Intermediate Nodes Research Articles

Data-driven approach to quantify trust in medical devices using Bayesian networks.

Bayesian networks are increasingly used to quantify the uncertainty of subjective and stochastic concepts such as trust. In this article, we propose a data-driven approach to estimate Bayesian parameters in the domain of wearable medical devices. Our approach extracts the probability of a trust factor being in a specific state directly from the devices (e.g. sensor quality). The strength of the relationship between related factors is defined by expert knowledge and incorporated into the model. We use propagation rules from requirements engineering to estimate how much each trust factor contributes to the related intermediate nodes in the network and ultimately compute the trust score. The trust score is a relative measure of trustworthiness when different devices are evaluated in the same test conditions and using the same Bayesian structure. To evaluate our approach, we developed Bayesian networks for the trust quantification of similar wearable devices from two manufacturers under identical test conditions and noise levels. The results demonstrated the learnability and generalizability of our approach.

In-cabin and outdoor environmental monitoring in vehicular scenarios with distributed computing

Accurate environmental monitoring is becoming the basis for assuring sustainable development in administrations at different levels, including cities and industry as key actors. However, current techniques rely on static stations that may not be representative of larger areas, for the case of outdoor scenarios, or even not considering indoor spaces where people can remain for long periods. This is the case of vehicles. The COVID-19 pandemic has remarked the importance of measuring air quality indoors, for instance. With the aim of solving this two-fold issue, this work proposes an in-cabin and outdoor air pollution monitoring system to assure healthy conditions when travelling, driving and operating vehicles, and to analyse the evolution of environmental parameters in cities. This effort is carried out exploiting distributed computing with micro-services, betting for an on-board hardware solution provided with sensors for measuring particulate matter, CO, CO2, NO2, O3, temperature and humidity. While basic data pre-processing is carried out in this acquisition unit, edge processing is performed on a single board computer aboard and intermediary communication nodes in the network path from the vehicle to the cloud. Vehicle connectivity is provided by 4G cellular and Low-Power Wide-Area (LPWAN) networks. Global environmental perception is acquired by cloud-based software powered by machine learning and time series analysis. The whole solution has been validated and tested in the city of Cartagena (Spain), with good performance in terms of data collection, communication links and service offered.

Percolation Theories for Quantum Networks.

Quantum networks have experienced rapid advancements in both theoretical and experimental domains over the last decade, making it increasingly important to understand their large-scale features from the viewpoint of statistical physics. This review paper discusses a fundamental question: how can entanglement be effectively and indirectly (e.g., through intermediate nodes) distributed between distant nodes in an imperfect quantum network, where the connections are only partially entangled and subject to quantum noise? We survey recent studies addressing this issue by drawing exact or approximate mappings to percolation theory, a branch of statistical physics centered on network connectivity. Notably, we show that the classical percolation frameworks do not uniquely define the network's indirect connectivity. This realization leads to the emergence of an alternative theory called "concurrence percolation", which uncovers a previously unrecognized quantum advantage that emerges at large scales, suggesting that quantum networks are more resilient than initially assumed within classical percolation contexts, offering refreshing insights into future quantum network design.

Network coding construction for a special class of three unicast sessions

For a special class of three unicast sessions, in which the maximum flow from each sender to each receiver is the same positive integer k, a network coding approach is proposed. A multigeneration mixed strategy, in which (2 × n + 1) consecutive generations are taken as a mixed set, is adopted. The precoding strategy is adopted at the senders, random linear network coding technology is adopted at intermediate nodes to transmit data, and interference alignment technology is used at the receivers to eliminate some interference information. The proposed approach can construct a network coding data transmission scheme, in which the transmission rate vector of the senders is [(n + 1) × k/(2 × n + 1), n × k/(2 × n + 1), n × k/(2 × n + 1)]. The feasibility of the proposed approach is mathematically deduced and proven, and the simulation results verify the conclusions of the theoretical analysis.

Long-term maintenance optimization for integrated mining operations

Maintenance activities are inevitable and costly in integrated mining operations. Conducting maintenance may require the whole system, or sub-units of the system, to be shut down temporarily. These maintenance activities not only disrupt the unit being shut down, but they also have consequences for inventory levels and product flow downstream. In this paper, we consider an interconnected mining system in which there are complicated maintenance relationships and stock accumulation at intermediate nodes. We propose a time-indexed mixed-integer linear programming formulation to optimize the long-term integrated maintenance plan and maximize the total throughput. We also devise an algorithm, which combines Benders decomposition and Lagrangian relaxation, to accelerate the computational speed. To validate our mathematical model, we perform simulations for a real-world case study in the iron ore industry. The results show that our method can yield better solutions than CPLEX optimization solver alone in faster time.

Experimental Demonstration of Secure Relay in Quantum Secure Direct Communication Network.

Quantum secure direct communication (QSDC) offers a practical way to realize a quantum network which can transmit information securely and reliably. Practical quantum networks are hindered by the unavailability of quantum relays. To overcome this limitation, a proposal has been made to transmit the messages encrypted with classical cryptography, such as post-quantum algorithms, between intermediate nodes of the network, where encrypted messages in quantum states are read out in classical bits, and sent to the next node using QSDC. In this paper, we report a real-time demonstration of a computationally secure relay for a quantum secure direct communication network. We have chosen CRYSTALS-KYBER which has been standardized by the National Institute of Standards and Technology to encrypt the messages for transmission of the QSDC system. The quantum bit error rate of the relay system is typically below the security threshold. Our relay can support a QSDC communication rate of 2.5 kb/s within a 4 ms time delay. The experimental demonstration shows the feasibility of constructing a large-scale quantum network in the near future.

Sequential Lateral Lymphatic Metastasis Shows Similar Oncologic Outcomes to Upward Spread in Advanced Rectal Cancer After Preoperative Chemoradiotherapy.

Whether lateral pelvic node metastasis should be considered as a regional or systemic disease is a long-standing debate. Although previous Japanese studies have considered it to be locoregional disease, Western countries consider it a systemic disease and do not perform lateral pelvic node dissection after preoperative chemoradiotherapy. To evaluate whether lateral pelvic node metastasis is a systemic or regional disease that is amenable to curative resection. Retrospective analysis of a prospectively collected database. This study was conducted at a tertiary cancer center. There were 616 consecutive patients who underwent curative total mesorectal excision alone or with lateral pelvic node dissection after preoperative chemoradiotherapy for locally advanced rectal cancer between 2011 and 2019. Three-year disease-free and overall survival. A total of 360 patients underwent total mesorectal excision, and 160 patients underwent total mesorectal excision with lateral pelvic node dissection. There was no difference in the 3-year disease-free survival (DFS; p = 0.844) or overall survival rates ( p = 0.921) between the groups. Patients with lateral pelvic node metastasis showed DFS similar to those with perirectal lymph node metastasis in the total mesorectal excision group. In a subgroup analysis, patients with internal iliac pelvic node metastasis showed a disease-free survival comparable to those with perirectal node involvement, and patients with other lateral pelvic node metastasis showed a DFS similar to those with intermediate node involvement. In the lateral pelvic node dissection group, the lateral pelvic node metastatic rate was 32.5%. On multivariate analysis, fewer than 8 of the unilateral harvested lateral pelvic nodes and advanced ypT stage were significantly associated with poor disease-free survival. The retrospective design. Lateral lymphatic metastasis showed oncologic outcomes similar to those of upward spread, especially perirectal lymph nodes metastasis. Large cohort studies with long-term follow-up are required to confirm these results. See Video Abstract . ANTECEDENTES:Es un debate muy antiguo si las metástasis en los ganglios pélvicos laterales deben considerarse una enfermedad regional o sistémica. Si bien estudios japoneses anteriores las consideran como una enfermedad locorregional, en los países de occidente se las considera como una enfermedad sistémica por la cual no se realiza disección de ganglios pélvicos laterales después de una radioquimioterapia preoperatoria.OBJETIVOS:Evaluar si la metástasis en los ganglios pélvicos laterales se consideran como enfermedad sistémica o enfermedad regional susceptible de resección curativa.DISEÑO:Análisis retrospectivo de una base de datos recopilada prospectivamente.AJUSTE:Este estudio se realizó en un centro oncológico terciario.PACIENTES:616 pacientes consecutivos se sometieron a excisión total del mesorrecto curativa sola o con disección de los ganglios pélvicos laterales después de radioquimioterapia preoperatoria en casos de cáncer de recto localmente avanzado entre 2011 y 2019.PRINCIPALES MEDIDAS DE RESULTADO:Sobrevida global y libre de enfermedad a 3 años.RESULTADOS:Un total de 360 pacientes se sometieron a excisión total del mesorrecto y 160 pacientes se sometieron a excisión total del mesorrecto con disección de ganglios pélvicos laterales.No hubo diferencias en la sobrevida libre de enfermedad a 3 años (p = 0,844) ni en las tasas de sobrevida general (p = 0,921) entre los grupos. Los pacientes con metástasis en los ganglios pélvicos laterales mostraron una sobrevida libre de enfermedad similar a aquellos con metástasis en los ganglios linfáticos perirrectales que se encontraban en el grupo de excisión total del mesorrecto.En el análisis de subgrupos, los pacientes con metástasis en los ganglios pélvicos ilíacos internos mostraron una sobrevida libre de enfermedad comparable a aquellos con afección de los ganglios perirrectales y los pacientes con otras metástasis en los ganglios pélvicos laterales mostraron una sobrevida libre de enfermedad similar a aquellos con afección de los ganglios intermedios.En el grupo de disección de los ganglios pélvicos laterales, la tasa de metástasis en dichos ganglios fué del 32,5%. En el análisis multivariado, < de 8 ganglios pélvicos laterales resecados unilateralmente y el estadio ypT avanzado se asociaron significativamente con una menor sobrevida libre de enfermedad.LIMITACIÓN:El diseño retrospectivo del estudio.CONCLUSIONES:Las metástasis linfáticas laterales mostraron resultados oncológicos similares a la diseminación ascendente, especialmente las metástasis en los ganglios linfáticos perirrectales. Se requieren grandes estudios de cohortes con seguimiento a largo plazo para confirmar estos resultados. (Traducción-Dr. Xavier Delgadillo ).

Analytical Model for Assessing the Impact of MANETs' Bottleneck Nodes

Objectives: Due to few resources, the most challenging task in mobile ad hoc networks is determining a route with the highest probability of success between a source and a destination. The objectives of this assignment are as follows: The limited power provided by batteries is one of the most significant challenges in routing. A great number of routing protocols have been devised to deal with the problem of limited battery power. Reliability, low energy consumption, and preferential treatment of nodes with greater energy levels are the cornerstones of the routing path made possible by these protocols. On the other hand, these protocols result in the creation of a bottleneck intermediate node, which is a node that must forward packets from numerous sources. The loss of packets is the most significant issue that emerges with a bottleneck intermediate node.&#x0D; Methods and Statistical Analysis: In this study, we employ a poison random process to assess an intermediate's performance of bottleneck node within a mathematical model we developed. Findings: The model that is being proposed calculates the typical length of the queue at the input buffer, the typical amount of time spent waiting in the buffer and the lifetime of the network.&#x0D; Application and enhancements: This model is put to use to regulate the incoming traffic of an intermediate node and to determine whether or not that node will participate in the route.

Irregularity Behaviour Detection - Ad-hoc On-Demand Distance Vector Routing Protocol (IBD - AODV): A Novel Method for Determining Unusual Behaviour in Mobile Ad-hoc Networks (MANET)

All the communication in the mobile ad-hoc network (MANET) will depend on the intermediate neighbour nodes or router nodes. Routing protocol is very important in MANET, because all the communication will be done in the MANET depending on the neighbour or intermediate node. If the intermediate node is a malicious node, all the data will be lost or changed by the intermediate or malicious node. Ad-hoc On-demand Distance Vector routing protocol is one of the moderate routing protocol in MANET. The Ad-hoc On-demand Distance Vector Routing protocol does not have any security mechanism. This work is going to find the Irregularity Behaviour Detection (IBD) over the Ad-hoc On-Demand Distance Vector Routing (AODV) protocol. IBD is finding a trusted node by using the trust value (TV) of the node. This TV includes network performance, node energy level, and node position value. IBD-AODV is implemented and tested in the OmNetpp 6.0 simulator.

On the causation analysis for hazards involved in the engine room fire-fighting system by integrating STPA and BN

A novel methodology is proposed in this study to investigate the hazards involved in the engine room fire-fighting system. In the proposed methodology, the fuzzy theory is applied to bridge the superiority of the systems theoretic process analysis (STPA) and Bayesian network (BN). The engine room fire-fighting system is initially audited within the framework of STPA qualitatively to identify unsafe control actions, and then, the causations contributing to these actions are determined, which are subsequently considered as the root nodes of the BN. To implementing Bayesian inference, the expert elicitation within fuzzy theory is employed to determine the probability distribution of the root nodes, and the conditional probability tables for intermediate nodes are obtained by leaky Noisy-OR function, based on which the developed BN is simulated. Finally, the results from BN simulation are interpretation from the perspectives of brief propagation analysis, sensitivity analysis and influence strength analysis. The proposed methodology can be applied to audit the integrity of engine room fire-fighting system to eliminate the potential causations contributing to engine room fire.

Artificial neural network for safety information dissemination in vehicle‐to‐internet networks

AbstractIn vehicular networks, diverse safety information can be shared among vehicles through internet connections. In vehicle‐to‐internet communications, vehicles on the road are wirelessly connected to different cloud networks, thereby accelerating safety information exchange. Onboard sensors acquire traffic‐related information, and reliable intermediate nodes and network services, such as navigational facilities, allow to transmit safety information to distant target vehicles and stations. Using vehicle‐to‐network communications, we minimize delays and achieve high accuracy through consistent connectivity links. Our proposed approach uses intermediate nodes with two‐hop separation to forward information. Target vehicle detection and routing of safety information are performed using machine learning algorithms. Compared with existing vehicle‐to‐internet solutions, our approach provides substantial improvements by reducing latency, packet drop, and overhead.

WALS: A Lightweight Stateless Label Switching Framework Based on Prime Theorem for MANETs

This article proposes an innovative unicast/multicast unified, cross-layer, and control/user-plane splitting forwarding method, called Wireless Arithmetic Label Switching (WALS), for mobile ad-hoc networks (MANETs). The WALS is based on a stateless approach: an arithmetic source-routed label is processed by the source and intermediate nodes without table lookup, causing lower protocol overheads and forwarding latency than the conventional stateless unicast/multicast routing protocols such as the Dynamic Source Routing (DSR) and the Differential Destination Multicast (DDM) respectively. Different from conventional unicasting/multicasting designs demanding distinct routing protocol stacks, the WALS requires a unified protocol stack, avoiding the need to distinguish unicast and multicast frames, thus simplifying the design of MANET devices. Mathematical analysis indicates that the WALS is highly scalable for both the network sizes and the number of multicast groups. Simulation results reveal that WALS effectively performs network scalability and the required overhead for the unicast and multicast capabilities. Additionally, WALS achieves superior to the other three state-of-the-art competitive multicast routing methods including DVMRP, ODMRP, and MAODV on the performance of the packet processing latency and packet delivery ratio. As a result, the WALS can significantly enable the employment and management of integrated unicast and multicast services for MANETs.

Linear constraints for ensuring k‐hop connectivity using mixed‐integer programming for multi‐agent systems

AbstractThis note concerns the problem of ‐hop connectivity in a network of mobile agents, which is achieved if any pair of agents can communicate with each other through a link of or fewer intermediate nodes. We propose linear constraints involving binary optimization variables to ensure ‐hop connectivity. Such constraints are then integrated into a mixed‐integer linear programming (MILP) trajectory planning model. Simulation results illustrate the application of the proposed method and the effect of varying in the context of a mission involving the visitation of multiple targets.

DDoS Attack Detection in WSN using Modified Invasive Weed Optimization with Extreme Learning Machine

Wireless sensor networks (WSN) are the wide-spread methodology for its distribution of the vast amount of devoted sensor nodes (SNs) that is employed for sensing the atmosphere and gather information. The gathered information was transmitted to the sink nodes via intermediate nodes. Meanwhile, the SN data are prone to the internet, and they are vulnerable to diverse security risks, involving distributed denial of service (DDoS) outbreaks that might interrupt network operation and compromises data integrity. In recent times, developed machine learning (ML) approaches can be applied for the discovery of DDoS attacks and accomplish security in WSN. To achieve this, this study presents a modified invasive weed optimization with extreme learning machine (MIWO-ELM) model for DDoS outbreak recognition in the WSN atmosphere. In the presented MIWO-ELM technique, an initial stage of data pre-processing is conducted. The ELM model can be applied for precise DDoS attack detection and classification process. At last, the MIWO method can be exploited for the parameter tuning of the ELM model which leads to improved performance of the classification. The experimental analysis of the MIWO-ELM method takes place using WSN dataset. The comprehensive simulation outputs show the remarkable performance of the MIWO-ELM method compared to other recent approaches.

A Method for Malicious Network Packet Detection based on Anomalous TTL Values

In the current digital age, a pervasive shift towards digitalization is evident in all aspects of life, encompassing entertainment, education, business, and more. Consequently, the demand for internet access has surged, paralleled therefore unfortunate escalation in cybercrimes. This study undertakes an exploration into the intrinsic nature of network packets, aiming to discern their potential for malice or legitimacy. In the internet, 32 intermediate nodes are encountered by a Network packet before it reaches its final host. Our findings suggest that the time-to-live (TTL) parameter in certain IP packets diverges from the initial TTL by more than 32 intermediary hops. It's likely that these packets are generated by specialized software. We anticipate that malicious IP packets exhibit unconventional TTL values, influenced by factors such as the source machine's operating system and protocols like TCP/ICMP/UDP, etc. To gauge the effectiveness and value of the proposed method, an experiment was conducted utilizing the SNORT NIDS system. Filtering rules based on signatures were formulated to thoroughly analyze the traffic. Real network data, along with DARPA and MACCDC 2012 datasets, were employed as inputs for the SNORT NIDS, and it has been observed that the suggested approach successfully detects the anomalous network packets.

Multipath Routing Scheme for Optimum Data Transmission in Dense Internet of Things

The Internet of Things (IoT) is an emerging technology that has recently gained significant interest, especially with the dramatic increase in connected devices. However, IoT networks are not yet standardized, and the design of such networks faces many challenges, including scalability, flexibility, reliability, and availability of such networks. Routing is among the significant problems facing IoT network design because of the dramatic increase in connected devices and the network requirements regarding availability, reliability, latency, and flexibility. To this end, this work investigates deploying a multipath routing scheme for dense IoT networks. The proposed method selects a group of routes from all available routes to forward data at a maximum rate. The choice of data transmission routes is a complex problem for which numerical optimization methods can be used. A novel method for selecting the optimum group of routes and coefficients of traffic distribution along them is proposed. The proposed method is implemented using dynamic programming. The proposed method outperforms the traditional route selection methods, e.g., random route selection, especially for dense IoT networks. The model significantly reduced the number of intermediate nodes involved in routing paths over dense IoT networks by 34%. Moreover, it effectively demonstrated a significant decrease of 52% in communication overhead and 40% in data delivery time in dense IoT networks compared to traditional models.

Topological and functional vulnerability analysis and mitigation for single-source heating networks based on tree models

The performance of district heating networks (DHNs) to provide heat to users under different operation conditions depends largely on their topology. It is of great significance to analyze the topological and functional characteristics of DHNs and propose design and reconstruction guidelines for vulnerability reduction. This study proposes binary tree and generalized tree models of DHNs, from which their topological characteristics can be quickly identified, and the functional analysis can be facilitated. Then, we propose a topology design and reconstruction framework for DHNs based on vulnerability assessment. Various characteristics of the branched and looped structure are studied in their influence on system vulnerability, based on which several design criteria for single-source DHNs are deduced. Specifically, single-source branched DHNs shall be designed as symmetrical to avoid long pipelines bearing enormous heat loads. When transforming the branched DHNs into looped DHNs, priority should be given to adding cotrees between the intermediate nodes of high importance, and the diameters of looped pipes should be enlarged. Finally, a detailed case study on a real-world single-source DHN demonstrates the effectiveness of the proposed approaches and design criteria.

Cross-timescale interaction of nonstationary hydrological responses in subtropical mountainous watersheds

Hydrological responses possess watershed systematicity and divergent features at various time scales, but how to quantitatively capture the cross-timescale interaction of nonstationary discharge and sediment concentration is unclear, thus limiting insight into hydrological responses to human alterations and stochastic hydroclimatic drivers. The present study analyzed the regimes of inter-annual, intra-annual, and event-scale hydrological responses using 40–60 years data from 15 subtropical watersheds. Bayesian networks of discharge and sediment concentration were constructed, incorporating watershed characteristics, climate effects, and three timescale hydrological indicators, to implement the nonlinear probability prediction. Broadly, annual discharge and sediment concentration followed a decreasing trend, with a key inflection in 2000. Hydrological nonstationarity was diagnosed in terms of magnitude, frequency, and time based on five temporal indicators and transient types of extreme events. In Bayesian networks, the interactions of hydrological indicators were synergistic, with intra-annual hydrological signatures as intermediate nodes between external drivers and annual trends, highly explaining the inconsistent discharge and sediment concentration. The sensitivity of marginal distribution in discharge and sediment concentration to watershed surface alterations was ranked in the terminal position. Nevertheless, the implementation of forest restoration could cause a 90.36% probability of enabling sediment concentration in a decreasing or stationary state under the restrictive scenario. These findings provide refinements in hydrological dynamic causality at different timescales for guiding watershed risk management.

A Machine Learning-Based Interest Flooding Attack Detection System in Vehicular Named Data Networking

A vehicular ad hoc network (VANET) has significantly improved transportation efficiency with efficient traffic management, driving safety, and delivering emergency messages. However, existing IP-based VANETs encounter numerous challenges, like security, mobility, caching, and routing. To cope with these limitations, named data networking (NDN) has gained significant attention as an alternative solution to TCP/IP in VANET. NDN offers promising features, like intermittent connectivity support, named-based routing, and in-network content caching. Nevertheless, NDN in VANET is vulnerable to a variety of attacks. On top of attacks, an interest flooding attack (IFA) is one of the most critical attacks. The IFA targets intermediate nodes with a storm of unsatisfying interest requests and saturates network resources such as the Pending Interest Table (PIT). Unlike traditional rule-based statistical approaches, this study detects and prevents attacker vehicles by exploiting a machine learning (ML) binary classification system at roadside units (RSUs). In this connection, we employed and compared the accuracy of five (5) ML classifiers: logistic regression (LR), decision tree (DT), K-nearest neighbor (KNN), random forest (RF), and Gaussian naïve Bayes (GNB) on a publicly available dataset implemented on the ndnSIM simulator. The experimental results demonstrate that the RF classifier achieved the highest accuracy (94%) in detecting IFA vehicles. On the other hand, we evaluated an attack prevention system on Python that enables intermediate vehicles to accept or reject interest requests based on the legitimacy of vehicles. Thus, our proposed IFA detection technique contributes to detecting and preventing attacker vehicles from compromising the network resources.

Optical-computing-enabled network: An avant-garde architecture to sustain traffic growth

A new architectural paradigm for future optical transport network, namely, optical-computing-enabled networking, is presented. As a major departure from traditional optical-bypass framework, the new paradigm is grounded on the idea of permitting superposition of transitional lightpaths crossing the same intermediate node to produce a more spectrally efficient lightpath bringing about spectral savings and potentially greater energy efficiency. A case study focusing on optical aggregation is provided to highlight how advantageous the proposed architecture is in comparison with the conventional one.