Distribution forecast can quantify forecast uncertainty and provide various forecast scenarios with their corresponding estimated probabilities. Accurate distribution forecast is crucial for planning – for example when making production capacity or inventory allocation decisions. We propose a practical and robust distribution forecast framework that relies on backtest-based bootstrap and adaptive residual selection. The proposed approach is robust to the choice of the underlying forecasting model, accounts for uncertainty around the input covariates, and relaxes the independence between residuals and covariates assumption. It reduces the Absolute Coverage Error by more than 63% compared to the classic bootstrap approaches and by 2%−32% compared to a variety of State-of-the-Art deep learning approaches on in-house product sales data and M4-hourly competition data.

Channel information plays an important role in modern wireless communication systems. Systems that use different frequency bands for uplink and downlink communication often need feedback between devices to exchange band specific channel information. The current state-of-the-art approach proposes a way to predict the channel in the downlink based on that of the observed uplink by identifying variables underlying the uplink channel. In this paper we present a solution that greatly reduces the complexity of this task, and is even applicable for single antenna devices. Our approach uses a neural network trained on a standard channel model to generate coarse estimates for the variables underlying the channel. We then use a simple and efficient single antenna optimization framework to get more accurate variable estimates, which can be used for downlink channel prediction. We implement our approach on software defined radios and compare it to the state-of-the-art through experiments and simulations. Results show that our approach reduces the time complexity by at least an order of magnitude (10x), while maintaining similar prediction quality.

The future Internet of Things (IoT) networks are expected to be composed of a large population of low-cost devices communicating dynamically with access points or neighboring devices to communicate small bundles of delay-sensitive data. To support the high-intensity and short-lived demands of these emerging networks, we propose an efficient MAC paradigm for IoT (EMIT). Our paradigm bypasses the high overhead and coordination costs of existing MAC solutions by employing an interference-averaging strategy that allows users to share their resources simultaneously. In contrast to the predominant interference-suppressing approaches, EMIT exploits the dense and dynamic nature of IoT networks to reduce the spatio-temporal variability of interference to achieve low-delay and high-reliability in service. This paper introduces foundational ideas of EMIT by characterizing the global interference statistics in terms of single-device operation and develops power-rate allocation strategies to guarantee low-delay high-reliability performance. A significant portion of our work is aimed at validating these theoretical principles in experimental test beds and simulations, where we compare the performance of EMIT with a CSMA-based MAC protocol. Our comparisons confirm the beneficial characteristics of EMIT and reveal significant gains over CSMA strategies in the case of IoT traffic.

In recent years there have been a few semi-supervised community detection approaches that use community membership information, or node metadata to improve their performance. However, communities have always been thought of as clique-like structures, while the idea of finding and leveraging other patterns in communities is relatively unexplored. Online social networks provide a corpus of real communities in large graphs which can be used to understand dataset specific community patterns. In this paper, we design a way to represent communities concisely in an easy to compute feature space. We design an efficient community detection algorithm that uses size and structural information of communities from a training set to find communities in the rest of the graph. We show that our approach achieves 10% higher F1 scores on average compared to several other methods on large real-world graph datasets, even when the training set is small.

Reliable and timely delivery of periodic V2V (vehicle-to-vehicle) broadcast messages is essential for realizing the benefits of connected vehicles. Existing MAC protocols for ad hoc networks fall short of meeting these requirements. In this paper, we present, CoReCast, the first collision embracing protocol for vehicular networks. CoReCast provides high reliability and low delay by leveraging two unique opportunities: no strict constraint on energy consumption, and availability of GPS clocks to achieve near-perfect time and frequency synchronization.

The future Internet of Things (IoT) networks are expected to be composed of a large population of low-cost devices communicating dynamically with access points or neighboring devices to communicate small bundles of delay-sensitive data. To support the high-intensity and short-lived demands of these emerging networks, we propose an Efficient MAC paradigm for IoT (EMIT). Our paradigm bypasses the high overhead and coordination costs of existing MAC solutions by employing an interference-averaging strategy that allow users to share their resources simultaneously. In contrast to the predominant interference-suppressing approaches, EMIT exploits the dense and dynamic nature of IoT networks to reduce the spatio-temporal variability of interference to achieve low-delay and high-reliability in service. This paper introduces foundational ideas of EMIT by characterizing the global interference statistics in terms of single-device operation and develops power-rate allocation strategies to guarantee low-delay high-reliability performance. A significant portion of our work is aimed at validating these theoretical principles in experimental testbeds, where we compare the performance of EMIT to a CSMA-based MAC protocol. Our comparisons confirm the beneficial characteristics of EMIT, and reveal significant gains over CSMA strategies in the case of IoT traffic.