Compressed data aggregation (CDA) over wireless sensor networks (WSNs) is task-specific and subject to environmental changes. However, the existing compressed data aggregation (CDA) frameworks (e.g., compressed sensing-based data aggregation, deep learning(DL)-based data aggregation) do not possess the flexibility and adaptivity required to handle distinct sensing tasks and environmental changes. Additionally, they do not consider the performance of follow-up IoT data-driven deep learning (DL)-based applications. To address these shortcomings, we propose OrcoDCS, an IoT-Edge orchestrated online deep compressed sensing framework that offers high flexibility and adaptability to distinct IoT device groups and their sensing tasks, as well as high performance for follow-up applications. The novelty of our work is the design and deployment of IoT-Edge orchestrated online training framework over WSNs by leveraging an specially-designed asymmetric autoencoder, which can largely reduce the encoding overhead and improve the reconstruction performance and robustness. We show analytically and empirically that OrcoDCS outperforms the state-of-the-art DCDA on training time, significantly improves flexibility and adaptability when distinct reconstruction tasks are given, and achieves higher performance for follow-up applications.
New vehicular applications demand more computing power and real-time processing. As modern vehicles are equipped with computationally powerful but often redundant and under-utilized onboard units for autonomous driving, a network of connected vehicles can form a vehicular cloud (VC) that can provide computing services among themselves or to other devices. In this paper, we evaluate the computing performance of a VC on a highway in congested traffic. We assume that the vehicles join and leave VC at random times. Thus, the number of vehicles in the VC will be time-varying. The residency times of the vehicles in the VC will be correlated because of traffic congestion. To enable the realization of VC in the future, we need to know its computing performance that considers its dynamic nature and concurrent execution of the tasks. In this work, we determine the completion time of a job with multiple tasks with random execution times. More specifically, we derive the probability density function of the job completion time as a function of the system parameters. We provide numerical results to demonstrate the utilization of the analysis and simulation results to confirm the correctness of the analysis.
Byzantine fault detection (BFD) techniques are promising approaches for better scalability and practicality, even though they cannot mask Byzantine faults like Byzantine fault tolerance (BFT) techniques. However, the existing BFD protocol for database systems has suffered from long latency since it synchronously makes an agreement on the order of transactions between replicas when executing the transactions. In this paper, we explore an alternative BFD approach for database systems, which defers the expensive agreement and detects Byzantine faults lazily rather than detecting them in real-time. We discuss the challenges and design overview of our approach. We also present preliminary experimental results showing the benefit of our approach.
This article explores the feasibility of developing cloud storage dApps on the Internet Computer (IC) blockchain, which can hold full-stack applications entirely on-chain and run at web speed. We analyze cloud storage services’ security, stability, cost, and performance issues and propose a multi-canister system design scheme for building cloud storage dApps on the IC. Our quantitative tests demonstrate that the blockchain-secured data I/O operations on the IC can result in ten times time savings and up to a million times cost savings compared to those on Ethereum; deploying small-scale data storage applications on the IC can even be more resource-efficient than on traditional servers. We also discuss the trade-offs and decision-making considerations in our conclusion.
The rapid advancements in science and technology have catalyzed a transformation in social structures, ushering in an era of unprecedented connectivity and proximity. Against this background, the concept of metaverse has received widespread attention and has become a highly anticipated industry across fields. This article critically examines the evolutionary trajectory of key technologies in the context of a society characterized by minimal physical barriers and explores novel approaches for addressing societal challenges through the integration of the metaverse. However, alongside its potential benefits, the metaverse also engenders noteworthy risks such as platform monopolies and compromises in user privacy. The meteoric rise of the metaverse is rooted in tangible realities, yet as its development progresses, it is imperative to remain cognizant of the social risks that may arise as a consequence.
Vehicular metaverses are an emerging paradigm that integrates extended reality technologies and real-time sensing data to bridge the physical space and digital spaces for intelligent transportation, providing immersive experiences for Vehicular Metaverse Users (VMUs). VMUs access the vehicular metaverse by continuously updating Vehicular Twins (VTs) deployed on nearby RoadSide Units (RSUs). Due to the limited RSU coverage, VTs need to be continuously online migrated between RSUs to ensure seamless immersion and interactions for VMUs with the nature of mobility. However, the VT migration process requires sufficient bandwidth resources from RSUs to enable online and fast migration, leading to a resource trading problem between RSUs and VMUs. To this end, we propose a learning-based incentive mechanism for migration task freshness-aware VT migration in vehicular metaverses. To quantify the freshness of the VT migration task, we first propose a new metric named Age of Twin Migration (AoTM), which measures the time elapsed of completing the VT migration task. Then, we propose an AoTM-based Stackelberg model, where RSUs act as the leader and VMUs act as followers. Due to incomplete information between RSUs and VMUs caused by privacy and security concerns, we utilize deep reinforcement learning to learn the equilibrium of the Stackelberg game. Numerical results demonstrate the effectiveness of our proposed learning-based incentive mechanism for vehicular metaverses.
Stock shocks are abrupt changes in a stock price time series. Major shocks, such as the 1929 Big Crash and, more recently, the subprime mortgage crisis, have greatly influenced the world economy in the last centuries. Nevertheless, shocks of minor intensity are frequent events that often go unnoticed but whose prediction can significantly help an investor to protect their investments. In this paper, we provide a formal definition of stock shocks and use limit order-book data to implement an algorithm to forecast stock shocks. The proposed algorithm is built upon a formal mathematical model approximating stock prices and return distributions with fat-tailed Lévy-stable models. A preliminary study of different machine-learning approaches allowed us to design an algorithm based on Random Forest, hierarchical clustering, and Bayesian optimization that outperforms other machine-learning methods that exhibit tunable and higher Precision and Recall values.
Metaverse advocates creating digital twins in the virtual world to faithfully emulate the behaviors of the physical counterparts. Following this idea, this paper explores how to create high-performance digital twins for network devices through network emulation. Existing network emulators are primarily used for network configuration verification. They can not emulate high-performance networks, and lose the ability to carry out performance-oriented tasks, like stress-testing datacenter networks. We present CNNet, a novel network emulator that is designed to emulate high-performance networks and execute performance-oriented tasks. CNNet adopts a cloud-native design and achieves the performance goal with the user-space dataplane, which provides high-performance virtual network links for different emulation containers. To further improve the emulation performance, a novel graph partition algorithm is proposed to embed the emulated network into the underlying server cluster. The embedding achieves two goals simultaneously, including balancing the number of emulation containers running on each worker server, and minimizing the bandwidth consumption of the cross-server virtual links. When emulating a datacenter network with over 100 nodes, CNNet can achieve the bi-section throughput of 96Gbps, which is an order of magnitude higher than existing network emulators.
Autonomous parking (AP) is an emerging technique to navigate an intelligent vehicle to a parking space without any human intervention. Existing AP methods based on mathematical optimization or machine learning may lead to potential failures due to either excessive execution time or lack of generalization. To fill this gap, this paper proposes an integrated constrained optimization and imitation learning (iCOIL) approach to achieve efficient and reliable AP. The iCOIL method has two candidate working modes, i.e., CO and IL, and adopts a hybrid scenario analysis (HSA) model to determine the better mode under various scenarios. We implement and verify iCOIL on the Macao Car Racing Metaverse (MoCAM) platform. Results show that iCOIL properly adapts to different scenarios during the entire AP procedure, and achieves significantly larger success rates than other benchmarks.
In this paper, we propose a novel simulation framework, O’Platoon, for the decentralized planning of truck platoons in road transport using Reinforcement Learning (RL). Truck platooning, a technique that enables densely-arranged trucks to travel together in a convoy, has the potential to improve fuel efficiency, reduce traffic congestion, and enhance road safety. Our study aims to address the challenges associated with the coordination and planning of these platoons in a decentralized manner, where individual trucks make independent decisions based on local information. The proposed conceptual framework integrates RL algorithms to optimize platoon formation. We present a streamlined simulator to study the effectiveness of RL approaches in the planning of platoons to tackle complex transportation problems.
A well-known use of the blockchain technology is Decentralized Finance (DeFi). DeFi makes financial information accessible to the public but raises potential privacy and security issues. In this study, we implemented a DeFi protocol that protects privacy, which is based on the Mystiko.Network protocol. As a proxy between the user and DeFi platforms, the Mystiko.Network protocol offers an auditable confidentiality mechanism for blockchain transactions. Via the new system, users may submit anonymous DeFi transactions and get income back into a shielded tokens pool. Moreover, we implemented a rollup approach to handle anonymous DeFi transactions in groups. The evaluation results suggest that the protocol is both practical and affordable, in fact it is able to save around 90% of the cost for DeFi transactions.
Bitcoin was released in 2008 as an electronic peer-to-peer payment system. The aim was to enable financial transactions in an anonymous manner between participating parties. However, Bitcoin is considered a pseudonymous network rather than anonymous, as the identity of the address owner is unknown, but every transaction is permanently stored on the Bitcoin blockchain and can be tracked by anyone. The structure of the blockchain, its transactions, and the Bitcoin network make it possible to deanonymize pseudonyms through various methods such as flow analysis, heuristics, and network traffic observations. Once a connection is established between pseudonyms used in the Bitcoin network and the real world, all previous transactions can be attributed to that identity. In 2021, Taproot was introduced to further increase privacy within the Bitcoin Network by introducing a new address format which will allow transactions to be more indistinguishable from one another. In this paper, we analyze current methods for deanonymizing Bitcoin transactions to understand which parts of the Bitcoin protocol they exploit. In addition, we look at the changes introduced by Taproot and determine the extent to which these changes affect the methods and what assumptions must be made for these methods to remain applicable.
Automated market makers (AMMs) are a new type of trading venue where the rules for liquidity provision and liquidity taking are considerably different from those of the traditional electronic trading venues. AMMs have become one of the key markets to trade crypto-currencies, whose liquidity is highly fragmented and prices exhibit high levels of cointegration. In this paper, we derive the optimal strategy for a liquidity taker (LT) who trades orders of large size and executes statistical arbitrages in a basket of crypto-currencies whose constituents co-move. The LT uses market signals and exchange rate information from relevant AMMs and traditional venues to enhance the performance of her strategy. We use stochastic control tools to derive a closed-form strategy that can be computed and implemented by the LT in real time. Finally, we use market data from two pools of Uniswap v3 and from the LOB-based exchange Binance to study co-movements between crypto-currencies and lead-lag effects between trading venues, and to showcase the performance of the strategy.
This paper aims to motivate the development of the metaverse by highlighting the potential of artificial-intelligence-generated content (AIGC) for the metaverse. We present the first literature review on AIGC in the metaverse with state-of-the-art research classified into 5 key application areas (avatars and Non-player Characters (NPCs), content creation, virtual world generation, automatic digital twin, and personalization). Having noticed a notable gap in research through our review, we propose ways in which state-of-the-art generative AI can be applied to the metaverse. Additionally, we offer a roadmap for future research with related ethical implications.
Cryptocurrencies and their underlying blockchains suffer from the scalability issue: they do not support efficient transaction processing. Payment channel networks were designed and implemented to alleviate the scalability issue. However, payment channel network is far from solving the scalability issue due to its routing fee, inefficient rebalancing, and limited accessibility. We propose the Practical Payment Channel Network. The Practical Payment Channel Network aims to minimize the routing fee, improve the rebalancing efficiency, and make payment channel network accessible to the general public.
The energy consumption of popular cryptocurrencies varies greatly: cryptocurrencies based on proof-of-work (e.g. Bitcoin) consume much more electricity than their counterparts that use alternative consensus mechanisms, such as proof-of-stake (e.g. Ethereum). Nevertheless, proof-of-work cryptocurrencies dominate the market. We investigate whether energy labelling, i.e., displaying electricity consumption information on centralized exchanges, influences consumers’ product preferences. We conduct a control/treatment study: during this study, participants with an interest in cryptocurrencies (N = 200) are presented with a fictitious cryptocurrency exchange user interface. The treatment group is shown a user interface that displays energy labels, while the control group receives no information related to electricity consumption. Participants then declare how likely they are to acquire particular cryptocurrencies. We measure the treatment effect and find a significant negative correlation (p = 0.002) between being exposed to energy labels and expressing a strong preference for energy-inefficient cryptocurrencies. Based on this finding, we reflect on the sustainability issues of cryptocurrencies and discuss how energy labelling on centralized exchanges can be applied to nudge investors away from energy-inefficient cryptocurrencies. This indicates that regulators would be well advised to consider energy labelling to address the adverse climate impacts of cryptoassets.
With the increasing time drivers spent behind the wheel, availability of accurate, reliable, and comprehensive information on real-time traffic conditions is becoming increasingly important for individual road users, industry, and traffic authorities. Due to a growing amount of traffic combined with insufficient transportation infrastructure, traffic congestion is one of the major challenges modern society has to face in urban areas. While this results in serious environmental issues such as pollution, congestions also have significant effects on drivers’ stress levels, which further is an essential factor in driver behavior and road safety. In this paper, we analyze real-time traffic situations on a fine-granular level by computing the local congestion factor, which represents traffic conditions on currently traversed route segments. We show that travel time predictions for short- and mid-term routes provided by four major public traffic information providers can be used for computing congestion factors in real-time, which can then be used as input for driver-centric applications.
Location-based services are at the heart of many applications that individuals use every day. However, there is often no guarantee of the truthfulness of users’ location data, since this information can be easily spoofed without a proof mechanism. In distributed system applications, preventing users from submitting counterfeit locations becomes even more challenging because of the lack of a central authority that monitors data provenance. In this work, we propose a decentralized architecture based on blockchains and decentralized technologies, offering a transparent solution for Proof of Location (PoL). We specifically address two main challenges, i.e., the issuing process of the PoL and the proof verification. We describe a smart contract based implementation in Reach, a blockchain-agnostic smart contract language, and the tests we conducted on different blockchains, i.e. Ethereum, Polygon, and Algorand, measuring latency and costs due to the payment of fees. Results confirm the viability of the proposal.
The Metaverse connects our physical reality with virtual worlds. Social VR platforms facilitate the creation of such virtual worlds, enabling activities such as interactive teaching, conferences, and community gatherings. These activities can be performed in mixed mode, with some participants physically present in the same location. In this paper, we evaluate the feasibility of such mixed-mode events by studying three leading social VR platforms. We uncover the network traffic patterns generated by these platforms, which affect the user experience when multiple users share the same network. We explore the traffic patterns to show that model loading creates a significant overhead and impacts user quality of experience. When the number of simultaneously connected users increases, some operations lead to network congestion that degrades or even interrupts service for most users. From these observations, we derive concrete action points to develop social VR platforms that enable mixed-mode events over the same network.
In this paper, we examine whether crypto tokens can boost portfolio performance and provide direct evidence on the claim that crypto tokens are potentially desirable alternatives for diversification. We use correlation-based networks to study the crypto token market and compare the optimal portfolio composed of tokens with that of tokens and stocks. We find that tokens with high Sharp ratios but low centrality can serve as the booster of portfolio performance. In addition, we discover that the token market resembles the stock market in terms of correlation network structure. The market is dominated by tokens from Defi, blockchain infrastructure, and GameFi sectors, and becomes more correlated during market downturns.
