Node ID Research Articles

Spatio-Temporal Transformer with Kolmogorov–Arnold Network for Skeleton-Based Hand Gesture Recognition

Manually crafted features often suffer from being subjective, having an inadequate accuracy, or lacking in robustness in recognition. Meanwhile, existing deep learning methods often overlook the structural and dynamic characteristics of the human hand, failing to fully explore the contextual information of joints in both the spatial and temporal domains. To effectively capture dependencies between the hand joints that are not adjacent but may have potential connections, it is essential to learn long-term relationships. This study proposes a skeleton-based hand gesture recognition framework, the ST-KT, a spatio-temporal graph convolution network, and a transformer with the Kolmogorov–Arnold Network (KAN) model. It incorporates spatio-temporal graph convolution network (ST-GCN) modules and a spatio-temporal transformer module with KAN (KAN–Transformer). ST-GCN modules, which include a spatial graph convolution network (SGCN) and a temporal convolution network (TCN), extract primary features from skeleton sequences by leveraging the strength of graph convolutional networks in the spatio-temporal domain. A spatio-temporal position embedding method integrates node features, enriching representations by including node identities and temporal information. The transformer layer includes a spatial KAN–Transformer (S-KT) and a temporal KAN–Transformer (T-KT), which further extract joint features by learning edge weights and node embeddings, providing richer feature representations and the capability for nonlinear modeling. We evaluated the performance of our method on two challenging skeleton-based dynamic gesture datasets: our method achieved an accuracy of 97.5% on the SHREC’17 track dataset and 94.3% on the DHG-14/28 dataset. These results demonstrate that our proposed method, ST-KT, effectively captures dynamic skeleton changes and complex joint relationships.

Noiseless Privacy-Preserving Decentralized Learning

Decentralized learning (DL) enables collaborative learning without a server and without training data leaving the users' devices. However, the models shared in DL can still be used to infer training data. Conventional defenses such as differential privacy and secure aggregation fall short in effectively safeguarding user privacy in DL, either sacrificing model utility or efficiency. We introduce Shatter, a novel DL approach in which nodes create virtual nodes (VNs) to disseminate chunks of their full model on their behalf. This enhances privacy by (i) preventing attackers from collecting full models from other nodes, and (ii) hiding the identity of the original node that produced a given model chunk. We theoretically prove the convergence of Shatter and provide a formal analysis demonstrating how Shatter reduces the efficacy of attacks compared to when exchanging full models between nodes. We evaluate the convergence and attack resilience of Shatter with existing DL algorithms, with heterogeneous datasets, and against three standard privacy attacks. Our evaluation shows that Shatter not only renders these privacy attacks infeasible when each node operates 16 VNs but also exhibits a positive impact on model utility compared to standard DL. In summary, Shatter enhances the privacy of DL while maintaining the utility and efficiency of the model.

The Application of National Security Algorithm in Wireless Networks for Industrial Automation Process Automation (WIA-PA) Network Data Security Transmission

Industrial wireless communication technology is the key to promoting the development of factory intelligence and automation. However, wireless network data security has hindered the development of industrial intelligence. Therefore, to solve the security issues of industrial wireless networks, a network data secure transmission technology based on the national security algorithm is proposed based on industrial wireless network standard protocols. Firstly, to address the vulnerability of network node identity authentication to attacks, the national security algorithm is used to construct a node authentication model. Secondly, a data security transmission model is constructed based on the improved national security algorithm, which can securely transmit industrial wireless networks by encrypting network application layer data. In network node identity authentication, when the step size was 50, the identity authentication average accuracy of the research mode reached over 98.95%, which was better than other models. When the step size was 50, the average accuracy of identity authentication in the research model was over 98.95%, showing the best performance among similar models. In the attack test of illegal client C, when the interaction history data was within 500, the node identity authentication was higher than 99.56%. However, when the interaction history data exceeded 500, the overall performance of the research model was still the best. In the comparison of node code storage and content usage overhead, when the code quantity was 15000, the storage overhead of the research model was 87356 bytes, with more node code storage and lower memory usage. The overall performance of the research model is better. From this, the research method performs the best in both identity recognition accuracy and security in network data security. The research content will provide technical support for the security management and data transmission of industrial wireless technology.

A Performance Survey and Simulated Residual Energy of Sensor Nodes in a Typical Wireless Sensor Network

This paper is a performance survey on the residual energy of nodes in a wireless sensor network (WSN). Wireless sensor nodes are limited in application due to constraints in energy of nodes’ battery and addressing the problem of energy management is essential to lengthen the life of a deployed network of wireless sensors. In the method used, Sensor nodes were equally distributed on the complete border of the network field. An exclusive cluster, six hierarchical, and eighteen hierarchical clusters were formed based on even partition of the border of the network field. The choice of Cluster Head (CH) within each cluster formed was based on selection of a sensor node with minimal transmission distance to the Base Station. A comparison of the various hierarchical scenarios was simulated in MATLAB. The mean residue energies (MREs) after the network lifetime are 3%, 52%, and 35% of the initial node energy for single, six and eighteen hierarchical clusters respectively. The residual energies of sensor nodes above the recorded MREs were taken on a class interval of 50’s of the node identity (nid) and respective averages of residual energies of sensor nodes (AEr) of each selected class interval were evaluated. Based on the result, certain categories of sensor nodes at a section of the network field indicated that the battery energy of the affected nodes was not maximally utilized thereby leading to enormous residual energy. It was concluded that the residual energy of nodes in a WSN could be optimized to extend the lifetime.

Federated Node Classification over Distributed Ego-Networks with Secure Contrastive Embedding Sharing.

Federated learning on graphs (a.k.a., federated graph learning- FGL) has recently received increasing attention due to its capacity to enable collaborative learning over distributed graph datasets without compromising local clients' data privacy. In previous works, clients of FGL typically represent institutes or organizations that possess sets of entire graphs (e.g., molecule graphs in biochemical research) or parts of a larger graph (e.g., sub-user networks of e-commerce platforms). However, another natural paradigm exists where clients act as remote devices retaining the graph structures of local neighborhoods centered around the device owners (i.e., ego-networks), which can be modeled for specific graph applications such as user profiling on social ego-networks and infection prediction on contact ego-networks. FGL in such novel yet realistic ego-network settings faces the unique challenge of incomplete neighborhood information for non-ego local nodes since they likely appear and have different sets of neighbors in multiple ego-networks. To address this challenge, we propose an FGL method for distributed ego-networks in which clients obtain complete neighborhood information of local nodes through sharing node embeddings with other clients. A contrastive learning mechanism is proposed to bridge the gap between local and global node embeddings and stabilize the local training of graph neural network models, while a secure embedding sharing protocol is employed to protect individual node identity and embedding privacy against the server and other clients. Comprehensive experiments on various distributed ego-network datasets successfully demonstrate the effectiveness of our proposed embedding sharing method on top of different federated model sharing frameworks, and we also provide discussions on the potential efficiency and privacy drawbacks of the method as well as their future mitigation.

A Secure Key Exchange and Authentication Scheme for Securing Communications in the Internet of Things Environment

In today’s advanced network and digital age, the Internet of Things network is experiencing a significant growing trend and, due to its wide range of services and network coverage, has been able to take a special place in today’s technology era. Among the applications that can be mentioned for this network are the field of electronic health, smart residential complexes, and a wide level of connections that have connected the inner-city infrastructure in a complex way to make it smart. The notable and critical issue that exists in this network is the extent of the elements that make up the network and, due to this, the strong and massive data exchanges at the network level. With the increasing deployment of the Internet of Things, a wide range of challenges arise, especially in the discussion of establishing network security. Regarding security concerns, ensuring the confidentiality of the data being exchanged in the network, maintaining the privacy of the network nodes, protecting the identity of the network nodes, and finally implementing the security policies required to deal with a wide range of network cyber threats are of great importance. A fundamental element in the security of IoT networks is the authentication process, wherein nodes are required to validate each other’s identities to ensure the establishment of secure communication channels. Through the enforcement of security prerequisites, in this study, we suggested a security protocol focused on reinforcing security characteristics and safeguarding IoT nodes. By utilizing the security features provided by Elliptic Curve Cryptography (ECC) and employing the Elliptic Curve Diffie–Hellman (ECDH) key-exchange mechanism, we designed a protocol for authenticating nodes and establishing encryption keys for every communication session within the Internet of Things. To substantiate the effectiveness and resilience of our proposed protocol in withstanding attacks and network vulnerabilities, we conducted evaluations utilizing both formal and informal means. Furthermore, our results demonstrate that the protocol is characterized by low computational and communication demands, which makes it especially well-suited for IoT nodes operating under resource constraints.

ASAP: A lightweight authenticated secure association protocol for IEEE 802.15.6 based medical BAN

Medical Body Area Networks (MBANs), a specialized subset of Wireless Body Area Networks (WBANs), are crucial for enabling medical data collection, processing, and transmission. The IEEE 802.15.6 standard governs these networks but falls short in practical MBAN scenarios. This paper introduces ASAP, a Lightweight Authenticated Secure Association Protocol integrated with IEEE 802.15.6. ASAP prioritizes patient privacy with randomized node ID generation and temporary shared keys, preventing node tracking and privacy violations. It optimizes network performance by consolidating Master Keys (MK), Pairwise Temporal Keys (PTK), and Group Temporal Keys (GTK) creation into a unified process, ensuring the efficiency of the standard four-message association protocol. ASAP enhances security by eliminating the need for pre-shared keys, reducing the attack surface, and improving forward secrecy. The protocol achieves mutual authentication without pre-shared keys or passwords and supports advanced cryptographic algorithms on nodes with limited processing capabilities. Additionally, it imposes connection initiation restrictions, requiring valid certificates for nodes, thereby addressing gaps in IEEE 802.15.6. Formal verification using Verifpal confirms ASAP's resilience against various attacks. Implementation results show ASAP's superiority over standard IEEE 802.15.6 protocols, establishing it as a robust solution for securing MBAN communications in medical environments.

A Privacy-Preserving Three-Factor Authentication System for IoT-Enabled Wireless Sensor Networks

Recently, Sahoo et al. introduced a three-factor authentication scheme for Wireless Sensor Networks (WSNs) based on an elliptic curve cryptosystem. Nonetheless, upon closer examination, we have identified critical vulnerabilities in their scheme, including susceptibility to user impersonation, gateway impersonation, sensor node impersonation attacks, and a breach in the three-factor security aspect. Further, the scheme fails to withstand offline sensor node identity guessing attacks, man-in-the-middle attacks, and known session-specific temporary information attacks. Intending to elevate both security and efficiency, we propose a novel three-factor authentication scheme that capitalizes on the strengths of a fuzzy extractor and a cryptographic one-way hash function. The proposed scheme’s security has been rigorously assessed using the SCYTHER tool, confirming its validity under the real-or-random (ROR) model. Moreover, a heuristic analysis exemplifies that the scheme effectively withstands various known cryptographic attacks. Consequently, the performance comparisons establish the superiority of our scheme over related approaches in terms of security and efficiency. Additionally, its suitability for WSNs is evident due to the minimal overhead on the sensor nodes, making it a highly promising solution for real-world implementation.

Brainstem Dbh+ neurons control allergen-induced airway hyperreactivity

Exaggerated airway constriction triggered by repeated exposure to allergen, also called hyperreactivity, is a hallmark of asthma. Whereas vagal sensory neurons are known to function in allergen-induced hyperreactivity1–3, the identity of downstream nodes remains poorly understood. Here we mapped a full allergen circuit from the lung to the brainstem and back to the lung. Repeated exposure of mice to inhaled allergen activated the nuclei of solitary tract (nTS) neurons in a mast cell-, interleukin-4 (IL-4)- and vagal nerve-dependent manner. Single-nucleus RNA sequencing, followed by RNAscope assay at baseline and allergen challenges, showed that a Dbh+ nTS population is preferentially activated. Ablation or chemogenetic inactivation of Dbh+ nTS neurons blunted hyperreactivity whereas chemogenetic activation promoted it. Viral tracing indicated that Dbh+ nTS neurons project to the nucleus ambiguus (NA) and that NA neurons are necessary and sufficient to relay allergen signals to postganglionic neurons that directly drive airway constriction. Delivery of noradrenaline antagonists to the NA blunted hyperreactivity, suggesting noradrenaline as the transmitter between Dbh+ nTS and NA. Together, these findings provide molecular, anatomical and functional definitions of key nodes of a canonical allergen response circuit. This knowledge informs how neural modulation could be used to control allergen-induced airway hyperreactivity.

Bayesian decision model based reliable route formation in internet of things

Security provisioning has become an important issue in wireless multimedia networks because of their crucial task of supporting several services. This paper presents Bayesian decision model based reliable route formation in internet of things (BDMI). The main objective of the BDMI approach is to distinguish unreliable sensor nodes and transmit the data efficiently. Active and passive attack recognition methods identify unreliable node sensor nodes. Remaining energy, node degree, and packet transmission rate parameters to observe their node possibilities for recognizing the passive unreliable nodes. In active recognition, the base station (BS) confirms every sensor node identity, remaining energy, supportive node rate, node location, and link efficiency parameters to detect active unreliable sensor nodes. The Bayesian decision model (BDM) efficiently isolates an unreliable sensor node in the multimedia network. Simulation outcomes illustrate that the BDMI approach can efficiently enhance unreliable node detection and minimize the packet loss ratio in the network.

Rich-club organization of whole-brain spatio-temporal multilayer functional connectivity networks

ObjectiveIn this work, we propose a novel method for constructing whole-brain spatio-temporal multilayer functional connectivity networks (FCNs) and four innovative rich-club metrics.MethodsSpatio-temporal multilayer FCNs achieve a high-order representation of the spatio-temporal dynamic characteristics of brain networks by combining the sliding time window method with graph theory and hypergraph theory. The four proposed rich-club scales are based on the dynamic changes in rich-club node identity, providing a parameterized description of the topological dynamic characteristics of brain networks from both temporal and spatial perspectives. The proposed method was validated in three independent differential analysis experiments: male–female gender difference analysis, analysis of abnormality in patients with autism spectrum disorders (ASD), and individual difference analysis.ResultsThe proposed method yielded results consistent with previous relevant studies and revealed some innovative findings. For instance, the dynamic topological characteristics of specific white matter regions effectively reflected individual differences. The increased abnormality in internal functional connectivity within the basal ganglia may be a contributing factor to the occurrence of repetitive or restrictive behaviors in ASD patients.ConclusionThe proposed methodology provides an efficacious approach for constructing whole-brain spatio-temporal multilayer FCNs and conducting analysis of their dynamic topological structures. The dynamic topological characteristics of spatio-temporal multilayer FCNs may offer new insights into physiological variations and pathological abnormalities in neuroscience.

Development of node architecture and emergence of molecular organizer characteristics in the pig embryo.

The avian node is the equivalent of the amphibian Spemann's organizer, as indicated by its ability to induce a secondary axis, cellular contribution, and gene expression, whereas the node of the mouse, which displays limited inductive capacities, was suggested to be a part of spatially distributed signaling. Furthermore, the structural identity of the mouse node is subject of controversy, while little is known about equivalent structures in other mammals. We analyzed the node and emerging organizer in the pig using morphology and the expression of selected organizer genes prior to and during gastrulation. The node was defined according to the "four-quarter model" based on comparative consideration. The node of the pig displays a multilayered, dense structure that includes columnar epithelium, bottle-like cells in the dorsal part, and mesenchymal cells ventrally. Expression of goosecoid (gsc), chordin, and brachyury, together with morphology, reveal the consecutive emergence of three distinct domains: the gastrulation precursor domain, the presumptive node, and the mature node. Additionally, gsc displays a ventral expression domain prior to epiblast epithelialization. Our study defines the morphological and molecular context of the emerging organizer equivalent in the pig and suggests a sequential development of its function.

Identity Based Authentication Scheme (IAS) for Securing WSN Based Internet of Things

The growing number of connected devices in IoT networks has raised security concerns. These networks face unique challenges, including limited resources, lack of standardized security protocols, and diverse devices and applications. Identity-based authentication is needed to address these threats and limitations. Implementing a multi-factor authentication system can enhance security and protect sensitive information from unauthorized access. The proposed Identity Based Authentication (IAS) scheme is implemented using the NS-2 simulator to address security challenges in IoT networks. Each node is assigned a unique identity for authentication, ensuring only authorized access and preventing security breaches. The IAS scheme enhances IoT network security by verifying node identities. Two scenarios are considered to evaluate the scalability and interoperability of the IAS scheme. The first scenario involves a random environment, assessing performance in real-world settings with varying device densities and network conditions. The second scenario involves a cluster-based environment, providing insights into the scheme's functionality and efficiency. By examining both scenarios, this research aims to provide a comprehensive understanding of how the authentication scheme performs in different deployment scenarios. The findings highlight the importance of selecting the appropriate authentication scheme based on the specific network environment and requirements. The superiority of the IAS authentication scheme suggests its potential for widespread adoption and implementation in various network scenarios over other authentication schemes.

SECURE and energy-efficient routing protocol based on micro-segmentation and batch authentication

An effective technique in designing routing algorithms for Wireless Sensor Networks (WSNs) is named clustering nodes, which augments the network lifespan. More tasks are performed by the Cluster Heads (CHs) of the clustered WSNs, thereby consuming more energy. Hence, a secure energy-efficient Routing Protocol (RP) centered on micro-segmentation and batch authentication is proposed here. The proposed method begins by initializing the WSN nodes. After that, a random key is engendered for every node centered on their Node ID. The nodes undergo a Micro-segmentation process; in addition, they are securely grouped utilizing the Supremum Distance based K-Prototype (SD-KP) algorithm. Now, a fog layer with a number of fog nodes is initialized; also, every single micro-segmented group is assigned with an optimal fog node. Then, by using the Quasi Deterministic Sequence-Black Widow Optimization (QDS-BWO) algorithm, the optimal fog nodes are selected. By using the Root Squared- Diffie Hellman (R2-DH) technique, a key agreement is created for each group centered on their Node IDs. By using the Bitwise Cyclic Shift –BLAKE 512 (BCS-BLAKE-512) algorithm, the created keys are converted into hashcode. Now, for the purpose of performing batch authentication, the hashcode of each group is sent to the trusted authority. Subsequent to verification, data sensing occurs. Lastly, by using the Elliptic Curve Cryptography (ECC) algorithm, the data is securely encrypted and it is transmitted via the routes selected utilizing the Learned Gradient Weight Initialization-based Geography and Energy Aware Routing (LGWI-GEAR) algorithm. The proposed framework's efficacy is proved by the experimental outcomes.

An efficient parameterized simulation framework for 3D scarf-repaired composite laminates

This paper proposes a parameterized simulation framework for predicting the load-bearing capacity of 3D scarf-repaired composite laminates which remarkably reduces the modeling time from several hours to just a few seconds. The specific implementation is demonstrated through a detailed step-by-step explanation. The main contribution lies in: (1) By introducing a reusable geometric partitioning method for composite layers and implementing a partitioning method-based numbering scheme for element and node, the effective management of element and node IDs of elements and nodes in such complex 3D models is greatly facilitated; (2) By ingeniously combining open-source meshing tools and text rewriting techniques, a highly efficient approach has been developed to construct complete models for such structures, showcasing the remarkable potential of these existing techniques in this field. The reliability of the efficient parameterized simulation framework proposed in this study is verified through the comparison between strength values acquired from progressive damage analysis (PDA) and experiment data reported in the existing literature.

Tell me who you are friends with and I will tell you who you are: Unique neighborhoods in random graphs

The identity of a physical entity in a network is traditionally defined by some secret knowledge, personal possession, or (biometric) property. What if no such unique property exists or can be defined “safely”, for example, in the interest of anonymity? In this work, we study the problem of defining an identity for an entity u under the constraint that there is no subjective property that u carries by itself, or in other words, any property that u has, may also be likewise present in another entity v. In the absence of an intrinsic property of u to define its identity, we thus consider the possibility of an “extrinsically defined identity” via the connections that u maintains to other entities. Letting u∈V be part of a graph G=(V,E), we study the question of whether the links that u has to its neighbors lend themselves to uniquely distinguish u from all other nodes v in the graph.A practical instance of this setting appears in symmetric cryptography, if we assume an edge between two nodes u,v if and only if u and v share a common secret. A single shared secret known by u is, in symmetric cryptography, also known to some other entity v. However, is the set of all secrets that u has likewise known to another node v in a network? If not, we can implement end-to-end authentication without shared secrets and exclusively using symmetric cryptography. This concept is here reviewed as peer-authentication. It works in a graph class that we call Unique-Neighborhood Network (UNN), which has been introduced in prior literature for the purpose of emulating public-key digital signatures with symmetric cryptography only. Our findings suggest that some networks naturally grow into UNNs, but even if not, we can efficiently identify substructures that allow to pin down a node's identity based on its “friend-nodes” in a graph.

Cross-Chain-Based Trustworthy Node Identity Governance in Internet of Things

Cross-Chain-Based Trustworthy Node Identity Governance in Internet of Things

Classification of HHO-based Machine Learning Techniques for Clone Attack Detection in WSN

Thanks to recent technological advancements, low-cost sensors with dispensation and communication capabilities are now feasible. As an example, a Wireless Sensor Network (WSN) is a network in which the nodes are mobile computers that exchange data with one another over wireless connections rather than relying on a central server. These inexpensive sensor nodes are particularly vulnerable to a clone node or replication assault because of their limited processing power, memory, battery life, and absence of tamper-resistant hardware. Once an attacker compromises a sensor node, they can create many copies of it elsewhere in the network that share the same ID. This would give the attacker complete internal control of the network, allowing them to mimic the genuine nodes' behavior. This is why scientists are so intent on developing better clone assault detection procedures. This research proposes a machine learning based clone node detection (ML-CND) technique to identify clone nodes in wireless networks. The goal is to identify clones effectively enough to prevent cloning attacks from happening in the first place. Use a low-cost identity verification process to identify clones in specific locations as well as around the globe. Using the Optimized Extreme Learning Machine (OELM), with kernels of ELM ideally determined through the Horse Herd Metaheuristic Optimization Algorithm (HHO), this technique safeguards the network from node identity replicas. Using the node identity replicas, the most reliable transmission path may be selected. The procedure is meant to be used to retrieve data from a network node. The simulation result demonstrates the performance analysis of several factors, including sensitivity, specificity, recall, and detection.

Trust And Energy-Aware Routing Protocol for Wireless Sensor Networks Based on Secure Routing

Wireless Sensor Network (WSN) is a network area that includes a large number of nodes and the ability of wireless transmission. WSNs are frequently employed for vital applications in which security and dependability are of utmost concern. The main objective of the proposed method is to design a WSN to maximize network longevity while minimizing power usage. In a WSN, trust management is employed to encourage node collaboration, which is crucial for achieving dependable transmission. In this research, a novel Trust and Energy Aware Routing Protocol (TEARP) in wireless sensors networks is proposed, which use blockchain technology to maintain the identity of the Sensor Nodes (SNs) and Aggregator Nodes (ANs). The proposed TEARP technique provides a thorough trust value for nodes based on their direct trust values and the filtering mechanisms generate the indirect trust values. Further, an enhanced threshold technique is employed to identify the most appropriate clustering heads based on dynamic changes in the extensive trust values and residual energy of the networks. Lastly, cluster heads should be routed in a secure manner using a Sand Cat Swarm Optimization Algorithm (SCSOA). The proposed method has been evaluated using specific parameters such as Network Lifetime, Residual Energy, Throughpu,t Packet Delivery Ratio, and Detection Accuracy respectively. The proposed TEARP method improves the network lifetime by 39.64%, 33.05%, and 27.16%, compared with Energy-efficient and Secure Routing (ESR), Multi-Objective nature-inspired algorithm based on Shuffled frog-leaping algorithm and Firefly Algorithm (MOSFA) , and Optimal Support Vector Machine (OSVM).

IOT based Intelligent Home Safety Control Centre

This research focuses on the development of an IOT (IoT) intelligent home safety control centre. System consists of a user side, a safety control centre, and a terminal node, with each component having specific functionalities to enhance the safety and safety of the intelligent home environment. Key modules include data encrypting/decrypting, safety communication, control centre of user access and verification of node identity, reliable platform for credibility verification, and log inspect and alarm. The system ensures data safety, authentication, credibility analysis, and system monitoring, thereby improving the safety performance and running efficiency of the intelligent home system.