Aware Routing Research Articles

Komodo Mlipir Algorithm-based optimal route determination mechanism for improving Quality of Service in Vehicular ad hoc network

In Vehicular ad hoc network (VANETs), Quality of Service (QoS)- aware protocols helps in handling the necessitated demand of delay sensitive applications for facilitating intelligent transportation. The fundamental challenge of VANETs lies in the process of establishing vehicle to infrastructure and vehicle-to-vehicle communication that are prone to link failure. Bio-inspired algorithms are identified to provide reliable solutions for securing the links of VANETs. In this paper, Komodo Mlipir Algorithm-based Optimal Route Determination Mechanism (KMAORDM) is proposed for achieving best optimal route that guarantees enhanced QoS. This QoS aware routing protocol utilizes the exceptional potentiality of Komodo Mlipir Algorithm attributed in terms of exploration and exploitation for the objective of evaluating and memorizing the impactful factors that helps in exchanging the necessitated messages to its neighbours. It adopts Mlipir as a best mode of modelling the behaviours of vehicular nodes during the process of data routing in VANETs. It uses a cache strategy which discovered reliable traversed routing paths such that the pre-cached route is used for data transmission. This cache strategy paves the way for preventing the route identification and maintenance process with minimized routing overhead. It also identifies the fittest vehicular node from its one hop distance as the successive forwarder in the absence of pre-cached route for addressing link failure during reliable packet transmission. The simulation results of this proposed KMAORDM approach with different vehicular nodes confirmed minimized communication overhead of 19.32 %, reduced network latency of 18.64 %, maximized throughput of 18.54 % better than benchmarked approaches.

OCC-MP: An optimal cluster based congestion aware technique multipath routing protocol in WSN using hybrid evolutionary techniques

Due to massive data traffic, wireless network congestion is inevitable, causing network performance deterioration and data transmission quality reduction. By adjustingthe traffic flow at the source node along the optimal path, Wireless sensor networks strive to evade and overcome congestion. Recently, numerous evolutionary approaches for congestion detection and avoidance have been presented, although they do not improve WSN performance. A hybrid evolutionary approach is attempted to avoid congestion in WSN and improve network performance. We present an optimum cluster-based congestion aware multipath routing protocol (OCC-MP). An improved atom search optimization (IASO) approach for efficient clustering is introduced initially in OCC-MP. Then the HSIPO method is used for decision making, which computes each node's trust degree. The HSIPO algorithm selects the cluster head (CH) from a group of nodes. Then a deep recurrent neural network (DRNN) is used to monitor congestion and provide congestion aware routing. Finally, multiple simulation situations like various node densities andat various simulation rounds supported our proposed OCC-MP techniqueoutperformedcurrent methodologies with respect to energy consumption, throughput, traffic load overflow, delivery ratio, and number of nodes alive.

Environmental Monitoring with Wireless Sensor Network for Energy Aware Routing and Localization

This paper introduces a novel approach to enhance the efficiency of environmental monitoring in Wireless Sensor Networks (WSNs) through the application of the Centroid Direction of Arrival Routing (CDAR) protocol. WSNs play a pivotal role in collecting and transmitting data for environmental surveillance, yet energy constraints and dynamic deployment scenarios pose challenges to their sustained operation. The CDAR protocol addresses these challenges by optimizing energy consumption, improving data transmission efficiency, and extending network lifetimes. We present comprehensive performance evaluations across ten deployment scenarios, revealing CDAR’s adaptability to varying node densities and environmental conditions. The protocol consistently demonstrates high energy efficiency percentages, showcasing its ability to strike a balance between energy conservation and reliable data transmission. Security measures integrated into CDAR ensure the protection of transmitted environmental data. This paper provides valuable insights into the applicability of CDAR in achieving sustainable and effective WSNs for environmental monitoring, setting the stage for further research and real-world implementations.

Priority based energy and load aware routing algorithms for SDN enabled data center network

Nowadays, to accommodate the swift expansion of cloud computing, big data, and other emerging technologies, integrating data center networks and SDN (Software Defined Network) is suggested, which also improves the network management’s convenience and flexibility. As network devices in these data centers continue to increase at a high rate, efficient route management to handle these devices’ vast amounts of data has emerged as a significant concern. This work suggests a novel admission and routing scheme considering the importance or priorities of the network flows, path energy, and routing path load. Using the SDN paradigm in DCN (Data Center Network), we first develop a Mixed Integer Linear Programming (MILP) formulation by jointly considering flow priority, path energy, and path load. The MILP formulation can maximize the number of flows as well as minimize the energy consumption and load variance in the network, or it can make a trade-off among all three. However, due to the potentially long running time, later, we introduce two greedy methods, Priority Based Energy Minimization Algorithm (PEMA) and Priority Based Evenly Load Distribution Algorithm (PEDL), where PEMA aims to maximize the flow with less energy, and PEDL focuses on maximizing the flow with reduced load variation. Finally, the developed routing strategies are implemented, and the simulation results demonstrate the out-performance of our work compared to the existing works in terms of successful flow ratio, energy savings, and load balancing.

Self‐attention based generative adversarial network with Aquila optimization algorithm espoused energy aware cluster head selection in WSN

SummaryWSNs have a wide range of applications, and the effective Wireless Sensor Network (WSN) design includes the best energy optimization techniques. The nodes in wireless sensor networks run on batteries. The existing cluster head selection methods do not take into account the latency and rate of wireless network traffic when optimizing the node's energy constraints. To overcome these issues, a self‐attention based generative adversarial network (SabGAN) with Aquila Optimization Algorithm (AqOA) is proposed for Multi‐Objective Cluster Head Selection and Energy Aware Routing (SabGAN‐AqOA‐EgAwR‐WSN) for secured data transmission in wireless sensor network. The proposed method implements the routing process through cluster head. SabGAN classifiers are utilized to select the CH based on firm fitness functions, including delay, detachment, energy, cluster density, and traffic rate. After the selection of the cluster head, the malicious node gains access to the cluster. Therefore, the ideal path selection is carried out by three parameters: trust, connectivity, and degree of amenity. These three parameters are optimized under proposed AqOA. The data are transferred to the base station with the support of optimum trust path. The proposed SabGAN‐AqOA‐EgAwR‐WSN method is activated in NS2 simulator. Finally, the proposed SabGAN‐AqOA‐EgAwR‐WSN method attains 12.5%, 32.5%, 59.5%, and 32.65% higher alive nodes; 85.71%, 81.25%, 82.63%, and 71.96% lower delay; and 52.25%, 61.65%, 37.83%, and 20.63% higher normalized network energy compared with the existing methods.

Energy Based Route Prioritization for Optimum Multi-path Selection

Energy–aware routing in wireless ad hoc networks is one of the demanding fields of research. Nodes of the network are battery operated that are difficult to recharge and replace, that's why while developing a routing protocol energy consumption metric should always be at high priority. Nodes of mobile ad hoc networks are distributed in different directions forming arbitrary topology instantly. To propose an energy-efficient routing protocol for such a dynamic, self-organized, self-configured, and self-controlled network is certainly a challenge and an open research problem. Energy constraints and mobility leading to link breakage are the motivating factors behind the development of the proposed Optimized Priority-based Ad Hoc on Demand Multi-path Distance Vector Energy Efficient Routing Protocol (OPAOMDV-EE). The routing protocol added three fields (CE, MAX_E, MIN_E) to the traditional AOMDV RREQ and RREP packets, which are further used for calculating total priority field value. This value is used by the source node for selecting an optimal prioritized energy-efficient route. The proposed OPAOMDV-EE protocol has been simulated on Network Simulator-2 (NS-2) for two different scenarios that prove the effectiveness of OPAOMDV-EE in terms of various performance metrics with reduced energy consumption.

Spectrum Sensing based Trust Aware Routing for Cognitive Radio Ad hoc Networks (CRAHNs)

Spectrum Sensing based Trust Aware Routing for Cognitive Radio Ad hoc Networks (CRAHNs)

Multi‐objective cluster head‐based energy aware routing using optimized auto‐metric graph neural network for secured data aggregation in Wireless Sensor Network

SummaryThe data are combined and transmitted through wireless sensor network (WSN) in internet of things applications. Multi‐Objective Cluster Head Based Energy Aware Routing using Auto‐Metric Graph Neural Network with Hybrid Balancing Composite Motion and Border Collie Optimization is proposed in this article for secured data aggregation (SDA) in WSN. The proposed method activates routing procedure via cluster head (CH). Hence, data aggregation basis Auto‐Metric Graph Neural Network selects CH depending upon multi‐objective fitness function. This fitness function deems factors, such as energy, delay, throughput, distance amid the nodes, capacity, collision, traffic rate, and cluster density. After CH selection, malicious node is present in the cluster. The optimum path is depending on three parameters: trust, connectivity, and quality of service (QoS). These three parameters have optimized under Hybrid Balancing Composite Motion and Border Collie Optimization method for ideal path selection. The ideal path is employed to transmit the gathered data to base station. The proposed technique is executed in network simulator, and its efficiency is evaluated under performance metrics, like network lifetime, packet delivery ratio, and delay. The proposed technique achieves 19.67%, 22.38%, and 27.45% better network lifetime; 28.92%, 25.83%, and 19.46% better packet delivery ratio; and 12.32%, 34.54%, and 22.33% lower delay while compared to the existing models: Taylor‐spotted hyena optimization for dependable and energy‐efficient CH selection‐based safe data routing and fault tolerance in WSN (T‐SHO‐DAWSN), multi‐objective cluster head utilizing SAPGAN for secured data aggregation (SAPGAN‐DAWSN), and energy efficient data accumulation design for protected routing protocol in WSN (MDAS‐DAWSN) methods, respectively.

An Energy Efficient and Trust-based Quality of Service Routing in Mobile Adhoc Networks

Mobile Ad-hoc Networks are widely used in all applications and are gaining attention towards reliable data delivery and efficient energy usage. Reliability of the data delivery is assured when the routing protocols are securely reliable and energy efficient. Limited resources being the main limitation of the wireless networks, there is a lack in providing security to the data and energy utilization. Trust aware routing can resolve the resource problems in MANETS. So, to overcome these limitations, an Energy Efficient and Trust-based Quality of Service Routing in Mobile Adhoc Networks is proposed. This protocol evaluates the trust score of the nodes based on the connectivity of the nodes and ACK sincerity, based on the comprehensive trust of the node's energy consumption required and communication cost for the communication between node is evaluated. This protocol efficiently identifies the malicious nodes based on the trust score it is the additional advantage of evaluating the trust score of nodes. The simulation results prove that the proposed protocol guarantees the reliable data delivery and aid to extend the life time of the network thereby improves the throughput of the network.

Trust And Energy-Aware Routing Protocol for Wireless Sensor Networks Based on Secure Routing

Wireless Sensor Network (WSN) is a network area that includes a large number of nodes and the ability of wireless transmission. WSNs are frequently employed for vital applications in which security and dependability are of utmost concern. The main objective of the proposed method is to design a WSN to maximize network longevity while minimizing power usage. In a WSN, trust management is employed to encourage node collaboration, which is crucial for achieving dependable transmission. In this research, a novel Trust and Energy Aware Routing Protocol (TEARP) in wireless sensors networks is proposed, which use blockchain technology to maintain the identity of the Sensor Nodes (SNs) and Aggregator Nodes (ANs). The proposed TEARP technique provides a thorough trust value for nodes based on their direct trust values and the filtering mechanisms generate the indirect trust values. Further, an enhanced threshold technique is employed to identify the most appropriate clustering heads based on dynamic changes in the extensive trust values and residual energy of the networks. Lastly, cluster heads should be routed in a secure manner using a Sand Cat Swarm Optimization Algorithm (SCSOA). The proposed method has been evaluated using specific parameters such as Network Lifetime, Residual Energy, Throughpu,t Packet Delivery Ratio, and Detection Accuracy respectively. The proposed TEARP method improves the network lifetime by 39.64%, 33.05%, and 27.16%, compared with Energy-efficient and Secure Routing (ESR), Multi-Objective nature-inspired algorithm based on Shuffled frog-leaping algorithm and Firefly Algorithm (MOSFA) , and Optimal Support Vector Machine (OSVM).

Reliable and Secure Congestion Aware Routing Protocol for Wireless Sensor Network

Reliable and Secure Congestion Aware Routing Protocol for Wireless Sensor Network

Rough Set Techniques in Wireless Sensor Networks using Membership Function and Rough Labelling Graphs for Energy Aware Routing

Rough set theory and rough graphs are employed for data analysis. Rough graphs utilize approximations, and this paper presents a method for implementing rough graphs using rough membership functions and graph labeling for data structures and reduction. This paper proposes a rough graph labeling method, termed rough -labeling similarity graph, that utilizes a similarity measure for vertex and edge labeling. This method aims to minimize boundary regions in rough graphs and is applicable to wireless sensor networks (WSNs). In WSN, the proposed algorithms such as PSO-LSTM, COA-LSTM, LOA-LSTM integrates with rough set theory based rough -labeling for boundary region identification. The proposed method incorporates the membership functions encapsulates the rough labeling graphs for cluster boundary region identification for WSN. The cluster boundary for WSN is based on rough set membership and rough labelling is termed as rough set membership boundary region (RMB). RMB in WSN and implementation of proposed routing algorithm such as LOA-LSTM provides high throughput and energy saving when compared to existing cluster boundary structure methods such as Voronoi, Spectrum and Chain.

Communication and Content Trust Aware Routing For Clustered IoT Network

Security has become a major concern in practical applications related to Internet of Things, a Trust Aware Routing is found as second line of defence. To ensure a secure and hassle-free communication in IoT, this paper proposes a new routing strategy called as Communication and Content Trust Aware Routing (CCTAR) for Clustered IoT network. CCTAR is applied on a clustered IoT network in which the entire nodes are clustered into different clusters. Distance, initial energy, transmission range, angle of overlap and the sensing range are the fur major metrics used to cluster the network into hierarchical clusters followed by Cluster Head Selection. Next, the Trust Aware routing computes three different trust metrics namely Nobility rust, bilateral trust and Data oriented trust to determine the trustworthiness of Cluster Heads. The experimental evaluation of the proposed mechanism shows its superiority in terms of malicious nodes identification, Storage overhead reduction and Network lifetime improvisation.

Energy efficient routing using adaptive elephant herding optimization for IoT-WSN

WSNs(Wireless Sensor Networks) has been developed with applications in many domains including agriculture, telecommunication, manufacturing industry, healthcare, and surveillance. More specifically, WSN plays a pivotal role in IoT (Internet of Things). The IoT sensors provide information about the physical phenomena in the deployed fields. As the sensors contain only limited resources, the factors like data processing, power consumption, transmission, and storage capabilities adversely affect the efficiency. Thus, the process of routing is necessary for network longevity. The data from IoT-based sensors is routed to the destination through a multi-hop routing system. The Energy aware Routing is motivated by the nature inspired Fuzzy Butterfly Optimization (E2RFBOA). Further a new data aggregation method is introduced in this article customized for IoT based WSN to acquaint higher crop yield in precision farming. Nevertheless, the scalability becomes a primary concern when deployed in larger and denser networks. This is due to the fact that all nodes in IoT and WSN are mostly alive depending on higher usage of bandwidth and power. The primal aim is to build a novel routing protocol developed for IoT-WSN. Apart from this, an Energy aware Clustered Routing that is motivated by Adaptive Elephant Herding Optimization (E2CR-AEHO) is proposed, which sensors collect data and find a group of Cluster Heads (CHs). In the AEHO Algorithm, the formed CH is rotated depending on power consumption. This also prevents frequent re-clustering; at the same time it can effectively adapt to the changes in network topology. According to the AEHOA, the node population comprises of nodes that can choose its CHs among the other nodes. This algorithm takes into account a number of criteria, including power consumption, residual power of Sensor Nodes (SN), network reliability, and data reliability. The suggested approach can efficiently represent the network environment, allowing the routing algorithm to avoid passing over marked zones. Network-specific performances measures including PDRs (Packet Delivery Ratios), NLs (Network Lifetimes), PLRs (Packet Loss Ratios), and AE2E (Average End To End) delay are used to evaluate simulation outcomes. This proposed framework aggregates IoT, which can gradually reduce the amount of data, hence extending network lifetime.

Energy aware routing with optimal deep learning based anomaly detection in 6G-IoT networks

The sixth generation (6G) wireless communication networks are being planned for the transformation of client services and applications through the Internet of Things (IoT) technology that is headed towards a completely intelligent and autonomous system. The IoT-6G network offers ubiquitous applications on the basis of wireless communication technologies. However, various problems need to be critically addressed in order to realize this technological application. Primarily, the devices encounter energy constraints that prevent it from smart functioning due to limited battery power; so the connectivity issue arises resulting in the failure of the connections, once the device’s energy gets exhausted. Besides, security also remains a challenging issue which can be solved with the help of Machine Learning (ML) and Deep Learning (DL)-based anomaly detection techniques. In this background, the current study presents a Metaheuristic Energy-Aware Routing with Optimal DL-based Anomaly Detection Technique (MER-ODLADT) for 6G-IoT networks. The presented MER-ODLADT technique encompasses two major processes namely, routing and anomaly detection. For the routing process, the proposed MER-ODLADT technique applies the Marine Predator’s Optimization (MPO) algorithm with a fitness function involving multiple input parameters. On the other hand, the MER-ODLADT technique employs the Eco-Geography Optimization with Deep Belief Network (EGODBN) model for anomaly detection and classification. The performance of the proposed MER-ODLADT technique was validated using a benchmark dataset and the results were examined under distinct measures. The experimental outcomes demonstrate the enhanced performance of the proposed MER-ODLADT technique over other existing approaches with a maximum sensitivity of 98.34 %, specificity of 97.91 %, accuracy of 99.43 %, precision of 99.58 % and an F1-score of 98.41 %.

Energy Efficient Trust Based Data Communication using AODV Protocol in MANET

MANET is a collection of mobile devices and small-scale infrastructure networks built without centralized control. Each node in these networks collaborates with the other nodes to serve as a host or router. Because the detection medium contains information about distributed communication transmitted through Internet resources, security is an essential aspect of the wireless sensor network. Due to security flaws in network routing attacks, most existing principles control power resources and topology structure responsibility in fewer security dependencies, resulting in various intrusion factors due to data breaches. This paper presents a secure routing mechanism for wireless sensor networks based on trust aware routing protocol. Most constraints the routing protocol is resilient in the presence of malicious nodes activities monitoring on the routing levels based on response portability and energy constraints. By concerning energy level-based security, we propose a Trust Aware Energy Efficient Routing Protocol (TAE2RP) using secure routing data transmission for improving lifetime maximization in WSN. Initially the Resistance Support Factor (RSF) was estimated based on the packet drop ratio by handling the data resemblance of route containing information. Then the routing levels behaviors are analyzed through Secure Surf-Channel Multicast Routing Protocol (SSCMRP). To evaluate the trust factor on packet transmission rate using TAE2RP. In addition, Redundant Array Shifting Encryption (RASE) was applied to secure the data packets. The authentication policy was enhanced through Master Node Digital signing authentication. This optimized the cost of security routing by handling energy balancing mechanisms to improve the higher security by concerning authentication to attain higher security.

Social context‐aware macroscopic routing scheme for opportunistic network

AbstractOpportunistic networks (OppNets) have attracted widespread attention as wireless technologies have advanced. OppNets are widely used in delay‐tolerant applications because they route messages using a store‐carry‐forward mechanism. Recently, socially aware routing has been increasingly modeled for message dissemination in OppNets, where the message is routed selectively through cooperative nodes based on user interests; however, routing becomes extremely difficult as node density and data size increase. However, the current method fails to reduce data redundancy, message overhead, delay, and improve performance efficiency. To address the issues, this article proposes social context‐aware microscopic routing (SCAMR) for OppNets. SCAMR uses cluster‐based communication, novel social‐context association mapping, and an improved lost packet retrieval mechanism with minimal messaging overhead. In this work, the experiment was performed by considering three scenarios: varying node size, varying buffer size, and varying time‐to‐live size. The experimental results show that the SCAMR scheme improves delivery ratio by 71.25%, 67.87%, 69.18%, reduces delay by 33.93%, 26.68%, 35.36%, reduces the number of hop nodes (i.e., messaging overhead) by 77.84%, 71.53%, 76.04% over existing approaches namely, SCARF (SoCial‐Aware Reliable Forwarding Technique for Vehicular Communications), SRS (secure routing strategy), and EDT (effective data transmission) considering different scenarios, respectively.

SBLDAR: A Link Score Based Delay Aware Routing for WSNs

SBLDAR: A Link Score Based Delay Aware Routing for WSNs

OPTIMIZATION OF ROUTING USING TRAFFIC CLASSIFICATION IN SOFTWARE DEFINED NETWORKING

Efficient routing is an essential task for any network as it directly impacts the network’s performance. In this paper, we have used traffic classification techniques to optimize routing in Software Defined Network (SDN) and provided a cost aware routing framework. Instead of classifying traffic based on a particular parameter, we perform the traffic classification using a hybrid approach that is based on machine learning techniques. Our framework uses supervised machine learning techniques to classify network flow and detect elephant flows which are too heavy in size and require significant bandwidth. As the requirements of each elephant flow vary with the application, we further perform Quality of Service (QoS) based traffic classification, which classifies these elephant flow into QoS classes as per their QoS requirements. For this purpose, we have used semi-supervised machine learning algorithms. Further, we have proposed a routing algorithm that makes use of the Dijkstra algorithm to compute the best possible shortest path based on the QoS requirements of the elephant flow. The proposed method was implemented and tested on Mininet using a POX controller. The simulation results show that our framework successfully classifies elephant flows with an accuracy of 80% and also computes a low-cost path for each elephant flow based on the actual internet data set.

Awareness routing algorithm in vehicular ad-hoc networks (VANETs)

AbstractThe behavior of a Vehicle Ad hoc Network (VANETs) is extremely unpredictable due to the high mobility and random network topology inherent to the nature of VANETs. Several problems, including frequent connection failures, scalability, multi-hop data transfer, and data loss, impact the performance of Transmission Control Protocols (TCP) in such wireless ad hoc networks. This study proposes using zone-based routing with consideration for mobility in VANETs as a means of avoiding this issue. A hybrid optimization approach is introduced and used to the routing process. Both Ant Colony Optimization (ACO) and Artificial Bee Colony Optimization (ABCO) are components of the hybrid algorithm (ABC). Link stability and Residual energy provide the basis of the fitness function. Several measures, including delivery ratio, time, and overhead, are used to evaluate the effectiveness of the suggested method. A comparison of the suggested method's efficiency with that of other algorithms.