Sketch is widely used in many traffic estimation tasks due to its good balance among accuracy, speed, and memory usage. In scenarios with priority flows, priority-aware sketch, as an emerging method, provides differentiated detection accuracy for flows of different priorities, optimizing resource allocation and improving the detection accuracy of high-priority flows. However, existing priority-aware sketches methods struggle to effectively handle the dynamic changes in flow priority distribution in realworld detection environments, leading to wasted or insufficient storage space. To address this issue, this paper proposes a new priority-aware sketch with Dynamic Memory Allocation called DMA-Sketch. It dynamically adjusts the detection framework based on flow priority distribution information and adaptively allocates appropriate memory space to each storage region. The experimental results show that DMA-Sketch improves the overall priority accuracy, high-priority accuracy and throughput by up to 1.33×, 16.39× and 1.88×, respectively, under the scenarios with changing flow priority distribution over the state-of-the-art schemes.

The rapid advancement of Industry 5.0 has accelerated the adoption of the Industrial Internet of Things (IIoT). However, challenges such as data privacy breaches, malicious attacks, and the absence of trustworthy mechanisms continue to hinder its secure and efficient operation. To overcome these issues, this paper proposes an enhanced blockchain-based data storage framework and systematically improves the Delegated Proof of Stake (DPoS) consensus mechanism. A four-party evolutionary game model is developed, involving agent nodes, voting nodes, malicious nodes, and supervisory nodes, to comprehensively analyze the dynamic effects of key factors—including bribery intensity, malicious costs, supervision, and reputation mechanisms—on system stability. Furthermore, novel incentive and punishment strategies are introduced to foster node collaboration and suppress malicious behaviors. The simulation results show that the improved DPoS mechanism achieves significant enhancements across multiple performance dimensions. Under high-load conditions, the system increases transaction throughput by approximately 5%, reduces consensus latency, and maintains stable operation even as the network scale expands. In adversarial scenarios, the double-spending attack success rate decreases to about 2.6%, indicating strengthened security resilience. In addition, the convergence of strategy evolution is notably accelerated, enabling the system to reach cooperative and stable states more efficiently. These results demonstrate that the proposed mechanism effectively improves the efficiency, security, and dynamic stability of IIoT data storage systems, providing strong support for reliable operation in complex industrial environments.

The explosive growth of Internet of Things (IoT) data demands secure and reliable storage, where traditional centralized solutions often fall short. Blockchain offers decentralization and tamper-resistance, making it a promising foundation for IoT. However, IoT blockchain systems based on Delegated Proof-of-Stake (DPoS) face challenges such as weak node incentives, unfair reward distribution, and low consensus efficiency. This paper proposes a fairness-aware incentive mechanism that accounts for both node capability and effort under information asymmetry. By incorporating fairness preferences into the contract design, the mechanism improves participation and motivates sustained effort. Theoretical analysis and simulation results show that our approach enhances throughput by about 15%, while achieving revenue fairness, incentive compatibility, and stronger consensus performance. The mechanism’s adaptability makes it suitable for diverse IoT application scenarios.

Internet of Things (IoT) systems that manage data across cloud, fog, and edge environments—and the devices that consume those services—face substantial challenges in confidentiality, privacy, and authentication. However, traditional Public Key Infrastructure (PKI) is too rigid and costly for massive, ephemeral IoT deployments. Moreover, device authentication is often overlooked in favor of service authentication, neglecting the security of the entire ecosystem. DNSSEC combined with DANE introduces a new paradigm in which service authentication can be managed globally, extending trust to locally generated, type-agnostic credentials. This framework can accommodate PKI certificates, self-signed credentials, and local keys, all of which can be verified by any client, local or remote. However, DNSSEC’s signature proofs grow linearly with the number of secured records, inflating communication overhead and energy consumption—an issue aggravated by the larger sizes of post-quantum signatures. Additionally, current DNSSEC delegation mechanisms lack the flexibility needed for secure load balancing and isolation. In this article, we present a collision-based DNSSEC signature-delegation mechanism designed to overcome these scalability limitations. By allowing a central DNS authority to delegate signing responsibilities to local DNS servers, our approach reduces certificate-management overhead and enables a dynamic, hierarchical trust model. It supports both service and device authentication in a unified DNS-name-based security context. Our evaluation shows that the proposed mechanism maintains a stable computational cost irrespective of credential count, a critical benefit for large-scale, resource-constrained IoT deployments. By leveraging existing DNS infrastructure and standards, this solution enhances scalability and efficiency compared to traditional PKI and DNSSEC, while promoting interoperability and ease of deployment. It also opens the adoption of future post quantum trapdoor systems still under research and development.

With the rapid development of the Internet of Things (IoT) and blockchain technology, e-voting has been widely used in all aspects of people’s lives. However, there is a common problem in the vast majority of e-voting solutions: the inability to complete vote counting without a trusted third-party organization, which may lead to security risks. When designing an e-voting system, ensuring the trustworthiness of the voting results as well as protecting the privacy of the voters are always the most important issues. To address this challenge, we propose improved secure and efficient (ISE)-Voting, an ISE e-voting scheme for blockchain-assisted IoT devices. Our proposed ISE-Voting achieves voter privacy anonymity, distributed vote counting, and public verifiability of counting results in e-voting systems by using secret-sharing and identity-based ring signatures in the blockchain system. In addition, we introduce a cloud service provider (CSP), which is used to share the computational pressure of the system and assist ISE-Voting to complete the final counting. According to the experimental analysis and results, our scheme is not only able to meet the basic security goals of satisfying correctness, anonymity, unforgeability and verifiability, and provide 128-bit identity security for the voters in the post-quantum environment. Moreover, it can complete the distributed counting of voters’ ballots within an effective time, which provides a feasible solution for future e-voting systems.

Sleep is a crucial period of rest necessary for optimal cognitive function, psychological well-being, and execution of everyday tasks. In the field of sleep healthcare, the primary objective is to identify and classify the various sleep states. Implementing sleep state detection in a system is problematic and essential for accurate diagnosis. Our study used an integrated framework to recognize sleep states. The dataset contained approximately eight lakh data points sorted into two groups: onset and wake-up. We successfully deployed a cutting-edge Convolutional Recurrent Transformer (CRT) model for sleep state detection. The training accuracy of our detection model was measured at 97.83%, a constant validation accuracy of 97.07%, and a testing measurement accuracy of 97.23%, were maintained. These scores indicate the model's proficiency in precisely recognizing the sleep states. Our system's detection capabilities demonstrate the ability to identify different sleep states, enhance the accuracy of diagnoses and increase healthcare outcomes in this specialized field.

The Internet of Things (IoT) has the potential to improve the complementary of communication, control, and information processing within the public transportation system. The IoT-enabled Intelligent Transportation System (ITS) ensures that automated transportation is networked and operated collaboratively. The IoT-enabled ITS has revolutionized the transportation industry by enabling the seamless integration of a wide range of devices and systems. It makes the strategic use of networked devices, sensors, and data analytics to improve transportation network efficiency, safety, and environmental friendliness. The usage of the IoT in the ITS has grown in popularity due to its capacity to improve traffic control, reduce congestion, facilitate live monitoring, and optimize transportation operations. The IoT-enabled ITS systems and devices must be protected from cyber-attacks for various reasons, including preserving sensitive data, guaranteeing privacy, preventing unauthorized access, and protecting against the risk of interruptions or manipulations. Malware attacks affect the working and performance of the deployed smart IoT devices. We propose a secure deep learning-enabled malware attack detection for IoT-enabled ITS (in short, SDLMA-IITS). The approach of explainable artificial intelligence (XAI) has been utilized for the effective detection of malware. A deep security analysis of the proposed SDLMA-IITS is presented to prove its security against various potential attacks. The comparative performance analysis of SDLMA-IITS is given with the other similar existing schemes. Finally, a practical implementation of SDLMA-IITS is provided to measure its impact on the security of the IoT-enabled ITS systems and devices.

Mobile Edge Computing (MEC) effectively alleviates the pressure on limited in-vehicle computing resources and energy supply caused by computation-intensive vehicular applications. However, the uneven spatial distribution of users leads to load imbalance among adjacent MEC servers, significantly increase the latency and energy consumption costs for vehicles. Therefore, achieving optimal configuration of available computing resources in MEC servers to accomplish the goal of low-latency and low-energy task offloading has become a critical issue to address. To tackle this problem, this study proposes a Multi-RSU Load Balancing (MRLB) strategy based on multi-hop network technology. This strategy dynamically allocates computing tasks to neighboring RSU server clusters with available computing resources through task segmentation and computation offloading mechanisms. Meanwhile, adaptive resource allocation strategies are implemented based on task quantity and task scale characteristics. Specifically, this study designs a multi-RSU collaborative offloading algorithm based on Deep Deterministic Policy Gradient (DDPG) to solve the optimal offloading decision. Additionally, by integrating the Lagrange multiplier method and Sequential Quadratic Programming (SQP) algorithm, the joint optimization of imbalanced task segmentation decisions and optimal CPU frequency allocation decisions for RSU servers is achieved. Experimental results demonstrate that the proposed method can achieve efficient multi-RSU resource allocation and ensure coordinated optimization of both system latency and energy consumption costs across diverse device conditions and varying network scenarios, particularly in load-imbalanced situations.

In the vehicular networks (VN) assisted by the integration of sensing and communication (ISAC), rapid processing of data from sensors is a necessary condition to ensure safe driving and enhance user experience. Utilizing the computational resources of the roadside unit (RSU) can effectively reduce the task processing delay. However, in some areas of the road, uneven distribution of task-vehicles can lead to severe load imbalance in neighbouring RSUs, and these tasks often have different delay requirements. The tasks in the high-load area can be offloaded to the low-load area to balance the load. We use the idle-vehicles in the low-load RSU area that are close to the task-vehicles as relays to hop and offload the tasks to the low-load RSUs. On the other hand, in order to satisfy the delay requirements of the heterogeneous tasks, this paper proposes the priority ordering of the heterogeneous tasks, the more delay-sensitive tasks require more resources to meet their delay requirements, i.e., the higher the priority. In order to both satisfy the delay requirements of heterogeneous tasks and maintain a small average system delay, we establish the optimization problem of minimizing the weighted average system delay and solve it by using the Relay Hopping and Differentiated Task Prioritization (RHATP) algorithm. Simulation results show that under the condition of guaranteeing the delay requirement of high-priority tasks, the strategy can achieve lower system delay and effectively reduce the processing delay in high-load areas. And it still maintains stable performance in different scenarios.

The current era witnesses the notable transition of society from an information-centric to a human-centric one aiming at striking a balance between economic advancements and upholding the societal and fundamental needs of humanity. It is undeniable that the Internet of Things (IoT) and artificial intelligence (AI) are the key players in realizing a human-centric society. However, for society and individuals to benefit from advanced technology, it is important to gain the trust of human users by guaranteeing the inclusion of ethical aspects such as safety, privacy, nondiscrimination, and legality of the system. Incorporating explainable AI (XAI) into the system to establish explainability and transparency supports the development of trust among stakeholders, including the developers of the system. This article presents the general class of vulnerabilities that affect IoT systems and directs the readers’ attention toward intrusion detection systems (IDSs). The existing state-of-the-art IDS system is discussed. An attack model modeling the possible attacks is presented. Furthermore, since our focus is on providing explanations for the IDS predictions, we first present a consolidated study of the commonly used explanation methods along with their advantages and disadvantages. We then present a high-level human-inclusive XAI framework for the IoT that presents the participating components and roles. We also hint upon a few approaches to upholding safety and privacy using XAI that we will be taking up in our future work. An attack model based on the study of possible attacks on the system is also presented in the article. The article also presents guidelines to choose a suitable XAI method and a taxonomy of explanation evaluation mechanisms, which is an important yet less visited aspect of explainable AI.

Human activity recognition is progressing from automatically determining what a person is doing and when, to additionally analyzing the quality of these activities—typically referred to as skill assessment. In this chapter, we propose a new framework for skill assessment that generalizes across application domains and can be deployed for near-real-time applications. It is based on the notion of repeatability of activities defining skill. The analysis is based on two subsequent classification steps that analyze (1) movements or activities and (2) their qualities, that is, the actual skills of a human performing them. The first classifier is trained in either a supervised or unsupervised manner and provides confidence scores, which are then used for assessing skills. We evaluate the proposed method in two scenarios: gymnastics and surgical skill training of medical students. We demonstrate both the overall effectiveness and efficiency of the generalized assessment method, especially compared to previous work.

Wearable on body sensors have been employed in many applications including ambulatory monitoring and pervasive computing systems. In this work, a wearable assistant has been created for people suffering from Parkinson’s disease (PD), specifically with the freezing of gait (FoG) symptom. Wearable accelerometers were placed on the person’s body and used for movement measure. When FoG is detected, a rhythmic audio signal was given from the wearable assistant to motivate the wearer to continue walking. Long-term monitoring results in collecting huge amounts of complex raw data; therefore, data analysis becomes impractical or infeasible resulting in the need for data reduction. In the present study, discrete wavelet transform (DWT) has been used to extract the main features inherent in the key movement indicators for FoG detection. The discrimination capacities of these features were assessed using (i) support vector machine using a linear kernel function and (ii) artificial neural network with a two-layer feed-forward with hidden layer of 20 neurons that trained with conjugate gradient back-propagation. Using these two different machine learning techniques, we were capable of detecting FoG with an accuracy of 87.50% and 93.8%, respectively. Additionally, the comparison between the extracted features from DWT coefficients with those using fast Fourier transform established accuracies of 93.8% and 81.3%, respectively. Finally, the discriminative features extracted from DWT yield to a robust multidimensional classification model compared to models in the literature based on a single feature. The work presented paves the way for reliable, real-time wearable sensors to aid people with PD.

Social media conveys a reachable platform for users to share information. The inescapable practice of social media has produced remarkable volumes of social data. Social media gathers the data in both structured-unstructured and formal-informal ways as users are not concerned with the exact grammatical structure and spelling when interacting with each other by means of various social networking websites (Twitter, Facebook, YouTube, LinkedIn, etc.). People are increasingly involved in and dependent on social media networks for data, news and opinions of other handlers on a variety of topics. The strong dependence on social media network sites contributes to enormous data generation characterized by three issues: scale, noise, and variety. Such problems also hinder social network data to be evaluated manually, resulting in the correct use of statistical analytical methods. Mining social media data can extract significant patterns that can be advantageous for consumers, users, and business. Pattern mining offers a wide variety of methods to detect valuable knowledge from huge datasets, such as patterns, trends, and rules. In this work, data was collected comprised of users’ opinions and sentiments and then processed using a significant number of pattern mining methods. The results were then further analyzed to attain meaningful information. The aim of this paper is to deliver a summary and a set of strategies for utilizing the ubiquitous pattern mining approaches, and to recognize the challenges and future research guidelines of dealing out social media data.

Glaucoma is a major public health problem that can lead to an optic nerve lesion, requiring systematic screening in the population over 45 years of age. The diagnosis and classification of this disease have had a marked and excellent development in recent years, particularly in the machine learning domain. Multimodal data have been shown to be a significant aid to the machine learning domain, especially by its contribution to improving data driven decision-making.Solving classification problems by combinations of classifiers has made it possible to increase the robustness as well as the classification reliability by using the complementarity that may exist between the classifiers. Complementarity is considered a key property of multimodality. A Convolutional Neural Network (CNN) works very well in pattern recognition and has been shown to exhibit superior performance, especially for image classification which can learn by themselves useful features from raw data. This article proposes a multimodal classification approach based on deep Convolutional Neural Network and Support Vector Machine (SVM) classifiers using multimodal data and multimodal feature for glaucoma diagnosis from retinal fundus images from RIM-ONE dataset. We make use of handcrafted feature descriptors such as the Gray Level Co-Occurrence Matrix, Central Moments and Hu Moments to co-operate with features automatically generated by the CNN in order to properly detect the optic nerve and consequently obtain a better classification rate, allowing a more reliable diagnosis of glaucoma.The experimental results confirm that the combination of classifiers using a new hybrid fusion approach and the BWWV technique is better than learning classifiers separately. The proposed method provides a computerized diagnosis system for glaucoma disease with impressive results comparing them to the main related studies that allow us to continue in this research path.

The general packet radio service (GPRS) has been developed for the mobile radio environment to allow the migration from the traditional circuit switched connection to a more efficient packet based communication link particularly for data transfer. GPRS requires the addition of not only the GPRS software protocol stack, but also more baseband functionality for the mobile as new coding schemes have been defined, uplink status flag detection, multislot operation and dynamic coding scheme detect. This paper concentrates on evaluating the performance of the GPRS coding scheme detection methods in the presence of a multipath fading channel with a single co-channel interferer as a function of various soft-bit data widths. It has been found that compressing the soft-bit data widths from the output of the equalizer to save memory can influence the likelihood decision of the coding scheme detect function and hence contribute to the overall performance loss of the system. Coding scheme detection errors can therefore force the channel decoder to either select the incorrect decoding scheme or have no clear decision which coding scheme to use resulting in the decoded ratio block failing the block check sequence and contribute to the block error rate. For correct performance simulation, the performance of the full coding scheme detection must be taken into account.

Sirens' used by police, fire and paramedic vehicles have been designed so that they can be heard over large distances, but unfortunately the siren noise enters the vehicle and corrupts intelligibility of voice communications from the emergency vehicle to the control room. Often the siren needs to be turned off to enable the control room to hear what is being said. This paper discusses a siren noise filter system that is capable of removing the siren noise picked up by the two-way radio microphone inside the vehicle. The removal of the siren noise improves the response time for emergency vehicles and thus save lives. To date, the system has been trialed within a fire tender in a non-emergency situation, with good results.

By using a deterministic approach, an exact form for the synchronous detected video signal under a ghosted condition is presented. Information regarding the phase quadrature-induced ghost component derived from the quadrature forming nature of the vestigial sideband (VSB) filter is obtained by crosscorrelating the detected video with the ghost cancel reference (GCR) signal. As a result, the minimum number of taps required to correctly remove all the ghost components is subsequently presented. The results are applied to both National Television System Committee (NTSC) and phase alternate line (PAL) television.