Edge computing has recently emerged as a promising paradigm to support mobile access in Internet of Things (IoT) multinetworks, where heterogeneous wireless communication solutions coexist. Meanwhile, software-defined networking (SDN) presents a potential infrastructure to monitor and manage mobile edge computing. However, resilient access in the integrated IoT-Edge-SDN environment is a key challenge. In this article, we present location-aware spatio-temporal solution (LASTS) as an edge computing-empowered software-defined system to scalably control mobile IoT access and detect sequential anomaly. LASTS utilizes a Personal access point protocol to enable switching between multiple networks. In addition, it supports efficient control and transfer of the mobile device’s spatio-temporal context. This context information plays an important role in a deep learning model employed for sequential anomaly detection in the LASTS system. Realistic testbed experiments confirm that LASTS can successfully achieve scalable access control and sequential anomaly detection in mobile IoT.

The accurate prediction of traffic conditions, including speed, flow, and travel time, poses a critical challenge in urbanization that significantly impacts car owners and road administrators. However, in certain scenarios with restricted road data availability (e.g. lack of traffic light status and signal control strategies, cooperation between road administrators and third parties, etc.), it is imperative to make effective use of basic road information (e.g. historical traffic data and road connectivity) to improve both prediction accuracy and scalability on various arterial road networks against state-of-art deep learning models. In this paper, we propose a spatial-temporal learning framework TPGraph for an accurate prediction of arterial roads’ traffic data by effectively utilizing upstream and downstream road information. TPGraph is composed of three major parts: 1) A multi-scale temporal feature fusion module that utilizes a multi-head attention mechanism to integrate recently-periodic features, daily-periodic features, and weekly-periodic features; 2) A multi-graph convolution module that employs graph fusion and graph convolution networks to capture richer spatial semantics, and 3) A dynamic spatial-temporal prediction module that leverages a spatial-temporal transformer for single or multiple traffic-state predictions. Our proposed framework, TPGraph, leverages just multi-scale historical traffic conditions and readily accessible spatial factors as input to generate accurate predictions of future traffic conditions. We mainly evaluate the performance of our approach through multi-step prediction experiments conducted at hourly intervals, forecasting travel time or travel speed for each road at 15 mins, 30 mins, and 1 hour. Furthermore, we conduct extensive experiments on real-world arterial road datasets to demonstrate the superior predictive performance of TPGraph compared to existing methods.

The “mind-controlling” capability has always been in mankind's fantasy. With the recent advancements in electroencephalograph (EEG) techniques, brain-computer interface (BCI) researchers have explored some solutions to allow individuals to perform various tasks using their minds. However, the commercial off-the-shelf devices to run accurate EEG signal collection are usually expensive and the comparably cheaper devices can only present coarse results, which prevents the practical application of these devices in domestic services. To tackle this challenge, we propose and develop an end-to-end solution that enables fine brain-robot interaction (BRI) through embedded learning of coarse EEG signals from low-cost devices, namely PerBCI, so that people having difficulty moving, such as the elderly, can mind command and control a robot to perform some basic household tasks. Our contributions are three folds: 1) We present a stacked long short-term memory (BiLSTM) structure, along with specific pre-processing techniques to handle the time-dependency of EEG signals and their classification. 2) We propose a personalized design to adaptively capture multiple features and achieve accurate recognition of individual EEG signals by enhancing the signal interpretation of BiLSTM with an attention mechanism. 3) We develop a low-cost, real-time and end-to-end BRI system that can run our PerBCI models and algorithms in the embedded robot platform to perform more than one type of domestic task based on the users' EEG signal inputs. Our real-world experiments with elderly participants of diverse backgrounds in a home setting and system comparison with other approaches show that the proposed end-to-end solution with low cost can achieve satisfactory run-time speed, accuracy and energy-efficiency.

Recent advancements in EEG-based BCI technologies have been explored to assist individuals in executing brain-to-robot tasks. In the future, using BCI systems in both personal and professional domains is anticipated in widespread adoption. One promising application is facing home life, to enable people to use commercial EEG equipment to implement daily tasks. However, current BCI studies mainly focus on single-brain-to-single-robot interaction, which has limitations in representing diverse human intentions. A generalized BCI system with multiple EEG devices allows users to more precisely or collaboratively control robots. Therefore, it is imperative to extend the BCI techniques to future collaboration scenarios. In this paper, we present a new system, NeuroBCI, for multi-brain-to-multi-robot interaction through the integration of sensing, computing, communication, and control. To improve sensing efficiency, NeuroBCI employs a sparse attention mechanism to extract joint features from heterogeneous EEG data. Parallel computation and transmission for multi-user multi-task scenarios are handled by semantic autoencoder and autodecoder communications. A code map is designed to ensure concurrent control and model compression methods are used on both transmitter and receiver sides. Our experiments in comparison with state-of-the-art works show the superior performance of NeuroBCI on sensing, computing, communication, and control as a holistic system.

Recent years have witnessed significant developments in point cloud processing, including classification and segmentation. However, supervised learning approaches need a lot of well-labeled data for training, and annotation is labor-and time-intensive. Self-supervised learning, on the other hand, uses unlabeled data, and pretrains a back-bone with a pretext task to extract latent representations to be used with the downstream tasks. Compared to 2D images, self-supervised learning of 3D point clouds is under-explored. Existing models, for self-supervised learning of 3D point clouds, rely on a large number of data samples, and require significant amount of computational re-sources and training time. To address this issue, we propose a novel contrastive learning approach, referred to as To ThePoint. Different from traditional contrastive learning methods, which maximize agreement between features obtained from a pair of point clouds formed only with different types of augmentation, ToThePoint also maximizes the agreement between the permutation invariant features and features discarded after max pooling. We first perform self-supervised learning on the ShapeNet dataset, and then evaluate the performance of the network on different downstream tasks. In the downstream task experiments, performed on the ModelNet40, ModelNet40C, ScanobjectNN and ShapeNet-Part datasets, our proposed ToThe-Point achieves competitive, if not better results compared to the state-of-the-art baselines, and does so with significantly less training time (200 times faster than baselines).