A Study of Connectivity in MIMO Fading Ad-Hoc Networks

We investigate the connectivity of fading wireless ad-hoc networks with a pair of novel connectivity metrics. Our first metric looks at the problem of connectivity relying on the outage capacity of MIMO channels. Our second metric relies on a probabilistic treatment of the symbol error rates for such channels. We relate both capacity and symbol error rates to the characteristics of the underlying communication system such as antenna configuration,modulation, coding, and signal strength measured in terms of Signal-to-Interference-Noise-Ratio (SINR). For each metric of connectivity, we also provide a simplified treatment in the case of ergodic fading channels. In each case, we assume a pair of nodes are connected if their bi-directional measure of connectivity is better than a given threshold. Our analysis relies on the central limit theorem to approximate the distribution of the combined undesired signal affecting each link of an ad-hoc network as Gaussian. Supported by our simulation results, our analysis shows that (1) a measure of connectivity purely based on signal strength is not capable of accurately capturing the connectivity phenomenon, and (2) employing multiple antenna mobile nodes improves the connectivity of fading ad-hoc networks

A Study of Connectivity in MIMO Fading Ad-Hoc Networks

We investigate the connectivity of fading wireless ad-hoc networks with a pair of novel connectivity metrics. Our first metric looks at the problem of connectivity relying on the outage capacity of MIMO channels. Our second metric relies on a probabilistic treatment of the symbol error rates for such channels. We relate both capacity and symbol error rates to the characteristics of the underlying communication system such as antenna configuration,modulation, coding, and signal strength measured in terms of Signal-to-Interference-Noise-Ratio (SINR). For each metric of connectivity, we also provide a simplified treatment in the case of ergodic fading channels. In each case, we assume a pair of nodes are connected if their bi-directional measure of connectivity is better than a given threshold. Our analysis relies on the central limit theorem to approximate the distribution of the combined undesired signal affecting each link of an ad-hoc network as Gaussian. Supported by our simulation results, our analysis shows that (1) a measure of connectivity purely based on signal strength is not capable of accurately capturing the connectivity phenomenon, and (2) employing multiple antenna mobile nodes improves the connectivity of fading ad-hoc networks

Artificial Intelligence Aided Agricultural Sensors for Plant Frostbite Protection

Frostbite and frost is one of the problems that endanger the health of crops and can ruin plants and fruits. Soil temperature is the most significant factor that influences the freezing depth. Therefore, monitoring and predicting this characteristic is crucial for frostbite protection. This study aims to predict soil temperature on cold days to prevent frostbite injury in crops. For this matter, we used the registered and logged hourly data by the HOBO U30 data logging device and predicted the soil temperature from air temperature, soil water content, and relative humidity. We used 80% of the data set for the training data and assigned the other 20% to the test data. RMSE and MSE were two of the evaluation criteria of the neural network in this study. Also, we calculated P-value and T-value for statistical hypothesis testing. In another approach for weighting the neural network, we used evolutionary algorithms such as Genetic Algorithm and Particle Swarm Optimization instead of the gradient-based methods. According to the results, Multi-layer perceptron neural network with the respective values 0.082 and 0.0068 for RMSE and MSE in training data and 0.085 and 0.0073 for RMSE and MSE in testing data proved to have a better performance in the soil temperature prediction compared to the ANN-GA and ANN-PSO models. Farmers, botanical researchers, and policymakers in food security can use these results