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.
