Transactions on Emerging Telecommunications Technologies最新文献

Nature-inspired algorithms revolve around the intersection of nature-inspired algorithms and the IoT within the healthcare domain. This domain addresses the emerging trends and potential synergies between nature-inspired computational approaches and IoT technologies for advancing healthcare services. Our research aims to fill gaps in addressing algorithmic integration challenges, real-world implementation issues, and the efficacy of nature-inspired algorithms in IoT-based healthcare. We provide insights into the practical aspects and limitations of such applications through a systematic literature review. Specifically, we address the need for a comprehensive understanding of the applications of nature-inspired algorithms in IoT-based healthcare, identifying gaps such as the lack of standardized evaluation metrics and studies on integration challenges and security considerations. By bridging these gaps, our paper offers insights and directions for future research in this domain, exploring the diverse landscape of nature-inspired algorithms in healthcare. Our chosen methodology is a Systematic Literature Review (SLR) to investigate related papers rigorously. Categorizing these algorithms into groups such as genetic algorithms, particle swarm optimization, cuckoo algorithms, ant colony optimization, other approaches, and hybrid methods, we employ meticulous classification based on critical criteria. MATLAB emerges as the predominant programming language, constituting 37.9% of cases, showcasing a prevalent choice among researchers. Our evaluation emphasizes adaptability as the paramount parameter, accounting for 18.4% of considerations. By shedding light on attributes, limitations, and potential directions for future research and development, this review aims to contribute to a comprehensive understanding of nature-inspired algorithms in the dynamic landscape of IoT-based healthcare services.

Driven by the rapid development of information technology, online social networks (OSNs) have experienced a fast development in recent years, allowing increasingly more people to share and spread information over OSNs. The rapid rise of OSN platforms such as Facebook and Twitter is sufficient evidence of such development. As one type of information, privacy information can also be created and disseminated over an OSN, posing a severe threat to individual privacy. This article attempts to construct a model for disseminating privacy information in OSNs and to analyze the model by simulating the dissemination process of privacy information in OSNs. First, we establish network models that exhibit the main characteristics of OSNs. Second, by considering the factors related to social relationships, especially intimacy between users and the attention of users to the privacy subject, we derive the parameters for privacy information dissemination models in OSNs. Third, based on the theory of information dissemination dynamics, we construct a model for information dissemination that conforms to the properties of privacy information. We also present some experimental results based on the constructed model and analyze the characteristics of privacy information dissemination. Fourth, we study and verify the various properties of the model through a set of experiments. The proposed model provides the opportunity to better understand the dynamics of privacy information dissemination in OSNs and the effect of user behavior on dissemination.

During signal acquisition, the signals are impacted by multiple noise sources that must be filtered before any analysis. However, many different filter implementations in VLSI are dispersed among many studies. This study aims to give readers a systematic approach to designing a Pipelined All-Pass Transformation based Variable digital filter (PAPT-VDF) to eliminate the high-frequency noise from ECG data. The modified design emphasizes first- and second-order responses to obtain high-speed filter realization with high operating frequencies. The addition of adder and multiplier designs to the hardware architecture of a filter design improves performance. The fundamental blocks of the filter design are the adder and multiplier. The adder and multiplier are employed with an Adaptable stage size-based concatenation, incremented carry-skip adder (ASS-CICSKA), and Improved reconfigurable compressed Vedic multiplier (IRCVM). Utilizing the adder design diminishes the delay with enhanced performance because receiving the carry from an incrementation block is not mandatory. In the multiplier design, the compressor and the reconfigurable approach are adapted with a data detector block to detect the redundant input and lower the logic gates' switching activity with less area overhead. The proposed filter design is implemented in vertex 7 FPGA family device, and the performance measures are analyzed regarding area utilization, delay, power, and frequency. Also, by using the denoised signal, the mean square error (MSE), and signal-to-noise ratio (SNR) are evaluated in the MATLAB platform.

Vehicular ad hoc Network (VANET) is a wireless self-organizing network for obtaining information about road conditions, vehicle speed, vehicle location and traffic congestion. Traditional key negotiation protocols create many problems when dealing with a group of vehicles that need to communicate over a public channel. For example, traditional key negotiation protocols rely too much on the participation of trusted institutions and suffer from a single point of failure. Meanwhile, group session key negotiation is usually inefficient with high computational cost and communication overhead. To solve these problems, this paper proposes a blockchain-based Chinese Remainder Theorem (CRT) VANET group key agreement. In addition, the protocol supports dynamic management of vehicles, including joining and exit. Formal security proofs show that our solution satisfies basic security requirements. Experiments using ProVerif show that the protocol functions properly even under many active and passive attacks, such as eavesdropping attacks. Performance analysis shows that the protocol is more efficient in the face of multi-vehicle communication, in particular by reducing the computational cost by up to 75% and the communication overhead by up to 66%.

Network covert channels use network resources to transmit data covertly, and their existence will seriously threaten network security. Therefore, an effective method is needed to prevent and detect them. Current network covert timing channel detection methods often incorporate machine learning methods in order to achieve generalized detection, but they consume a large amount of computational resources. In this paper, we propose a generalized detection framework for covert channels based on perceptual hashing without relying on machine learning methods. And we propose a one-dimensional data feature descriptor for feature extraction of perceptual hash for the data characteristics of covert timing channels. We first generate the hash sequence of the corresponding channel to get the average hash, which is used for comparison in the test phase. The experimental results show that the feature descriptor can capture the feature differences of one-dimensional data well. When compared to machine learning methods, this perceptual hashing algorithms enable faster traffic detection. Meanwhile, our method is able to detect the effectiveness with the smallest coverage window compared with the latest solutions. Moreover, it exhibits robustness in jitter network environment.

VNFs boost data processing efficiency in Mobile Edge Computing (MEC)-driven Internet of Things (IoT) for healthcare, smart cities, and industrial automation. VNF-based IoT MEC systems encounter a significant security threat due to unauthorized access, posing risks to data privacy and system integrity. Existing approaches struggle to adapt to dynamic environments and lack tamper-proof enforcement mechanisms. In this work, we propose a novel system combining Reinforcement Learning (RL) and blockchain technology to revoke unauthorized access in VNF-based IoT MEC. We introduce the Integrated Action-selection DRL Algorithm for Unauthorized Access Revocation (IASDRL-UAR), a novel RL approach that excels in dynamic environments by handling both continuous and discrete actions, enabling real-time optimization of security risk, execution time, and energy consumption. A behavior control contract (BCC) is proposed and integrated into the RL system, automating behavior checks and enforcement, streamlining security management, and reducing manual intervention. RL feedback plays a pivotal role in steering dynamic security adjustments, gaining valuable perspectives from user behavior via trust scores in the behavior contract. The security features of the proposed method are analyzed. Performance comparisons reveal a substantial improvement, with the proposed system outperforming existing methods by 30% in terms of throughput, 21.7% in system stability, and 26% in access revocation latency. Additionally, the system demonstrates a higher security index, energy efficiency, and scalability.

Recent device-level cyber-attacks have targeted IoT critical applications in power distribution systems integrated with the Internet communications infrastructure. These systems utilize group domain of interpretation (GDOI) as designated by International Electrotechnical Commission (IEC) power utility standards IEC 61850 and IEC 62351. However, GDOI cannot protect against novel threats, such as IoT device-level attacks that can modify device firmware and configuration files to create command and control malicious communication. As a consequence, the attacks can compromise substations with potentially catastrophic consequences. With this in mind, this article proposes a permissioned/private blockchain-based authentication framework that provides a solution to current security threats such as the IoT device-level attacks. Our work improves the GDOI protocol applied in critical IoT applications by achieving decentralized and distributed device authentication. The security of our proposal is demonstrated against known attacks as well as through formal mechanisms via the joint use of the AVISPA and SPAN tools. The proposed approach adds negligible authentication latency, thus ensuring appropriate scalability as the number of nodes increases.

Sensitive data identification is the prerequisite for protecting critical user and business data. Traditional methods usually only target a certain type of application scenario or a certain type of data, thus making it difficult to meet the needs of enterprise-level data protection. This paper proposes an introduction to the end-to-end sensitive data identification system of Beike Inc. The system consists of the data identification & annotation platform, dataset management platform, and sensitive data identification model, which propose different governance methods for batch data and streaming data respectively. Specifically, we propose a sliding window-based identification method for long text to improve the identification of streaming data. Evaluation results show that this method can improve the effect of identifying long text sensitive data without losing the ability on short text, for the open source test dataset, the value can be up to 94.15, so it is applicable in diverse scenarios.

Web service reliability and scalability is an important mission that keeps web services running normally. Within web service, the web services invoked by users not only depend on the service itself, but also on web load condition. Due to the features of web dynamics, traditional reliability and scalability methods have become inappropriate; at the same time, the web condition parameter sparsity problem will cause inaccurate reliability prediction. To address these challenges, Web Service Reliability and Scalability Determination Using ResNet Convolutional Neural Network optimized with Zero Optimization Algorithm (WRS-ResNetCNN-ZOA) is proposed in this manuscript. Initially, the input data is collected from WSRec dataset. The ResNet convolutional neural network (ResNetCNN) with Business Process Execution Language (BPEL) specification is introduced to forecast the reliability and scalability of web service. The results are categorized as right and wrong based on ResNetCNN. The weight parameters of the ResNetCNN is optimized by Zebra Optimization Algorithm to improve accuracy of the prediction. The performance of the proposed method is examined under some performance metrics, like F-measure, reliability, scalability, accuracy, sensitivity, specificity, and precision. The proposed technique attains 15.36%, 35.39%, 23.87%, 20.67% better reliability, 42.39%, 11.39%, 34.16%, 25.78% better accuracy when analyzed to the existing methods, like Web Reliability based on K-clustering, (WRS-KClustering), Web Reliability prediction based on AdaBoostM1 and J48 (WRS-AdaM1-J48), Web Reliability prediction based on Online service Reliability (WRS-OPUN), and Web Reliability prediction based on Dynamic Bayesian Network (WRS-DBNS), respectively.

Virtualized fog–cloud computing (VFCC) has emerged as an optimal platform for processing the increasing number of emerging Internet of Things (IoT) applications. VFCC resources are provisioned to IoT applications in the form of virtual machines (VMs). Effectively utilizing VMs for diverse IoT tasks with varying requirements poses a significant challenge due to their heterogeneity in processing power, communication delay, and energy consumption. In addressing this challenge, in this article, we propose a system model for scheduling IoT tasks in VFCCs, considering not only individual task deadlines but also the system's overall energy consumption. Subsequently, we employ a greedy randomized adaptive search procedure (GRASP) to determine the optimal assignment of IoT tasks among VMs. GRASP, a metaheuristic-based technique, offers appealing characteristics, including simplicity, ease of implementation, a limited number of tuning parameters, and the potential for parallel implementation. Our comprehensive experiments evaluate the effectiveness of the proposed method, comparing its performance with the most advanced algorithms. The results demonstrate that the proposed approach outperforms the existing methods in terms of deadline satisfaction ratio, average response time, energy consumption, and makespan.