Link State Research Articles

Clustered OLSR routing and FPGA parameter analysis for mobile ad hoc communication

ABSTRACT The research letter emphasises the hardware chip design of homogeneous clustered optimised link state routing (HMC-OLSR) protocol for Mobile Ad-hoc Networks (MANET). The homogeneously distributed clustering allows for parallel processing and distributes the burden evenly among the clusters to reduce the network complexity and computational time. The chip design is evaluated based on field programmable gate array (FPGA) performance metrics for the proposed HMC-OLSR protocol, which significantly reduced hardware utilisation by 27.45%, 48.8% of slices, 28.71%, 55.82% of flip-flops, 8.35%, 13.60% of look-up tables (LUTs), and 36.4% of input/output blocks (IoBs) compared to existing OLSR and destination-Sequenced distance vector (DSDV) protocols, respectively. The recommended protocol also outperformed OLSR and DSDV routing protocols in FPGA timing constraints such as minimum time before the clock, maximum time after the clock, and minimum propagation period. The novelty of the work is in the incorporation of an FPGA-integrated scalable network design that surpasses maximum throughput, packet delivery ratio (PDR), minimum power consumption, end-to-end delay, and control overhead.

Multitopology Routing With Virtual Topologies and Segment Routing

ABSTRACTMultitopology routing (MTR) provides an attractive alternative to segment routing (SR) for traffic engineering when network devices cannot be upgraded. However, due to a high overhead in terms of link state messages exchanged by topologies and the need to frequently update link weights to follow evolving network conditions, MTR is often limited to a small number of topologies and the satisfaction of loose QoS constraints. To overcome these limitations, we propose virtual MTR (vMTR), an MTR extension where demands are routed over virtual topologies that are silent; that is, they do not exchange LSA messages and that are continuously derived from a very limited set of real topologies, optimizing each QoS parameter. In this context, we present a polynomial and exact algorithm for vMTR and, as a benchmark, a local search algorithm for MTR. We show that vMTR helps to reduce drastically the number of real topologies and that it is more robust to QoS changes. In the case where SR can actually be rolled‐out, we also show that vMTR allows to drastically reduce SR overhead.

Оценка активности глутатионового звена антиоксидантной системы у беременных женщин коренного и пришлого населения Приамурья с экологических и этнических позиций

In order to study the state of the glutathione link of the antioxidant system (AOS) in pregnant women of the alien and indigenous population living in urban and rural areas, using the example of the Amur region, 175 patients were examined during their initial visit in the first and second trimesters and in the absence of iron deficiency states. Depending on their ethnicity and place of residence, the pregnant women were conditionally divided into 3 groups: urban aliens (Caucasians) (n=67); rural aliens (Caucasians) (n=74); rural indigenous people of the Amur region (Nanai), belonging to the Mongoloid race (n=34). Comparative characteristics of the indicators of women in groups 1 and 2 (urban and rural aliens) indicated environmental features of glutathione antioxidant protection (AOP) during pregnancy. Analysis of data from groups 2 and 3 (rural newcomers and natives) revealed ethnic peculiarities of the studied detoxification zone. Taking into account the participation of iron in the processes of free radical oxidation and antioxidant protection, the level of hemoglobin, serum ferritin in women was determined to exclude iron deficiency conditions, as well as the content of total glutathione, reduced (GSH) and oxidized (GSSG) forms in whole blood with the calculation of redox status (GSH/GSSG ratio) as a biomarker of AOS. Statistical data processing was performed using the programs "Microsoft Excel 2010", "Statsoft Statistica", version 6.1, 10.01. As a result of the studies, a reliable decrease in the content of total glutathione was found in urban patients of the newcomer population compared to rural newcomers due to a decrease in the reduced form both in the first and second trimesters with a multidirectional trend of the oxidized fraction in these groups, which showed the ecological orientation of the AOS functioning. Ethnic features in native women were revealed when comparing the parameters under study with newcomers living in similar rural areas, in the form of reliably low values of the reduced form of glutathione in the 2nd trimester of gestation, as well as a reliable decrease in the level of total and reduced glutathione in the 2nd trimester compared to the 1st. The optimal variant of the functional state of the glutathione link of the AOS was determined by the indicators of the oxidative stress level - the redox status in newcomer pregnant women of the village, based on its reliably high indicators in the 1st and 2nd trimesters compared to pregnant women of the native population of the village and especially with newcomers living in the city. The obtained data should be taken into account when carrying out therapeutic and preventive measures for complicated pregnancy, taking into account the ecological and ethnic features of oxidation-reduction processes. Key Words: pregnant women, newcomers, indigenous people, city, village, glutathione, ferritin

Predictive Forwarding Rule Caching for Latency Reduction in Dynamic SDN

In mission-critical environments such as industrial and military settings, the use of unmanned vehicles is on the rise. These scenarios typically involve a ground control system (GCS) and nodes such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). The GCS and nodes exchange different types of information, including control data that direct unmanned vehicle movements and sensor data that capture real-world environmental conditions. The GCS and nodes communicate wirelessly, leading to loss or delays in control and sensor data. Minimizing these issues is crucial to ensure nodes operate as intended over wireless links. In dynamic networks, distributed path calculation methods lead to increased network traffic, as each node independently exchanges control messages to discover new routes. This heightened traffic results in internal interference, causing communication delays and data loss. In contrast, software-defined networking (SDN) offers a centralized approach by calculating paths for all nodes from a single point, reducing network traffic. However, shifting from a distributed to a centralized approach with SDN does not inherently guarantee faster route creation. The speed of generating new routes remains independent of whether the approach is centralized, so SDN does not always lead to faster results. Therefore, a key challenge remains: determining how to create new routes as quickly as possible even within an SDN framework. This paper introduces a caching technique for forwarding rules based on predicted link states in SDN, which was named the CRIMSON (Cashing Routing Information in Mobile SDN Network) algorithm. The CRIMSON algorithm detects network link state changes caused by node mobility and caches new forwarding rules based on predicted topology changes. We validated that the CRIMSON algorithm consistently reduces end-to-end latency by an average of 88.96% and 59.49% compared to conventional reactive and proactive modes, respectively.

INT-MC: Low-Overhead In-Band Network-Wide Telemetry Based on Matrix Completion

In-band Network Telemetry (INT) enhances real-time, high-resolution network monitoring capabilities by incorporating fine-grained internal state information into packets. Utilizing INT for network-wide visualization can significantly bolster network management and operation. Although existing studies have made significant contributions, they have not been able to simultaneously meet the following two objectives: 1) Comprehensive visibility of the network's internal state, which refers to obtaining information on all switch ports and their corresponding link states; 2) Low measurement overhead, which involves reducing measurement bandwidth overhead and minimizing the impact of the measurement process on the network. We designed INT-MC to meet both of these objectives. INT-MC is an efficient and cost-effective network-wide telemetry solution based on matrix completion. By modeling link metadata as a matrix and leveraging its low-rank property, INT-MC selectively measures certain links. It then employs matrix completion algorithms to infer information about unmeasured links, thereby achieving low-overhead network-wide telemetry. Designing paths to cover the target links involves an NP-complete problem, and the high computational complexity may delay the measurement tasks. To circumvent this, we propose an improved scheme based on Eulerian digraph decomposition, transforming the path calculation problem into a high-information path selection problem, significantly reducing computational costs. We have implemented an INT-MC prototype within the NSFNet topology, consisting of 14 Tofino switches and 10 end-hosts, and conducted extensive experiments and evaluations. The results indicate that INT-MC incurs only 16% of the measurement overhead compared to existing network-wide telemetry solutions, while achieving nearly identical accuracy. Even under high-frequency measurements of 20 times per second, the bandwidth overhead of INT-MC is approximately 0.075% of the total bandwidth, exerting minimal impact on the network.

Enhancing Stability and Efficiency in Mobile Ad Hoc Networks (MANETs): A Multicriteria Algorithm for Optimal Multipoint Relay Selection

Mobile ad hoc networks (MANETs) are autonomous systems composed of multiple mobile nodes that communicate wirelessly without relying on any pre-established infrastructure. These networks operate in highly dynamic environments, which can compromise their ability to guarantee consistent link lifetimes, security, reliability, and overall stability. Factors such as mobility, energy availability, and security critically influence network performance. Consequently, the selection of paths and relay nodes that ensure stability, security, and extended network lifetimes is fundamental in designing routing protocols for MANETs. This selection is pivotal in maintaining robust network operations and optimizing communication efficiency. This paper introduces a sophisticated algorithm for selecting multipoint relays (MPRs) in MANETs, addressing the challenges posed by node mobility, energy constraints, and security vulnerabilities. By employing a multicriteria-weighted technique that assesses the mobility, energy levels, and trustworthiness of mobile nodes, the proposed approach enhances network stability, reachability, and longevity. The enhanced algorithm is integrated into the Optimized Link State Routing Protocol (OLSR) and validated through NS3 simulations, using the Random Waypoint and ManhattanGrid mobility models. The results indicate superior performance of the enhanced algorithm over traditional OLSR, particularly in terms of packet delivery, delay reduction, and throughput in dynamic network conditions. This study not only advances the design of routing protocols for MANETs but also significantly contributes to the development of robust communication frameworks within the realm of smart mobile communications.

A resilient routing strategy based on deep reinforcement learning for urban emergency communication networks

In the context of urban informatization, meeting the stringent requirements of emergency communication presents a significant challenge for Urban Emergency Communication Networks (UECNs). Mobile ad hoc networks deployed in these environments often experience node degradation and link disruptions due to the complex urban landscape, leading to frequent communication failures. This paper introduces a novel resilient routing strategy, termed Deep Reinforcement Learning-based Resilient Routing (DRLRR). The proposed routing strategy first utilizes node and link state information to accurately characterize dynamic changes in network topology. The routing decision-making process is then formalized as a Markov decision process, integrating multiple performance metrics into a reward function tailored for the specific demands of urban emergency communications. By leveraging deep reinforcement learning, DRLRR effectively adapts to the complexities of urban environment, enabling intelligent and optimal route selection during network topology fluctuations to ensure seamless data transmission during emergencies. Comparative simulations conducted using NS3(Network simulator 3) demonstrate that DRLRR significantly outperforms three other routing protocols, achieving notable improvements in packet delivery rate, average end-to-end delay, and throughput, thus fulfilling the requirements for reliable and consistent communication in urban emergency scenarios.

Musings on SVD and pseudo entanglement entropies

Pseudo-entropy and SVD entropy are generalizations of the entanglement entropy that involve post-selection. In this work we analyze their properties as measures on the spaces of quantum states and argue that their excess provides useful characterization of a difference between two (i.e. pre-selected and post-selected) states, which shares certain features and in certain cases can be identified as a metric. In particular, when applied to link complement states that are associated to topological links via Chern-Simons theory, these generalized entropies and their excess provide a novel quantification of a difference between corresponding links. We discuss the dependence of such entropy measures on the level of Chern-Simons theory and determine their asymptotic values for certain link states. We find that imaginary part of the pseudo-entropy is sensitive to, and can diagnose chirality of knots. We also consider properties of entropy measures for simpler quantum mechanical systems, such as generalized SU(2) and SU(1,1) coherent states, and tripartite GHZ and W states.

Fusion of irreducible modules in the periodic Temperley–Lieb algebra

We propose a new family \mathsf{Y}_{k,\ell,x,y,[z,w]}𝖸k,ℓ,x,y,[z,w] of modules over the enlarged periodic Temperley–Lieb algebra \mathsf{\mathcal EPTL}_N(\beta)ℰ𝖯𝖳𝖫N(β). These modules are built from link states with two marked points, similarly to the modules \mathsf{X}_{k,\ell,x,y,z}𝖷k,ℓ,x,y,z that we constructed in a previous paper. They however differ in the way that defects connect pairwise. We analyse the decomposition of \mathsf{Y}_{k,\ell,x,y,[z,w]}𝖸k,ℓ,x,y,[z,w] over the irreducible standard modules \mathsf{W}_{k,x}𝖶k,x for generic values of the parameters zz and ww, and use it to deduce the fusion rules for the fusion \mathsf{W} × \mathsf{W}𝖶×𝖶 of standard modules. These turn out to be more symmetric than those obtained previously using the modules \mathsf{X}_{k,\ell,x,y,z}𝖷k,ℓ,x,y,z. From the work of Graham and Lehrer, it is known that, for \beta = -q-q^{-1}β=−q−q−1 where qq is not a root of unity, there exists a set of non-generic values of the twist yy for which the standard module \mathsf{W}_{\ell,y}𝖶ℓ,y is indecomposable yet reducible with two composition factors: a radical submodule \mathsf{R}_{\ell,y}𝖱ℓ,y and a quotient module \mathsf{Q}_{\ell,y}𝖰ℓ,y. Here, we construct the fusion products \mathsf{W}×\mathsf{R}𝖶×𝖱, \mathsf{W}×\mathsf{Q}𝖶×𝖰 and \mathsf{Q} × \mathsf{Q}𝖰×𝖰, and analyse their decomposition over indecomposable modules. For the fusions involving the quotient modules \mathsf{Q}𝖰, we find very simple results reminiscent of \mathfrak{sl}(2)𝔰𝔩(2) fusion rules. This construction with modules \mathsf{Y}_{k,\ell,x,y,[z,w]}𝖸k,ℓ,x,y,[z,w] is a good lattice regularization of the operator product expansion in the underlying logarithmic bulk conformal field theory. Indeed, it fits with the correspondence between standard modules and connectivity operators, and is useful for the calculation of their correlation functions. Remarkably, we show that the fusion rules \mathsf{W}×\mathsf{Q}𝖶×𝖰 and \mathsf{Q}×\mathsf{Q}𝖰×𝖰 are consistent with the known fusion rules of degenerate primary fields.

Cross-layer UAV network routing protocol for spectrum denial environments

Unmanned Aerial Vehicles (UAVs), which connect to one another over wireless networks, are being used in warfare more frequently. Nevertheless, adversarial interference has the potential to disrupt wireless communication, and the UAV routing methods in use today struggle to handle interference. In this paper, we propose a Cross-Layer UAV Link State Routing protocol, CLUN-LSR, to combat against jamming attacks. CLUN-LSR features three designs. First, it obtains real-time spectrum status from the link layer. Such capabilities are provided by many existing radios, especially the ones in military applications, but are ignored by traditional routing protocols. Second, based on the cross-layer information, CLUN-LSR adds efficient routing functions during routing, including the use of the number of two-hop neighbor nodes as a metric for route selection. Third, CLUN-LSR selects nodes that are not in the interference area, thereby reducing network interruptions and improving data transmission efficiency. All table-driven routing protocols can apply CLUN-LSR for better performance. We apply CLUN-LSR to the existing routing protocol MP-OLSR and simulate it using a commercial network simulator. Simulation results show that our innovative routing protocol demonstrates superior performance compared to existing table-driven routing methods, particularly in terms of packet transmission rate and overall throughput.

An efficient DDoS attack detection and prevention model using fusion heuristic enhancement of deep learning approach in FANET sector

Problem overviewA Flying Ad-hoc Network (FANET) is a decentralized communication network formed by Unmanned Aerial Vehicles (UAVs). However, it faces significant security challenges due to its decentralized nature and mobility. One of the most critical threats is the Distributed Denial-of-Service (DDoS) attack, which aims to overcome the network by flooding it with malicious actions, disrupting communication, and causing system failures. The primary challenge lies in detecting and mitigating such attacks in real-time, given the dynamic and complex nature of FANETs, where the rapid movement of UAVs complicates monitoring and defending mechanisms. MethodologyA novel detection and prevention model has been proposed to address the issue of DDoS attacks utilizing a hybrid heuristic-based machine learning approach tailored for FANET environments. The model begins by collecting necessary information using benchmark datasets to train and enhance the detection performance. The collected data is used for detecting the DDoS attack using Hybrid Deep Learning (HDL). The HDL model developed by combining Deep Temporal Convolutional Networks (DTCN) and Long Short-Term Memory (LSTM) networks, offers a powerful solution. DTCNs excel at detecting short-term, rapidly changing patterns in network traffic, which is essential for recognizing the instant effects of topology changes in FANETs. At the same time, LSTMs are designed to handle long-term dependencies and can used to the evolving patterns of UAV movement and traffic behavior over time. Here, the hyper-parameters are optimized by proposing the Hybrid of Water Strider and Cuckoo Search (HWSCS). Water Strider optimization (WSA) and Cuckoo Search Optimizer (CSO) are combined in the HWSCS algorithm. This algorithm is a versatile and powerful tool for optimizing multifaceted systems across different fields. Its combination of local and global search abilities allows it to find optimal solutions in complicated environments, making it invaluable for tasks ranging from machine learning to network security and beyond. In network security, HWSCS is effective in optimizing the parameters of intrusion detection systems, particularly in detecting complex cyber threats like DDoS attacks, ensuring more accuracy while reducing false positives. After detecting the DDoS attack, it is prevented from communication by establishing the routing process. This routing process involves rerouting the network traffic away from the affected areas and towards secure servers, effectively isolating the attack and minimizing its impact on the network. Additionally, it allows for greater flexibility and adaptability in responding to evolving threats in realtime. Here, the attack mitigation is accomplished by finding the Optimal Link State Routing (OLSR), estimated by the hybrid HWSCS algorithm. This algorithm determines the most efficient path for redirecting traffic away from the targeted servers, preventing overload, and maintaining network stability. The integration of HWSCS with OLSR not only improves network security but also proves the importance of innovative solutions in protecting malicious activities. Lastly, the model's performance is validated and measured with different metrics. ResultsThe proposed model demonstrated superior performance compared to traditional models. It achieved a recall of 93.87, significantly higher than other approaches like 89.22 for MobileNet, 89.40 for DTCN, 88.44 for LSTM, and 88.35 for DTCN-LSTM. This improved detection accuracy, combined with the effective routing mechanism, ensures better prevention and mitigation of DDoS attacks in FANETs. The results confirm the model's ability to not only detect attacks but also minimize network disruption, providing a robust solution for maintaining secure and stable communication in FANET environments.

Inverse Coupled Simulated Annealing for Enhanced OSPF Convergence in IoT Networks

The current Internet of Things (IoT) network structure is evolving from small-scale distributed systems to a large-scale hierarchical collaboration between backbone and access networks. In this context, the dynamic changes in backbone node connections and the surge in service demands, coupled with sluggish fault detection speeds, significantly shorten effective service transmission time. To address this issue, this paper proposes an inverse coupled simulated annealing for enhanced OSPF route convergence in IoT networks (OSPF-ICSA). Initially, the link state is derived from the statistical characteristics of Hello packets, while the aggregated characteristics of the link state are employed to characterize the node state, providing data support for the reverse coupled simulated annealing algorithm. Subsequently, the Hello packet is refined, and a mechanism is designed to synchronize OSPF intervals and transmit node states. This ensures that nodes within the same subnet synchronize their sending intervals and fault detection times while sharing their node states. Finally, building upon this foundation, the reverse coupled simulated annealing algorithm is introduced to jointly optimize the Hello packet sending interval and fault detection time. Compared to the traditional AODV protocol, OSPF-ICSA reduces the average fault detection time by over 37.38%, improves the average fault detection accuracy by more than 3.1%, decreases the average routing overhead by over 20%, and increases the average packet delivery rate by over 5.1%.

Traffic Prevention and Security Enhancement in VANET Using Deep Learning With Trusted Routing Aided Blockchain Technology

ABSTRACTVehicular ad hoc networks (VANETs) in portable broadband networks are a revolutionary concept with enormous potential for developing safe and efficient transportation systems. Because VANETs are open networks that require regular information sharing, it might be difficult to ensure the security of data delivered through VANETs as well as driver privacy. This paper proposes a blockchain technology that supports trusted routing and deep learning for traffic prevention and security enhancement in VANETs. Initially, the proposed Feature Attention‐based Extended Convolutional Capsule Network (FA_ECCN) model predicts the driver's behaviors such as normal, drowsy, distracted, fatigued, aggressive, and impaired. Next, the Binary Fire Hawks‐based Optimized Link State Routing Protocol (BFH_OLSRP) is used to route traffic after trust values have been assessed. Furthermore, Binary Fire Hawks Optimization (BFHO) determines the best routing path based on criteria such as link stability and node stability degree. Finally, blockchain storage is supported by the Interplanetary File System (IPFS) technology to improve the security of VANET data. Additionally, the validation process is established by using Delegated Practical Byzantine Fault Tolerance (DPBFT). As a result, the proposed study employs the blockchain system to securely send data to neighboring vehicles via trust‐based routing, thereby accurately predicting the driver's behavior. The proposed method achieves a better outcome in terms of latency, packet delivery ratio (PDR), overhead packets, throughputs, end‐to‐end delay, transmission overhead, and computational cost. According to simulation results and efficiency evaluation, the proposed approach outperforms existing approaches and enhances vehicle communication security in an effective manner.

A novel energy efficient QoS secure routing algorithm for WSNs

Quality of Service (QoS) routing protocol is a hot topic in the research field of wireless sensor networks (WSNs). However, the task of identifying an optimal path that simultaneously meets multiple QoS constraints is acknowledged as an NP-hard problem, with its complexity intensifying in proportion to the network’s nodal count. Therefore, a novel heuristic multi-objective trust routing method, the Levy Chaos Adaptive Snake Optimization-based Multi-Trust Routing Method (LCASO-MTRM), is proposed, aiming to enhance link bandwidth while simultaneously reducing latency, packet loss, and energy consumption. The proposed method incorporates innovative chaos and adaptive operators within the LCASO framework. The chaos operator enhances population diversity, expands the solution space, and accelerates the search process. Meanwhile, the adaptive operator improves convergence, enhances robustness, and effectively prevents stagnation. Additionally, this paper introduces a novel multi-objective QoS routing model that integrates a link trust mechanism, allowing for a more accurate assessment of link trust levels and a precise reflection of the current link status. The effectiveness of LCASO-MTRM is demonstrated through simulation comparisons with the Improved Particle Swarm Optimization (IPSO), Improved Artificial Bee Colony Algorithm (IABC), and Cloned Whale Optimization Algorithm (CWOA). Simulation results demonstrate that LCASO-MTRM significantly reduces energy consumption by 49.53%, latency by 22.56%, and packet loss by 40.21%, while increasing bandwidth by 6.13%, outperforming the other algorithms.

Research on AUV Multi-Node Networking Communication Based on Underwater Electric Field CSMA/CA Channel.

To address the issues of high attenuation, weak reception signal, and channel blockage in the current electric field communication of underwater robots, research on autonomous underwater vehicle (AUV) multi-node networking communication based on underwater electric field Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) channel was conducted. This article, first through simulation, finds that the Optimized Link State Routing (OLSR) protocol has a smaller routing packet delay time and higher reliability compared to the Ad Hoc On-Demand Distance Vector (AODV) protocol on underwater electric field CSMA/CA channels. Then, a 2FSK underwater electric field communication system was established, and dynamic communication experiments were carried out between two AUV nodes. The experimental results showed that within a range of 0 to 3.5 m, this system can achieve underwater dynamic electric field communication with a bit error rate of 0 to 0.628%. Finally, to avoid channel blockage during underwater AUV multi-node communication, this article proposes a dynamic backoff method for AUV multi-node communication based on CSMA/CA. This system can achieve dynamic multi-node communication of underwater electric fields with an error rate ranging from 0 to 0.96%. The research results have engineering application prospects for underwater cluster operations.

Monitoring changes in walkability over time: An environmental exposure change detection framework with implications for equity and social justice

Evidence suggests that walkability and greenspace impact travel related activity patterns and vehicle emissions which affect sustainability, public health, and equity. Resulting levels of physical activity, active, or sedentary travel time impact obesity, diabetes, and heart disease which impact COVID-19 mortality. It is now possible to track changes in locally controlled land use characteristics known to impact sustainability and health. This information can provide decisionmakers with feedback required to spatially prioritize and better link state and nationally funded transportation investments with locally sanctioned land use actions. Linking the achievement of established benchmarks of health equity-based indicators with funding establishes a more performance-based approach connecting land use with transportation investment. This study longitudinally tracks neighborhood-level walkability features at the census tract level for 2013 and 2020 for the entire USA. Longitudinal volatility-based change detection models are developed to examine how changes in walkability over time correlate with racialization and social justice. Walkability tends to increase over time with significant variations across metro regions and the urban-rural continuum. Largest and smallest increases in walkability were observed in Western Pacific and Northwest states, respectively. Increased racial and social justice disparities were observed in access to more walkable infrastructure by marginalized populations (such as less-educated, older, unemployed, and black individuals). Significant heterogeneity in the spatial distribution of walkability was observed, over the variation captured by observed sociodemographic, regional, and urban/rural factors. The findings highlight the potential for an “environmental surveillance” system to support a “performance-based” approach to transportation funding that prioritizes resource allocation consistent with Justice40 and United Nation's Sustainable Development Goals.

Data transmission optimization based on multi-objective deep reinforcement learning

Abstract Simultaneously reducing network energy consumption and delay is a hot topic today. This paper addresses this issue by designing a novel multi-objective data transmission optimization algorithm based on deep reinforcement learning. A three-layer back propagation (BP) neural network is designed to improve the accuracy of environmental state prediction, by learning from historical state and action sequence data, which can help the agent make better decision for routing selection in complex network environment. Based on this, we use Q-Learning to find routing for transmission demands, aggregating more traffic through less links and routers, to reduce energy consumption and delay. To enhance the efficiency and robustness of the algorithm, a new reward mechanism is designed based on the traffic demand and the link state. The algorithm divides candidate links into three levels for path selection so that a better solution can be obtained on the basis of ensuring feasible solutions are obtained. Continuous updating of the Pareto set through multiple state steps approximates the optimal solution. We leverage the Euclidean distance to the reference point to measure the optimization effect of the two objectives. The simulation results show that this algorithm outperforms existing algorithms in reducing energy consumption and network delay.

STATE OF CELLULAR IMMUNITY IN PURULENT CHOLANGITIS

Abstract. Objective: To study the state of the cellular link of immunity in acute and chronic cholangitis. Materials and methods. Cholangitis always worsens the condition of the biliary system. Depending on the magnitude of the pathological changes in the bile ducts, we distinguished three groups of patients. Group I – cholangitis caused by obstruction of the main extrahepatic bile ducts due to choledocholithiasis, group II – cholangitis in conditions of cicatricial strictures of the main bile ducts, and group III – cholangitis in conditions of already performed reconstruction of the duct system. The indicators of the cellular link of immunity were studied separately in each group. The results. The conducted studies indicate an imbalance of the immune system in patients with different courses of cholangitis. The presence of inflammation of a mild degree of severity causes stimulation of almost all links of the immune system, while the cellular branch reacts first. Conclusions. Long-term cholangitis was accompanied by suppression of the main markers of T-lymphocyte differentiation — CD2+, CD3+, CD4+ and CD8+, which take part in antigen presentation, signal transmission to other cells and affect their adhesive properties.

Optimizing Routing Protocol Design for Long-Range Distributed Multi-Hop Networks

The advancement of communication technologies has facilitated the deployment of numerous sensors, terminal human–machine interfaces, and smart devices in various complex environments for data collection and analysis, providing automated and intelligent services. The increasing urgency of monitoring demands in complex environments necessitates low-cost and efficient network deployment solutions to support various monitoring tasks. Distributed networks offer high stability, reliability, and economic feasibility. Among various Low-Power Wide-Area Network (LPWAN) technologies, Long Range (LoRa) has emerged as the preferred choice due to its openness and flexibility. However, traditional LoRa networks face challenges such as limited coverage range and poor scalability, emphasizing the need for research into distributed routing strategies tailored for LoRa networks. This paper proposes the Optimizing Link-State Routing Based on Load Balancing (LB-OLSR) protocol as an ideal approach for constructing LoRa distributed multi-hop networks. The protocol considers the selection of Multipoint Relay (MPR) nodes to reduce unnecessary network overhead. In addition, route planning integrates factors such as business communication latency, link reliability, node occupancy rate, and node load rate to construct an optimization model and optimize the route establishment decision criteria through a load-balancing approach. The simulation results demonstrate that the improved routing protocol exhibits superior performance in node load balancing, average node load duration, and average business latency.

SIMULASI JARINGAN VPN ROUTER DENGAN METODE LINK STATE ROUTING PROTOCOL DI SMK KORPRI MAJALENGKA

At this time the progress of information technology is growing so rapidly. The internet is a product of advances in information technology that can be accessed by anyone and anywhere. The increasingly sophisticated information technology helps a lot in human life today, but not a few also have a negative impact on the individual and group levels. Therefore, it is necessary to have a data and information security system. SMK Korpri Majalengka is an education provider institution in Majalengka Regency that has not carried out a data communication security system that is not optimal, such as confidential data that can still be accessed by just anyone. The agency needs a local network that has a wide range, cannot be accessed by just anyone, but only people who have access rights are connected to the local network so that agency security can be maintained properly. To solve this problem, a Virtual Private Network (VPN) network is needed. Therefore, the author intends to answer the above problems by simulating the "VPN Router Network with the Link State Routing Protocol Method" with the aim of maintaining the data and information security system at SMK Korpri Majalengka Regency.