ICT Express最新文献

Due to the complex functioning of Smart Healthcare Systems (SHS), many security concerns have been raised in the past. It provisions the attackers to hamper the working of SHS in a variety of ways, e.g., injection of false data to replace vital signs, tampering of medical devices to prevent informing critical situations, etc. In this work, a novel ML-based framework, i.e., SmartHealth is proposed to secure IoMT devices in SHS. SmartHealth watches the vital signs gathered through different IoMT to analyze the change in various body activities to differentiate between normal activities and dangerous security attacks. The performance of the SmartHealth is also analyzed for three different dangerous attacks. During performance analysis, it has been observed that SmartHealth can identify wicked activities in IoMT 92% times accurately with an F1-score of 90%.

Maintaining a service continuum is crucial for providing a reliable service in an Internet-of-Things (IoT) system based on Network Function Virtualization (NFV) due to the distribution of Virtual Network Functions (VNF). We address the optimization problem of VNF migration in an NFV-enabled IoT system (NIoT), considering the dynamics of traffic volume processed by an individual IoT function. We develop an optimization model based on integer linear programming and a function traffic-aware heuristic algorithm to solve the problem. The evaluation results show that our proposed heuristic algorithm offers an approximation solution very close to the optimal VNF migration in significantly reduced time. It outperforms a benchmark solution based on Simulated Annealing in both the service restoration cost and computation time. Furthermore, our findings provide valuable insights into an investment strategy for an IoT service provider seeking to enhance the system performance in the presence of a failure.

In this paper, we propose and analyze an unmanned aerial vehicle (UAV)-assisted non-orthogonal multiple access (NOMA) short packet communication (SPC) system, where finite-size data packets are transmitted from a UAV to multiple users. The block error rate (BLER), throughput, and energy efficiency (EE) are used as metrics to evaluate the proposed system performance. We adopt the Gaussian-Chebyshev quadrature and the first-order Riemann integral to derive the approximate expressions of BLERs. The accuracy of the derived expressions is verified by presenting that numerical results are perfectly matched with analysis results. We also formulate optimization problems to maximize the throughput with respect to the number of transmitted bits and the altitude of the UAV. Numerical results in this paper provide useful knowledge about the relationships among BLER, throughput, EE, transmitted packet size, the altitude of the UAV, and the Rician factor.

In this paper, we will consider the problem of multi-unmanned aerial vehicles (UAVs) deployment among users’ hotspots. UAVs carry reconfigurable intelligent surface (RIS) boards to strengthen millimeter wave (mmWave) coverage at these hotspots. UAVs should maximize hotspots’ sum data rates while minimizing their flights’ cost, i.e., hovering and flying energy consumptions. Moreover, collisions should be avoided among UAVs, i.e., one hotspot should be served by only one UAV at a time. In this paper, this problem is considered as a multi-player multi-armed bandit (MP-MAB) game with budget constraint and collision avoidance. In this context, budget constraint Thompson sampling (TS) with collision avoidance MAB algorithm (BTSCA-MAB) is proposed to efficiently implement the formulated MP-MAB game. Numerical analysis confirms the superior performance of the proposed BTSCA-MAB over other benchmarks.

In 1997, the US National Science Foundation recognized the need to unite research communities together to solve mutual problems, overcome challenges, future development and practical applications of cryptography and error correction coding. Recent works in this area are connected with turbo codes, which have a special ability to provide error correction characteristics of the information transmission close to theoretically obtained values. Besides, they are used for television (DVB-RCS, DVB-RCT), wireless local networks (WiMAX), software-defined radio systems, mobile (3G, 4G, Inmarsat) and space (CCSDS) communications etc. In this article is presented the crypto coding system based on turbo codes with secret keys used by interleavers. All calculations are performed during data encoding, which reduces the computational complexity of data pre-encryption. The necessary mathematical apparatus was presented, the security level of the communication system was estimated, a simulation of the error correction characteristics was performed, and multimedia information was processed. The working characteristics of the classical and proposed turbo codecs do not differ statistically. The results can be used in communication systems for various functional purposes.

This work investigates the problem of efficient packet scheduling and transmission for IEEE 802.15.4e TSCH based on the Traffic-Aware Scheduling Algorithm (TASA). Three Integer Linear Programming (ILP) formulations are proposed, namely ILP1, ILP2, and ILP3, to comprehensively address key aspects of how packets are optimally delivered from child nodes to the root node, including node parent selection, packet scheduling, and frequency assignment. ILP1 is proposed to model the fundamental operations of TASA including packet scheduling and frequency assignment, while ILP2 further takes node parent selection into consideration for jointly optimizing node parent selection, packet scheduling, and frequency assignment. Despite its superior performance, ILP2 is only applicable to relatively small network problems due to high computational complexity. Therefore, we finally propose ILP3 for fast and efficient node parent selection. ILP3 can be combined with ILP1 and, most importantly, TASA for achieving effective node parent selection atop packet scheduling and frequency assignment for practical TSCH networks. ILP3 combined with ILP 1 led to computational complexity while ILP3 combined with TASA yielded good performance where it required less than one second of execution time and less packet delivery time.

This paper presents bidirectional encoder representations from transformers (BERT)-based deep learning model for the classification of scientific articles. This model aims to increase the efficiency and reliability of human health risk assessments related to electromagnetic fields (EMF). The proposed model takes the title and abstract of EMF-related articles and classifies them into four categories: animal exposure experiment, cell exposure experiment, human exposure experiment, and epidemiological study. We conducted a performance evaluation to verify the superiority of the proposed model. The results demonstrated that the proposed model outperforms other deep learning models that use pre-trained embeddings, with an average accuracy of 98.33%.

The decentralization paradigm has made blockchain one of the most disruptive technologies today. When evaluating the level of decentralization, the key metric for most public blockchain networks is the degree of decentralization of the resources responsible for determining who generates the blocks. In turn, it facilitates a greater understanding of both security and scalability on a blockchain. This work provides an overview of the current state-of-the-art on wealth decentralization, which has not yet received the attention it deserves. We collect data, calculate various wealth decentralization metrics, and compare our results with research on the same methodology. As the amount of data for various blockchains increases rapidly, it is helpful to have techniques to aggregate data for statistical analysis. We introduce and provide conservative estimates of decentralized group metrics based on the reduced data and compare them with full-data measurements. Our research considers both the Layer 1 blockchains of Bitcoin and Ethereum, along with Layer 2 blockchains such as Arbitrum, Optimism, and Polygon.

Numerous consensus mechanisms have been suggested to cater to the specific characteristics of fog computing. To comprehensively understand their unique features, performance, and applications in fog computing, it is crucial to conduct a systematic analysis of these mechanisms. For this study, 79 relevant articles were carefully selected based on predefined criteria. Among these articles, 35 employed work-proof-based consensus mechanisms, 24 utilized voting-based mechanisms, and 22 adopted capability-based mechanisms. Among the 26 identified consensus mechanisms, proof of work remains the most prevalent one. It is important to note that the scope of this paper is limited to the research available in the predominant databases at the time of writing. Future research may expand to include additional databases and more recent literature in this domain.

Nowadays, research works into the dynamic and static human activities on Smart spaces abounds. Artificial Intelligence (AI) and low cost non-privacy invasive ambient sensors have made this ubiquitous. This review presents a state-of-the-art analysis, performance evaluation, and future research direction. One of the aims of activity recognition (especially that of humans) systems using thermal sensors and AI is the safety of persons in Smart spaces. In a Smart home, human activity detection systems are put in place to ensure the safety of persons in such an environment. This system should have the ability to monitor issues like fall detection, a common home-related accident. In this work, a review of trends in thermal sensor deployment, an appraisal of the popular datasets, AI algorithms, testbeds, and critical challenges of the recent works was provided to direct the research focus. In addition, a summary of AI models and their performance under various sensor resolutions was presented.