Packet Delivery Research Articles

Improved MPR Selection Algorithm-Based WS-OLSR Routing Protocol

Vehicle Ad Hoc Networks (VANETs) have become a viable technology to improve traffic flow and safety on the roads. Due to its effectiveness and scalability, the Wingsuit Search-based Optimised Link State Routing Protocol (WS-OLSR) is frequently used for data distribution in VANETs. However, the selection of MultiPoint Relays (MPRs) plays a pivotal role in WS-OLSR's performance. This paper presents an improved MPR selection algorithm tailored to WS-OLSR, designed to enhance the overall routing efficiency and reduce overhead. The analysis found that the current OLSR protocol has problems such as redundancy of HELLO and TC message packets or failure to update routing information in time, so a WSOLSR routing protocol based on improved-MPR selection algorithm was proposed. Firstly, factors such as node mobility and link changes are comprehensively considered to reflect network topology changes, and the broadcast cycle of node HELLO messages is controlled through topology changes. Secondly, a new MPR selection algorithm is proposed, considering link stability issues and nodes. Finally, evaluate its effectiveness in terms of packet delivery ratio, end-to-end delay, and control message overhead. Simulation results demonstrate the superior performance of our improved MR selection algorithm when compared to traditional approaches.

EECRPSID: Energy-Efficient Cluster-Based Routing Protocol with a Secure Intrusion Detection for WSN-Assisted IoT

A revolutionary idea that has gained significance in technology for Internet of Things (IoT) networks backed by WSNs is the " Energy-Efficient Cluster-Based Routing Protocol with a Secure Intrusion Detection" (EECRPSID). A WSN-powered IoT infrastructure's hardware foundation is hardware with autonomous sensing capabilities. The significant features of the proposed technology are intelligent environment sensing, independent data collection, and information transfer to connected devices. However, hardware flaws and issues with energy consumption may be to blame for device failures in WSNassisted IoT networks. This can potentially obstruct the transfer of data. A reliable route significantly reduces data retransmissions, which reduces traffic and conserves energy. The sensor hardware is often widely dispersed by IoT networks that enable WSNs. Data duplication could occur if numerous sensor devices are used to monitor a location. Finding a solution to this issue by using clustering. Clustering lessens network traffic while retaining path dependability compared to the multipath technique. To relieve duplicate data in EECRPSID, we applied the clustering technique. The multipath strategy might make the provided protocol more dependable. Using the EECRPSID algorithm, will reduce the overall energy consumption, minimize the End-to-end delay to 0.14s, achieve a 99.8% Packet Delivery Ratio, and the network's lifespan will be increased. The NS2 simulator is used to run the whole set of simulations. The EECRPSID method has been implemented in NS2, and simulated results indicate that comparing the other three technologies improves the performance measures.

IoT-based MANET performance improvement against jamming attackers in different mobile applications

Internet of Things (IoT) is a cutting-edge technology and effective solution that enables real-time communication between physical objects. In order to manage the mobility of nodes in the IoT, mobile ad hoc networks (MANETs) are crucial. It enables IoT-based systems to construct and connect networks autonomously. IoT based MANET is more vulnerable to Jamming attacks (Jammers) due to the decentralized nature of the ad hoc network. Therefore, secure communication is a crucial requirement for nodes. In this paper, jammers interfere with the IoT MANET communication resulting in order to degrade network performance in terms of delay, throughput and PDR (Packet Delivery Rate) which are the important QoS (Quality of Service) parameters to measure the network performance. The IoT ad hoc system's networking is based on MANET routing protocols so that (GRP, AODV and OLSR) routing protocols had been examined in this paper to improve the QoS (Packet Delivery rate, throughput and delay) of the IoT-MANET system using Riverbed Modeler (17.5) simulation program for different mobile applications. The outcomes showed that the selection of a suitable routing protocol would improve the degradation in delay, throughput and PDR of the ad hoc network.

An Integrated DQN and RF Packet Routing Framework for the V2X Network

With the development of artificial intelligence technology, deep reinforcement learning (DRL) has become a major approach to the design of intelligent vehicle-to-everything (V2X) routing protocols for vehicular ad hoc networks (VANETs). However, if the V2X routing protocol does not consider both real-time traffic conditions and historical vehicle trajectory information, the source vehicle may not transfer its packet to the correct relay vehicles and, finally, to the destination. Thus, this kind of routing protocol fails to guarantee successful packet delivery. Using the greater network flexibility and scalability of the software-defined network (SDN) architecture, this study designs a two-phase integrated DQN and RF Packet Routing Framework (IDRF) that combines the deep Q-learning network (DQN) and random forest (RF) approaches. First, the IDRF offline phase corrects the vehicle’s historical trajectory information using the vehicle trajectory continuity algorithm and trains the DQN model. Then, the IDRF real-time phase judges whether vehicles can meet each other and makes a real-time routing decision to select the most appropriate relay vehicle after adding real-time vehicles to the VANET. In this way, the IDRF can obtain the packet transfer path with the shortest end-to-end delay. Compared to two DQN-based approaches, i.e., TDRL-RP and VRDRT, and traditional VANET routing algorithms, the IDRF exhibits significant performance improvements for both sparse and congested periods during intensive simulations of the historical GPS trajectories of 10,357 taxis within Beijing city. Performance improvements in the average packet delivery ratio, end-to-end delay, and overhead ratio of the IDRF over TDRL-RP and VRDRT under different numbers of pairs and transmission ranges are at least 3.56%, 12.73%, and 5.14% and 6.06%, 11.84%, and 7.08%, respectively.

A Blockchain-Enabled Trust Management Framework for Energy-Efficient and Secure Routing in Mobile Ad-Hoc Networks

This study principally addresses the challenges associated with streaming videos in wireless Mobile ad hoc networks (MANETs), primarily constrained by wireless channels and node mobility. This study proposes a novel routing protocol incorporating a feedback mechanism and cross-layer architecture, supported by three key enhancements. First, it incorporates a route recovery strategy for detecting link failures and re-establishing connections based on predefined Quality of Service (QoS) metrics. Second, a novel algorithm estimates the available bandwidth, enabling dynamic adaptation of bitrate at the application layer concerning the source video. This novel rate-adaptive approach utilizes a scalable layered structure of the video coding, thereby removing layers that the network cannot efficiently support due to bandwidth limitations. Third, a gateway discovery procedure enhances the connectivity among MANETs and other infrastructure networks. This method employs available bandwidth as a parameter while selecting appropriate gateways and dynamically adjusts operational parameters like proactive area size and advertisement message frequency. The performances of the proposed Enhanced OLSR (E-OLSR) protocol are compared with the state-of-the-art routing procedures such as Adhoc on-demand distance vector (AODV), destination sequenced distance vector (DSDV), and optimized link state routing (OLSR), in terms of metrics like packet delivery ratio (PDR), end-to-end (E2E) delay, throughput, as well as energy consumption. The simulation results demonstrated notable improvements, including reduced packet delay, fewer dropped packets, and decreased link failures, indicating effective utilization of available bandwidth. The overall performance of the proposed E-OLSR protocol exceeds other traditional protocols and its security is enhanced through the integration of Blockchain Technology.

A lightweight packet forwarding verification in SDN using sketch

By decoupling the control plane and the data plane, Software Defined Networking (SDN) has reshaped the ossified network architecture and improved the programmability of the network. However, SDN is also susceptible to malicious injection, tampering, dropping and hijacking attacks against forwarding packets, and the SDN architecture cannot perceive the real behavior of switches in the data plane. Packet forwarding verification and exception localization are recognized as effective and promising methods, enabling reliable packet delivery in the data plane and allowing the controller in SDN to identify abnormal links. While existing mechanisms embed the linear-scale cryptographic tags into the packet header space as the transmission path lengthens to realize packet verification and exception localization, which cannot achieve a feasible tradeoff between efficiency and security. Leveraging the central controllability of SDN, we propose a lightweight packet forwarding verification mechanism. This mechanism splits the runtime of a flow into consecutive epochs by address hopping. The switches in the data plane forward packets based on hopping address, collecting the information of packets forwarded in a compact data structure, namely traffic sketch. The controller verifies traffic sketches and localizes exception in the epoch. We further prototype the proposed mechanism based on simulation network. The analysis and experiments demonstrate that the cost of proposed scheme is lower than the similar mechanisms, introducing no more than 9 % additional forwarding delay and less than 8 % throughput degradation.

Enhanced Whale Optimization Algorithm for Multi-Objective Node Disjoint Routing

Objectives: The research goal is to identify node-disjoint pathways maximizing path lifetime, delivery ratio, etc. while establishing several node-disjoint paths to optimize routing and enhance interference reduction and PDR efficiency. Methods: In the MANET, the Enhanced Whale Optimization Algorithm (EWOA) is utilized to discover the most secure routing path. The dataset used in this research comprises simulated scenarios representing various network conditions and configurations, allowing for a comprehensive evaluation of the proposed work. The software employed for simulation and analysis includes an NS-3 network simulation tool, allowing the model to evaluate the performance of the proposed routing protocol in diverse MANET scenarios. The parameters considered during the evaluation include throughput, end-to-end delay, packet delivery ratio (PDR), and routing overhead. The modifications involve enhancing the routing algorithm to prioritize node-disjoint pathways, thus improving interference reduction, energy efficiency, and overall network performance. Findings: The EWOA for Multi-Objective Node Disjoint Routing demonstrates superior performance compared to existing routing protocols such as SRABC and OLSR across various key metrics. Specifically, in terms of end-to-end delay, EWOA consistently outperforms SRABC and OLSR by reducing delay by 1.3% and 4.7%, respectively. Additionally, EWOA achieves a higher packet delivery ratio, surpassing SRABC and OLSR by 4.3% and 0.4%, respectively. Moreover, EWOA exhibits higher throughput, with a throughput increase of 11.3% compared to SRABC and 6.2% compared to OLSR. Furthermore, EWOA demonstrates lower routing overhead, reducing overhead by 9.6% compared to SRABC and 3.1% compared to OLSR. These findings highlight the efficacy of EWOA in optimizing multi-objective routing in MANETs, offering improved network performance and efficiency compared to existing protocols. Novelty: The Multi-Objective Node Disjoint Routing Protocol with Enhanced Whale Optimization is utilized in MANET to choose the best route, reducing latency resulting from link failure and distributing traffic loads across multiple paths. Keywords: Optimization, Whale Optimization Algorithm (WOA), Optimized Link State Routing (OLSR), Secure Routing Algorithm Blockchain Technology (SRABC)), MANET

Safeguarding the Internet of Things: Elevating IoT routing security through trust management excellence

This study presents an innovative IoT routing security model that integrates trust management to bolster network reliability, improve resilience against routing attacks, and isolate malicious activities. The model, emphasizing node behavior, reputation, and past performance, offers a nuanced approach to network security. Through comprehensive comparisons between dynamic and static models in IoT routing, the impact on crucial performance parameters, including throughput, packet delivery ratio, control traffic overhead, and energy consumption, is quantified. Simulations showcase the model's effectiveness in securing IoT communication, achieving an impressive 98 % accuracy in detecting and mitigating attacks. Comparative analysis against prior studies underscores its exceptional performance, particularly in identifying and classifying attack types such as wormhole, Sybil, and rank, alongside normal traffic. This trust-based IoT routing security model represents a substantial advancement in securing dynamic IoT environments, standing out as a valuable contribution. Noteworthy is its low average power consumption, contributing to its exceptional lightweight nature.

Energy-Efficient and Highly Reliable Geographic Routing Based on Link Detection and Node Collaborative Scheduling in WSN.

Energy efficiency and data reliability are important indicators to measure network performance in wireless sensor networks. In existing research schemes of routing protocols, the impact of node coverage on the network is often ignored, and the possibility that multiple sensor nodes may sense the same spatial point is not taken into account, which results in a waste of network resources, especially in large-scale networks. Apart from that, the blindness of geographic routing in data transmission has been troubling researchers, which means that the nodes are unable to determine the validity of data transmission. In order to solve the above problems, this paper innovatively combines the routing protocol with the coverage control technique and proposes the node collaborative scheduling algorithm, which fully considers the correlation characteristics between sensor nodes to reduce the number of active working nodes and the number of packets generated, to further reduce energy consumption and network delay and improve packet delivery rate. In order to solve the problem of unreliability of geographic routing, a highly reliable link detection and repair scheme is proposed to check the communication link status and repair the invalid link, which can greatly improve the packet delivery rate and throughput of the network, and has good robustness. A large number of experiments demonstrate the effectiveness and superiority of our proposed scheme and algorithm.

Integrating Bio-Inspired Approaches with UAV-FSN Systems for Clustered Target Area Detection in Agriculture

Abstract: Target area detection in agriculture plays a crucial role in optimizing crop management and resource allocation. Traditional methods often lack the precision and efficiency required for modern farming practices. Integrating bio-inspired approaches with UAV-FSN systems offers a promising solution to this challenge. By harnessing principles from nature, such as swarm intelligence and reinforcement learning, it becomes possible to optimize the deployment and coordination of UAVs within FSN for clustered target area detection in agriculture. This research explores the integration of bio-inspired approaches with UAV-FSN systems for clustered target area detection in agriculture. The increasing demand for precision agriculture necessitates efficient methods for identifying target areas affected by various factors. In this study, we introduce a novel algorithm, the QL-ABC Algorithm, which combines the Artificial Bee Colony (ABC) Algorithm with Q-Learning to optimize the deployment and movement of unmanned aerial vehicles (UAVs) within flying sensor networks (FSN) for target area detection. The performance outcomes of the proposed QL-ABC Algorithm on comparison with the existing algorithms are analysed using extensive simulation analysis with the appropriate simulation metrics: packet delivery ratio, mean end-to-end delay, and energy consumption. Results validate the usefulness of the proposed QL-ABC algorithm in accurately detecting clustered target areas in agricultural landscapes, thus contributing to advancements in precision farming practices and sustainable agriculture. Keywords: Flying Sensor Networks, Artificial Bee Colony Algorithm, Clustered Target Area Detection, Q-Learning, Unmanned Aerial Vehicles.

Joint routing and computation offloading based deep reinforcement learning for Flying Ad hoc Networks

Flying ad-hoc networks (FANETs) consisting of multiple Unmanned Aerial Vehicles (UAVs) are widely used due to their flexibility and low cost. In scenarios such as crowdsensing and data collection, data collected by UAVs are transmitted to base stations for processing and then sent to data centers. Still, the deployment of base stations is costly and inflexible. To address this issue, this paper introduces a position-based Computing First Routing (CFR) protocol designed for efficient task transmission and computation offloading in FANETs. This protocol facilitates task processing during data transfer and ensures the delivery of fully processed results to the data center. Considering the dynamically changing topology of the FANETs and the uneven distribution of the UAVs’ computation power, deep reinforcement learning is used to make multi-objective decisions based on the Q-values computed by the model. FANETs are centerless clusters, and two-hop neighbor tables containing position and computing power information are used to make less costly decisions. Simulation experiments demonstrate that CFR outperforms other benchmark schemes with an approximately 6% higher packet delivery rate, an approximately 21% reduction in end-to-end delay, and about a 34% decrease in total cost. Furthermore, it effectively ensures the completion of task offloading before reaching the destination node. This occurs due to the design of a hierarchical reward function that takes into account dynamic changes in delay and energy consumption, as well as the injection of neighbor computing power information into the two-hop neighbor table.

Selection of relay nodes in Bluetooth Mesh network for smart home application

Bluetooth mesh (BM) is increasingly growing and appearing in Internet of Things (IoT) systems. BM uses a managed flooding mechanism to transmit the message. Nodes with a relay feature will receive and forward the message to neighboring nodes, so that the message can be spread to the whole network. The default configuration of BM is that all nodes are relays, ensuring that every node in the network can connect to each other. However, when all nodes act as relays, it results in a lot of messages being transmitted through the network at a time, which increases the possibility of packet collisions, thereby reducing the packet delivery rate (PDR) and increasing the end-to-end delay (E2E delay). These issues will greatly affect the user experience, especially in smart home systems where users interact near the device. Therefore, it is necessary to choose the relay nodes in the network appropriately to improve the quality of the network. This paper will select and analyze some relay node selection algorithms in the BM network applied in the smart home system, and compare them with the default configuration of the network (all relays) in terms of PDR and E2E delay criteria.

Enhancement of VANETS Security against Gray Hole Attack with ANFIS-GWO and LBK

Vehicular Ad Hoc Networks (VANETs) have emerged as a critical component of intelligent transportation systems, facilitating real time communication among vehicles and infrastructure. However, the open and dynamic nature of VANETs makes them vulnerable to various security threats, among which gray hole attacks pose significant challenges. In gray hole attacks, malicious nodes manipulate or selectively drop data packets, disrupting communication and compromising network integrity. In response to these challenges, this paper proposes an innovative approach that integrates the Adaptive Neuro Fuzzy Inference System (ANFIS) with Grey Wolf Optimization (GWO). The main objective of this research is to develop a robust ANFIS GWO framework capable of identifying and mitigating gray hole attacks in real time VANET scenarios. Through extensive simulations and experiments, the performance of proposed technique in terms of average energy consumed, throughput, Packet Delivery Ratio (PDR) and packet drop is examined. The results demonstrate that the ANFIS GWO approach not only improves the resilience of VANETs against gray hole attacks but also guarantees reliable and secure communication among vehicles and infrastructure.

Implementation and optimization of Burg’s method for real-time packet loss concealment in networked music performance applications

AbstractIn networked music performance (NMP) applications, which entail real-time audio streaming over the Internet, strict latency requirements are needed to ensure a realistic interaction between geographically dispersed musicians. Thus, NMP applications typically leverage uncompressed audio and unreliable transport protocols to avoid unnecessary processing and re-transmission delays. Given that no guarantee on packet delivery is offered, NMP applications must deal with late/lost audio packets to mitigate the impact of the resulting audio artifacts on the quality of the playback audio stream. This paper explores an audio packet loss concealment (PLC) technique based on autoregressive (AR) models. In particular, it investigates the algorithmic implementation of Burg’s method and the parameters configuration that offers the best trade-off between prediction error and computational time requirements. The purpose is to find the most suitable solution capable of running on a Raspberry Pi 4B within the real-time audio boundaries imposed by NMP applications. Additionally, we analyze the computational time required to fit the model and predict future samples by considering six implementations and various compilation flags. Results confirm that AR models can predict future audio samples more accurately than traditional PLC approaches, which consist of filling audio gaps with silence or repeating the last received audio segment. Furthermore, results demonstrate the effectiveness of the proposed solution in meeting the strict latency requirements when deployed on a Raspberry Pi 4B.

Optimizing Energy Efficiency in Opportunistic Networks: A Heuristic Approach to Adaptive Cluster-Based Routing Protocol

Opportunistic Networks (OppNets) are characterized by intermittently connected nodes with fluctuating performance. Their dynamic topology, caused by node movement, activation, and deactivation, often relies on controlled flooding for routing, leading to significant resource consumption and network congestion. To address this challenge, we propose the Adaptive Clustering-based Routing Protocol (ACRP). This ACRP protocol uses the common member-based adaptive dynamic clustering approach to produce optimal clusters, and the OppNet is converted into a TCP/IP network. This protocol adaptively creates dynamic clusters in order to facilitate the routing by converting the network from a disjointed to a connected network. This strategy creates a persistent connection between nodes, resulting in more effective routing and enhanced network performance. It should be noted that ACRP is scalable and applicable to a variety of applications and scenarios, including smart cities, disaster management, military networks, and distant places with inadequate infrastructure. Simulation findings demonstrate that the ACRP protocol outperforms alternative clustering approaches such as kRop, QoS-OLSR, LBC, and CBVRP. The analysis of the ACRP approach reveals that it can boost packet delivery by 28% and improve average end-to-end, throughput, hop count, and reachability metrics by 42%, 45%, 44%, and 80%, respectively.

Next‐generation energy‐efficient optical networking: DQ‐RGK algorithm for dynamic quality of service and adaptive resource allocation

SummaryIn green optical networking, designing an adaptive energy‐saving scheme plays a vital role, in optimizing energy consumption by dynamically adjusting resources based on network traffic and environmental conditions, to a more sustainable and efficient optical communication infrastructure. Traditional methods in optical networking face challenges such as static resource allocation, limited adaptability, inefficient power usage, environmental insensitivity, and scalability issues. Therefore this article proposed a novel method named Dynamic Quality of Service based Random update Genghis Khan (DQ‐RGK) algorithm, the proposed model can tackle the abovementioned complexities. In this study, cluster head dynamic placement is utilized to optimize the network's performance by adapting the placement of cluster heads to the current topology, load distribution, and energy levels in the network nodes. Additionally, Dynamic Quality of Service (QoS) is employed to respond dynamically to changes in network conditions, adapting to varying traffic patterns and resource availability. In this work, the Genghis Khan Shark optimization with a random update strategy is implemented for hyperparameter optimization to enhance the performance of the DQ‐RGK method. The DQ‐RGK adjusts the parameters of QoS in real‐time, and this ensures that network resources based on the requirements changed and priorities of applications, which ultimately optimizes performance and enhances user experience. By dynamically assigning and reallocating resources based on the current demand the algorithm enhances overall network efficiency and reduces energy consumption. Then, this work analyzes the experimental results, where some evaluation measures estimate the DQ‐RGK method's performance. Routing efficiency, latency, scalability, spectral efficiency, Packet Delivery Ratio, throughput, network lifetime, energy consumption, jitter, and energy consumption are the measures employed by the DQ‐RGK model. In The results, other routing models that do not provide efficiency are utilized, a comparison of these other routing models is represented in results. The overall DQ‐RGK model's effectiveness is represented in the experimental results and its effectiveness is greater among other methods.

An Optimized Fraud Identification Framework Using DCNN For Wireless Communication

Convolution Neural Network (Cnn) Is The Powerful Framework To Solve The Lot Of Issues Of Link Detection In Unlabeled Wireless Communication. Last Few Decades Lot Of Fraud Detection Strategies Has Been Projected Bit That All Are Inefficient For Detecting The Frauds. Therefore, The Great Need For Effective And Fast Fraud Detection Scheme With Higher Detection Accuracy. In This Research, Spider Based Convolution Neural Network (Sbcnn) Model To Detect The Frauds In The Wireless Communication. Initially, Create The Wireless Channel To Transmit The Messages From Source To Destination. Here, The Fraudulent Activities Are Detected Based On The Packet Delivery Time Of The Source To Destination Of The Wireless Medium. Moreover, The Proposed System Implementation Is Done In The Matlab Frame Work Additionally; The Obtained Results Are Validated With Prevailing Methods For Evaluating The Efficiency Of The Proposed Sbcnn Approach. Wireless communication fraud poses a significant threat as unauthorized use of services, compromising the security of cellular networks and infrastructure. With the surge in online services and users, the reliance on wireless communication for high-speed internet applications has grown. Despite the convenience brought by technologies like net banking, credit cards, and online services, financial frauds and unauthorized payments remain substantial risks. The intricate nature of wireless communication, illustrated in Fig. 1, where devices use signals between source and destination nodes, leads to challenges like network interference and loss rate, often exacerbated by fraudulent activities. Numerous techniques, including Artificial Intelligence (AI), hybrid ensemble models, oversampling, and machine learning, have been explored but haven't provided satisfactory solutions. In this study, an optimization-assisted intelligent framework is proposed to maximize communication performance, addressing the limitations of existing approaches. The subsequent sections analyse related research, identify issues with conventional methods, elaborate on the functioning of the proposed framework, discuss results, and conclude with research inferences

Fuzzy based trusted malicious unmanned aerial vehicle detection using in flying ad-hoc network

The communication security of unmanned aerial vehicles (UAVs) or drones based on flying ad hoc networks (FANETs) can be enhanced and maintained by assessing vulnerabilities, threats, and attacks. Thus, trust, high mobility, and complex communication interfaces are crucial for effective coordination and segregation of malicious drones from the genuine. A new efficient honesty-based detection scheme for malicious drones has been proposed to distinguish between intentional and unintentional misbehavior of the UAVs. The proposed scheme configures a drone system for packet transmission within the FANET, specifying buffer size and packet size variables to control data flow and prevent congestion. It then computes energy used for packet reception and transmission, optimizes energy consumption, and evaluates the drone’s mobility through the link stability index (LSI) and honesty UAV. The honesty UAV parameter categorizes UAVs into unintentional malicious and intentional malicious categories. Fuzzy logic helps identify intentional and unintentional misbehaving drones and improves performance in the network. It continuously generates the malicious UAV detection mechanism by assessing the parameters i.e., energy, pattern of movements, and the honesty score about the UAVs as a whole. Thereby a dynamic rating system to adeptly identify and differentiate the cooperative and non-cooperative nature of drones from the network is assimilated in the paper. Simulation results show the proposed scheme approximately aligns with the actual cases for varying numbers of malicious drones. Finally, the observation of the simulation result reflects substantial enhancement in performance metrics, with a significant cutback in end to end delay and packet delivery ratio by 10–30% and 20–50% respectively, contrasting with the existing techniques.

Intelligent Packet Priority Module for a Network of Unmanned Aerial Vehicles Using Manhattan Long Short-Term Memory

Unmanned aerial vehicles (UAVs) are becoming more common in wireless communication networks. Using UAVs can lead to network problems. An issue arises when the UAVs function in a network-access-limited environment with nodes causing interference. This issue could potentially hinder UAV network connectivity. This paper introduces an intelligent packet priority module (IPPM) to minimize network latency. This study analyzed Network Simulator–3 (NS-3) network modules utilizing Manhattan long short-term memory (MaLSTM) for packet classification of critical UAV, ground control station (GCS), or interfering nodes. To minimize network latency and packet delivery ratio (PDR) issues caused by interfering nodes, packets from prioritized nodes are transmitted first. Simulation results and evaluation show that our proposed intelligent packet priority module (IPPM) method outperformed previous approaches. The proposed IPPM based on MaLSTM implementation for the priority packet module led to a lower network delay and a higher packet delivery ratio. The performance of the IPPM averaged 62.2 ms network delay and 0.97 packet delivery ratio (PDR). The MaLSTM peaked at 97.5% accuracy. Upon further evaluation, the stability of LSTM Siamese models was observed to be consistent across diverse similarity functions, including cosine and Euclidean distances.

Fuzzy inference logic assisted rendezvous points based effective routing strategy in wireless sensor networks

SummaryIn this proposed strategy, an effective routing strategy is introduced for routing based on Rendezvous Points assisted fuzzy inference logics. First, sensor nodes in the cluster are created using the mean‐shift clustering mechanism (MS‐ClusT). A cluster head is selected for the clusters to gather aggregated data from the nodes of clusters. The cluster head selection is done using the Manta Ray optimization‐assisted modified network‐based fuzzy inference logic (MMFiL) approach. Rendezvous points are then selected for routing to minimize the delay and energy consumption for visiting all cluster heads. The rendezvous points are used to define the shortest route for routing. The shortest path from clusters to the base station by selecting rendezvous points is implemented using an integrated neural network with an Archer‐Fish optimization algorithm (InnAFo). In the end, the performance attained by the model is compared with the results of the classical strategy and returns a 0.976563 packet delivery rate and 0.029297 loss rate in 500 nodes. Furthermore, the throughput attained by the proposed model is 0.992874 Mbps, and the delay is 67.30 ms at 500 nodes. The analysis of the proposed model will result in less time needed for cluster head selection.