Packet Routing Research Articles

PARouting: Prediction-supported adaptive routing protocol for FANETs with deep reinforcement learning

Flying Ad-hoc Networks (FANETs) are becoming increasingly popular for various applications. Effective routing protocols for FANETs are essential yet challenging due to the high dynamic nature of Unmanned Aerial Vehicles (UAVs). Most existing routing protocols require the periodic broadcast of Hello packets to maintain neighbor tables that store the locations of neighbors, mobility patterns, etc. However, the frequent exchange of Hello packets leads to a large routing overhead in FANETs. This paper proposes PARouting, a prediction-supported adaptive routing protocol with Deep Reinforcement Learning, which introduces a novel UAV mobility prediction algorithm using Deep Learning (DL-UMP) to estimate the locations of UAVs. Based on DL-UMP, we design an adaptive Hello packet mechanism to realize on-demand broadcasting of Hello packets, which reduces routing overhead. The routing process is formulated as a Partially Observable Markov Decision Process, and a new Q-network structure is proposed to select the optimal next hop. Simulation results confirm the accuracy of the DL-UMP and show that PARouting outperforms benchmark routing protocols in terms of packet delivery rate, end-to-end delay, and routing overhead.

An effective routing strategy using congestion signaling for various types of communication networks

Finding the shortest paths for packets in communication networks is one of the vital engineering challenges. Recently, a routing strategy using gravitational centrality has been proposed to find efficient transmitting routes for packets to their destinations. This routing strategy successfully mitigates the congestion of packets in the networks and effectively transmits many packets to their destinations by using gravitational centrality. However, this routing strategy is difficult to transmit the packets to their destinations if large volumes of packets flow in the communication networks because fixed costs are used to determine the shortest paths for every source-destination pair. To overcome these problems, we have already proposed a routing strategy using congestion signals. This routing strategy further mitigates the congestion of packets compared to conventional routing strategies such as the routing strategy with gravitational centrality. In this study, we comprehensively evaluate the routing strategy with the congestion signals if different types of centralities are incorporated into the costs of links and various topologies are applied to the communication network models. Numerical experiments illustrate that our routing strategy using the congestion signals mitigates the congestion of packets even if other types of centralities are incorporated. In addition, our proposed routing strategy shows effective transmitting performance for the various topologies of networks.

Efficient route discovery method in MANETs and packet loss reduction mechanisms

In the route of expansion in mobiles, quantity of nodes in the mobile ad-hoc networks (MANETs) must be expanded. The MANETs being active in nature and it starts issues in deciding the most ideal route for packets. In this paper an attempt to propose another routing method which consolidates the properties of both static and dynamic routing method from that point tries to take out the issues innate in the system through size-based routing is done. The paper concentrates on efficient route discovery process for secure data transfer with less ratio of packet loss. For the minimisation of assault and packet dropping different authors manufactured different technique such strategy is node validation, distant analysis scheme, ACK-based method, status-based plan and motivating force dependent plan. ACK-based plan endured an issue of monstrous overhead because of additional affirmation packet and it likewise endured choice equivocalness if asked for node decline to send back acknowledgement. In this paper we utilises 2-ACK-based plan utilising protected channel for conquering the issue of choice equivocalness for asked for node, enhanced node confirmation and limit packet dropping in ad-hoc network.

MRCN: Throughput-Oriented Multicast Routing for Customized Network-on-Chips

The relentless proliferation of Big Data and artificial intelligence has compelled computing platform architectures to evolve into heterogeneous multicores for greater energy efficiency. A customized network-on-chip (NoC) supporting interconnection diversity is pivotal for the asymmetric data-access traffic requirements of modern heterogeneous multicore system-on-chip (SoC). A significant portion of on-chip data access comprises single-source multi-destination (SSMD) traffic, which supports barrier synchronization, multi-threading, cache coherency protocols, and deep neural network (DNN) acceleration. By amortizing SSMD traffic, multicast routing is essential for effectively utilizing communication bandwidth. One of the primary concerns in supporting multicast routing in NoCs is to circumvent the additional deadlock conditions caused by branch operations among the active routers. However, it is challenging to implement the throughput-optimized multicast routing in irregular topology-based NoCs because the deadlock conditions become highly complicated, and the Hamiltonian path required to apply the labeling rule may not exist. Two important observations were identified regarding multicast routing in customized NoCs: 1) Even if the NoC lacks a Hamiltonian path, deadlock-freedom can be guaranteed by restricting branch operations to a specific destination. 2) A variable path diversity in a custom topology can be leveraged in routing path allocation and branch. Based on these properties, this study proposes a deadlock-free and throughput-enhanced multicast routing for customized NoC (MRCN). MRCN ensures deadlock freedom by utilizing extended routing and router labeling rules. Furthermore, destination router partitioning and traffic-aware adaptive branching are incorporated to reduce packet routing hops and disperse channel traffic. The effectiveness of MRCN was verified using Noxim, a well-known cycle-accurate NoC simulator, under various topologies and traffic patterns. The simulation revealed that MRCN improved the average latency by 13.98 % and the throughput by 12.16 % under the saturated traffic conditions over the previous multicast routings in customized NoCs.

Optimized Tuning of LOADng Routing Protocol Parameters for IoT

Interconnected devices and intelligent applications have slashed human intervention in the Internet of Things (IoT), making it possible to accomplish tasks with less human interaction. However, it faces many problems, including lower capacity links, energy utilization, enhancement of resources and limited resources due to its openness, heterogeneity, limited resources and extensiveness. It is challenging to route packets in such a constrained environment. In an IoT network constrained by limited resources, minimal routing control overhead is required without packet loss. Such constrained environments can be improved through the optimal routing protocol. It is challenging to route packets in such a constrained environment. Thus, this work is motivated to present an efficient routing protocol for enhancing the lifetime of the IoT network. Lightweight On-demand Ad hoc Distance-vector Routing Protocol—Next Generation (LOADng) protocol is an extended version of the Ad Hoc On-Demand Distance Vector (AODV) protocol. Unlike AODV, LOADng is a lighter version that forbids the intermediate nodes on the route to send a route reply (RREP) for the route request (RREQ), which originated from the source. A resource-constrained IoT network demands minimal routing control overhead and faster packet delivery. So, in this paper, the parameters of the LOADng routing protocol are optimized using the black widow optimization (BWO) algorithm to reduce the control overhead and delay. Furthermore, the performance of the proposed model is analyzed with the default LOADng in terms of delay, delivery ratio and overhead. Obtained results show that the LOADng-BWO protocol outperforms the conventional LOADng protocol.

Machine Learning and Trust Based AODV Routing Protocol to Mitigate Flooding and Blackhole Attacks in MANET

The latest advancements in wireless communication promotes the researchers to concentrate more in the expansion of Mobile Ad hoc Networks (MANETs), where nodes communicate each other to provide the demanded real time infotainment services. However, due to wireless connectivity and decentralized architecture, constructing secure routing is still a challenging issue in MANETs. Therefore, in this paper, A Machine Learning and Trust Based AODV Routing Protocol (ML-AODV) to mitigate Flooding and Blackhole Attacks in MANET is proposed. Primarily, cooperative intermediate nodes in the network are selected through trust estimation at each node to avoid (flooding attack) unnecessary transmission of huge number of routing packets to nonexistent destinations. Towards this, the node distinct metrics like Hop Count (HC), Residual Energy (RE), and Link Expiration Time (LET) are considered for trust estimation. The nodes with highest trust value are chosen as trusted relay forwarders to find the optimal paths with reduced packet drop rate (Blackhole attack). Further, finds best optimal path to eliminate the energy disparity and delay for packet transmission using machine learning based Artificial Neural Network (ANN) with Support Vector Machine (SVM) classifier. Here, SVM is used to identify the intruder in the selected route to improve efficiency and intrusion detection accuracy. The performance of the proposed ML-AODV is carried out using NS-2 and compared over existing routing schemes under attack. Based on the simulation results, the proposed ML based AODV exhibits improved throughput to 4% and reliability to 44% over existing approaches. In addition, delay, routing overhead, packet loss ratio are reduced to 12%, 15%, and 10% respectively.

Combined Distributed Shared-Buffered and Diagonally-Linked Mesh Topology for High-Performance Interconnect.

Networks-on-Chip (NoCs) have become the de-facto on-chip interconnect for multi/manycore systems. A typical NoC router is made up of buffers used to store packets that are unable to advance to their desired destination. However, buffers consume significant power/area and are often underutilized, especially in cases of applications with non-uniform traffic patterns thus leading to performance degradation for such applications. To improve network performance, the Roundabout NoC (R-NoC) concept is considered. R-NoC is inspired by real-life multi-lane traffic roundabouts and consists of lanes that are shared by multiple input/output ports to maximize buffering resource utilization. R-NoC relies on router-internal adaptive routing that decides the lane path based on back pressure. Back pressure makes it possible to assess lane utilization and route packets accordingly. This is made possible thanks to the use of elastic buffers for control flow, a clever type of handshaking in a way similar to asynchronous circuits. Another prominent feature of R-NoC is that internal routing and arbitration are completely distributed which allows for significant freedom in deciding internal router topology and parameters. This work leverages this property and proposes novel yet unexplored configurations for which an in-depth evaluation of corresponding implementations on 45 nm CMOS technology is given. Each configuration is evaluated performance and power-wise on both synthetic and real application traffic. Several R-NoC configurations are identified and demonstrated to provide very significant performance improvements over standard mesh configurations and a typical input-buffered router, without compromising area and power consumption. Exploiting the distributed nature of R-NoC routers, a diagonally-linked configuration is then proposed which incurs moderate area overhead and features yet better performance and energy efficiency.

COMPUTER SIMULATION MODEL OF A COMPUTER NETWORK WITH FRACTAL TRAFFIC FOR TESTING ROUTING ALGORITHMS


 
 
 The object of research in this article is simulation modeling of a computer network and the process of traffic routing. The relevance of the research is due to the importance of ensuring the quality of service in computer networks, in particular, by necessity reducing the number of lost IP-packets at high traffic intensity values. Determining the routing of traffic packets is a complex process and is based on various indicators or combinations of indicators. If the routing process takes place in a dynamic mode, then the complexity of the route calculation increases, in this case, one of the tools for research and comparison of different routing algorithms can be a computer simulation model of a computer network. The goal of the work is the development and research of a computer simulation model of a computer network for testing traffic routing algorithms. The tasks: to develop computer simulation model of a computer network to generate the network structure and simulate the traffic distribution process with the ability to test different routing algorithms. Research methods: theory of computer networks, theory of fractal analysis, object-oriented programming, theory of algorithms and data structures, theory of complex networks, theory of Markov processes. Conclusions. The paper investigated the basic principles of traffic routing in computer networks. A simulation model of a computer network for testing traffic routing algorithms has been developed. A method based on the theory of complex networks has been developed to generate the structure of a computer network. Theory of fractal analysis and Markov processes are used for traffic generation. A series of experiments was conducted on a developed model to determine how different fractal dimensions of traffic at high traffic intensity values affect the number of lost packets, and therefore the quality of service. Analyzing the results of the experiment, the following conclusions can be drawn: the least number of lost packets occurs when the process is random or has weak trends. The fewest lost packets were at fractal dimension 1.5, i.e., when the process is completely random, there were also few lost packets at fractal dimensions close to this; persistent and anti-persistent processes (those with memory) cause more packet loss for the same traffic intensity and maximum number of packets sent from one device per unit of time. Moreover, anti-persistent processes cause significantly more losses than persistent ones. Thus, when performing traffic routing and finding optimal paths for sending IP-packets, it can be useful to determine and take into account the fractal dimension of traffic at the entrance of each router and use it when calculating metrics to determine the best routes.
 
 

TGRV: A trust-based geographic routing protocol for VANETs

Vehicular ad-hoc networks (VANETs) consist of highly mobile and self-organized nodes that wirelessly communicate with one another and transmit a variety of information, some of which may be critical information. Due to the rapidly changing topology of VANETs and the high-speed mobility of the participating vehicles, the routing of packets in these networks is a challenging task. Moreover, the presence of malicious vehicles in VANETs makes the correct routing of packets more challenging. In this paper, we propose a trust-based geographic routing protocol for VANETs (TGRV) that limits the participation of malicious vehicles in routing. In TGRV, to select the next-hop, a sender not only considers the distance, speed, and direction of its neighbors but also evaluates their direct trust and recommendation trust. For this purpose, TGRV uses a monitoring system that allows each vehicle to monitor the correct packet forwarding rate of its next-hop. In this way, the vehicle updates its direct trust to the next-hop and retransmits the packets if the packets are lost. The vehicle also sends its observations of the next-hop to its neighbors with a push-based notification, and based on that, the neighbors can update their recommendation trust about the next-hop. The monitoring system applies distance prediction in a modified promiscuous mode to better estimate the correct packet forwarding rate of the next-hop. To enhance the accuracy of trust management, the trust values of interactions are reduced over time by using a decay factor. TGRV uses the number and trust of two-hop neighbors, which helps in selecting the next-hop that is in a more trusted zone. Our extensive simulations on OMNeT++ show that when the percentage of malicious vehicles in an urban scenario increases from 0 to 25%, the packet delivery ratio of TGRV decreases from 95.1 to 88.7%, which performs very well compared to the GPSR and PGRP protocols. Also, the end-to-end delay of TGRV increases from 3.79 to 7.07 s and the average hop count of TGRV increases from 6.7 to 9.9, which are significant increases compared to the other two protocols. These increases are acceptable because, in the other two protocols, there is no monitoring and retransmission, and the probability of packet delivery failure on longer routes is higher. The cost and security analysis also show that the memory, communication, and computational overheads of TGRV are acceptable, and TGRV is resistant and resilient to some important trust-based attacks.

Multi-agent adaptive routing by multi-head-attention-based twin agents using reinforcement learning

A regular condition, typical for packet routing, for the problem of cargo transportation, and for the problem of flow control, is the variability of the graph. Reinforcement learning based adaptive routing algorithms are designed to solve the routing problem with this condition. However, with significant changes in the graph, the existing routing algorithms require complete retraining. To handle this challenge, we propose a novel method based on multi-agent modeling with twin-agents for which new neural network architecture with multi-headed internal attention is proposed, pre-trained within the framework of the multi-view learning paradigm. An agent in such a paradigm uses a vertex as an input, twins of the main agent are placed at the vertices of the graph and select a neighbor to which the object should be transferred. We carried out a comparative analysis with the existing DQN-LE-routing multi-agent routing algorithm on two stages: pre-training and simulation. In both cases, launches were considered by changing the topology during testing or simulation. Experiments have shown that the proposed adaptability enhancement method provides global adaptability by increasing delivery time only by 14.5 % after global changes occur. The proposed method can be used to solve routing problems with complex path evaluation functions and dynamically changing graph topologies, for example, in transport logistics and for managing conveyor belts in production.

A Learned Bloom Filter-Assisted Scheme for Packet Classification in Software-Defined Networking

Traditional routing technologies based on a single IP address domain faces the challenge to meet the increasing demand for network services and security. Packet classification is a technique to differentiate multi-domain network traffic in a fine-grained manner using packet header fields. Packet classification requires to operate efficiently to avoid it becoming a bottleneck in the packet routing process. Tuple space search (TSS) used in SDN supports fast rule updates but low-speed packet classification. In this paper, we propose a learned Bloom filter (LBF)-based packet classification algorithm that combines LBF and TSS to promote classification speed by avoiding invalid hash table accesses. Specifically, LBF consists of multiple RNN models and one support Bloom filter (SBF), in which the learned models are trained with the positive and negative sets, and used as a pre-filter to identify the two sets. For the filter outcomes with a negative result from learned models, SBF is constructed to perform the second filtration. To ensure efficiency of RNN and SBF, we carefully select key features to be used in RNN and SBF, which can also maintain efficient search in the final stage of hash checking. Our experimental results show that the proposed algorithm saves more memory space than Tuple space pruning (TSP) given the same false positive rate. The proposed algorithm is competitive in terms of the number of memory accesses, while achieving almost one order of magnitude improvement on pre-processing time over NeuroCuts which is an advanced decision tree classifier.

IQoR-LSE: An Intelligent QoS On-Demand Routing Algorithm With Link State Estimation

With the rapid development of Internet applications, diversified Quality of Service (QoS) has been required in packet routing to meet the demand of various types of applications. In this article, an Intelligent QoS on-demand Routing (IQoR) framework has been proposed to support multiclass QoS Provisioning for packet forwarding. In addition, we present an IQoR with Link State Estimation (IQoR-LSE) algorithm with the assistance of link congestion inference to guide the exploration of action space in deep reinforcement learning, to seek optimal routing policies. The IQoR-LSE algorithm is proposed to solve the non-convergence problem at exploring the high-dimensional action space by jointly estimating the link congestion. Extensive simulations show that IQoR-LSE algorithm outperforms other benchmark routing algorithms with efficient learning and a significant reduction in average delay, jitter and packet loss.

A cascaded federated deep learning based framework for detecting wormhole attacks in IoT networks

The growth of the internet over the years has resulted in massive use and spread of the Internet of Things (IoT) in many areas. From home networks to industrial IoT, from medicine to transportation, IoT is everywhere. This massive usage has come with a price as attackers make use of sophisticated techniques and malware to target these heterogeneous IoT networks. Moreover, the low processing power and minimal resources of the IoT devices make security and privacy a major challenge. This weakness has motivated attackers to target IoT networks in a wide variety of ways by launching different types of attacks. One such kind of attack is the Wormhole attack which belongs to the family of routing attacks and disturbs the normal routing and flow of network packets in the IoT network. Many techniques involving rule-based, trust-based, machine learning-driven and deep learning assisted have been devised to counter wormhole attacks. The proposed research work presents a cascaded wormhole detection technique for IoT networks which is based on the federated deep learning technique and a Dynamic Trust Factor (DTF). The DTF is based on two trust attributes, whereas Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) deep learning models have been trained using the federated approach, which guarantees data security and privacy at the node level. The proposed technique achieves the accuracy of 96% and is lightweight due to the cascaded and federated learning approach.

Enhancement of the performance of MANET using machine learning approach based on SDNs

Deep learning (DL) is a subdivision of machine learning (ML) that employs numerous algorithms, each of which provides various explanations of the data it consumes; mobile ad-hoc networks (MANET) are growing in prominence. For reasons including node mobility, due to the potential wireless sensor network (WSN) to provide a small-cost solution to real-world contact challenges. But the lifespan in this network is restricted lifespan. Therefore, the wireless sensor network (WSN) is more vulnerable to battery consumption. On the other hand, routing packets in a Wireless Sensor Network (WSN) is a challenging task, according to the limited resources available on the nodes of these networks, especially their energy sources. The use of Machine Learning (ML) techniques in a Software-Defined Network (SDN) topology has shown good potential for solving such a complex task. However, existing techniques emphasize finding the shortest paths to deliver the packets, which can overload certain nodes in the network, depending on their positioning. In this study, a new method is proposed to extend the lifetime of the WSN by balancing the loading on the nodes, using a Deep Reinforcement Learning (DRL) approach. By emphasizing the lifetime of the network, the proposed method has been able to discover and use alternative routes to deliver the packets, avoiding the use of nodes with low energy. Hence, the average number of hops the packets travel through has been increased, but the time required for the first node to exhaust its energy has been significantly increased.

Multipath Routing in Wireless Body Area Sensor Network for Healthcare Monitoring.

Mobility and low energy consumption are considered the main requirements for wireless body area sensor networks (WBASN) used in healthcare monitoring systems (HMS). In HMS, battery-powered sensor nodes with limited energy are used to obtain vital statistics about the body. Hence, energy-efficient schemes are desired to maintain long-term and steady connectivity of the sensor nodes. A sheer amount of energy is consumed in activities such as idle listening, excessive transmission and reception of control messages, packet collisions and retransmission of packets, and poor path selection, that may lead to more energy consumption. A combination of adaptive scheduling with an energy-efficient protocol can help select an appropriate path at a suitable time to minimize the control overhead, energy consumption, packet collision, and excessive idle listening. This paper proposes a region-based energy-efficient multipath routing (REMR) approach that divides the entire sensor network into clusters with preferably multiple candidates to represent each cluster. The cluster representatives (CRs) route packets through various clusters. For routing, the energy requirement of each route is considered, and the path with minimum energy requirements is selected. Similarly, end-to-end delay, higher throughput, and packet-delivery ratio are considered for packet routing.

An IDS based on modified chaos Elman’s neural network approaches for securing mobile ad hoc networks against DDoS attack

A mobile ad hoc network (MANETs) is a collection of moving nodes that combine into a network with no predefined infrastructure. There are many types of attacks that could target MANETS, one among them is Distributed Denial of service attacks (DDoS). DDoS is defined as attacking routing functions and taking down the entire operation of the mobile ad hoc network. The two primary victims of DDoS attacks are the functions of routing and battery capacity. The DDoS attack can cause routing table overflow which in turn can potentially cause the infected node floods. The routing overflow is followed by creating a fake route packet to consume the available resources of the participating active nodes. This cause disrupts the normal functioning of legitimate routes. In recent years, different approaches are implemented to improve the security level of MANET. In this work, the Cuckoo Search Algorithm-based Modified Elman’s Neural Network (CSA - MENN) approaches have been proposed to overcome DDoS attacks. The CSA - MENN approaches consists of three-part which are Cuckoo search algorithm clustering area to enhance the route from source to destination, chaos theory module is used to detect the abnormal nodes, then the Modified Elman Neural Network (MENN) is employed to prevent a malicious node from sending data to the destination by determining node that consumed more resources. Packets could be lost or the victim could reset the path between the attacker and itself. CICIDS dataset has been used to test and evaluate the performance of the proposed approach based on the criteria of accuracy, packet loss, and jitter. The data set, CICIDS 2017, used in this article divides the data into 7 groups: 5 for training, 1 for validation, and 1 for generalization. In summary, approximately 71.4 percent of data is used for training and 28.6 percent for validation and generalization.

Q-Learning Driven Routing for Aeronautical Ad-Hoc Networks

The aeronautical ad-hoc network (AANET) is one of the critical methodologies to satisfy the Internet connectivity requirement of airplanes during their flights. However, the ultra-dynamic topology and unstable air-to-air link characteristics increase the need for AANETs to a particular routing algorithm compared to the terrestrial networks. This need is mainly because these AANET-specific characteristics increase the delays, packet losses, and network load with accuracy reduction by continuously changing topology and breaking air-to-air links during routing. The works in the literature do not satisfy the ultra-dynamic topology and unstable air-to-air link characteristics of AANETs during routing. On the other hand, the routing algorithm can adapt to the dynamic conditions of AANETs by utilizing Artificial Intelligence (AI) based methodologies. For adaptation to this dynamic environment, we aim to let the airplanes find their routing path through exploration and exploitation by mapping the AANET environment to QLR. Clearly, this article proposes an updated Layered Hidden Markov Model (updated-LHMM) estimation-based Q-learning (QLR) routing for AANETs to solve the delay, packet loss, network load, and accuracy problems. For this aim, the Bellman Equation is adapted to the AANET environment by proposing different methodologies for its related QLR components. Results reveal that the proposed strategy mainly reduces the routing delay and packet losses by 30% and 33% compared to the methods in the literature.

Bio-Inspired Dynamic Trust and Congestion-Aware Zone-Based Secured Internet of Drone Things (SIoDT)

The Internet of Drone Things (IoDT) is a trending research area where drones are used to gather information from ground networks. In order to overcome the drawbacks of the Internet of Vehicles (IoV), such as congestion issues, security issues, and energy consumption, drones were introduced into the IoV, which is termed drone-assisted IoV. Due to the unique characteristics of the IoV, such as dynamic mobility and unsystematic traffic patterns, the performance of the network is reduced in terms of delay, energy consumption, and overhead. Additionally, there is the possibility of the existence of various attackers that disturb the traffic pattern. In order to overcome this drawback, the drone-assisted IoV was developed. In this paper, the bio-inspired dynamic trust and congestion-aware zone-based secured Internet of Drone Things (BDTC-SIoDT) is developed, and it is mainly divided into three sections. These sections are dynamic trust estimation, congestion-aware community construction, and hybrid optimization. Initially, through the dynamic trust estimation process, triple-layer trust establishment is performed, which helps to protect the network from all kinds of threats. Secondly, a congestion-aware community is created to predict congestion and to avoid it. Finally, hybrid optimization is performed with the combination of ant colony optimization (ACO) and gray wolf optimization (GWO). Through this hybrid optimization technique, overhead occurs during the initial stage of transmission, and the time taken by vehicles to leave and join the cluster is reduced. The experimentation is performed using various threats, such as flooding attack, insider attack, wormhole attack, and position falsification attack. To analyze the performance, the parameters that are considered are energy efficiency, packet delivery ratio, routing overhead, end-to-end delay, packet loss, and throughput. The outcome of the proposed BDTC-SIoDT is compared with earlier research works, such as LAKA-IOD, NCAS-IOD, and TPDA-IOV. The proposed BDTC-SIoDT achieves high performance when compared with earlier research works.

Routing flying Ad Hoc network using salp swarm algorithm

An Ad-hoc network is collection of mobile nodes without the necessity of existing any centralized access points. In this paper introduce novel protocol that used to improveAdHoc protocol with salp swarm algorithm to routed flying Ad Hoc Network (FANET) by method of simulation. FANET predicated unmanned aerial vehicles (UAV), it designed wireless network has nodes withwith high mobility, actively changing topology and movement in 3D space. The main problem for FANET manner of routing packets among managed nodes. The new protocol based on salp swarm algorithm called “SalpAdHoc” protocol to solve routing problem and less the conjunction, thesimulation results of an experimental study confirming the feasibility of using salp swarm algorithms for routing in FANET are presented.

Sustainable Multipath Routing for Improving Cross-Layer Performance in MANET Using an Energy Centric Tunicate Swarm Algorithm

Generally, battery power is a valuable resource for mobile devices in a Mobile Ad Hoc Network (MANET). Therefore, energy efficiency and network lifetime should be taken into account when developing control strategies. However, designing an energy-efficient routing mechanism necessitates consideration of many nodes from many layers, such as remaining energy, overall traffic load, and channel assumptions. The traditional layered strategy is unsuccessful in dealing with power-related issues that might affect all layers of the stack. In this paper, the Energy Centric Tunicate Swarm Algorithm (ECTSA) is proposed to perform the cross-layer routing over MANET. The fitness metrics considered in the ECTSA to improve the cross-layer routing are residual energy, communication cost, Data Success Rate (DSR), and mobility. Additionally, an Adaptive Competition Window (ACW) adjustment is used for minimizing the energy consumption caused by the contentions. The performance of the proposed ECTSA is analyzed by means of energy consumption, Packet Delivery Ratio (PDR), End-to-End Delay (EED), and routing overhead. Next, the existing techniques, namely, CEELBRP and EECRP-PSO, are used to evaluate the efficiency of the ECTSA method. The energy consumption of the ECTSA is 7.1 joules and prolongs the network lifetime up to 1603 s for 50 nodes, which is better when compared to the existing CEELBRP and EECRP-PSO techniques.