models predict link performance as a function of inter-node distance and radio power level. The models are culled from extensive experimentation in open grass field and dense forest environments using all radio power levels and covering up to the maximum distances reachable by the radio. Second, we provide a simulation framework for simulating self-stabilizing algorithms. The framework provides three feature extensions: (i) fault injection to study algorithm behavior under various fault scenarios, (ii) automated detection of non-stabilizing behavior; and (iii) integration of the link quality models described above. Our contributions aim at avoiding problems that could result in the need for network redeployment

Functional ANOVA with random functional effects: an application to event-related potentials modelling for electroencephalograms analysis

The di erential e ects of basic visual or auditory stimuli on electroencephalograms (EEG), named event related potentials (ERPs), are often used to evaluate the impact of treatments on brain performances. In the present paper, we propose a P-splines based model that can be used to evaluate treatment e ect on the timing and the amplitude of some peaks of the ERPs curves. Functional ANOVA is an adaptation of linear model or analysis of variance to analyse functional observations. The changes in the functional of interest e ects are generally described using smoothing splines. Eilers and Marx proposed to work with P-splines, a combination of B-splines and di erence penalties on coe cients. We de ne a Psplines model for ERPs curves combined with random e ects. In particular, we show that it is a useful alternative to classical strategies requiring the visual and usually imprecise localization of speci c ERP peaks from curves with a low signal-to-noise ratio.IAP network No. P5=2