We consider a distributed composite hypothesis testing problem in which sensor nodes share a collision channel to send their decisions and the fusion center (FC) has a limited time to collect these decisions. When the FC does not have enough time to collect all local decisions successfully, we propose a transmission protocol called sensor censoring random access as the multiple access scheme used by sensor nodes to send their decisions to the FC. By using this protocol, the collection time is divided into frames, where each frame consists of a number of time slots. The sensor nodes whose observations are within a specific range will send their decisions in a specific frame by using slotted ALOHA. Thereafter, we derive a Rao test used by the FC to decide whether the event is happening. Since this Rao test is aware of packet collisions and exploits them to make a global decision, we call it a collision-aware Rao test (CA-Rao test). Its asymptotic performance (the probabilities of detection and false alarm) is determined. The receiver operating characteristics of the CA-Rao test are evaluated and compared to those of a Rao test of distributed detection using parallel access channels.
As short-term load forecasting is essential for the day-to-day operation planning of power systems, we built an ensemble learning model to perform such forecasting for Thai data. The proposed model uses voting regression (VR), producing forecasts with weighted averages of forecasts from five individual models: three parametric multiple linear regressors and two non-parametric machine-learning models. The regressors are linear regression models with gradient-descent (LR), ordinary least-squares (OLS) estimators, and generalized least-squares auto-regression (GLSAR) models. In contrast, the machine-learning models are decision trees (DT) and random forests (RF). To select the best model variables and hyper-parameters, we used cross-validation (CV) performance instead of the test data performance, which yielded overly good test performance. We compared various validation schemes and found that the Blocked-CV scheme gives the validation error closest to the test error. Using Blocked-CV, the test results show that the VR model outperforms all its individual predictors.
We consider the composite hypothesis testing problem of time-bandwidth-constrained distributed detection. In this scenario, the probability distribution of the observed signal when the event of interest is happening is unknown. In addition, local decisions are censored and only those uncensored local decisions will be sent to the fusion center over a shared and noisy collision channel. The fusion center also has a limited time duration to collect transmitted decisions and make a final decision. Two types of medium access control that the sensor nodes apply to send their decisions are investigated: time division multiple access and slotted-Aloha. Unlike using the time division multiple access protocol, the slotted-Aloha-based distributed detection will experience packet collisions. However, in this article, since only uncensored decisions are sent, packet collisions are informative. We derive fusion rules according to generalized likelihood ratio test, Rao test, and Wald test for both the time division multiple access–based distributed detection and the slotted-Aloha-based distributed detection. We see that the fusion rules for the slotted-Aloha-based distributed detection here also exploit packet collisions in the final decision-making. In addition, the asymptotic performances and energy consumption of both schemes are analyzed. Extensive simulation and numerical results are provided to compare the performances of these two schemes. We show that, for a given time delay, the slotted-Aloha-based distributed detection can outperform the time division multiple access–based distributed detection by increasing the number of sensor nodes which results in higher energy consumption.
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