Democracy is crucial to a cryptocurrency ecosystem, as the diversity of miners (farms, personal computers, web clients, or even cloud functions) underlays the credibility of the cryptocurrency. Among miners, web clients used to be the vast majority, e.g., 50M+ as of March 2018. As time went on, however, cryptomining was gradually monopolized by mining farms with dedicated hardware (e.g., ASICs), and web clients scaled down to ∼0.1M. To suppress mining farms, certain cryptocurrencies (like Monero) adopted new mining algorithms such as RandomX whose execution relies on general-purpose hardware architectures. Unfortunately, this further impairs web-based cryptomining as web clients cannot provide the desired architecture support to these algorithms. This paper explores how to revive software democracy of efficient web-based crypto-mining, using a novel program transformation technique termed Vectra. Vectra employs just-in-time (JIT) transformations of mining programs for web architectures; it effectively identifies and merges isomorphic instructions upon execution. Vectra ensures correct transformations based on symbolic constraints of the instructions. Real-world deployments show that Vectra reduces WASM instructions by about 7× and achieves a 3× –16× speedup for web cryptomining in diverse execution environments like PCs, mobile phones, and serverless platforms, which translates to a high (69%–274%) return-on-investment (ROI) for common users.

Emerging communication-based control and virtually coupled train formations are critical foundations for automatic train operations to improve capacity, safety and flexibility toward intelligent rail transportation systems. High-reliability cooperative control of heavy haul train (HHT) formations encounters greater challenges due to the distinctive dynamic characteristics and uncertainties. This paper proposes a communication-based distributed global composite learning cooperative control protocol for virtually coupled heavy haul train formations that enables all HHTs to achieve autonomous dynamic steady coordination with a minimum safe interval and collision avoidance. A pneumatic braking algorithm is first presented for each HHT’s vehicles in the formation to satisfy the distinctive electro-pneumatic braking mode of HHTs and attain improved longitudinal impulses. Then, the complicated dynamic characteristics and uncertainties of HHTs are considered based on a constructed control-oriented dynamic model for HHTs and a neural network (NN) approximation. Additionally, corresponding control protocols are proposed, where the interpretability of the NN approximation and the globally uniform ultimate boundedness property hold. Finally, the control feasibility and abilities of the proposed control protocol are analyzed and demonstrated to be effective via simulation experiments.

With rapid development of vehicular sensing and mobile communication technologies, cooperative sensing becomes an emerging paradigm of future intelligent transportation systems (ITSs). This paper investigates a LiDAR based cooperative sensing scenario in Vehicular Edge Computing (VEC). Specifically, we present the system architecture, in which vehicles with on-board LiDAR are able to detect objects via local processing of the sensed point-cloud data, and the outputs can be further shared via vehicle-to-vehicle (V2V) /vehicle-to-infrastructure (V2I) communications and fused in edge nodes. Then, we formulate the Edge Assisted Task Offloading (EATO) problem by considering the heterogeneous computation and communication capacities of vehicles and edge nodes, aiming at minimizing the average delay of the cooperative sensing tasks. Further, we propose a Multi-Armed Bandit (MAB)-based algorithm to make task offloading decisions adaptively. Finally, we implement the system prototype and give a comprehensive performance evaluation, which demonstrates the effectiveness of the proposed algorithm.

The variation of wireless signal in dynamic indoor parking environments may seriously compromise the performance of fingerprint-based localization methods. In this regard, this paper investigates the problem of robust WiFi fingerprint-based vehicle tracking in dynamic indoor parking environments, aiming at designing an online learning framework to continuously train the localization model and counteract the effect of signal variation. Specifically, a Hidden Markov Model (HMM) based Online Evaluation (HOE) method is firstly proposed to assess the accuracy of localization results by measuring the inconsistency of locations inferred by WiFi fingerprinting and Dead Reckoning (DR). Further, an Online Transfer Learning (OTL) algorithm is designed to improve the robustness of the fingerprinting localization, which consists of a weight allocation scheme to combine two classification models (i.e., the batch model and the online model) and an instance-based transferring scheme to resample the offline fingerprints and retrain the batch model. Finally, we implement the system prototype and give comprehensive performance evaluation, which demonstrates that the proposed solutions can outperform the state-of-the-art localization algorithms around 28%$\sim$58% on vehicle tracking accuracy in dynamic indoor parking environments.

Traditional traffic event monitoring and detection solutions mainly rely on roadside surveillance cameras. However, existing solutions cannot be applied for traffic event augmentation due to both restricted monitoring angles and limited camera coverage. Therefore, this paper investigates a novel architecture for traffic event augmentation via vehicular edge computing. In particular, multiple vehicles can collaborate with roadside infrastructures for detecting, re-identification and augmenting certain traffic event via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. To enable such an application, we formulate the problem of multi-view augmentation task offloading (MATO) by considering the heterogeneous capabilities of vehicles and edge servers, which aims at minimizing average request delay. On this basis, we design the offloading scheduling framework and propose an adaptive real-time offloading algorithm (ARTO), which makes online offloading decision of object detection and re-identification, by balancing real-time workload among heterogeneous devices. Finally, we implement the hardware-in-the-loop testbed for performance evaluation. The comprehensive results demonstrate the superiority of the proposed algorithm in various realistic traffic scenarios.

Recent advances in wireless communication, sensing, and computing technologies have paved the way for the development of a new era of Internet of Vehicles (IoV). Nevertheless, it is challenging to process data and computation intensive tasks with strict time constraints due to heterogeneous communication, storage, and computation capacities of IoV network nodes, spotty wireless connections in vehicles and infrastructures, unevenly distributed workload, and high vehicles mobility. In this paper, we propose a two-layer vehicular fog computing (VFC) architecture to explore the synergistic effect of the cloud, the fog nodes, and the terminals on processing data and computation intensive IoV tasks. Then, we formulate the real-time task offloading model, aiming at maximizing the task service ratio. Further, considering the dynamic requirements and resource constraints, we propose a real-time task offloading algorithm to adaptively categorize all tasks into four types, and then cooperatively offload them. Finally, we build the simulation model and give a comprehensive performance evaluation, which validates the performance of the proposed method.

Although indoor localization has been studied over a decade, it is still challenging to enable many IoT applications, such as activity tracking and monitoring in smart home and customer navigation and trajectory mining in smart shopping mall, which typically require meter-level localization accuracy in a highly dynamic and large-scale indoor environment. Therefore, this article aims at designing and implementing an adaptive and scalable indoor tracking system in a cost-effective way. First, we propose a zero site-survey overhead (ZSSO) algorithm to enhance the system scalability. It integrates the step information and map constraints to infer user's positions based on the particle filter and supports the auto labeling of scanned Wi-Fi signal for constructing the fingerprint database without the extra site-survey overhead. Further, we propose an iterative-weight-update (IWU) strategy for ZSSO to enhance system robustness and make it more adaptive to the dynamic changing of environments. Specifically, a two-step clustering mechanism is proposed to delete outliers in the fingerprint database and alleviate the mismatch between the auto-tagged coordinates and the corresponding signal features. Then, an iterative fingerprint update mechanism is designed to continuously evaluate the Wi-Fi fingerprint localization results during online tracking, which will further refine the fingerprint database. Finally, we implement the indoor tracking system in real-world environments and conduct a comprehensive performance evaluation. The field testing results conclusively demonstrate the scalability and effectiveness of the proposed algorithms.

With rapid development of Internet of Things (IoT) and pervasive computing, indoor localization and tracking has attracted considerable attentions. This work aims at designing an effective and scalable indoor tracking system based on smart phones embedded with Wi-Fi interfaces and inertial sensors. Specifically, we first propose a zero site-survey overhead algorithm (ZSSO), which includes a step detection mechanism, a map constraint construction method and a customized particle filter. The step detection mechanism is used to count user steps based on raw data extracted from inertial sensors. The map constraint construction method is adopted to generate obstacle constraints of the indoor environment based on a two-step conversion method designed for indoor map. Finally, a customized particle filter is proposed to track user's positions continuously. Further, we propose an enhanced version of ZSSO (i.e., E-ZSSO) to improve tracking performance by incorporating with Wi-Fi fingerprint based localization technique. First, an automatic Wi-Fi fingerprint collection mechanism is developed for building the fingerprint database without extra site-survey overhead. Then, the Wi-Fi fingerprint based localization results are further adopted to speed up the convergence of the particle filter as well as to better calibrate the localization results. We have implemented the indoor tracking system in real-world environments and conducted comprehensive performance evaluation. The field testing results conclusively demonstrate the scalability and effectiveness of our proposed algorithms.

This work aims to design and implement a scalable and easy-deployed indoor localization system based on particle filter and Wi-Fi fingerprint techniques. Specifically, our system leverages particle filter to estimate user's location and automatically scans Wi-Fi fingerprints. Then, we utilize the collected fingerprints to speed up the convergence of particles. Finally, the system iteratively refines the collected fingerprints by evaluating their performance duration the on-line localization phase, which is able to further enhance the positioning accuracy. We implement the system on Android platform and give a comprehensive performance evaluation by setting up the system in our lab area and comparing the algorithm with conventional fingerprint-based solutions. Experimental results demonstrate the scalability and effectiveness of the proposed solution.