Accurate and timely traffic flow prediction is crucial for intelligent transportation systems (ITS). Recent advances in graph-based neural networks have achieved promising prediction results. However, some challenges remain, especially regarding graph construction and the time complexity of models. In this paper, we propose a multi-stream feature fusion approach to extract and integrate rich features from traffic data and leverage a data-driven adjacent matrix instead of the distance-based matrix to construct graphs. We calculate the Spearman rank correlation coefficient between monitor stations to obtain the initial adjacent matrix and fine-tune it while training. As to the model, we construct a multi-stream feature fusion block (MFFB) module, which includes a three-channel network and the soft-attention mechanism. The three-channel networks are graph convolutional neural network (GCN), gated recurrent unit (GRU) and fully connected neural network (FNN), which are used to extract spatial, temporal and other features, respectively. The soft-attention mechanism is utilized to integrate the obtained features. The MFFB modules are stacked, and a fully connected layer and a convolutional layer are used to make predictions. We conduct experiments on two real-world traffic prediction tasks and verify that our proposed approach outperforms the state-of-the-art methods within an acceptable time complexity.

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.

IoT services hosted by low-power devices rely on the cloud infrastructure to propagate their ubiquitous presence over the Internet. A critical challenge for IoT systems is to ensure continuous provisioning of IoT services by overcoming network breakdowns, hardware failures, and energy constraints. To overcome these issues, we propose a cloud-based framework namely SensorClone, which relies on virtual devices to improve IoT resilience. A virtual device is the digital counterpart of a physical device that has learned to emulate its operations from sample data collected from the physical one. SensorClone exploits the collected data of low-power devices to create virtual devices in the cloud. SensorClone then can opportunistically migrate virtual devices from the cloud into other devices, potentially underutilized, with higher capabilities and closer to the edge of the network, e.g., smart devices. Through a real deployment of our SensorClone in the wild, we identify that virtual devices can be used for two purposes, 1) to reduce the energy consumption of physical devices by duty cycling their service provisioning between the physical device and the virtual representation hosted in the cloud, and 2) to scale IoT services at the edge of the network by harnessing temporal periods of underutilization of smart devices. To evaluate our framework, we present a use case of a virtual sensor created from an IoT service of temperature. From our results, we verify that it is possible to achieve unlimited availability up to 90% and substantial power efficiency under acceptable levels of quality of service. Our work makes contributions towards improving IoT scalability and resilience by using virtual devices.

Computational offloading can improve user experience of mobile apps through improved responsiveness and reduced energy footprint. A fundamental challenge in offloading is to distinguish situations where offloading is beneficial from those where it is counterproductive. Currently, offloading decisions are predominantly based on profiling performed on individual devices. While significant gains have been shown in benchmarks, these gains rarely translate to real-world use due to the complexity of contexts and parameters that affect offloading. We contribute by proposing crowdsensed evidence traces as a novel mechanism for improving the performance of offloading systems. Instead of limiting to profiling individual devices, crowdsensing enables characterizing execution contexts across a community of users, providing better generalisation and coverage of contexts. We demonstrate the feasibility of using crowdsensing to characterize offloading contexts through an analysis of two crowdsensing datasets. Motivated by our results, we present the design and development of the EMCO toolkit and platform as a novel solution for computational offloading. Experiments carried out on a testbed deployment in Amazon EC2 Ireland demonstrate that EMCO can consistently accelerate app execution while at the same time reduce energy footprint. We also demonstrate that EMCO provides better scalability than current cloud platforms, being able to serve a larger number of clients without variations in performance. Ourframework, use cases, and tools are available as open source from GitHub.