Eliciting Truthful Data from Crowdsourced Wireless Monitoring Modules in Cloud Managed Networks

To facilitate efficient cloud managed resource allocation solutions, collection of key wireless metrics from multiple access points (APs) at different locations within a given area is required. In unlicensed shared spectrum bands collection of metric data can be a challenging task for a cloud manager as indepen- dent self-interested APs can operate in these bands in the same area. We propose to design an intelligent crowdsourcing solution that incentivizes independent APs to truthfully measure/report data relating to their wireless channel utilization (CU). Our work focuses on challenging scenarios where independent APs can take advantage of recurring patterns in CU data by utilizing distribution aware strategies to obtain higher reward payments. We design truthful reporting methods that utilize logarithmic and quadratic scoring rules for reward payments to the APs. We show that when measurement computation costs are considered then under certain scenarios these scoring rules no longer ensure incentive compatibility. To address this, we present a novel reward function which incorporates a distribution aware penalty cost that charges APs for distorting reports based on recurring patterns. Along with synthetic data, we also use real CU data values crowdsourced using multiple independent measuring/reporting devices deployed by us in the University of Oulu

Green areas around homes reduce atopic sensitization in children

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Statistical modeling and bit error rate analysis for bio-sensor receivers in molecular communication

The behavior of bio-sensor receivers is studied for molecular communication (MC). Bacteria can be engineered as a bio-sensor receiver to produce an output signal, e.g., produce green fluorescent protein, with respect to an external concentration pulse (MC signal). The signal transduction of bacteria, i.e., bacteria response, can be used to detect the pulse- amplitude modulated MC signals. In this work, a statistical model for the bacteria-based bio-sensor receivers is developed. Statistical signal models are useful to evaluate the reliability of the communication systems. The bacteria response is modeled by approximating a first-order model of signal transduction in the linear ramp-up region. The bacteria response is found to be a function of the response rate (linear ramp-up slope) and the time. Bacterial signal transduction is inherently noisy due to the cascades of biochemical reactions to produce the output signal. Therefore, the first-order model is extended incorporating the noise in both the rate and the timing (random delay) of the bacteria response. The bit error rate performance is studied to reveal the impact of the timing noise against the response rate noise. The developed statistical signal model can aid performance evaluation of bacteria-based bio-sensor receivers in MC and biological sensing