Routing Techniques Research Articles

Survey on Wireless Sensor Network Structure Attacks and Energy Optimization

A Wireless Sensor Network (WSN) is a novel form of embedded real-time device that can be used for a variety of applications that make conventional networking impractical. Concerning the energy production of the nodes, WSN has significant issues that may affect the system's stability. Researchers have developed a range of methods and strategies to decrease wireless sensor network energy consumption. This paper has brief WSN structure for the optimization of communication with routing techniques. As network is vulnerable hence attacks were also present. This paper has list attacks as per number of layers. Further paper has list some of latest research work that not only optimize the network energy but also increases the robustness. Index Terms— Adhoc Network, Wireless Sensor Network, Communication Attacks, Virtual machines.

Admission Allotment Scheme Base Load Balancing Techniques for Clustering Routing in Wireless Sensor Network

Admission Allotment Scheme Base Load Balancing Techniques for Clustering Routing in Wireless Sensor Network

Optimizing Wireless Sensor Network longevity with hierarchical chain-based routing and vertical network partitioning techniques

Efficient utilization of energy is a crucial concern in Wireless Sensor Networks (WSNs) to increase the network's longevity. However, it is impossible to investigate routing without considering the effective formation of chains or clustering methods to optimize the problem in WSNs. The proposed routing technique aims to extend the lifespan of sensors using various network partitioning techniques. The approach utilized in the strategy is PEGASIS (Power EfficientGathering in Sensor Information Systems) protocol, it uses Prim's Algorithm to modify the chain structure and is based on hierarchical chain-based routing. In order to transmit information from the working nodes to the base station (BS), we employ and vertical network partitioning techniques named EEPEG-PA-V. According to this approach, the transition is carried out when the node's residual energy is about to run out. The suggested method has the potential to enhance the average network longevity substantially when compared to existing routing techniques. For instance, EEPEG-PA improves it by 21.7092 % and EEPEG-PA-V by 29.9056 % compared to PEGASIS. Similarly, EEPEG-PA-V by 6.1708 % compared to EEPEG-PAacross various network sizes.

Air quality index prediction with optimisation enabled deep learning model in IoT application

ABSTRACT The development of industrial and urban places caused air pollution, which has resulted in a variety of effects on individuals and the atmosphere over the years. The measurement of the air quality index (AQI) depends on various environmental situations, such as emissions, dispersions, and chemical reactions. This paper developed the Internet of Things (IoT)-based Deep Learning (DL) technique for predicting air quality. Initially, the IoT simulation is performed, where the nodes receive input data. The routing technique is used to identify the best route toward the Base station (BS). The proposed Tangent Two-Stage Algorithm (TTSA) is used in the routing mechanism. For AQI prediction, the time series data is transmitted to the BS. The Z-score normalisation is employed to neglect the unessential data. Furthermore, feature indicator extraction is employed to extract the relevant feature indicators. The Deep Feedforward Neural Network (DFNN) is used to predict air quality. Furthermore, the proposed Fractional Tangent Two-Stage Optimisation (FTTSA) is employed for the training process of DFNN. Moreover, metrics such as energy, time, and distance are used to evaluate the routing process, and superior results such as 0.979J, 0.025s and 0.196 m are obtained. Furthermore, the AQI is predicted by metrics like root mean square error (RMSE), R-squared (R2), mean square error (MSE), and mean absolute percentage error (MAPE), whereas the superior values such as 0.602, 0.598, 0.362, and 0.456 are attained.

Bio-Inspired Based Optimization of Machine Learning Techniques for Energy Efficient Routing for UWSN

Bio-Inspired Based Optimization of Machine Learning Techniques for Energy Efficient Routing for UWSN

Performance Analysis of Multi-Core Systems in Multistage Interconnection Networks: Investigating Challenges in Inter-Processor Communication

For high-performance computers and data-heavy apps, how well multi-core systems work in multistage interconnection networks (MINs) is very important. As the need for parallel processing grows, inter-processor communication (IPC) has become a major issue that affects both the speed of work and the ability of the system to grow. This essay looks into the problems with IPC in multi-core systems set up with MINs, mainly delay, bandwidth limits, and network congestion. imulation-based performance review is used to test multi-core systems with different amounts of traffic, message sizes, and route techniques. A mix of mathematical models and simulation tools are used to test different MIN designs on measures like delay, speed, and fault tolerance. It is also looked at how network structures and methods for controlling crowding affect IPC efficiency. According to the results, MINs can be used to connect multiple devices, but when there is a lot of traffic, the delay goes up a lot because of bottlenecks in some parts of the network. Also, in higher-dimensional networks, processors fighting over bandwidth causes speed to drop. Some of these problems can be solved with optimized transport methods and adaptable congestion control systems, which also make IPC work better overall. This study shows how important it is to keep improving how processors talk to each other in multi-core systems, especially when it comes to making network switching and managing congestion more efficient.

Link-Quality Aware Routing for Software-Defined Vehicular Networks

The routing protocol for Vehicular Ad Hoc Networks (VANETs) uses hop-count as a routing metric to determine the shortest route from source to destination. The presence of unstable links in the selected route may cause packet failures, resulting in an increase in packet retransmissions, causing network congestion, increasing total end-to-end delay, and reducing throughput. Thus, route selection based on hop count may not always be optimal in terms of route link quality. In Software-Defined Vehicular Networks (SDVN), the global view of link-state information for the vehicular networks is created at the controller; thus, a better route can be computed. Our study proposes an SDVN routing technique that considers link quality while determining the route. Our proposed framework examines the link quality using two metrics: Expected Transmission Count (ETX) and Expected Transmission Time (ETT), and this information is used by the controller to compute the flow rule. The routing performance of the proposed scheme is compared to traditional VANETs and other existing works with the help of network simulations using the NS-3 simulator. The obtained simulation results for our proposed scheme outperform in terms of performance metrics such as packet delivery ratio, average throughput, and end-to-end delay.

Intelligent System for Tourist Guidance in Tumbes-Perú

An Intelligent System (IS) for Tourist Guidance was developed for the city of Tumbes, Peru. Considering the preferences of the tourist, the IS obtains the optimal route for the visit and provides a visit plan with schedules and times of stay in each place. It is an innovative hybrid system since it is complemented by two artificial intelligence techniques, a rule-based expert system and a genetic algorithm. The methodology for the implementation of the IS consisted of the collection of tourist, geographic, georeferenced information and photographs of 28 tourist places, grouped into Beaches, Nature, Adventure, Culture and Medicinal Sites, then the logical rules, the distance matrix and time of stay in each place were developed, and finally with the vehicle routing technique the genetic algorithm is implemented that allows obtaining the optimal route. The results indicate that the GA provides optimal solutions with an execution time of 5.6 seconds and zero errors. This contribution is very important for future work on tourist profiles and new tourist routes, since the intelligent system can be adapted for use in other cities.

Multi-objective-derived efficient energy saving in multipath routing for mobile ad hoc networks with the modified aquila–firefly heuristic strategy

Mobile ad hoc networks (MANETs) suffer from a major problem with the energy drainage of network nodes, as the mobile nodes rely on batteries and do not have a permanent power supply. Therefore, a new multipath routing technique, using a novel hybrid heuristic algorithm, is designed to solve the diverse metrics of a multi-objective function, including energy consumption, distance, routing overhead ratio, throughput, end-to-end delay and packet delivery ratio. This approach implements a new hybrid algorithm termed the modified aquila–firefly heuristic strategy (MA-FHS), which integrates the aquila optimizer and the firefly algorithm. This derived fitness function is applied to achieve optimal paths in multipath routing with minimization of the multi-objective function. The simulation demonstrates that the developed routing protocols outperformed traditional protocols under several network constraints.

Effect of Yttria and Ceria Particulates Inclusion on Corrosion Characteristics and Mechanical Properties of AZ91D Mg Alloy Composites Fabricated Through Metallurgical Routing Technique

Effect of Yttria and Ceria Particulates Inclusion on Corrosion Characteristics and Mechanical Properties of AZ91D Mg Alloy Composites Fabricated Through Metallurgical Routing Technique

Correction to: Secure and efficient message transmission in MANET using hybrid cryptography and multipath routing technique

Correction to: Secure and efficient message transmission in MANET using hybrid cryptography and multipath routing technique

An effective attack detection framework using multi-scale depth-wise separable 1DCNN via fused grasshopper-based lemur optimizer in IoT routing system

A secured IoT routing model against different attacks has been implemented to detect attacks like replay attacks, version attacks, and rank attacks. These attacks cause certain issues like energy depletion, minimized packet delivery, and loop creation. By mitigating these issues, an advanced attack detection approach for secured IoT routing techniques with a deep structured scheme is promoted to attain an efficient attack detection rate over the routing network. In the starting stage, the aggregation of data is done with the help of IoT networks. Then, the selected weighted features are subjected to the Multiscale Depthwise Separable 1-Dimensional Convolutional Neural Networks (MDS-1DCNN) approach for attack detection, in which the parameters in the 1-DCNN are tuned with the aid of Fused Grasshopper-aided Lemur Optimization Algorithm (FG-LOA). The parameter optimization of the FG-LOA algorithm is used to enlarge the efficacy of the approach. Especially, the MDS-1DCNN model is used to detect different attacks in the detection phase. The attack nodes are mitigated during the routing process using the developed FG-LOA by formulating the fitness function based on certain variables such as shortest distance, energy, path loss and delay, and so on in the routing process. Finally, the performances are examined through the comparison with different traditional methods. From the validation of outcomes, the accuracy value of the developed attack detection model is 96.87%, which seems to be better than other comparative techniques. Also, the delay analysis of the routing model based on FG-LOA is 17.3%, 12.24%, 10.41%, and 15.68% more efficient than the classical techniques like DHOA, HBA, GOA, and LOA, respectively. Hence, the effectualness of the offered approach is more enriched than the baseline approaches and also it has mitigated diverse attacks using secured IoT routing and different attack models.

Advancements in IoT Routing and Energy Efficiency: A Comprehensive Review of Algorithms and Technologies

The rapid expansion of the Internet of Things (IoT) has led to a significant increase in connected devices, resulting in growing challenges related to efficient data transmission, energy consumption, and network scalability. Routing protocols and energy-efficient algorithms play a pivotal role in addressing these challenges, enabling IoT systems to optimize performance while minimizing power usage. This review provides a comprehensive analysis of recent advancements in IoT routing techniques and energy-efficient solutions, focusing on multi-objective optimization algorithms such as fractional gravitational search, grey wolf optimization, and hybrid salp swarm-differential evolution algorithms. Additionally, we explore context-aware routing, cluster-based methods, and the integration of machine learning to enhance decision-making in dynamic IoT environments. The review also highlights the critical role of energy-efficient routing in IoT applications, such as smart agriculture, healthcare, and smart cities, emphasizing its importance in prolonging network lifetime and reducing overall operational costs. The potential of blockchain technology and AI-driven algorithms to enhance security and energy efficiency in IoT networks is discussed, alongside future directions in green routing solutions. By addressing the complexities of IoT routing and energy management, this review aims to provide insights into the latest research trends and guide future innovations in IoT systems.

Energy efficient routing for improving lifetime in MWSN: A clustering approach

A Mobile wireless sensor network (MWSN) consists of mobile sensor nodes, which can be deployed in any specific environment, and due to its’ mobility it can perform with rapid topological transformations of a network. The sensor nodes having limited battery power are used to collect specific data and this raw data is sent to a static sink node of the network. Under such a scenario, to avoid frequent disconnections due to topological change in the network and can avail more reliable data transmissions in energy awareness perspective, an energy efficient routing protocol for MWSNs to improve its lifetime is proposed in this paper by utilizing a clustering approach. A MWSN with random number of sensor nodes are initially considered and then, clustering algorithm K-means is used to find a predefined number of clusters with their initial cluster heads (CHs) and the centroid location of these clusters is also determined. The role of these CHs is to elect our DDBLACH (distance to sink and cluster centroid with battery level aware cluster head) nodes from each cluster, by sending and receiving intra-cluster messages among other member sensor nodes. A DDBLACH node is determined by using three factors, such as minimum distance from cluster centroid location, minimum distance from sink and the maximum battery level of the node from each cluster. These DDBLACH nodes are used to collect data from intra-cluster sensors and thereafter, send those towards sink node for further processing using tree based hierarchical routes. Finally, an energy efficient routing technique for MWSNs is proposed for data transmission from these DDBLACH nodes of clusters to a sink of the network. Here simulation results indicate the supremacy of our proposed scheme over other existing methods in various aspects, such as improving the presence of alive nodes and average network lifetime.

ATENA: Adaptive TEchniques for Network Area Coverage and Routing in IoT-Based Edge Computing

The Internet of Things (IoT) and Edge Computing (EC) are now pervasive. IoT networks are made up of several objects, deployed in an area of interest (AoI), that can communicate with each other and with a remote computing centre for decision-making. EC reduces latency and data congestion by bringing data processing closer to the source. In this paper, we address the problems of network coverage and data collection in IoT-based EC networks. Several solutions exist designed to solve these problems unfortunately, they are either not energy-efficient or do not consider connectivity and they do not cover AoI. The proposed routing mechanisms are often not suited for AoI coverage schemes and lead to poor data routing delay or high packet losses. To address these shortcomings, we propose ATENA, a periodic, lightweight and energy-efficient protocol that aims to improve network coverage based on the two new schemes used to define a few number of objects to be kept awake at each period it also uses an adaptive routing scheme to send the collected data to the computing centre. This protocol is designed to take into account the limited resources of objects and ensures a longer network lifetime. A comparison of ATENA’s simulation results with recent existing protocols shows that it significantly improves network coverage, network lifetime and end-to-end delay to the computing centre.

Time-Critical Unified Rendezvous Guidance for an Unmanned Autonomous Vehicle

Abstract This paper addresses the time-critical rendezvous problem for a pursuing autonomous unmanned vehicle, e.g., an unmanned aerial vehicle (UAV), guided using the concept of true-proportional navigation guidance, which is a variant of proportional-navigation guidance. In existing vehicle routing and flight time-constrained guidance techniques, specific rendezvous guidance commands are designed based on the specific motion of the target. In contrast to that, we propose a unified guidance command for a UAV that guarantees a time-critical rendezvous with a target that moves arbitrarily. We explore the purview of true-proportional navigation guidance and posit that a guidance law thus designed may be a potential candidate for designing time-critical rendezvous strategies against various target motions, even when the pursuer does not necessarily have a speed advantage over the target. We first derive a closed-form expression for the flight duration until rendezvous, over which we exercise control to make the pursuing vehicle rendezvous with the target at any feasible time prescribed a priori. Next, we ensure that the necessary flight time-based error variable converges to zero with an optimal convergence pattern with respect to a suitable cost function. We finally validate the efficacy of the proposed unified guidance command via numerical simulations.

Optimized Resource Management and Dynamic Routing Protocol for Wireless Sensor Networks Through Load Balancing, Packet Scheduling, and Intelligent Clustering

Heterogeneous Wireless Sensor Networks (HWSNs) are pivotal for providing weather-related event data, enabling universal location access, and facilitating remote monitoring through multi-hop transmission. Efficient energy utilization is critical in ensuring the optimal functioning of HWSNs. Previously, Compressive Sensing (CS) technology was established to enhance communication efficiency within HWSNs. While previous methods were effective in managing energy consumption and reducing transmission delays across network devices, the increased number of devices has impacted their efficacy. Consequently, energy becomes a vital limitation in constructing HWSNs. In order to address these challenges, this study introduces Load Balancing and Packet Scheduling with Intelligent Clustering based Improved Routing Protocol (LPICR). This integrates load balancing, packet scheduling, intelligent clustering, and enhanced routing techniques. The protocol is structured into three main categories: intelligent route selection, load balancing-based Cluster Head (CH) selection, and path scheduling. Initially, an efficient opportunistic routing is conducted by the intelligent route selection process. This routing method minimizes data forwarding during communication and significantly decreases energy consumption in the HWSN. Furthermore, by using a load balancing-oriented procedure for selecting cluster heads, the system achieves efficient determination of cluster heads and construction of clusters, resulting in the most efficient use of energy in communication. Path scheduling reduces the probability of delays by facilitating effective data flow between the source and destination in the HWSN. The NS2 platform is used to implement the proposed LPICR-HWSN protocol. The calculation of the result and comparison analysis is considered for the parameters are Data loss rate, communication time, packet success rate, malicious detection ratio, throughput, Routing overhead and energy efficiency. The results are thoroughly investigated by accounting for factors like the quantity of nodes and the varying speed of the network. To assess the efficacy of this proposed protocol, we conduct a comparative analysis using established methodologies such as CDAS-WSN, EEPC-WSN, TCCS-WSN, and MTODS-HWSN. The results suggest that the proffered LPICR-HWSN model demonstrates superior performance compared to previous methods.

Novel green CQDs/ZnO binary photocatalyst synthesis for efficient visible light irradiation of organic dye degradation for environmental remediation

The advancement of effective and sustainable water treatment depends on the creation of a highly active and controllable photocatalyst. A suitable site for increasing the activity of such a photocatalyst is the heterojunction. Organic dyes have become a major source of concern in recent years because they are so common in wastewater. Heavy metals have a high permanence in wastewater and may be carcinogenic. Low-waste, high-performance technologies are needed to remove organic dyes and heavy metals from wastewater. To determine their structural, photochemical, and optical properties, Carbon Quantum Dots (CQDs) (made from Tamarindus indica shell waste), ZnO (from Ficus Benghalensis), and CQDs/ZnO photocatalysts were synthesised using green route techniques, followed by calcination. Malachite Green (MG) and Methylene Blue (MB) are effectively photo-degraded by the synthesised CQDs, ZnO, and Green CQDs/ZnO Binary Photocatalyst under the irradiation of naturally occurring solar light. The maximum photocatalytic activity for MG and MB within 60 min was shown by the Green CQDs/ZnO Binary Photocatalyst, with 100 % and 100 %, respectively. The reduced rate of electron-hole recombination causes effective photocatalytic activity. According to experiments with free radical entrapment, O2 and h+ play an important role in photodegradation. The study thus presents a fresh approach to the synthesis of highly effective binary photocatalysts for their application in environmental remediation.

Intelligent Routing Approaches Based on Ant Colony Optimization for Dynamic Internet of Things Network

Introduction: This paper introduces two meta-heuristic approaches utilizing Swarm Intelligence and ant colony optimization techniques. The strategy comprises applying smart routing technology to optimize a dynamic IoT network computed path. Method: The issue of route selection to achieve the target and critical factors such as network energy, left energy in each gadget, run out IoT nodes count has been explored. After rigorous iterations extending up to 1000, the simulation has yielded results for two distinctive routing approaches. The ABED (ACO- Breadth first search- Euclidean- Dynamic) and the ADED (ACODijkstra algorithm -Euclidean- Dynamic) have simulated and compared their network efficiencies using MATLAB. Results: At node 200, ABED exhibits a performance advantage over ADED of 1.6%. This efficiency differential between ABED and ADED expands to 2.9% at 300 nodes and further to 2.6% at 400 nodes. Furthermore, ABED showcases superior network stability in routing techniques compared to ADED. Specifically, ABED's routing technique achieves a more consistent network compared to ADED. Conclusion: In networks comprising 500 nodes, ABED surpasses ADED by 13.33% in the context of HND (Half Node Dead) and by 6.7% in the case of LND (Last Node Dead).

Cluster-Based Hybrid Routing Technique for Wireless Sensor Networks

Cluster-Based Hybrid Routing Technique for Wireless Sensor Networks