The rise of edge computing gives birth to a spectrum of delay-sensitive applications. Many of these applications build their services atop the functionality that the edge nodes quickly negotiate a unique order on the events received from a massive number of client devices, even under very high event rates. To this end, we propose a protocol, called Nomad, for achieving fast event ordering in edge computing environments. Nomad is designed as a consensus protocol that employs a lease-based approach to take advantage of the locality of the unbalanced workload across the system. It also dynamically adjusts the leadership distribution on the edge nodes based on the recent running history, and relies on a cloud-based arbitrator to resolve contentions. Experiments demonstrate that Nomad outperforms the existing solutions, such as Multi-Paxos, Mencius and E-Paxos, in achieving fast event ordering for large-scale, delay-sensitive edge-cloud applications.

Many edge computing systems rely on virtual machines (VMs) to deliver their services. It is challenging, however, to deploy the virtualization mechanisms on edge computing hardware infrastructures. In this paper, we introduce the engineering and research trends of achieving efficient VM management in edge computing. We elaborate on: 1) the virtualization frameworks for edge computing developed in both the industry and the academia; 2) the virtualization techniques tailored for edge computing; 3) the placement and scheduling algorithms optimized for edge computing; and 4) the research problems in security related to virtualization of edge computing.

Many times, training a large scale deep learning neural network on a single machine becomes more and more difficult for a complex network model. Distributed training provides an efficient solution, but Byzantine attacks may occur on participating workers. They may be compromised or suffer from hardware failures. If they upload poisonous gradients, the training will become unstable or even converge to a saddle point. In this paper, we propose FABA, a Fast Aggregation algorithm against Byzantine Attacks, which removes the outliers in the uploaded gradients and obtains gradients that are close to the true gradients. We show the convergence of our algorithm. The experiments demonstrate that our algorithm can achieve similar performance to non-Byzantine case and higher efficiency as compared to previous algorithms.

Fast event ordering is critical for delay-sensitive edge computing applications that serve massive geographically distributed clients. Using a centralized cloud to determine the event order suffers from unsatisfactory latency. Naive edge-centric solutions, which designate one edge node to order all the events, have scalability and single point of failure issues. To address these problems, we propose EdgeCons, a novel consensus algorithm optimized for edge computing networks. EdgeCons achieves fast consensus by running a sequence of Paxos instances among the edge nodes and dynamically distributing their leadership based on the recent running history. It also guarantees progressiveness by incorporating a reliable, backend cloud. A preliminary evaluation shows that EdgeCons works more efficiently than the state-of-theart consensus algorithms, in the context of achieving fast event ordering in edge computing networks.

Blockchain-based cryptocurrency systems such as Bitcoin and Ethereum have attracted much attention during the last decade. In recent years, a trend of combining Internet of Things (IoT) devices with blockchain technology has emerged. Digital payments can be made on front-end IoT devices, while a back-end blockchain serves as a distributed ledger to ensure the validity of payments across the system. Nevertheless, fast payments are usually on demand in such a scenario, but an open problem still remains on how to protect blockchain-based systems from double-spending attacks in the context of fast payment. Off-chain techniques, such as Lightning Network and Raiden Network, act as countermeasures to this problem, but they all suffer from the hidden transactions problem. To combat this problem, we propose FastPay, a solution for achieving secure fast payments in blockchain-backed edge-IoT systems. Preliminary evaluation on our prototype demonstrates the effectiveness of FastPay.

We present LAVEA, a system built for edge computing, which offloads computation tasks between clients and edge nodes, collaborates nearby edge nodes, to provide low-latency video analytics at places closer to the users. We have utilized an edge-first design to minimize the response time, and compared various task placement schemes tailed for inter-edge collaboration. Our results reveal that the client-edge configuration has task speedup against local or client-cloud configurations.

This article presents a detailed description of fog computing ( also known as edge computing) and explores its research challenges and problems. Based on the authors' understanding of these challenges and problems, they propose a flexible software architecture, which can incorporate different design choices and user-specified polices. They present their design of WM-FOG, a computing framework for fog environments that embraces this software architecture, and evaluate their prototype system.

Mobile devices have become our true companions in recent years. While bringing plenty of convenience, they also come with many security and privacy concerns. Being small, a mobile device is prone to loss or theft. Privacy data such as emails in a saved Gmail or Yahoo account on a lost device can be easily accessed by an unwanted visitor. Therefore, it is essential to research methods protecting mobile devices from any such unauthorized access. In this paper, we explore the potential of re-authenticating mobile device users by exploiting the biometrics derived from their tapping behaviors on the on-screen keyboard. We conduct an offline analysis on a dataset collected from 33 subjects using a Google Nexus S phone. The results show that more than 90% accuracy can be achieved for text input with 20 continuous key-taps.

We present EdgeStore, a cloud-based storage system that integrates edge computing for better performance. Preliminary results on our prototype system demonstrate the efficiency of EdgeStore when working in an edge computing environment. We believe that EdgeStore provides a new perspective on how to exploit the potential of edge computing in cloud-based systems.

The smartphone has become an important part of our daily lives. However, the user experience is still far from being optimal. In particular, despite the rapid hardware upgrades, current smartphones often suffer various unpredictable delays during operation, e.g., when launching an app, leading to poor user experience. In this paper, we investigate the behavior of reads and writes in smartphones. We conduct the first large-scale measurement study on the Android I/O delay using the data collected from our Android application running on 2611 devices within nine months. Among other factors, we observe that reads experience up to 626% slowdown when blocked by concurrent writes for certain workloads. Additionally, we show the asymmetry of the slowdown of one I/O type due to another, and elaborate the speedup of concurrent I/Os over serial ones. We use this obtained knowledge to design and implement a system prototype called SmartIO that reduces the application delay by prioritizing reads over writes, and grouping them based on assigned priorities. SmartIO issues I/Os with optimized concurrency parameters. The system is implemented on the Android platform and evaluated extensively on several groups of popular applications. The results show that our system reduces launch delays by up to 37.8%, and run-time delays by up to 29.6%.

In recent years mobile smart devices such as tablets and smartphones have exploded in popularity. We are now in a world of ubiquitous smart devices that people rely on daily and carry everywhere. This is a fundamental shift for computing in two ways. Firstly, users increasingly place unprecedented amounts of sensitive information on these devices, which paints a precarious picture. Secondly, these devices commonly carry many physical world interfaces. In this paper, we propose information leakage malware, specifically designed for mobile devices, which uses covert channels over physical "real-world" media, such as sound or light. This malware is stealthy; able to circumvent current, and even state-of-the-art defenses to enable attacks including privilege escalation, and information leakage. We go on to present a defense mechanism, which balances security with usability to stop these attacks.

Mobile devices are now ubiquitous in the modern world. In this paper, we propose a novel and practical mobile-cloud platform for smart mobile devices. Our platform allows users to run the entire mobile device operating system and arbitrary applications on a cloud-based virtual machine. It has two design fundamentals. First, applications can freely migrate between the user's mobile device and a backend cloud server. We design a file system extension to enable this feature, so users can freely choose to run their applications either in the cloud (for high security guarantees), or on their local mobile device (for better user experience). Second, in order to protect user data on the smart mobile device, we leverage hardware virtualization technology, which isolates the data from the local mobile device operating system. We have implemented a prototype of our platform using off-the-shelf hardware, and performed an extensive evaluation of it. We show that our platform is efficient, practical, and secure.

Despite the broad utilization of cloud computing, some applications and services still cannot benefit from this popular computing paradigm due to inherent problems of cloud computing such as unacceptable latency, lack of mobility support and location-awareness. As a result, fog computing, has emerged as a promising infrastructure to provide elastic resources at the edge of network. In this paper, we have discussed current definitions of fog computing and similar concepts, and proposed a more comprehensive definition. We also analyzed the goals and challenges in fog computing platform, and presented platform design with several exemplar applications. We finally implemented and evaluated a prototype fog computing platform.