ORCID Profile
0000-0003-1199-6678
Current Organisation
Roskilde University
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Publisher: MDPI AG
Date: 06-2021
DOI: 10.3390/S21113838
Abstract: COVID-19 has disrupted normal life and has enforced a substantial change in the policies, priorities and activities of in iduals, organisations and governments. These changes are proving to be a catalyst for technology and innovation. In this paper, we discuss the pandemic’s potential impact on the adoption of the Internet of Things (IoT) in various broad sectors, namely healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT. Our perspective and forecast of this impact on IoT adoption is based on a thorough research literature review, a careful examination of reports from leading consulting firms and interactions with several industry experts. For each of these sectors, we also provide the details of notable IoT initiatives taken in the wake of COVID-19. We also highlight the challenges that need to be addressed and important research directions that will facilitate accelerated IoT adoption.
Publisher: Association for Computing Machinery (ACM)
Date: 03-02-2022
DOI: 10.1145/3498338
Abstract: With the continued deployment of the Internet of Things (IoT) , increasing volumes of devices are being deployed that emit massive spatially referenced data. Due in part to the dynamic, decentralized, and heterogeneous architecture of the IoT, the varying and often low quality of spatial IoT data (SID) presents challenges to applications built on top of this data. This survey aims to provide unique insight to practitioners who intend to develop IoT-enabled applications and to researchers who wish to conduct research that relates to data quality in the IoT setting. The survey offers an inventory analysis of major data quality dimensions in SID and covers significant data characteristics and associated quality considerations. The survey summarizes data quality related technologies from both task and technique perspectives. Organizing the technologies from the task perspective, it covers recent progress in SID quality management, encompassing location refinement, uncertainty elimination, outlier removal, fault correction, data integration, and data reduction and it covers low-quality SID exploitation, encompassing querying, analysis, and decision-making techniques. Finally, the survey covers emerging trends and open issues concerning the quality of SID.
Publisher: Association for Computing Machinery (ACM)
Date: 08-2020
Abstract: Modern navigation systems do not only provide shortest paths but also some alternative paths to provide more options to the users. This paper is the first to study the problem of continuously reporting alternative paths for a user traveling along a given path. Specifically, given a path P on which a user is traveling, we continuously report to the user k paths from the user's current location on P to the target t. We present several algorithms each improving on the previous based on non-trivial observations and novel optimisations. The proposed algorithms maintain and exploit the previously computed useful information to efficiently update the k alternative paths as the user moves. We provide space and time complexity analysis for each proposed algorithm. We conduct a comprehensive experimental study on large real-world road networks. The results demonstrate that the proposed algorithms are up to several orders of magnitude faster than the straightforward algorithms.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2022
Publisher: IEEE
Date: 04-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2021
Publisher: Association for Computing Machinery (ACM)
Date: 13-06-2023
DOI: 10.1145/3589270
Abstract: Correlated time series (CTS) forecasting plays an essential role in many practical applications, such as traffic management and server load control. Many deep learning models have been proposed to improve the accuracy of CTS forecasting. However, while models have become increasingly complex and computationally intensive, they struggle to improve accuracy. Pursuing a different direction, this study aims instead to enable much more efficient, lightweight models that preserve accuracy while being able to be deployed on resource-constrained devices. To achieve this goal, we characterize popular CTS forecasting models and yield two observations that indicate directions for lightweight CTS forecasting. On this basis, we propose the LightCTS framework that adopts plain stacking of temporal and spatial operators instead of alternate stacking that is much more computationally expensive. Moreover, LightCTS features light temporal and spatial operator modules, called L-TCN and GL-Former, that offer improved computational efficiency without compromising their feature extraction capabilities. LightCTS also encompasses a last-shot compression scheme to reduce redundant temporal features and speed up subsequent computations. Experiments with single-step and multi-step forecasting benchmark datasets show that LightCTS is capable of nearly state-of-the-art accuracy at much reduced computational and storage overheads.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Publisher: IEEE
Date: 04-2012
DOI: 10.1109/ICDE.2012.44
No related grants have been discovered for Hua Lu.