Packet Networks Research Articles

Visual identification of some regularities in packet network traffic

Modern heterogeneous packet networks generate network traffic with a complex structure. In this article, the object of study is a time series. The total number of User Datagram Protocol (UDP) packets has reached 250242. According to analysts, the growth trend of traffic, including real-time applications, will continue and the volume of data will grow, which may lead to the formation of packet queues when processed by network devices. In this case, there may be losses in case of long queues. To solve this problem, a power spectrum assessment was carried out. The AR maximum entropy estimator has been shown to be more sensitive than the auxiliary Fourier estimator. Accounting for non-stationarity by spectral methods is possible only through estimation in a sliding time window. Nine diagrams of spectral-temporal analysis of the original series, its increments, and the mixed series of increments were obtained: with default parameters, with small and large windows. Diagrams related to the original series reflect the dynamics of changes in data transmission intensity in the network; they show higher temporal resolution, indicating the presence of high-frequency components (noise) and the presence of low-frequency components (trend). Diagrams with increments describe signals of periodic components; changing the length of the window did not reflect the presence of noise or trend signs. Diagrams with mixed increments show that frequency components are uniformly distributed. The uniqueness of this work lies in the real measured data, and a distinctive feature of the obtained results is the visual examination of the complex traffic structure, allowing for the resolution of the investigated problem. Practical application of the results obtained can be applied in Quality of Service (QoS) management, resource planning, and network performance optimization

Analysis of multimedia network data security based on routing algorithm

In today’s society, the application of multimedia data is increasing, and the accompanying data security is also an important issue. This paper will analyze the data security of multimedia network based on routing algorithm. The advantages of wireless local area networks (WLAN) and ad hoc networks are combined in wireless mesh networks (WMNs), which have steadily grown to be a reliable method of wireless broadband access. The application of multi RF and multi-channel technology improves the performance of WMN. However, the limitation that includes the number of wireless network interface cards and channels in the network, results in the great challenges ofrouting and channel allocation in multi RF and multi-channel WMN. Based on AODV algorithm, this paper proposes a new cross layer routing algorithm which is JRCA-AODV algorithm. The simulation results demonstrate that the JRCA-AODValgorithm can successfully increase multiRF multi-channel WMN throughput while lowering packet loss rate and network delay. Take the packet rate of 8 Mbps as an example, the network throughput based on JRCA-AODV algorithm is 1.14 times that of MRMC-AODV algorithm, the packet loss rate based on JRCA-AODV algorithm is 14.06% lower than that of MRMC-AODV algorithm, and the network time delay based on JRCA-AODV algorithm is 14.27% lower than that of MRMC-AODV algorithm. At the same time, JRCA-AODV algorithm can effectively reduce the probability of data loss caused by the competitive and shared access mechanism of the channel, improve the success rate of data transmission, thus reduce the probability of data eavesdropping and tampering in the process of repeated transmission, and improve the security of multimedia network data.This will provide a new direction for promoting the idea of multimedia data security.

CTJIF-ICN: A Coadjuvant Trust Joint Interest Forwarding Mechanism in Information Centric Networks

The Information centric networks (ICN) transforms the focal point of current Internet paradigm to data centric approach from host centric approach by allowing content driven forwarding and in-network caching mechanisms. Though NDN (Named data networking) paradigm of ICN assures a secure content communication, it is vulnerable to different attacks by the malicious nodes. To minimize the hazards from compromised nodes and to improve the network security, the remaining nodes should transparently receive information about such nodes. This will restrict the forwarding strategy to exploit these malicious nodes for forwarding interest and content as well. Our protocol introduces a dynamic model for prediction of trust in order to evaluate the node trust. Proposed approach observes the historical behaviors of node and uses extended fuzzy logic rules for the prediction of future behaviors to evaluate the node’s trust value. This prediction model is incorporated within the trust based forwarding mechanism that aims to forward interest through secure and shortest path. The extensive simulation study has been carried out to analyze the protocol performance in ns-3 driven ndnSIM-2.0 simulator for performance metrics such as data discovery latency, packet delivery ratio, network overhead, detection ratio and cache hit ratio. When we integrate our trust joint forwarding strategy to state-of-the-art protocols, their performance is significantly improved up to approximately 10-35% against stated performance measures for realistic network topology.

SWARAM: Osprey Optimization Algorithm-Based Energy-Efficient Cluster Head Selection for Wireless Sensor Network-Based Internet of Things.

The Internet of Things (IoT) has transformed various aspects of human life nowadays. In the IoT transformative paradigm, sensor nodes are enabled to connect multiple physical devices and systems over the network to collect data from remote places, namely, precision agriculture, wildlife conservation, intelligent forestry, and so on. The battery life of sensor nodes is limited, affecting the network's lifetime, and requires continuous maintenance. Energy conservation has become a severe problem of IoT. Clustering is essential in IoT to optimize energy efficiency and network longevity. In recent years, many clustering protocols have been proposed to improve network lifetime by conserving energy. However, the network experiences an energy-hole issue due to picking an inappropriate Cluster Head (CH). CH node is designated to manage and coordinate communication among nodes in a particular cluster. The redundant data transmission is avoided to conserve energy by collecting and aggregating from other nodes in clusters. CH plays a pivotal role in achieving efficient energy optimization and network performance. To address this problem, we have proposed an osprey optimization algorithm based on energy-efficient cluster head selection (SWARAM) in a wireless sensor network-based Internet of Things to pick the best CH in the cluster. The proposed SWARAM approach consists of two phases, namely, cluster formation and CH selection. The nodes are clustered using Euclidean distance before the CH node is selected using the SWARAM technique. Simulation of the proposed SWARAM algorithm is carried out in the MATLAB2019a tool. The performance of the SWARAM algorithm compared with existing EECHS-ARO, HSWO, and EECHIGWO CH selection algorithms. The suggested SWARAM improves packet delivery ratio and network lifetime by 10% and 10%, respectively. Consequently, the overall performance of the network is improved.

Research on islanding detection of solar distributed generation based on best wavelet packet and neural network

Research on islanding detection of solar distributed generation based on best wavelet packet and neural network

Priority RPL for IOT Based Smart Manufacturing Industries

A routing protocol used in heterogeneous transport networks for low-power, lossy networks. This is a routing protocol for wireless networks. This protocol follows the same specifications as Zigbee, 6 lopan is IEEE 802.15. 4 Enables both many-to-one and one-to-one communication. To address the need for enhancing in this study proposes a novel methodology called RPL-PG (Routing Protocol for Low-Power and Lossy Networks Priority Generation). Initially sensors like Temperature, Humidity, Vibration, Proximity, Gas and Current Monitoring Sensors are used for smart manufacturing. Consequently, Destination Oriented Directed Acyclic Graph (DODAG) is used for RPL configuration. Based on selected RPL configuration the priority is generated using assign priority count and priority-based queuing. Finally, Fuzzy rules are used to select the RPL path and then update the DODAG finally reached the destination. The study involves setting up a simulated environment using appropriate tools, such as MATLAB. Experimental findings evaluate and compares performance measures, such as Energy Consumption, Network Life Time, Packet Loss Ratio, Packet Delivery Ratio (PDR), E2E Delay, and Network Throughput. The Energy Consumption of the proposed RPL-PG method achieves 43.6 % lower than 38 % and 35.8 % in terms of OMC-RPL and RMA-RP respectively.

Algorithm for diagnosing and warning of vibration caused by loosened screws on motor base using wavelet packet and neural network

Motor base screw loosening is a common problem in motor operation, which, if not dealt with in time, may lead to motor failure and damage. However, few studies have focused on the diagnosis and warning of this problem. Based on wavelet packet and neural network analysis, this paper presents a new algorithm for monitoring, diagnosing, and warning vibration caused by loose screws in the motor base. The vibration signal generated by the base screw loosening is monitored and sampled with sensors, and the wavelet packet is used to decompose, reconstruct, and reduce the noise of the vibration signal to enhance the time–frequency characteristics of the signal. After analyzing the fault data by wavelet, the feature vector characterizing the fault is extracted, and then, the vector and the corresponding fault type are used as the input and output of the neural network, respectively, and the non-mapping relationship between them is built to complete the diagnosis and early warning of the fault. Finally, the method is used to compare the motor base screw loosening operation and normal operation. The experiments show that the new algorithm based on wavelet packet and neural network can complete the health diagnosis and early warning of motor in the early stage of motor base screw loosening, reduce the loss caused by subsequent faults, and provide a new reference scheme for the motor fault diagnosis field.

Gaussian Harris Hawks Optimizer (GHHO) Based Cluster Head Selection (CHS) And Enhanced Energy Harvesting Clustering (EEHC) Protocol for WSN

Designing routing protocols for Energy Harvesting- Wireless Sensor Network (EH-WSN) has two key challenges like improving harvested energy utilization and examination harvested energy utilization. However, the clustering and routing phases of the clustering-based routing protocols employed in EH-WSN need a lot of message overhead. Cluster Head Selection (CHS) is a crucial component of clustering that improves energy efficiency. An Enhanced Energy Harvesting Clustering (EEHC) protocol with two phases for cluster setup and data transmission is presented in this research. Sensor nodes are grouped into clusters during cluster construction, and CHS using the Gaussian Harris Hawks Optimizer (GHHO) algorithm is used for data transmission. Hawks chasing behaviours serve as the search agent in the GHHO algorithm, while prey serves as the ideal CH position. Data from each cluster node is collected by CHS, which then sends the aggregated data to the base station. Following CHS, connecting Cluster Member (CM) to a suitable CH is done using a variety of factors like energy, distance to neighbours, distance to the Base Station (BS), node density, and Signal-to-Noise Ratio (SNR). Each CM awakens during its designated working time slot during the DT stage, and transfers the data it has gathered towards the CH of the same cluster. A mobile sink is employed to enhance network performance and relay transmission distance is changed to match the amount of gathered energy. Residual Energy (RE), Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), Network Lifetime, and average delay are metrics used to measure the results of clustering methods.

A Design of Improved Trust-based Clustering Approach to Enhance the Energy Computation in Secured Internet of Things (IoT)

The Internet of Things (IoT) is the trending area that occupies maximum of the applications of intelligent communication. The nodes in the IoT system are located in the monitoring environment to attain stable and reliable communication. In between the nodes and the sink, similar data is sensed and transmitted at maximum of the period and that leads to high energy consumption. On the other hand, it increases the security issues of the network. So the main objective is reducing power utilization and increasing the IoT network security. In this paper, we suggest an improved trust-based clustering approach to enhance energy computation and security (ITCES-IoT) in IoT networks which can able to reduce energy consumption and increase the security among the nodes in the network. We first introduced an effective system model that can able to reduce the transmitting and receiving power during communication. Secondly, the initial cluster setup phase, LEACH-based cluster head (CH) selection phase, and re-clustering principles are discussed. Then, the trust calculation is established for both the cluster members (CM) and the CH through direct and indirect trust calculation processes followed by the best solution of path selection is performed between the CM and the sink node. We constructed Bad mouthing attacks, Ballot stuffing attacks, and detection mechanisms with a Fast Entropy algorithm. Finally, intra-cluster and inter-cluster communication are elaborated in the communication phase. Through this process, the nodes and CHs can able to perform effective communication with minimum power utilization, and the nodes with the transmitted information are secured from the external environment. At the end stage, we validate our proposed ITCES-IoT approach in certain scenarios in network simulator NS2 and their performance analysis includes the parameters of packet delivery ratio, network throughput, and packet loss and energy consumption. Then the final outcomes are compared with the base methods such as ECET-IoT, DRTP-IoT, and RCDA-IoT. From the results it is understood that the proposed ITCES-IoT outperforms the baseline methods in terms of delivery ratio and throughput and as well it consumes less power at the time of data transmission.

Performance Analysis of Routing Protocol Using Trust-Based Hybrid FCRO-AEPO Optimization Techniques

Mobile Ad hoc Networks (MANETs) offer numerous benefits and have been used in different applications. MANETs are dynamic peer-to-peer networks that use multi-hop data transfer without the need for-existing infrastructure. Due to their nature, for secure communication of mobile nodes, they need unique security requirements in MANET. In this work, a Hybrid Firefly Cyclic Rider Optimization (FCRO) algorithm is proposed for Cluster Head (CH) selection, it efficiently selects the CH and improves the network efficiency. The Ridge Regression Classification algorithm is presented in this work to sense the malicious nodes in the network and the data is transmitted using trusted Mobile nodes for the QoS enactment metric improvement. A trust-based routing protocol (TBRP) is introduced utilizing the Atom Emperor Penguin Optimization (AEPO) algorithm, it identifies the best-forwarded path to moderate the routing overhead problem in MANET. The planned method is implemented using Matlab software and the presentation metrics are packet delivers ratio, packet loss ratio (PLR), routing overhead, throughput, end-to-end delay (E2ED), transmission delay, energy consumption and network lifetime. The suggested AEPO algorithm is compared with the prevailing PSO-GA, TID-CMGR, and MFFA. The AEPO algorithm’s performance is approximately 1.5%, 3.2%, 2%, 3%, and 4% higher than the existing methods for PLR, packet delivers ratio, throughput, and E2ED and network lifetime. The sender nodes can increase their information transmission rates and reduce delays in appreciation of this evaluation. Additionally, the suggested technique has a perfect benefit in terms of demonstrating the genuine contribution of distinct nodes to trust evaluation (TE).

A novel Q-learning-based routing scheme using an intelligent filtering algorithm for flying ad hoc networks (FANETs)

The flying ad hoc network (FANET) is an emerging network focused on unmanned aerial vehicles (UAVs) that has attracted the attention of researchers around the world. Due to the cooperation between UAVs in this network, data transfer between these UAVs is very essential. Routing protocols must determine how to make routing paths for each UAV with others in a wireless ad hoc network to facilitate the data transmission between UAVs. Nowadays, reinforcement learning (RL), especially Q-learning, is an effective response for solving existing challenges in the routing approaches and adding features such as autonomous, self-adaptive, and self-learning to these approaches. In this paper, Q-learning is used to enhance and increase network performance, and a Q-learning-based routing method using an intelligent filtering algorithm called QRF is presented for FANETs. The main innovation in this paper is that QRF manages the size of the state space using the proposed filtering algorithm. This will increase the convergence rate of the Q-learning-based routing algorithm. On the other hand, QRF regulates the learning parameters related to Q-learning so that this scheme is better adapted to the FANET environment. In the last step, the network simulator version 2 (NS2) is employed to execute the simulation process related to QRF. In this process, five evaluation criteria, namely energy consumption, packet delivery rate, overhead, end-to-end delay, and network longevity are evaluated, and the results obtained from QRF are compared with those of QFAN, QTAR, and QGeo. The simulation results in this paper show that QRF makes a balanced energy distribution between UAVs and thus extends the network longevity. Moreover, the intelligent filtering algorithm designed in QRF has reduced delay in the routing process but is associated with communication overhead.

An improved fault diagnosis method for rolling bearings based on wavelet packet decomposition and network parameter optimization

The diagnosis of faults in rolling bearings plays a critical role in monitoring the condition and maintaining the performance of rotating machinery, while also preventing major accidents. In this article, a new approach to diagnosing faults in rolling bearings is proposed, using wavelet packet decomposition (WPD) for features extraction and the chaotic sparrow search optimization algorithms (CSSOAs) to optimize the parameters of a deep belief network (DBN). Firstly, the WPD method is used for the decomposition of vibration signals in rolling bearings, which are decomposed into three layers, and reconstruction is performed on the nodes of the last layer based on the decomposition. Furthermore, the energy characteristics of the reconstructed nodes are then utilized as inputs to DBN, and the CSSOA is employed to optimize the hyperparameters of DBN. Ultimately, a fault diagnosis model combining WPD with optimizing parameters is presented. This model is validated on bearing datasets from Case Western Reserve University (CWRU) and Jiangnan University (JNU). Experimental results indicate that the average accuracy achieved when modeling with WPD-CSSOA-DBN on the CWRU dataset is , with a root mean square error of 0.0713. On the JNU bearing dataset, the modeling achieves an average accuracy of with a root mean square error of 0.1018. Compared to other methods, this approach demonstrates stronger feature extraction capabilities and outstanding rolling bearing fault diagnosis abilities.

Research on image analysis and processing method based on compressed perception technology

Abstract This paper analyzes the traditional Shannon-Nyquist sampling theorem, introduces the process of compressive perception theory and the key techniques of compressive perception in image sparse representation, design of measurement matrix and signal reconstruction, and explores the application of compressive perception in the field of image analysis and processing. Meanwhile, the image system imaging is constructed based on the compressive perception technique, and the process of wavelet packet subspace decomposition and reconstruction constructs the compressive perception image algorithm based on the optimal wavelet packet basis. The algorithm simulation results show that the minimum signal entropy is 16*4 in the minimum wavelet chunking way, at which the minimum values are -0.35, -0.04, -0.07, and -0.01, respectively.

SRP-HEE: A Modified Stateless Routing Protocol based on Homomorphic Energy based Encryption for Wireless Sensor Network

Due to the wireless nature, the sensors node data are prone to location privacy of source and classification of the packet by unauthorized parties. Data encryption is one of the most effective ways to thwart unauthorized access to the data and trace information. Traditional wireless network security solutions are not viable for WSNs In this paper, a novel distributed forward aware factor based heuristics towards generating greedy routing using stateless routing is SRP-HEE for wireless sensor network. The model employs the homomorphic Energy based encryption technique. Energy based Encryption model is devoted as homomorphic mechanism due to their less computational complexity. Additionally, privacy constraint becoming a critical issue in the wireless sensor networks (WSNs) because sensor nodes are generally prone to attacks which deplete energy quickly as it is exposed to mobile sink frequently for data transmission. Through inclusion of the Forward aware factor on the Greedy routing strategies, it is possible to eliminate the attacking node which is depleting the energy of the source node. Heuristic conditions are used for optimizing the sampling rate and battery level for tackling the battery capacity constraints of the wireless sensor nodes. The Node characteristics of the propagating node have been analysed utilizing kalman filter and linear regression. The cooperative caching of the network information will enable to handle the fault condition by changing the privacy level of the network. The Simulation results demonstrate that SRP-HEE model outperforms existing technique on basis of Latency, Packet Delivery Ratio, Network Overhead, and Energy Utilization of nodes.

GET 200-2023 State Primary Standard of the transmitted (received) information (data) unit and the units of packet network parameters

GET 200-2023 State Primary Standard of the transmitted (received) information (data) unit and the units of packet network parameters

Multi level trust calculation with improved ant colony optimization for improving quality of service in wireless sensor network

Wireless sensor network (WSN) is the most integral parts of current technology which are used for the real time applications. The major drawbacks in currect technologies are threads due to the creation of false trust values and data congestion. Maximum of the concept of WSNs primarily needs security and optimization. So, we are in the desire to develop a new model which is highly secured and localized. In this paper, we introduced a novel approach namely multi level trust calculation with improved ant colony optimization (MLT-IACO). This approach mainly sub-divided into two sections they are multi level trust calculation which is the combination three levels of trust such as direct trust, indirect trust and random repeat trust. Secondly, improved ant colony optimization technique is used to find the optimal path in the network. By transmitting the data in the optimal path, the congestion and delay of the network is reduced which leads to increase the efficiency. The outcome values are comparatively analyzed based the parameters such as packet delivery ratio, network throughput and average latency. While compared with the earlier research our MLT-IACO approach produce high packet delivery ratio and throughput as well as lower latency and routing overhead.

Linear Packet Network Coding to Enhance Reliability and Resiliency of Next Generation Wireless Networks with Topological Redundancies

Linear Packet Network Coding to Enhance Reliability and Resiliency of Next Generation Wireless Networks with Topological Redundancies

Robustness and Reliability Provided by Deterministic Packet Networks (TSN and DetNet)

Converged networks require new service protection functionalities to ensure reliable and robust communications. This paper provides insights into the evolution of deterministic communication and the importance of service protection in converged networks. We examine the per-packet-based replication/elimination service protection mechanism, ongoing standardization efforts, and potential areas for further development. We also explore the practical implementation of the service protection technique in real network scenarios, including its impact on operation and maintenance. The main contributions of the paper are: (1) new cloud-specific extensions to the service protection functionality, (2) introduction of redundancy domains to avoid replication/elimination-related failure propagation, (3) new service protection specific extensions to the network operation toolset, and (4) new diagnostic capabilities to monitor the healthiness of the service protection functionalities. Additionally, we evaluate the effectiveness and operation of newly proposed service protection extensions based on experimental results from a real 5G testbed.

Arithmetic Optimization AOMDV Routing Protocol for FANETs.

Flying ad hoc networks (FANETs), composed of small unmanned aerial vehicles (UAVs), possess characteristics of flexibility, cost-effectiveness, and rapid deployment, rendering them highly attractive for a wide range of civilian and military applications. FANETs are special mobile ad hoc networks (MANETs), FANETs have the characteristics of faster network topology changes and limited energy. Existing reactive routing protocols are unsuitable for the highly dynamic and limited energy of FANETs. For the lithium battery-powered UAV, flight endurance lasts from half an hour to two hours. The fast-moving UAV not only affects the packet delivery rate, average throughput, and end-to-end delay but also shortens the flight endurance. Therefore, research is urgently needed into a high-performance routing protocol with high energy efficiency. In this paper, we propose a novel routing protocol called AO-AOMDV, which utilizes arithmetic optimization (AO) to enhance the ad hoc on-demand multi-path distance vector (AOMDV) routing protocol. The AO-AOMDV utilizes a fitness function to calculate the fitness value of multiple paths and employs arithmetic optimization for selecting the optimal route for routing selection. Our experiments were conducted using NS3 with three evaluation metrics: the packet delivery ratio, network lifetime, and average end-to-end delay. We compare this algorithm to routing protocols including AOMDV and AODV. The results indicate that the proposed AO-AOMDV attained a higher packet delivery ratio, network lifetime, and lower average end-to-end delay.

An Efficient Routing Awareness Based Scheduling Approach in Energy Harvesting Wireless Sensor Networks

Wireless sensor networks (WSNs) have emerged as a promising technology for various Internet of Things (IoT)-based sensing applications. Generally, sensor nodes are powered by non-rechargeable and low-capacity batteries; they only last for a small amount of time. Due to their dispersed nature, sensor nodes cannot have their batteries replaced or maintained locally. The energy harvesting (EH) concept shows commitment as a method for addressing the current energy shortage. Another problem is the intra-energy control of various network load functions, including coverage preservation, routing, clustering, etc. This study proposes an ERAS (efficient routing awareness scheduling) algorithm to address the abovementioned issues. The ERAS method employs a solar harvesting system as a hierarchical clustering-based routing protocol. The presented scheme dynamically alters nodes between states and adjusts to new states based on the insides of sensed data packets. Synchronization-based scheduling ensures proper coordination between the cluster head (CH) and cluster members (CMs). It selects the CH based on both remaining and harvested energy and the number of active nodes to improve throughput. Surprisingly proposed EASR algorithm performs efficiently while considering various performance matrices, including network lifetime, packet delivery ratio, energy consumption, and network sustainability. Extensive simulation results exhibit the network lifetime of the proposed ERAS technique (79.8%) compared to ERA, SEED and CREST methods as 67.3%, 73.8%, and 75.2%, respectively.