Routing Nodes Research Articles

Design and Implementation of Intelligent Fire Monitoring System Based on Multi-Sensor Data Fusion

Abstract To reduce the losses caused by fire, this paper proposes an intelligent fire monitoring system based on multi-sensor data fusion. Through the design of the temperature, smoke and flame sensors, based on the weighted least squares WLS and improved fuzzy support degree of similar sensors and dissimilar sensors data fusion, respectively, in the ZigBee wireless network based on the design of the fire remote monitoring and control alarm for monitoring, and add the local storage and emergency module. In addition, whether the router nodes work on the ZigBee wireless network during the fire test depends on multi-sensor data fusion. The test shows that the node communication distance is extended and can reach the acceptable requirements in the range of 70m, the highest probability of fire occurrence is 0.92, the lowest probability is 0.14, the overall probability of occurrence is in the range of 0.4 to 0.6, and the error between the predicted value of the probability of occurrence of the fire and the expected value is within ±0.05.

Changes in the attraction area and network structure of recreation flows in urban green, blue and grey spaces under the impact of the COVID-19 pandemic

Despite increasing attention to the impact of the COVID-19 pandemic on outdoor recreation, there is limited knowledge about its effects on visitor travel across cities for recreation and changes in the recreation response of blue, green and grey spaces within cities. This study examines changes in the attraction area of inter-city recreation flows within Wuhan, China, before and after the COVID-19 pandemic. The results showed a significant decrease in average attraction distance from 861.74 km to 787.77 km after the pandemic, while attraction directions remained stable. Intra-city recreation routes tend to be simplified, with average length decreasing from 40.86 km to 30.52 km and node number of recreation routes reducing from 7.02 to 5.00. However, the proportion of blue nodes in recreation routes increased significantly from 17.45 % to 23.25 %, while the proportion of grey nodes decreased noticeably from 57.36 % to 52.75 %. The proportion of green nodes did not show a significant change, but when the number of nodes exceeded 6.18, the proportion of green nodes slightly decreased below pre-pandemic levels. With the increase in the length of recreation routes, the proportion of grey nodes gradually converged to around 42 % from around 54 % before the pandemic. The total proportion of agglomeration and diffusion nodes increased from 13.71 % to 20.01 %, while the balanced nodes decreased from 8.87 % to 6.93 % after the pandemic. Notably, 35.71 % of the functional nodes before the pandemic remained their original functional type after the pandemic, but 7.14 % of functional nodes converted to weak function. These findings can assist tourism departments and scenic spot managers in developing strategies that balance pandemic control and recreation needs.

Spectrum-Aware Transitive On-Demand Routing Protocol for Military Cognitive Radio Ad Hoc Networks

The advancement of wireless technology is increasing the demand for scarce spectrum. Cognitive radio ad hoc networks (CRAHNs) were proposed as a solution to spectrum scarcity which is also deployed in combat. However, military cognitive radio ad-hoc networks (MCRAHNs) are subject to destruction and frequent link breakages. The challenge with MCRAHNs routing is the timing-out of packets. This paper proposes the spectrum-aware transitive multi-cast on-demand distance vector (SAT-MAODV), optimised for throughput and delay. The relay nodes are selected based on zonal data and mobility. This is achieved through handshaking and sharing of location data. The nodes are expected to store location data of nodes encountered, which is used in routing and in determining the movement of the node. The mobility of military nodes is organised and structured, which simplifies routing. The SAT-MAODV was evaluated in network simulator 2 and the results show that the scheme is effective. UsingSAT-MAODV instead of xWCETT reduced routing path and node relay delay by at least 65% and 13% respectively, and increased achievable throughput by 31%. It also improved the delivery ratio by 9% while reducing latency by 27% in comparison with MARSA.

An investigation of tendon strains in jersey finger injury load cases using a finite element neuromuscular human body model.

Introduction: A common hand injury in American football, rugby and basketball is the so-called jersey finger injury (JFI), in which an eccentric overextension of the distal interphalangeal joint leads to an avulsion of the connected musculus flexor digitorum profundus (FDP) tendon. In the field of automotive safety assessment, finite element (FE) neuromuscular human body models (NHBMs) have been validated and are employed to evaluate different injury types related to car crash scenarios. The goal of this study is to show, how such a model can be modified to assess JFIs by adapting the hand of an FE-NHBM for the computational analysis of tendon strains during a generalized JFI load case. Methods: A jersey finger injury criterion (JFIC) covering the injury mechanisms of tendon straining and avulsion was defined based on biomechanical experiments found in the literature. The hand of the Total Human Model for Safety (THUMS) version 3.0 was combined with the musculature of THUMS version 5.03 to create a model with appropriate finger mobility. Muscle routing paths of FDP and musculus flexor digitorum superficialis (FDS) as well as tendon material parameters were optimized using literature data. A simplified JFI load case was simulated as the gripping of a cylindrical rod with finger flexor activation levels between 0% and 100%, which was then retracted with the velocity of a sprinting college football player to forcefully open the closed hand. Results: The optimization of the muscle routing node positions and tendon material parameters yielded good results with minimum normalized mean absolute error values of 0.79% and 7.16% respectively. Tendon avulsion injuries were detected in the middle and little finger for muscle activation levels of 80% and above, while no tendon or muscle strain injuries of any kind occurred. Discussion: The presented work outlines the steps necessary to adapt the hand model of a FE-NHBM for the assessment of JFIs using a newly defined injury criterion called the JFIC. The injury assessment results are in good agreement with documented JFI symptoms. At the same time, the need to rethink commonly asserted paradigms concerning the choice of muscle material parameters is highlighted.

An efficient algorithm for optimal route node sensing in smart tourism Urban traffic based on priority constraints

An efficient algorithm for optimal route node sensing in smart tourism Urban traffic based on priority constraints

Energy efficient data transmission using multiobjective improved remora optimization algorithm for wireless sensor network with mobile sink

<span lang="EN-US">A wireless sensor network (WSN) is a collection of nodes fitted with small sensors and transceiver elements. Energy consumption, data loss, and transmission delays are the major drawback of creating mobile sinks. For instance, battery life and data latency might result in node isolation, which breaks the link between nodes in the network. These issues have been avoided by means of mobile data sinks, which move between nodes with connection issues. Therefore, energy aware multiobjective improved remora optimization algorithm and multiobjective ant colony optimization (EA-MIROA-MACO) is proposed in this research to improve the WSN’s energy efficiency by eliminating node isolation issue. MIRO is utilized to pick the optimal cluster heads (CHs), while multiobjective ant colony optimization (MACO) is employed to find the path through the CHs. The EA-MIROA-MACO aims to optimize energy consumption in nodes and enhance data transmission within a WSN. The analysis of EA-MIROA-MACO’s performance is conducted by considering the number of alive along with dead nodes, average residual energy, and network lifespan. The EA-MIROA-MACO is compared with traditional approaches such as mobile sink and fuzzy based relay node routing (MSFBRR) protocol as well as hybrid neural network (HNN). The EA-MIROA-MACO demonstrates a higher number of alive nodes, specifically 192, over the MSFBRR and HNN for 2,000 rounds.</span>

Cycle‐Consistent Generative Adversarial Network and Crypto Hash Signature Token‐based Block chain Technology for Data Aggregation with Secured Routing in Wireless Sensor Networks

SummaryA fundamental component of Wireless Sensor Networks (WSN) is routing, because it is responsible for data transmission to the base stations (BS). Routing attacks are attacks that have the ability to interfere with the operation of WSN. A reliable routing system is needed for guaranteeing routing security including WSN effectiveness. Many studies have been conducted to improve trust among routing nodes, cryptographic algorithms and centralized routing decisions. Nevertheless, most of the routing techniques are not practical because it is challenging to detect suspicious actions of routing nodes. Generally, there is lack of reliable technique for preventing malicious node attacks. Therefore in this manuscript, Cycle‐Consistent Generative Adversarial Network (CCGAN) optimized with Ebola Optimization Search Algorithm (EOSA) and Crypto Hash Signature (CHS) Token‐based Block chain (BC) Technology for Data Aggregation with Secured Routing in Wireless Sensor Networks (SR‐CCGAN‐EOSA‐BDA‐WSN) is proposed for data aggregation with secured optimum routing in WSN. The proposed methodology uses a Proof of Authority (PoA) method in the block chain network to authenticate the process of node transmission. A Cycle‐Consistent Generative Adversarial Network optimized with the Ebola optimization algorithm methodology (CCGAN‐EOSA) is used to select the validation group required for proofing and select the proper next hop as a forwarding node proficient of securely and easily transmitting messages. The performance metrics, like delay, average latency with energy consumption, block chain token transactions throughput is analyzed. The performance of SR‐CCGAN‐EOSA‐BDAWSN proposed method provides 76.26%, 65.57%, and 42.9% lesser delay under 30% spiteful routing environment; 73.06% are compared with existing methods.

WiCHORD+: A Scalable, Sustainable, and P2P Chord-Based Ecosystem for Smart Agriculture Applications

In the evolving landscape of Industry 4.0, the convergence of peer-to-peer (P2P) systems, LoRa-enabled wireless sensor networks (WSNs), and distributed hash tables (DHTs) represents a major advancement that enhances sustainability in the modern agriculture framework and its applications. In this study, we propose a P2P Chord-based ecosystem for sustainable and smart agriculture applications, inspired by the inner workings of the Chord protocol. The node-centric approach of WiCHORD+ is a standout feature, streamlining operations in WSNs and leading to more energy-efficient and straightforward system interactions. Instead of traditional key-centric methods, WiCHORD+ is a node-centric protocol that is compatible with the inherent characteristics of WSNs. This unique design integrates seamlessly with distributed hash tables (DHTs), providing an efficient mechanism to locate nodes and ensure robust data retrieval while reducing energy consumption. Additionally, by utilizing the MAC address of each node in data routing, WiCHORD+ offers a more direct and efficient data lookup mechanism, essential for the timely and energy-efficient operation of WSNs. While the increasing dependence of smart agriculture on cloud computing environments for data storage and machine learning techniques for real-time prediction and analytics continues, frameworks like the proposed WiCHORD+ appear promising for future IoT applications due to their compatibility with modern devices and peripherals. Ultimately, the proposed approach aims to effectively incorporate LoRa, WSNs, DHTs, cloud computing, and machine learning, by providing practical solutions to the ongoing challenges in the current smart agriculture landscape and IoT applications.

Activation dynamics of antigen presenting cells in vivo against Mycobacterium bovis BCG in different immunized route

BackgroundControl of Tuberculosis (TB) infection is mainly the result of productive teamwork between T-cell populations and antigen presenting cells (APCs). However, APCs activation at the site of initiating cellular immune response during BCG early infection is not completely understood.MethodsIn this study, we injected C57BL/6 mice in intravenous (i.v) or subcutaneous (s.c) route, then splenic or inguinal lymph node (LN) DCs and MΦs were sorted, and mycobacteria uptake, cytokine production, antigen presentation activity, and cell phenotype were investigated and compared, respectively.ResultsAg85A-specific T-cell immune response began at 6 days post BCG infection, when BCG was delivered in s.c route, Th17 immune response could be induced in inguinal LN. BCG could induce high level of activation phenotype in inguinal LN MΦs, while the MHC II presentation of mycobacteria-derived peptides by DCs was more efficient than MΦs.ConclusionsThe results showed that BCG immunized route can decide the main tissue of T-cell immune response. Compared with s.c injected route, APCs undergo more rapid cell activation in spleen post BCG i.v infection.

The optimization method for alleviating the congestion during peak hours of Guangzhou metro line 3 is based on thorough research and analysis

The proportion of subway usage in citizens' daily travel increases everyday, but overcrowding during peak hours remains a problem. Studying the characteristics of passenger flows in subways during peak hours is meaningful and a basic requirement to ensure the operation of stations. Although there are various reasons for the surge in passenger flows during peak hours, the fundamental solution is to control the passenger volume within the acceptable range of the station. Therefore, studying appropriate flow control methods to alleviate overcrowding during peak hours is practically valuable for urban development and citizens' lives. To solve this problem, the existing solutions usually involve flow restrictions or diversion, resulting in additional consumption with limited effectiveness. This paper focuses on the overcrowding phenomenon during the evening rush hour of the subway. A route selection model is proposed based on passengers' tendency to choose the route with the minimum negative utility (long travel time, discomfort, etc.) to achieve the feasibility of reducing passenger flow during peak hours, with the objective function set as the minimum value. To optimize the model, decision nodes for constrained routes are provided based on the calculation process of the search decision points and passenger volume threshold set by facility capacity, i.e., when the actual capacity exceeds the threshold. Subsequently, reasonable restrictions are set on the decision nodes of the minimum utility function route to prevent many passengers from entering the platform. Several methods, case analysis and data importation, can effectively ensure the safe and efficient operation of the station.

Construction of a simulated dynamic model of data packet routing on a telecommunication network fragment

The object of the current research is the process of routing data packets in a telecommunications network. It was established that the state and parameters of data transmission channels and routers could have a negative impact and need to be taken into account in the process of modeling the routing of data packets in telecommunication networks. A model of simulated dynamic modeling of data packet routing in telecommunication networks has been devised and proposed. The suggested model makes it possible to establish quantitative values of the delay time of processing data packets during their routing along a separate segment of the telecommunications network, taking into account the state and parameters of the data transmission channel and routers. It has been established that the achievement of the minimum delay time of data packets during routing is achieved by choosing the path of their transmission under the condition of minimum transmission time by communication lines and the capabilities of routers in terms of their accumulation and service speed. At the same time, the reduction of packet transmission delay time relative to the average for the time network segment can reach from 21 to 38 percent. It is shown that the main factor affecting the value of the delay time is the speed of data processing along the selected path of packet transmission. The number of routing nodes in a separate data transmission path can affect the delay time only if the parameters of the routers are equal compared to others that are included in alternative routes. The model of simulated dynamic modeling of data packet routing reported in this work, unlike the existing ones, takes into account the state and parameters of data transmission channels and routers. It can be used in practical improvement of existing and development of new multi-service telecommunication communication networks

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.

Threat Detection Model for WLAN of Simulated Data Using Deep Convolutional Neural Network

Security identification solutions against WLAN network attacks according to straightforward digital detectors, such as SSID, IP addresses, and MAC addresses, are not efficient in identifying such hacking or router impersonation. These detectors can be simply mocked. Therefore, a further protected key uses new information by combining these simple digital identifiers with an RF signature of the radio link. In this work, a design of a convolutional neural network (CNN) based on fingerprinting radio frequency (RF) is developed with computer-generated data. The developed CNN is trained with beacon frames of a wireless local area network (WLAN) that is simulated as a result of identified and unidentified router nodes of fingerprinting RF. The proposed CNN is able to detect router impersonators by comparing the data pair of the MAC address and RF signature of the received signal from the known and unknown routers. ADAM optimizer, which is the extended version of stochastic gradient descent, has been used with a developed deep learning convolutional neural network containing three fully connected and two convolutional layers. According to the training progress graphic, the network converges to around 100% accuracy within the first epoch, which indicates that the developed architecture was efficient in detecting router impersonations.

Research on Deployment Scheme and Routing Optimization Algorithm of Distribution Cable Condition Monitoring Devices

In order to achieve more effective online monitoring of distribution cables, a deployment scheme of the distribution cable condition monitoring devices based on a wireless sensor network (WSN) has been proposed. The proposed deployment scheme can improve the coverage rate and extend the lifetime of the sensor network. Moreover, the clustering method, node density, and node deployment method of the wireless sensor routing algorithm are improved, and based on isosceles triangle non-uniform deployment, a routing optimization algorithm has been proposed to achieve the balance of the energy consumption of each node in the network. Firstly, according to the energy consumption model of the network, the energy consumption of each cluster is calculated, and then by taking the minimum energy consumption of data transmission between clusters as the constraint condition, the optimal clustering distance of the network is solved. Then, according to the scale of network nodes, the density of routing nodes in each cluster is calculated, and the routing nodes in the cluster are deployed by an isosceles triangle. Finally, according to the cluster head election formula, the routing cluster head nodes in each cluster are selected, and the network data information is transmitted to the base station in a multi-hop manner through the routing cluster head nodes. The simulation results indicated that, compared with the traditional routing optimization algorithms, the proposed algorithm could keep the routing coverage at more than 200% all the time, and can effectively balance the energy consumption of nodes, improve the energy utilization efficiency of the routing nodes, and at least extend the lifetime of the network by two times. Moreover, the longer the cables, the more cost savings and the better the life cycle optimization effect of the proposed scheme. In addition, the proposed scheme can greatly reduce the economic cost of network investment, compared with using the demodulator to obtain monitoring data.

MO-CBACORP: A new energy-efficient secure routing protocol for underwater monitoring wireless sensor network

With the rapid progress of sensor technology, underwater monitoring wireless sensor network (UMWSN) equipped with water resource monitoring capabilities have surfaced. UMWSN is currently emerging as a popular research and application area, with a wide range of applications in various fields, including but not limited to disaster warning, pollutant monitoring and hydrological data monitoring. However, due to the limited size and computing power of wireless sensors, UMWSN are exceedingly susceptible to network security attacks by contaminating entities. In order to tackle the problems of inadequate security, elevated network energy consumption, and high system latency in existing QoS routing research, this paper puts forward an innovative multi-objective routing protocol named multi-objective chaotic Boltzmann ant colony optimization routing protocol (MO-CBACORP). MO-CBACORP can minimize the energy consumption of sensor nodes in UMWSN routing, thereby effectively diminishing system latency and significantly enhancing routing security. During the routing optimization process, MO-CBACORP proposed an innovative design of the chaos operator and Boltzmann selection strategy, which effectively avoids the issue of being stuck in local optimal solutions and greatly enhances the convergence speed of the algorithm. Additionally, a novel experiment for underwater monitoring environments is presented that precisely replicates underwater monitoring conditions and attack scenarios. According to the experimental results, MO-CBACORP exhibits superior performance compared to the latest state-of-the-art research including THOA-MHR, ACO-MCMC, and MAP-ACO, with a minimum improvement of 7.25%, 9.31%, and 7.68% in terms of route security, network energy consumption, and delay, respectively. All in all, MO-CBACORP exhibits superior performance compared to the three latest research algorithms.

Heuristic approaches for the cutting path problem

With the extensive application of laser cutting, the proper planning of the cutting path has a significate impact on industrial production, like clothing, metalware, and furniture. This paper proposes heuristic approaches for the two-dimensional cutting path problem, which aims to find the shortest path to cut pieces from the master plate. Two cutting methods, the arc routing method, and the node routing method, are studied based on whether the piece must be cut separately. A two-step heuristic approach is proposed for the arc routing method. A model based on a generalized traveling salesman problem (GTSP) and a variable neighborhood search (VNS) is introduced for the node routing method. The results show that the proposed two-step heuristic quickly finds the optimal or near-optimal solution. CPLEX using the GTSP model can solve small-size instances, while the VNS can find the solution with good quality in a reasonable time for medium-size instances. In addition, the model and approaches are also tested on larger instances with more than 250 pieces and 20,000 nodes. The results show that the proposed heuristics can handle these instances within a reasonable time.

An algorithm for discovering vital nodes in regional networks based on stable path analysis

Vital node discovery is a hotspot in network topology research. The key is using the Internet’s routing characteristics to remove noisy paths and accurately describe the network topology. In this manuscript, a vital regional routing nodes discovery algorithm based on routing characteristics is proposed. We analyze the stability of multiple rounds of measurement results to overcome the single vantage point’s path deviation. The unstable paths are eliminated from the regional network which is constructed through probing for target area, and the pruned topology is more in line with real routing rules. Finally, we weight the edge based on the actual network’s routing characteristics and discover vital nodes in combination with the weighting degree. Unlike existing algorithms, the proposed algorithm reconstructs the network topology based on communication and transforms unweighted network connections into weighted connections. We can evaluate the node importance in a more realistic network structure. Experiments on the Internet measurement data (275 million probing results collected in 107 days) demonstrate that: the proposed algorithm outperforms four existing typical algorithms. Among 15 groups of comparison in 3 cities, our algorithm found more (or the same number) backbone nodes in 10 groups and found more (or the same number) national backbone nodes in 13 groups.

Cohort-based kernel principal component analysis with Multi-path Service Routing in Federated Learning

Federated Learning (FL) is a machine learning (ML) strategy that is performed in a decentralized environment. The training is performed locally by the client on the global model shared by the server. Federated learning has recently been used as a service (FLaaS) to provide a collaborative training environment to independent third-party applications. However, the widespread adoption in distributed settings of FL has opened venues for a number of security attacks. A number of studies have been performed to prevent multiple FL attacks. However, sophisticated attacks, such as label-flipping attacks, have received little or no attention. From the said perspective, this research is focused on providing a defense mechanism for the aforesaid attack. The proposed approach is based on Type-based Cohorts (TC) with Kernel Principal Component Analysis (KPCA) to detect and defend against label-flipping attacks. Moreover, to improve the performance of the network, we will deploy Multi-path Service Routing (MSR) for edge nodes to work effectively. The KPCA will be used to secure the network from attacks. The proposed mechanism will provide an effective and secure FL system. The proposed approach is evaluated with respect to the following measures: execution time, memory consumption, information loss, accuracy, service request violations, and the request’s waiting time.

Path loss model based on exponential water cycle algorithm for wireless sensor network

SummaryWireless sensor networks (WSNs) are frequently employed in the agriculture field to improve the quality and crop yield. The WSN might reduce the quality of the communication link because of the absorption, dispersion, and attenuation through the leaves of plants. Therefore, estimating the path loss due to signal attenuation before WSN deployment is crucial for the smooth operation of the network. In this research paper, three innovative path loss models are defined based on the MATLAB curve fitting tool: polynomial water cycle (PWC), exponential water cycle (EWC), and Gaussian water cycle (GWC) algorithm. Here, the path loss between the router node and the coordinator node is modeled on the basis of the received signal strength indicator (RSSI) and time of arrival (TOA) measurements in a sugarcane field. The correlation coefficient between the RSSI measurement and the distance must be increased to create a precise path loss model. This paper integrates the exponential, polynomial, and Gaussian functions with the water cycle algorithm (WCA) to evaluate the optimal coefficients that would lead to precise path loss models. The performance of the proposed models that determines the optimum linear fit between RSSI and distance is validated using the correction coefficient . The results show that the proposed path loss model is superior to existing path loss models. The correlation coefficient of the proposed EWC model is 0.9993, whereas the existing PE‐PSO, LNSM, and PSO‐Exponential models yield 0.98, 0.87, and 0.93, respectively. Also, the proposed models attain the best mean absolute error (MAE) of 0.2187, 0.2951, and 0.3457 dBm for EWC, PWC, and GWC algorithms, respectively.

Mathematical Model of Network and Communication Node State in TensorField Algebraic Space Subject to Electromagnetic Pulse Lesion

The possibility of applying a tensor approach to the mathematical model construction of the network and communication node state in the algebraic space of tensor field is proposed, taking into account the factors of destructive impact by an electromagnetic pulse (EMP) causing functional damage to the structural elements of communication equipment (routers, controls, computing equipment and monitoring) of networks and communication nodes. To construct the specified mathematical model under conditions of destructive influence, taking into account the provisions of network tensor analysis, a 2-simplex structure at the router level is introduced: a set of closed and open paths connecting nodes with N possible connections in total. The whole set of structures is considered as a particular coordinate system within N-dimensional space, corresponding to various ways of connecting N branches, while maintaining their original number or moving from one set of independent paths to another within the analyzed space without loss of quality and performance characteristics of the communication equipment of networks and communication nodes. Taking into account the size, quantity of source data and multi-criteria, the solution of problems of optimizing the characteristics, properties and structure of a communication system that is subject to destructive influence, including networks and routing nodes, is carried out in parts, iteratively involving special methods. In future, the use of this model at the stage of the network and communication node parameter optimizing will allow to account them in the benefit of protection from negative (destructive) impact threats, followed by the mathematical model formation to ensure functional stability of networks and communication nodes of critical purpose.