555 research outputs found
Adaptive Fuzzy Game-Based Energy-Efficient Localization in 3D Underwater Sensor Networks
No full textNumerous applications in 3D underwater sensor networks (UWSNs), such as pollution detection, disaster prevention, animal monitoring, navigation assistance, and submarines tracking, heavily rely on accurate localization techniques. However, due to the limited batteries of sensor nodes and the difficulty for energy harvesting in UWSNs, it is challenging to localize sensor nodes successfully within a short sensor node lifetime in an unspecified underwater environment. Therefore, we propose the Adaptive Energy-Efficient Localization Algorithm (Adaptive EELA) to enable energy-efficient node localization while adapting to the dynamic environment changes. Adaptive EELA takes a fuzzy game-theoretic approach, whereby the Stackelberg game is used to model the interactions among sensor and anchor nodes in UWSNs and employs the adaptive neuro-fuzzy method to set the appropriate utility functions. We prove that a socially optimal Stackelberg-Nash equilibrium is achieved in Adaptive EELA. Through extensive numerical simulations under various environmental scenarios, the evaluation results show that our proposed algorithm accomplishes a significant energy reduction, e.g., 66% lower compared to baselines, while achieving a desired performance level in terms of localization coverage, error, and delay.</p
LbSP : Load-Balanced Secure and Private Autonomous Electric Vehicle Charging Framework With Online Price Optimization
No full textNowadays, autonomous electric vehicles (AEVs) are increasingly popular due to low resource consumption, low pollutant emission, and high efficiency. In practice, Vehicle-to-Grid (V2G) networks supply energy power to EVs to ensure the usage of EVs. However, there are still certain security and privacy concerns in V2G connections, such as identity impersonation and message manipulation. Additionally, the widespread usage of EVs brings significant pressure on the power grid, leading to undesirable effects like voltage deviations if EVs' charging is not well coordinated. In this article, to tackle these issues, we design a novel load-balanced secure and private EV charging framework named load-balanced secure and private framework (LbSP) for secure, private, and efficient EV charging with a minimal negative effect on the existing power grid. It assures reliable and efficient charging services by a lightweighted encryption technique. Also, it balances the energy consumption of power grids via an online pricing strategy that minimizes load variance by optimizing energy prices in real time. Moreover, it preserves users' privacy while not affecting online pricing using an advanced differential privacy technique. Furthermore, LbSP deploys on an edge-cloud structure for fast response and more precise pricing, where clouds balance overall load consumption by online price optimization while edges gather data for clouds and respond to charging requests from EVs. The evaluation results show that the proposed framework ensures secure and private EV charging, balances energy load consumption, and preserves users' privacy
FedRD:Privacy-preserving adaptive Federated learning framework for intelligent hazardous Road Damage detection and warning
Full text linkRoad damages have caused numerous fatalities. Therefore, the study of road damage detection, especially hazardous road damage detection and warning, is critical in improving traffic safety. Existing road damage detection systems mainly process data on clouds, however, they are not able to warn users timely due to the long latency. Recent edge-computing techniques mitigate this problem while users can only receive warnings of hazardous road damages within a small area due to the limited communication range of edges. Besides, untrusted edges might misuse users’ sensitive information. In this paper, we propose FedRD: a novel privacy-preserving edge-cloud and Federated learning-based framework for intelligent hazardous Road Damage detection and warning. In FedRD, a new hazardous road damage detection model is developed leveraging the advantages of hierarchical feature fusion. A novel adaptive federated learning strategy is designed for robust model learning from different edges with limited and unequally-sized datasets. A new individualized differential privacy approach with pixelization is proposed to protect users’ privacy before sharing data. Simulation results demonstrate that FedRD achieves a high detection performance and provides fast responses with accurate warning information covering a wider area while preserving users’ privacy, even when some edges have limited data
EcRD:Edge-Cloud Computing Framework for Smart Road Damage Detection and Warning
No full textRoad damages have caused numerous fatalities, thus the study of road damage detection, especially hazardous road damage detection and warning is critical for traffic safety. Existing road damage detection systems mainly process data at cloud, which suffers from a high latency caused by long-distance. Meanwhile, supervised machine learning algorithms are usually used in these systems requiring large precisely labeled data sets to achieve a good performance. In this article, we propose EcRD: an edge-cloud-based road damage detection and warning framework, that leverages the fast-responding advantage of edge and the large storage and computation resources advantages of cloud. There are three main contributions in this article: we first propose a simple yet efficient road segmentation algorithm to enable fast and accurate road area detection. Then, a light-weighted road damage detector is developed based on gray level co-occurrence matrix features at edge for rapid hazardous road damage detection and warning. Furthermore, a multitypes road damage detection model is introduced for long-term road management at cloud, embedded with a novel image generator based on cycle-consistent adversarial networks which automatically generates images with labels to further improve road damage detection accuracy. By comparing with the state-of-the-art, we demonstrate that the proposed EcRD can accurately detect both hazardous road damages at edge and multitypes road damages at cloud. Besides, it is around 579 times faster than cloud-based approaches without affecting users' experience and requiring very low storage and labeling cost
New discoveries of Mesolithic sites in the Thar Desert (Upper Sindh, Pakistan)
Full text linkThe author discusses the Mesolithic finds from the Thar Desert in Upper Sindh, and compares them to those from the Mulri Hils near Karachi (Lower Sindh, Pakistan
Tsering Thar, Nangshig. A Tibetan Bonpo Monastery and its Family in Amdo
Full text linkFor those actively involved in the study of the Tibetan Bon religion, Tsering Thar is a familiar name in the small roster of scholars who have contributed significantly to our understanding of the contemporary history and institutions (monastic and otherwise) of Bon. The author of this monograph is Professor at the Central University for Nationalities in Beijing. He is a seasoned Tibetologist and has conducted field based research on Bon since the mid 1980s. In the course of these research tr..
BDASE 2018 Workshop Chairs Welcome Message
No full textWelcome to the first edition of the International Workshop on Big Data Analytics for Sustainable Environments—BDASE 2018
Identification of "Candidatus Thioturbo danicus," a Microaerophilic Bacterium That Builds Conspicuous Veils on Sulfidic Sediments
No full textMolecular analysis of bacteria enriched under in situ-like conditions and mechanically isolated by micromanipulation showed that a hitherto-uncultivated microaerophilic bacterium thriving in oxygen-sulfide counter-gradients (R. Thar and M. Kühl, Appl. Environ. Microbiol. 68:6310-6320, 2000) is affiliated with the ε-subdivision of the Proteobacteria. The affiliation was confirmed by the use of whole-cell hybridization with newly designed specific oligonucleotide probes. The bacterium belongs to a new genus and received the provisional name "Candidatus Thioturbo danicus." Copyright © 2005, American Society for Microbiology. All Rights Reserved
SaS-BCI:a new strategy to predict image memorability and use mental imagery as a brain-based biometric authentication
Full text linkSecurity authentication is one of the most important levels of information security. Nowadays, human biometric techniques are the most secure methods for authentication purposes that cover the problems of older types of authentication like passwords and pins. There are many advantages of recent biometrics in terms of security; however, they still have some disadvantages. Progresses in technology made some specific devices, which make it possible to copy and make a fake human biometric because they are all visible and touchable. According to this matter, there is a need for a new biometric to cover the issues of other types. Brainwave is human data, which uses them as a new type of security authentication that has engaged many researchers. There are some research and experiments, which are investigating and testing EEG signals to find the uniqueness of human brainwave. Some researchers achieved high accuracy rates in this area by applying different signal acquisition techniques, feature extraction and classifications using Brain–Computer Interface (BCI). One of the important parts of any BCI processes is the way that brainwaves could be acquired and recorded. A new Signal Acquisition Strategy is presented in this paper for the process of authorization and authentication of brain signals specifically. This is to predict image memorability from the user’s brain to use mental imagery as a visualization pattern for security authentication. Therefore, users can authenticate themselves with visualizing a specific picture in their minds. In conclusion, we can see that brainwaves can be different according to the mental tasks, which it would make it harder using them for authentication process. There are many signal acquisition strategies and signal processing for brain-based authentication that by using the right methods, a higher level of accuracy rate could be achieved which is suitable for using brain signal as another biometric security authentication.</p
Knowledge-Based Decision Support Systems for Personalized u-lifecare Big Data Services
Full text linkThe emergence of information and communications technology (ICT) and rise in living standards necessitate knowledge-based decision support systems that provide services anytime and anywhere with low cost. These services assist individuals for making right decisions regarding lifestyle choices (e.g., dietary choices, stretching after workout, transportation choices), which may have a significant impact on their future health implications that may lead to medical complications and end up with a chronic disease. In other words, the knowledge-based services help individuals to make a personal and conscious decision to perform behaviour that may increase or decrease the risk of injury or disease. The main aim of this chapter is to provide personalized ubiquitous lifecare (u-lifecare) services based on users’ generated big data. We propose a platform to acquire knowledge from diverse data sources and briefly explain the potential underlying technology tools. We also present a case study to show the interaction among the platform components and personalized services to individuals
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