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

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

A study on false alarm cancellation for spectrum usage measurements

Abstract Two-layer smart spectrum access (SSA) consists of spectrum sharing based on dynamic spectrum access (DSA: first layer) and spectrum awareness system (SAS: second layer). A main role of SAS is providing useful statistical information in terms of spectrum usage by long term, wide-band and wide-area measurements. In this paper, we focus on signal area (SA) estimation which is a core signal processing in SAS to understand the spectrum usage. Specifically, SA estimation is used as post processing for energy detector outputs. It has been shown that Simple-SA (S-SA) estimation can enhance the spectrum usage measurement performance, but it inherently increases false alarms. For this issue, efficient false alarm cancellation technique, L-shaped false alarm cancellation (L-FC), is proposed in this paper. Numerical evaluations show that the proposed method can achieve proper detection performance while the computational cost is small compared to other methods

Time and frequency varying noise floor estimation for spectrum usage measurement

Abstract We investigate noise floor (NF) estimation for FFT-ED (Energy Detection based on FFT)-based spectrum usage measurement in the context of smart spectrum access (SSA), in which spectrum usage information of primary users (PUs), such as channel occupancy rate (COR), will be exploited by secondary users (SUs). In FFT-ED, the NF has to be estimated to set a decision threshold for ED appropriately. In general, the NF is frequency-dependent and its level changes with time leading to the need of estimating the NF regularly while performing the spectrum usage measurement. In this paper, we propose an NF estimation method which exploits prior information regarding the shape of NF and forward consecutive mean excision (FCME) algorithm. Numerical and experimental evaluations show the proposed method enables an accurate NF estimation considering the time and frequency dependencies of the NF. Moreover, we show the proposed method can obtain the almost desired detection performance, but can not the comparative method (the original FCME method)