Real-time Optimal Resource Allocation for Embedded UAV Communication Systems

We consider device-to-device (D2D) wireless information and power transfer systems using an unmanned aerial vehicle (UAV) as a relay-assisted node. As the energy capacity and flight time of UAVs is limited, a significant issue in deploying UAV is to manage energy consumption in real-time application, which is proportional to the UAV transmit power. To tackle this important issue, we develop a real-time resource allocation algorithm for maximizing the energy efficiency by jointly optimizing the energy-harvesting time and power control for the considered (D2D) communication embedded with UAV. We demonstrate the effectiveness of the proposed algorithms as running time for solving them can be conducted in milliseconds.Comment: 11 pages, 5 figures, 1 table. This paper is accepted for publication on IEEE Wireless Communications Letter

Initial trust establishment for personal space IoT systems

© 2017 IEEE. Increasingly, trust has played a crucial role in the security of an IoT system from its inception to the end of its lifecycle. A device has to earn some level of trust even before it is authenticated for admission to the system. Furthermore, once the device is admitted to the system, it may behave maliciously over time; hence its behavior must be evaluated constantly in the form of trust to ensure the integrity of the system. Currently, no mechanism exists to establish an initial trust on a device, without prior knowledge, before its admission to an IoT system. Even when trust is applicable, trust evaluation models require direct/indirect observations over time, historical data on past encounters, or third party recommendations. However, this type of past data is not available in the first encounter between the system and the device. The question is how to establish whether a device can be trusted to a level that merits further evaluation for admission into a mobile and dynamic IoT system when it encounters the system for the first time? This paper addresses this challenge by proposing a challenge-response method and a trust assessment model to establish, without prior knowledge, the initial trust that a device places on another in a mobile and dynamic environment called personal space IoT. The initial trust is established before further interaction can take place and under the assumption that only a limited window of time is available for the trust assessment. The paper describes and evaluates the proposed model theoretically and by simulation. It also describes a practical scheme for realizing the proposed solution

Sound-Dr: Reliable Sound Dataset and Baseline Artificial Intelligence System for Respiratory Illnesses

As the burden of respiratory diseases continues to fall on society worldwide, this paper proposes a high-quality and reliable dataset of human sounds for studying respiratory illnesses, including pneumonia and COVID-19. It consists of coughing, mouth breathing, and nose breathing sounds together with metadata on related clinical characteristics. We also develop a proof-of-concept system for establishing baselines and benchmarking against multiple datasets, such as Coswara and COUGHVID. Our comprehensive experiments show that the Sound-Dr dataset has richer features, better performance, and is more robust to dataset shifts in various machine learning tasks. It is promising for a wide range of real-time applications on mobile devices. The proposed dataset and system will serve as practical tools to support healthcare professionals in diagnosing respiratory disorders. The dataset and code are publicly available here: https://github.com/ReML-AI/Sound-Dr/.Comment: 9 pages, PHMAP2023, PH

Challenge-response trust assessment model for personal space IoT

© 2016 IEEE. Internet of Things (IoT) embraces the interconnection of identifiable devices that are capable of providing services through their cooperation. The cooperation among devices in such an IoT environment often requires reliable and trusted participating members in order to provide useful services to the end user. Consequently, an IoT environment or space needs to evaluate the trust levels of all devices in contact before admitting them as members of the space. Existing trust evaluation models are based on resources such as historical observations or recommendations information to evaluate the trust level of a device. However, these methods fail if there is no existing trust resource. This paper introduces a specific IoT environment called personal space IoT and proposes a novel trust evaluation model that performs a challenge-response trust assessment to evaluate the trust level of a device before allowing it to participate in the space. This novel challenge-response trust assessment model does not require the historical observation or previous encounter with the device or any existing trusted recommendation. The proposed challenge-response trust assessment model provides a reliable trust resource that can be used along with other resources such as direct trust, recommendation trust to get a comprehensive trust opinion on a specific device. It can also be considered as a new method for evaluating the trust value on a device

Coconut in the Mekong Delta: An Assessment of Competitivenessand Industry Potential

The numbers surrounding the world coconut industry are substantial – 55,500,000,000 coconuts produced every year from 12,000,000 hectares supporting an industry worth USD 6 billion at wholesale. Yet despite the size and wealth of the industry most coconut growers are among the poorest in their society and over 1 million tonnes of coconut dust are dumped into the environment every year. In the Mekong Delta, riverbanks shaded with coconut trees are an iconic part of the landscape, but only in the last decade has the local coconut industry taken the first steps to becoming a modern, competitive industry. Much of this recent development has happened in Ben Tre province, at the heart of the industry in the Delta with the greatest concentration of coconut trees and businesses. The Ben Tre authorities and industry leaders are now looking to help the industry mature into an internationally competitive and sustainable coconut industry that maximises the value created for the local community, businesses and coconut farmers. This study is part of that process and aims to provide evidence of the current state of the global coconut industry and the local industry in Ben Tre and the wider Mekong Delta and to assess specific opportunities for the industry’s future development. The study also identifies several promising commercial opportunities for local coconut businesses and the impacts these could have on the company’s own bottom-line profits as well as the wider industry. It supplements extensive secondary data with insights and evidence gathered through an international benchmarking exercise with leading competitor countries, including the Philippines, Sri Lanka and Thailand as well as the local industry in Ben Tre

Retrieval of interatomic separations of molecules from laser-induced high-order harmonic spectra

We illustrate an iterative method for retrieving the internuclear separations of N$_2$, O$_2$ and CO$_2$ molecules using the high-order harmonics generated from these molecules by intense infrared laser pulses. We show that accurate results can be retrieved with a small set of harmonics and with one or few alignment angles of the molecules. For linear molecules the internuclear separations can also be retrieved from harmonics generated using isotropically distributed molecules. By extracting the transition dipole moment from the high-order harmonic spectra, we further demonstrated that it is preferable to retrieve the interatomic separation iteratively by fitting the extracted dipole moment. Our results show that time-resolved chemical imaging of molecules using infrared laser pulses with femtosecond temporal resolutions is possible.Comment: 14 pages, 9 figure

Proof-of-Stake Consensus Mechanisms for Future Blockchain Networks: Fundamentals, Applications and Opportunities

© 2013 IEEE. The rapid development of blockchain technology and their numerous emerging applications has received huge attention in recent years. The distributed consensus mechanism is the backbone of a blockchain network. It plays a key role in ensuring the network's security, integrity, and performance. Most current blockchain networks have been deploying the proof-of-work consensus mechanisms, in which the consensus is reached through intensive mining processes. However, this mechanism has several limitations, e.g., energy inefficiency, delay, and vulnerable to security threats. To overcome these problems, a new consensus mechanism has been developed recently, namely proof of stake, which enables to achieve the consensus via proving the stake ownership. This mechanism is expected to become a cutting-edge technology for future blockchain networks. This paper is dedicated to investigating proof-of-stake mechanisms, from fundamental knowledge to advanced proof-of-stake-based protocols along with performance analysis, e.g., energy consumption, delay, and security, as well as their promising applications, particularly in the field of Internet of Vehicles. The formation of stake pools and their effects on the network stake distribution are also analyzed and simulated. The results show that the ratio between the block reward and the total network stake has a significant impact on the decentralization of the network. Technical challenges and potential solutions are also discussed

Cyberattack detection in mobile cloud computing: A deep learning approach

© 2018 IEEE. With the rapid growth of mobile applications and cloud computing, mobile cloud computing has attracted great interest from both academia and industry. However, mobile cloud applications are facing security issues such as data integrity, users' confidentiality, and service availability. A preventive approach to such problems is to detect and isolate cyber threats before they can cause serious impacts to the mobile cloud computing system. In this paper, we propose a novel framework that leverages a deep learning approach to detect cyberattacks in mobile cloud environment. Through experimental results, we show that our proposed framework not only recognizes diverse cyberattacks, but also achieves a high accuracy (up to 97.11%) in detecting the attacks. Furthermore, we present the comparisons with current machine learning-based approaches to demonstrate the effectiveness of our proposed solution