Data Routing Research Articles

CLUSTER-BASED GRID COMPUTING ON WIRELESS NETWORK DATA TRANSMISSION WITH ROUTING ANALYSIS PROTOCOL AND DEEP LEARNING

Grid computing based on clusters has emerged as a promising strategy for improving the efficacy of wireless network data transmission. This study examines the incorporation of cluster-based grid computing, routing analysis protocols, and deep learning techniques to optimize data transmission in wireless networks. The proposed method utilizes clusters to distribute computing duties and enhance resource utilization, resulting in efficient data transmission. To further improve the routing process, a novel routing analysis protocol is introduced, which dynamically adapts to network conditions and chooses the most optimal routes. In addition, deep learning algorithms are used to analyze network data patterns, allowing for intelligent data routing and resource allocation decisions. Experiment results exhibit the efficacy of the proposed method, revealing substantial enhancements in network performance metrics such as throughput, latency, and energy consumption. This research contributes to the development of cluster-based grid computing and offers valuable insights for the design of efficient wireless network data transmission systems.

Analytical Study for Optimization Techniques to Prolong WSNs Life

Wireless Sensor Networks (WSNs) have become the most cost-effective monitoring solution due to their low cost, despite their major drawback of limited power due to dependence on batteries. Each Sensor Node (SN) is clustered in a particular location and forms a network by self-organizing. They often operate in some of the world's most unusual or dangerous conditions. Networking errors, memory and processor limitations, and energy constraints all pose problems for WSN developers. Many problems in WSNs are expressed as multivariate optimization problems that are solved using biologically inspired techniques. Particle swarm optimization (PSO) is an easy, algorithmically sound, and robust optimization technique. It has been used to address problems like Clustering, data routing, Cluster Head (CH) collection, and data collecting in WSNs. This paper presents a brief analysis of WSN studies in which the PSO algorithm was used as the primary or secondary algorithm for enhancing lifespan of WSNs, focusing on results that show energy efficiency in the sensors, extending the network's life. Index Term— Cluster Head selection, Clustering, PSO, Routing, Wireless Sensor Networks.

Weight-Based PA-GPSR Protocol Improvement Method in VANET.

Vehicle Ad-hoc network (VANET) can provide technical support and solutions for the construction of intelligent and efficient transportation systems, and the routing protocol directly affects the efficiency of VANET. The rapid movement of nodes and uneven density distribution affect the routing stability and data transmission efficiency in VANET. To improve the local optimality and routing loops of the path-aware greedy perimeter stateless routing protocol (PA-GPSR) in urban sparse networks, a weight-based path-aware greedy perimeter stateless routing protocol (W-PAGPSR) is proposed. The protocol is divided into two stages. Firstly, in the routing establishment stage, the node distance, reliable node density, cumulative communication duration, and node movement direction are integrated to indicate the communication reliability of the node, and the next hop node is selected using the weight greedy forwarding strategy to achieve reliable transmission of data packets. Secondly, in the routing maintenance stage, based on the data packet delivery angle and reliable node density, the next hop node is selected for forwarding using the weight perimeter forwarding strategy to achieve routing repair. The simulation results show that compared to the greedy peripheral stateless routing protocol (GPSR), for the maximum distance-minimum angle greedy peripheral stateless routing (MM-GPSR) and PA-GPSR protocols, the packet loss rate of the protocol is reduced by an average of 24.47%, 25.02%, and 14.12%, respectively; the average end-to-end delay is reduced by an average of 48.34%, 79.96%, and 21.45%, respectively; and the network throughput is increased by an average of 47.68%, 58.39%, and 20.33%, respectively. This protocol improves network throughput while reducing the average end-to-end delay and packet loss rate.

An intelligent active probing and trace-back scheme for IoT anomaly detection

Due to their simple hardware, sensor nodes in IoT are vulnerable to attack, leading to data routing blockages or malicious tampering, which significantly disrupts secure data collection. An Intelligent Active Probing and Trace-back Scheme for IoT Anomaly Detection (APTAD) is proposed to collect integrated IoT data by recruiting Mobile Edge Users (MEUs). (a) An intelligent unsupervised learning approach is used to identify anomalous data from the collected data by MEUs and help to identify anomalous nodes. (b) Recruit MEUs to trace back and propose a series of trust calculation methods to determine the trust of nodes. (c) The last, the number of active detection packets and detection paths are designed, so as to accurately identify the trust of nodes in IoT at the minimum cost of the network. A large number of experimental results show that the recruiting cost and average anomaly detection time are reduced by 6.5 times and 34.33% respectively, while the accuracy of trust identification is improved by 20%.

Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring.

Wireless Sensor Networks (WSNs) have been adopted in various environmental pollution monitoring applications. As an important environmental field, water quality monitoring is a vital process to ensure the sustainable, important feeding of and as a life-maintaining source for many living creatures. To conduct this process efficiently, the integration of lightweight machine learning technologies can extend its efficacy and accuracy. WSNs often suffer from energy-limited devices and resource-affected operations, thus constraining WSNs' lifetime and capability. Energy-efficient clustering protocols have been introduced to tackle this challenge. The low-energy adaptive clustering hierarchy (LEACH) protocol is widely used due to its simplicity and ability to manage large datasets and prolong network lifetime. In this paper, we investigate and present a modified LEACH-based clustering algorithm in conjunction with a K-means data clustering approach to enable efficient decision making based on water-quality-monitoring-related operations. This study is operated based on the experimental measurements of lanthanide oxide nanoparticles, selected as cerium oxide nanoparticles (ceria NPs), as an active sensing host for the optical detection of hydrogen peroxide pollutants via a fluorescence quenching mechanism. A mathematical model is proposed for the K-means LEACH-based clustering algorithm for WSNs to analyze the quality monitoring process in water, where various levels of pollutants exist. The simulation results show the efficacy of our modified K-means-based hierarchical data clustering and routing in prolonging network lifetime when operated in static and dynamic contexts.

Efficient determination of driving resistance through system identification based on driving route information and weather data

Predictive driving functions based on load profile prediction can make an important contribution to the urgently needed further reduction in energy consumption of road vehicles. In this context, this paper presents a method that precisely and efficiently predicts the driving resistances of an upcoming trip by means of online parameter estimation and system identification based on the driving route and weather data. A parameter estimation has been implemented that determines at the beginning of each trip different parameters such as the total vehicle mass, the dynamic rolling radius as well as the absolute and relative inclination of the vehicle relative to the road. It then performs an online calibration of the speed and acceleration calculation. In addition, a method was developed that allows the prediction of driving resistances based on the estimated parameters as well as environmental data such as road roughness and wind conditions. The developed functions were validated against real-world measurements under different environmental conditions and were able to reduce the prediction error in parameter estimation to a median of less than 2 %, while the prediction error of the driving resistances was reduced from sometimes more than 40 % to a few percentage points compared to conventional approaches.

Lightweight Transmission Behavior Audit Scheme for NDN Industrial Internet Identity Resolution and Transmission Based on Blockchain

The Named Data Network (NDN) enables efficient content dissemination through interest-based retrieval, name-based routing, and content caching. In the industrial Internet architecture based on NDN, device identity distribution, identification, resolution, and routing rely on identification resolution technology. However, this approach presents challenges such as cache poisoning, interest packet flood attacks, and black hole attacks. Existing security schemes primarily focused on routing forwarding and verification fail to address critical concerns, including routing environment credibility and data leakage, while exhibiting poor time and space efficiency. To address these challenges, this paper proposes a lightweight behavior auditing scheme using blockchain technology. The scheme utilizes an improved Bloom filter to compress behavioral information like interest and data packets during the identification transmission process. The compressed data are subsequently uploaded to a blockchain for auditing, achieving efficient space and time utilization while maintaining feasibility.

Intelligent-Slicing: An AI-Assisted Network Slicing Framework for 5G-and-Beyond Networks

5G-and-beyond networks are designed to fulfill the communication and computation requirements of various industries, which requires not only transporting the data, but also processing them to meet/address diverse key performance indicators (KPIs). Network Function Virtualization (NFV) has emerged to enable this vision by: (i) collecting the requirements of diverse services, using graphs of Virtual Network Functions (VNFs); and (ii) mapping these requirements into network management decisions. Because of the latter, we need to efficiently allocate computing and network resources to support the desired services, and because of the former such decisions must be jointly optimized considering all KPIs associated with supported services. Thus, this paper proposes an optimized, intelligent network slicing framework to maintain a high performance of network operation by supporting diverse and heterogeneous services, while meeting new KPIs, e.g., reliability, energy consumption, and data quality. Different from the existing works, which are mainly designed considering traditional metrics like throughput and latency, we present a novel methodology and resource allocation schemes that enable high-quality selection of radio points of access, VNF placement and data routing, as well as data compression ratios, from the end users to the cloud. Our results depict the efficiency of the proposed framework in enhancing the network performance when compared to baseline approaches that consider partial network view or fair resource allocation.

LEACH-based hierarchical energy efficient routing in wireless sensor networks

Wireless Sensor Networks (WSNs) not only have a constantly increasing range of applications but also stimulate the amazing growth of 5G mobile communications and the Internet of Things (IoT). Yet, the lifetime of WSNs is restricted due to the severe energy limitations of their components. For this reason, the achievement of energy sustainability in WSNs is extremely important. Given that the greatest part of energy consumption in a WSN takes place during data routing, numerous research works are carried out in order to develop protocols for energy efficient routing. This article aims to clarify the most popular subdivision of this category of protocols i.e. the so-called hierarchical energy efficient routing protocols. Specifically, LEACH, which is considered to be the pioneer protocol of this kind, is studied along with 18 of its descendant protocols. A theoretical comparison of these protocols in terms of various metrics is also performed. Additionally, the performance of LEACH is compared with that of 3 descendant protocols through simulation tests that are carried out. Finally, a discussion takes place and concluding remarks are drawn.

Pythagorean Fuzzy Sets-based VIKOR and TOPSIS-based multi-criteria decision-making model for mitigating resource deletion attacks in WSNs

In Wireless Sensor Networks (WSNs), resource depletion attacks that focusses on the compromization of routing protocol layer is identified to facilitate a major influence over the network. These resource depletion attacks drain the batter power of the sensor nodes drastically with persistent network disruption. Several protocols were established for handling the impact of Denial of Service (DoS) attack, but majority of them was not able to handle it perfectly. In specific, thwarting resource depletion attack, a specific class of DoS attack was a herculean task. At this juncture, Multicriteria Decision Making Model (MCDM) is identified as the ideal candidate for evaluating the impact introduced by each energy depletion compromised sensor nodes towards the process of cooperation into the network. In this paper, A Pythagorean Fuzzy Sets-based VIKOR and TOPSIS-based multi-criteria decision-making model (PFSVT-MCDM) is proposed for counteracting with the impacts of resource depletion attacks to improve Quality of Service (QoS) in the network. This PFSVT-MCDM used the merits of Pythagorean Fuzzy Sets information for handling uncertainty and vagueness of information exchanged in the network during the process of data routing. It utilized VIKOR and TOPSIS for exploring the trust of each sensor nodes through the exploration of possible dimensions that aids in detecting resource depletion attacks. The experimental results of PFSVT-MCDM confirmed better throughput of 21.29%, enhanced packet delivery fraction of 22.38%, minimized energy consumptions 18.92%, and reduced end-to-end delay of 21.84%, compared to the comparative resource depletion attack thwarting strategies used for evaluation.

Harris Hawks Optimization-Based Clustering Algorithm for Vehicular Ad-Hoc Networks

Vehicular ad-hoc network (VANET) is highly dynamic due to the high speed and sparse distribution of vehicles on the road. This creates major challenges (e.g., network fragmentation, packet routing) for the researchers to enable robust, reliable, and scalable communication, especially in a highly dense network. Clustering in VANET is one of the remedies to address the scalability issue. However, it is observed in the literature, that existing clustering techniques produce a high number of clusters for the vehicular environment. Consequently, it increases the consumption of scarce resources in a wireless network. Furthermore, it also increases the communication overhead as well as the number of hops for data routing. As a result communication latency also increases and the reliability of communication protocol decreases. So it is highly desirable to find out the optimal clusters for a given vehicular environment. As finding optimal clusters is a multi-objective combinatorial optimization problem, therefore by employing nature-inspired meta-heuristic algorithms we can optimize the multi-objective problem. To this end, we proposed a novel clustering algorithm based on the Harris Hawks Optimization (HHO) algorithm for VANET (HHOCNET). HHO algorithm is a nature-inspired meta-heuristic algorithm inspired by the foraging maneuver of hawks called surprise pounce. The proposed framework imitates the cooperative foraging maneuver of hawks (i.e., surprise pounce for creating optimized vehicular clusters). The stochastic operators of the HHO algorithm and proper maintenance of the equilibrium state between the operations of exploration and exploitation enable the proposed algorithm to escape from the local optima and provide a globally optimal solution (i.e., the optimal number of vehicular clusters). Simulations are performed in MATLAB and the results are compared with the state-of-art schemes (i.e., Gray Wolf optimization-based clustering algorithm (GWOCNET), Multi-objective Particle Swarm Optimization (MO, PSO), and Comprehensive Learning Particle Swarm Optimization (CLPSO)) using different performance metrics. The results demonstrate that the proposed approach is an effective approach for clustering in VANET and outer performs the other benchmark algorithms in terms of optimizing the multi-objective clustering problem. HHOCNET algorithm selects 36.04% of nodes as cluster heads while the existing state-of-the-art schemes are providing 50.42%, 56.7%, and 60.89% for GWOCNET, CLPSO, and Multi-objective Particle Swarm Optimization (MOPSO). The proposed HHOCNET algorithm enhances the performance of the vehicular network by up to 15%. Consequently, it increases network efficiency by reducing the consumption of the required wireless resources. It also reduces the number of hops for packet routing. Hence it achieves a minimum end-to-end communication latency.

Oil tanker under ice loadings

As a result of global warming, the area of the polar pack ice is diminishing, making merchant travel more practical. Even if Arctic ice thickness reduced in the summer, fractured ice is still presenting operational risks to the future navigation. The intricate process of ship-ice interaction includes stochastic ice loading on the vessel hull. In order to properly construct a vessel, the severe bow forces that arise must be accurately anticipated using statistical extrapolation techniques. This study examines the severe bow forces that an oil tanker encounters when sailing in the Arctic Ocean. Two stages are taken in the analysis. Then, using the FEM program ANSYS/LS-DYNA, the oil tanker bow force distribution is estimated. Second, in order to estimate the bow force levels connected with extended return periods, the average conditional exceedance rate approach is used to anticipate severe bow forces. The vessel’s itinerary was planned to take advantage of the weaker ice. As a result, the Arctic Ocean passage took a meandering route rather than a linear one. As a result, the ship route data that was investigated was inaccurate with regard to the ice thickness data encountered by a vessel yet skewed with regard to the ice thickness distribution in the region. This research intends to demonstrate the effective application of an exact reliability approach to an oil tanker with severe bow forces on a particular route.

PERFORMANCE EVALUATION OF SA-LA ROUTING PROTOCOL FOR WSN INTEGRATED IOT

WSN is considered one of the prominent technology due to the different kinds of operations they are involved in. Secured and energy-efficient data routing is very important in the WSN framework. The main aim of this research is to evaluate the performance of a secured and energy-efficient routing protocol named Self Adaptive-Lion Algorithm (SA-LA) in WSN, as well as in WSN assisted IoT framework. This protocol is evaluated by means of different parameters like residual energy, delay, network lifetime, Node Trust Factor (NTF), Interference factor (IF), throughput and security risk probability. Achieved improved outcomes as network lifetime rose up to 14% also risk probability decreased marginally through 18% compared to previous methods.

RF-BBFT: a random forest based multimedia big data routing technique for social opportunistic IoT networks

RF-BBFT: a random forest based multimedia big data routing technique for social opportunistic IoT networks

MHSEER: A Meta-Heuristic Secure and Energy-Efficient Routing Protocol for Wireless Sensor Network-Based Industrial IoT

Several industries use wireless sensor networks (WSN) for various tasks such as monitoring, data transmission, and data gathering. They find applications in the industrial internet of things (IIoT). WSNs are utilized to track and monitor changes in the environment. Since they include multiple small sensor nodes (SN), they are severely constrained, so resource management geared toward energy efficiency is crucial in this kind of network. Minimizing the power to interpret, transmit, and store data between various sensors poses important challenges. Experts have considered various ways to address these issues that unavoidably affect the network’s performance: reducing energy usage while maintaining system throughput remains the primary research issue. Another important concern relates to network security. Specifically, intrusion detection and avoidance are major concerns. In this work, we introduce the meta-heuristic-based secure and energy-efficient routing (MHSEER) protocol for WSN-IIoT. The protocol learns the forwarding decisions using the number of hops, connection integrity characteristics, and accumulated remaining energy. To make the method more secure, the protocol also employs counter-encryption mode (CEM) to encrypt the data. A meta-heuristics study designed to achieve reliable learning is used in the suggested protocol. The protocol consists of two stages. The first stage uses a heuristics method to improve the option for dependable data routing. Security based on a computationally simple and random CEM is accomplished in the second stage. The proposed MHSEER protocol has been compared to the secure trust routing protocol for low power (Sectrust-RPL), heuristic-based energy-efficient routing (HBEER), secure and energy-aware heuristic-based routing (SEHR), and secure energy-aware meta-heuristic routing (SEAMHR) in terms of packet drop ratio, throughput, network delay, energy usage, and faulty pathways. The proposed protocol increases throughput to 95.81% and decreases the packet drop ratio, packet delay, energy consumption, and faulty pathways to 5.12%, 0.10 ms, 0.0102 mJ, and 6.51%, respectively.

Z number improved reference ideal method‐based decision‐making process for enforcing cooperation during data dissemination in mobile ad hoc networks

SummaryIn mobile ad hoc networks (MANETs), cooperative communication and resource constraint are the two important core characteristics essential to guarantee trusted data dissemination. The cooperative communication between mobile nodes depends on the trust rendered by them towards the process of reliable data routing. However, stringent resource constraints of mobile nodes such as energy, memory, communications, and computations result in the introduction of selfish and malicious node that completely degrades the network performance in different dimensions. In this paper, Z number improved reference ideal method (RIM)‐based decision‐making process (NIRIMDMP) is proposed with the merits of maximizing deviation method (MDM) and best–worst method (BWM) to ensure reliable data routing by modeling the cooperation degree in terms of Z number. This NIRIMDMP adopted Z number to represent the information reliability and handle the problem of inherent uncertainty during the process of evaluating each mobile node in the routing process. In specific, MDM and BWM are included into the proposed NIRIMDMP to determine comprehensive attribute weights based on the calculated objective and subjective weights that could be possible derived in routing. It extended the merits of classical RIM using Z numbers to confirm reliable ranking of mobile nodes, even when the optimal solution exists amid extreme values taken into consideration for assessing the mobile nodes during decision making. Simulation investigations of the proposed NIRIMDMP confirmed improved throughput and network lifetime with reduced control overhead, energy consumptions, and delay independent of the amount of malicious and non‐cooperative nodes.

Fully Homomorphic Encryption with Optimal Key Generation Secure Group Communication in Internet of Things Environment

The Internet of Things or “IoT” determines the highly interconnected network of heterogeneous devices where each type of communication seems to be possible, even unauthorized. Consequently, the security requirement for these networks became crucial, while conventional Internet security protocol was identified as unusable in these types of networks, especially because of some classes of IoT devices with constrained resources. Secure group communication (SGC) in the IoT environment is vital to ensure the confidentiality, integrity, and availability (CIA) of data swapped within a collection of IoT devices. Typically, IoT devices were resource-constrained with limited memory, processing, energy, and power, which makes SGC a difficult task. This article designs a Fully Homomorphic Encryption with Optimal Key Generation Secure Group Communication (FHEOKG-SGC) technique in the IoT environment. The presented FHEOKG-SGC technique mainly focuses on the encryption and routing of data securely in the IoT environment via group communication. To accomplish this, the presented FHEOKG-SGC technique initially designs an FHE-based encryption technique to secure the data in the IoT environment. Next, the keys in the FHE technique are chosen optimally using the sine cosine algorithm (SCA). At the same time, the plum tree algorithm (PTA) is applied for the identification of the routes in the IoT network. Finally, the FHEOKG-SGC technique employs a trust model to improve the secure communication process, and the key management center is used for optimal handling of the keys. The simulation analysis of the FHEOKG-SGC technique is tested using a series of experiments, and the outcomes are studied under various measures. An extensive comparative study highlighted the improvement of the FHEOKG-SGC algorithm over other recent approaches.

Enhanced ant colony optimization algorithm for packet delivery with improved energy efficiency in wireless sensor networks

This paper aims to formulate an enhanced ant colony optimization algorithm that helps find a suitable data route that improves energy efficiency and reduces the time consumption for the delivery of packets. Energy equations have been framed to analyze energy used at the transmitting, receiving, and relay nodes. The network is segmented into smaller virtual segments for easier analysis of the proposed algorithm. Each segment is assumed to have nodes with a cluster head. The sink gathers information from different cluster heads as it moves from one segment to another. The nodes not in close connection with the network used the overhearing mechanism to share the information with the sink. The simulation has been done using Cisco Packet Tracer software. The proposed algorithm has been applied to different types of wireless networks to determine their efficiency. The wireless networks considered for this purpose are Bluetooth, Wi-Max, and Wi-Fi. Packet delivery ratio, end-to-end delay, collision, and lifetime are evaluated for the different types of wireless networks. The obtained results are analyzed, and graphs are plotted.

Demonstration of a photonic router via quantum walks

Motivated by the need for quantum computers to communicate between multiple, well separated qubits, we introduce the task of routing the quantum state from one input mode to a superposition of several output modes coherently. We report an experimental demonstration of a deterministic photonic routing protocol applied to an entangled state. We show in a quantum walk architecture, quantum networks perfectly route entangled states from an initial input mode to an arbitrary output mode coherently and deterministically. Our results demonstrate the key principle of a perfect router, opening a route toward data routing and transferring for quantum computing systems. The routing algorithm in our work can be applied to a wide range of physical systems, which provides a way for effective design of efficient routing protocols on practical quantum networks.

NDN-BDA: A Blockchain-Based Decentralized Data Authentication Mechanism for Vehicular Named Data Networking

Named Data Networking (NDN) is an implementation of Information-Centric Networking (ICN) that has emerged as a promising candidate for the Future Internet Architecture (FIA). In contrast to traditional networking protocols, NDN’s focus is on content, rather than the source of the content. NDN enables name-based routing and location-independent data retrieval, which gives NDN the ability to support the highly dynamic nature of mobile networks. Among other important features, NDN integrates security mechanisms and prioritizes protecting content over communication channels through cryptographic signatures. However, the data verification process that NDN employs may cause significant delays, especially in mobile networks and vehicular networks. This aspect makes it unsuitable for time-critical and sensitive applications such as the sharing of safety messages. Therefore, in this work, we propose NDN-BDA, a blockchain-based decentralized mechanism that provides a faster and more efficient data authenticity mechanism for NDN-based vehicular networks.