Ant Colony Optimization Routing Research Articles

Optimise Energy Consumption of Wireless Sensor Networks by using modified Ant Colony Optimization

Routing represents a pivotal concern in the context of Wireless Sensor Networks (WSN) owing to its divergence from traditional network routing paradigms. The inherent dynamism of the WSN environment, coupled with the scarcity of available resources, engenders considerable challenges for industry and academia alike in devising efficient routing strategies. Addressing these challenges, a viable recourse is applying heuristic search methodologies to ascertain the best path in WSNs. Ant Colony Optimization (ACO) is a well-established heuristic algorithm that has demonstrated notable advancements in routing contexts. This paper presents an altered routing protocol that is based on ant colony optimization. In this protocol, we incorporate the inverse of the distance between nodes and their neighbours in the probability equations of ACO, along with considering pheromone levels and residual energy. These formulation modifications facilitate the selection of the most suitable candidate for the subsequent hop, effectively minimising the average energy consumption across all nodes in each iteration. Furthermore, in this protocol, we iteratively fine-tune ACO's parameter values based on the outcomes of several experimental trials. The experimental analysis is conducted through a diverse set of network topologies, and the results are compared against well-established ACO algorithms and routing protocols. The efficacy of the proposed protocol is assessed based on various performance metrics, encompassing throughput, energy consumption, network lifetime, energy consumption, the extent of data transferred over the network, and the length of paths traversed by packets. These metrics collectively provide a comprehensive evaluation of the performance attainments of the routing protocols.

Comparative analysis of reactive routing protocols for vehicular adhoc network communications

In the recent past, vehicular adhoc networks (VANETs) have gained a lot of importance. The routing protocols play a vital role to deliver payloads from one vehicle to another one while they are moving at relative speeds. It is a challenging task to select a routing protocol for VANETs because of the uneven distribution and high mobility of vehicles. In this paper, we have analysed the two standard reactive protocols, adhoc on-demand distance vector (AODV) and ant colony optimization (ACO). The performance comparison of AODV and ACO routing protocols has been presented in this paper. The results show AODV performed better in terms of energy consumption and routing overhead. While considering the throughput, energy loss ratio, and delay ACO has performed. ACO resulted as upperhand.

Energy Efficient Quantum-Informed Ant Colony Optimization Algorithms for Industrial Internet of Things

One of the most prominent research areas in information technology is the Internet of things (IoT) as its applications are widely used such as structural monitoring, health care management systems, agriculture and battlefield management, and so on. Due to its self-organizing network and simple installation of the network, the researchers have been attracted to pursue research in the various fields of IoTs. However, a huge amount of work has been addressed on various problems confronted by IoT. The nodes densely deploy over critical environments and those are operated on tiny batteries. Moreover, the replacement of dead batteries in the nodes is almost impractical. Therefore, the problem of energy preservation and maximization of IoT networks has become the most prominent research area. However, numerous state-of-the-art algorithms have addressed this issue. Thus, it has become necessary to gather the information and send it to the base station in an optimized method to maximize the network. Therefore, we propose a novel quantum-informed ant colony optimization (ACO) routing algorithm with the efficient encoding scheme of cluster head selection and derivation of information heuristic factors. The algorithm has been tested by simulation for various network scenarios. The simulation results of the proposed algorithm show its efficacy over a few existing evolutionary algorithms using various performance metrics such as residual energy of the network, network lifetime, and the number of live IoT nodes.

An Enhanced Location-Aided Ant Colony Routing for Secure Communication in Vehicular Ad Hoc Networks

The dynamic characteristics of vehicular ad hoc networks (VANETs) demand reliable and secure communication over wireless media. However, there are significant contradictions in autonomous vehicular systems related to security and privacy. Furthermore, VANETs require seamless data transmission to exchange time-critical data about accidents and traffic. Therefore, VANETs require secure seamless data transmission mechanisms to provide secure reliable communication without communication interruption to exchange time-critical data between vehicles. This paper proposes an Enhanced Location-Aided Ant colony Routing (ELAACR) for VANETs to achieve seamless secure data transmission. The proposed ELAACR is a two-fold scheme in which Phase 1 implements Location-Aided Key Management (LAKM) and Phase 2 implements Ant Colony Routing (ACR). An ACR combined with an LAKM pattern for offering secure and reliable communication. The LAKM scheme works based on the group signature concept to construct a secure network, and an ACR is used to find an efficient secure shortest path for seamless data transmission. The proposed work is simulated in Network Simulator (NS 2.35). By using an efficient and secure shortest path with secure seamless data transmission, the proposed ELAACR achieves an average throughput (T) of 640 Kbps, packet loss rate (PLR) of 0.018%, packet delivery ratio (PDR) of 98.5%, overhead (OH) of 0.157%, and 0.16 s end-to-end delay (EED) compared to Enhanced Hybrid Ant Colony Optimization Routing Protocol (EHACORP) and Fuzzy Based Ant Colony Optimization (F-ANT). The simulation results show that the proposed ELAACR registers better performance in all aspects compared to the existing EHACORP and F-ANT.

Fuzzified swarm intelligence framework using FPSOR algorithm for high-speed MANET- Internet of Things (IoT)

Internet of things (IoT) is an inventive technology which permit the association of physical things by means of the digital world throughout the use of heterogeneous networks and communication technologies. Ad-hoc routing protocols may be dispersed and involve every node in route discovery process by making routing data more reliable. Mobile-Adhoc-Networks (MANETs) consist of many portable nodes that can commune directly with each other or through intermediate nodes. Repeatedly, nodes in a MANETs operate with batteries and can roam freely, and thus, a node may exhaust its energy or move away without providing any notice to its cooperative nodes. IoT system using a combination of MANET, a route consists of numerous links in sequence, and so, its lifetime is based on the lifetime of every node and also the wireless links among adjacent nodes. In this research work Fuzzified Particle Swarm Optimization oriented Routing (FPSOR) algorithm is planned to minimize data loss and computational overhead, which in turn maximizes the lifetime of MANETs. Particle Swarm Optimization oriented Routing protocol (PSOR) have taken energy competence as significant criterion for processing routing and driving optimized path for the data accelerating and processing to the source node. The PSOR produces a fresh route for routing by considering fitness value of energy to evaluate diverse path and to choose best-optimized path whose energy consumption is less as compared to ant colony optimization routing paths. This algorithm utilizes a good approach focusing energy levels/status of the nodes through fuzzification and the lengths of the routed ways. NS2 simulator is casted for performance evaluation.

MO-CBACORP: A new energy-efficient secure routing protocol for underwater monitoring wireless sensor network

With the rapid progress of sensor technology, underwater monitoring wireless sensor network (UMWSN) equipped with water resource monitoring capabilities have surfaced. UMWSN is currently emerging as a popular research and application area, with a wide range of applications in various fields, including but not limited to disaster warning, pollutant monitoring and hydrological data monitoring. However, due to the limited size and computing power of wireless sensors, UMWSN are exceedingly susceptible to network security attacks by contaminating entities. In order to tackle the problems of inadequate security, elevated network energy consumption, and high system latency in existing QoS routing research, this paper puts forward an innovative multi-objective routing protocol named multi-objective chaotic Boltzmann ant colony optimization routing protocol (MO-CBACORP). MO-CBACORP can minimize the energy consumption of sensor nodes in UMWSN routing, thereby effectively diminishing system latency and significantly enhancing routing security. During the routing optimization process, MO-CBACORP proposed an innovative design of the chaos operator and Boltzmann selection strategy, which effectively avoids the issue of being stuck in local optimal solutions and greatly enhances the convergence speed of the algorithm. Additionally, a novel experiment for underwater monitoring environments is presented that precisely replicates underwater monitoring conditions and attack scenarios. According to the experimental results, MO-CBACORP exhibits superior performance compared to the latest state-of-the-art research including THOA-MHR, ACO-MCMC, and MAP-ACO, with a minimum improvement of 7.25%, 9.31%, and 7.68% in terms of route security, network energy consumption, and delay, respectively. All in all, MO-CBACORP exhibits superior performance compared to the three latest research algorithms.

Enhancing the Lifetime of WSN Using a Modified Ant Colony Optimization Algorithm

Wireless sensor networks (WSNs) have been extensively used in various fields, such as health, defense, education, and industrial applications, to collect and transmit environmental data to the base station. However, energy efficiency is a significant challenge in WSNs, as data transmission is typically limited to a single route, leading to excessive energy consumption by the nodes along that route. This can lead to a decrease in the network's overall efficiency and effectiveness. To address this issue, this study aims to extend the lifespan of WSNs by optimizing route selection based on three variables: residual node energy, distance to the base station, and number of shared neighbors. In this paper, the authors propose three systematic approaches, namely Energy-Aware ACO Routing (EACO), Cost-Effective ACO Routing (CEACO), and Cost-Efficient Node Replacement Strategies ACO (CERACO), to enhance the lifetime of WSNs. These approaches consider various factors such as cost, energy consumption, replacement, and reliability. The paper provides a practical guide for researchers and practitioners to overcome the challenges related to energy efficiency and cost-effectiveness in WSNs. Experimental results demonstrate that the first dead node occurs later with the proposed methods than with the traditional Ant Colony Optimization (ACO) algorithm.

A committee machine-based estimation of shear velocity log by combining intelligent systems and rock-physics model using metaheuristic algorithms

Estimation of reservoir parameters such as porosity, lithology and determination of elastic moduli is based on well-logging data and seismic velocity information. However, the lack of shear velocity log in many wells, and the high costs of its acquisition have led to developing different approaches for shear velocity estimation. Currently, shear velocity estimation is accomplished by common industrial methods such as empirical equations, regression analysis, intelligent methods and rock-physics models (RPMs). The current study proposes that robust, accurate and cost-effective intelligent methods coupled with suitable RPM provide reasonable estimations of the shear velocity in an optimal processing time. Hybridizing intelligent models with RPMs in the framework of a committee machine is an optimal solution for shear velocity estimation of carbonate reservoirs. The intelligent methods used in this study include neural network (NN), fuzzy logic (FL) and an adaptive-neuro fuzzy inference system (ANFIS). In this research, for each individual intelligent model and a developed RPM, the optimal weights were allocated by Metaheuristic optimization algorithms including genetic algorithm (GA), ant colony optimization routing (ACOR) and simulated annealing (SA)). To obtain the best results with the lowest mean squared error (MSE), a total of 9408, 10801 and 12288 models were created and tested in MATLAB to optimally parametrize the GA, ACOR and SA, respectively. The results showed that the combination of the applied intelligent models with the developed RPMs leads to a higher correlation coefficient with reference to the measured Shear velocity in test dataset. The use of a combination of intelligent methods in the form of committee-machine instead of individual methods has led to a reduction in error and improved the results by up to 2% Considering the high sensitivity of shear wave velocity in geomechanical and reservoir characterization studies, even small improvements in its estimation can significantly reduce error in the characterization of hydrocarbon reservoirs.

IATLR: Improved ACO and TOPSIS-Based Layering Routing Protocol for Underwater Acoustic Networks

Aiming at the challenges of underwater acoustic networks (UANs) such as low bandwidth, long propagation delay, dynamic network topology, and energy limitation, an improved ant colony optimization (IACO) and technique for order preference by similarity to an ideal solution (TOPSIS)-based layering routing (IATLR) protocol for UANs is proposed. In the IATLR protocol, the transfer probability of the ant colony optimization (ACO) is optimized by comprehensively considering multiple factors such as energy consumption, propagation distance, and forwarding angle. Moreover, the ACO is improved and the optimized ACO is combined with the TOPSIS method by using the pheromone concentration of ACO as the decision basis of TOPSIS. Furthermore, the multifactor decision ranking of the TOPSIS method is optimized by the transfer probability of the ACO. Consequently, the best next-hop node is determined. In addition, the energy consumption is balanced through the whole network by the node silence mechanism in the IATLR protocol. Simulation results show that the IATLR protocol has significant advantages compared with the depth-based routing for underwater sensor networks (DBR), layer-based energy-efficient routing (LEER), and depth and distance based layering routing protocol for underwater sensor networks (DDLR) routing protocols in terms of packet delivery rate (PDR), energy consumption, and end-to-end delay (EED). The PDR increases by 5%–30%, the EED decreases by about 1–7 s, and the average energy consumption (AEC) reduces by 0.5–6 J.

An Improved Machine Learning Model with Hybrid Technique in VANET for Robust Communication

The vehicular ad hoc network, VANET, is one of the most popular and promising technologies in intelligent transportation today. However, VANET is susceptible to several vulnerabilities that result in an intrusion. This intrusion must be solved before VANET technology can be adopted. In this study, we suggest a unique machine learning technique to improve VANET’s effectiveness. The proposed method incorporates two phases. Phase I detects the DDoS attack using a novel machine learning technique called SVM-HHO, which provides information about the vehicle. Phase II mitigates the impact of a DDoS attack and allocates bandwidth using a reliable resources management technique based on the hybrid whale dragonfly optimization algorithm (H-WDFOA). This proposed model could be an effective technique predicting and utilizing reliable information that provides effective results in smart vehicles. The novel machine learning-based technique was implemented through MATLAB and NS2 platforms. Network quality measurements included congestion, transit, collision, and QoS awareness cost. Based on the constraints, a different cost framework was designed. In addition, data preprocessing of the QoS factor and total routing costs were considered. Rider integrated cuckoo search (RI-CS) is a novel optimization algorithm that combines the concepts of the rider optimization algorithm (ROA) and cuckoo search (CS) to determine the optimal route with the lowest routing cost. The enhanced hybrid ant colony optimization routing protocol (EHACORP) is a networking technology that increases efficiency by utilizing the shortest route. The shortest path of the proposed protocol had the lowest communication overhead and the fewest number of hops between sending and receiving vehicles. The EHACORP involved two stages. To find the distance between cars in phase 1, EHACORP employed a method for calculating distance. Using starting point ant colony optimization, the ants were guided in phase 2 to develop the shortest route with the least number of connections to send information. The relatively short approach increases protocol efficiency in every way. The pairing of DCM and SBACO at H-WDFOA-VANET accelerated packet processing, reduced ant search time, eliminated blind broadcasting, and prevented stagnation issues. The delivery ratio and throughput of the H-WDFOA-packet VANET benefitted from its use of the shortest channel without stagnation, its rapid packet processing, and its rapid convergence speed. In conclusion, the proposed hybrid whale dragonfly optimization approach (H-WDFOA-VANET) was compared with industry standard models, such as rider integrated cuckoo search (RI-CS) and enhanced hybrid ant colony optimization routing protocol (EHACORP). With the proposed method, throughput could be increased. The proposed system had energy consumption values of 2.00000 mJ, latency values of 15.61668 s, and a drop at node 60 of 0.15759. Additionally, a higher throughput was achieved with the new method. With the suggested method, it is possible to meet the energy consumption targets, delay value, and drop value at node 60. The proposed method reduces the drop value at node 80 to 0.15504, delay time to 15.64318 s, and energy consumption to 2.00000 mJ. These outcomes demonstrate the effectiveness of our proposed method. Thus, the proposed system is more efficient than existing systems.

An Enhanced Bio-Inspired Routing Algorithm for Vehicular Ad Hoc Networks

Vehicular ad hoc networks (VANETs) demands reliable communication mechanisms for time-critical communication between vehicles. In VANETs, communication links between vehicles are prone to frequent breaks due to high mobility and topology changes. In this context, this work presents an enhanced bio-inspired routing algorithm (EBIRA) to provide reliable communication. In EBIRA, enhanced ant colony optimization (EACO) finds the optimal long-life short-distance routes with the minimum hops based on distance, received signal strength metric, hop count, and evaporation rate. In EBIRA, the selected path has a short distance and a high level of connectivity at the link level with minimum hops. Choosing the shortest path through minimum hops with high connectivity level links improves route lifetime and reduces frequent link breaks between vehicles. Simulation results show that the performance of EBIRA is better than reliable route discovery by using ant colony optimization (RDACO) and road-aware geographic routing protocol (RAGR) in terms of packet delivery ratio, throughput, and latency. Furthermore, variations of the received signal strength based on vehicle density and speed are evaluated, and the EBIRA route discovery success ratio is estimated and shown based on vehicle density at speed.
 HIGHLIGHTS
 
 Vehicular ad hoc networks (VANETs) demands reliable communication mechanisms for time-critical communication between vehicles. In VANETs, communication links between vehicles are prone to frequent breaks due to high mobility and topology changes
 VANET routing faces various challenges due to its unique characteristics such as high mobility, dynamic topology, unlimited network size, no infrastructure, and wireless communication
 Choosing the shortest path through minimum hops with high connectivity level links improves route lifetime and reduces frequent link breaks between vehicles
 The Less complexity, adaptability and self-organizing characteristics of ACO can cope with frequent topology changes, high mobility, absence of infrastructure, and wireless communication. The robustness feature of the ACO often helps to overcome network interruptions in the form of disconnections
 
 GRAPHICAL ABSTRACT

An Ant Colony Optimization-Based Routing Algorithm for Load Balancing in LEO Satellite Networks

Satellite networks can provide a wider service range and lower delay than traditional terrestrial optical fiber networks. However, due to the bursty characteristic of the Internet traffic and the distributive feature of satellite links, traffic-intensive areas often suffer from link congestion while links in other areas are underutilized, i.e., the traffic imbalance problem in LEO satellite networks. In this paper, an ant colony optimization routing algorithm with window reduction for LEO satellite networks, ACORA-WR, is proposed to achieve load balancing. ACORA-WR limits the movement of the ant colony to a specific range and comprehensively considers the path distance, transmission direction, and link load to find a path with low delay and overhead. Simulation results verify that the proposed ACORA-WR scheme demonstrates high data delivery ratio and network throughput, while ensuring low average delay and network transmission overhead.

An enhanced distance and residual energy‐based congestion aware ant colony optimization routing for vehicular ad hoc networks

SummaryDue to the ad hoc nature of vehicular ad hoc networks (VANETs), routing in VANETs is challenging. Using an energy‐efficient shortest path without congestion (EESPWC) increases routing efficiency by satisfying specific parameters (high throughput, high packet delivery ratio, low overhead, low end‐to‐end delay, low packet loss ratio, and low energy consumption). In this work, enhanced distance and residual energy‐based congestion aware ant colony optimization (EDR‐CAACO) for VANETs is proposed to find an EESPWC. The proposed EESPWC produces high throughput and high packet delivery ratio with low overhead and low delay. In EDR‐CAACO, the roulette wheel selects the EESPWC based on the link's fitness values. The fitness value is computed based on the pheromone level of the link. The enhanced ACO (ant colony optimization) of the proposed model relies on the distance, residual, and congestion levels of the vehicles to estimate pheromone levels. For path selection, the roulette wheel relies on links that have the highest fitness value. The combination of high fitness value links provides an energy‐efficient non‐congested shortest route. The simulation results prove that the overall performance of EDR‐CAACO is better than Improved Distance Based Ant Colony Optimization Routing (IDBACOR), Fuzzy‐based Ant colony optimization (F‐ANT), ant colony optimization routing algorithm (ARA), and AntNet in terms of throughput, routing overhead, packet delivery ratio, end to end delay, and packet loss rate.

Tuning the Evaporation Parameter in ACO MANET Routing Using a Satisfaction-Form Game-Theoretic Approach

A Mobile Ad Hoc Network (MANET) is a communication network that links communicating devices (nodes) and does not contain permanent infrastructure. There are no dedicated routing devices in MANETs, where the routing task is assigned to a routing algorithm installed on all communicating nodes. In this work, communicating nodes utilize one of the most widely used algorithms: Ant Colony Optimization (ACO) routing algorithms. The ACO algorithms aim to balance between exploring new routes for the communication packets vs. utilizing the best-known routes discovered during the communication session. Achieving the optimality in this tradeoff is traditionally set manually by assigning many values to some parameters and measuring the network performance after the simulation session. This manual optimality tuning approach depends on human intuition and does not cope with MANET’s dynamic topology. In this research, we introduce a novel method to find an optimal balance for the exploration-exploitation tradeoff during the communication session. We formulate weighing the benefits of exploring new routes vs. exploiting known ones upon the MANET performance as a game between the two semantic players. This equilibrium is reflected as an optimal value for the pheromone evaporation parameter of the ACO algorithm during the communication session. Experimental results show a higher performance of this online tuning algorithm than the traditional offline tuning algorithms.

Hybrid Genetic Firefly Algorithm-Based Routing Protocol for VANETs

Vehicular Adhoc Networks (VANETs) are used for efficient communication among the vehicles to vehicle (V2V) infrastructure. Currently, VANETs are facing node management, security, and routing problems in V2V communication. Intelligent transportation systems have raised the research opportunity in routing, security, and mobility management in VANETs. One of the major challenges in VANETs is the optimization of routing for desired traffic scenarios. Traditional protocols such as Adhoc On-demand Distance Vector (AODV), Optimized Link State Routing (OLSR), and Destination Sequence Distance Vector (DSDV) are perfect for generic mobile nodes but do not fit for VANET due to the high and dynamic nature of vehicle movement. Similarly, swarm intelligence routing algorithms such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) routing techniques are partially successful for addressing optimized routing for sparse, dense, and realistic traffic network scenarios in VANET. Also, the majority of metaheuristics techniques suffer from premature convergence, being stuck in local optima, and poor convergence speed problems. Therefore, a Hybrid Genetic Firefly Algorithm-based Routing Protocol (HGFA) is proposed for faster communication in VANET. Features of the Genetic Algorithm (GA) are integrated with the Firefly algorithm and applied in VANET routing for both sparse and dense network scenarios. Extensive comparative analysis reveals that the proposed HGFA algorithm outperforms Firefly and PSO techniques with 0.77% and 0.55% of significance in dense network scenarios and 0.74% and 0.42% in sparse network scenarios, respectively.

Dynamic and Optimized Routing Approach (DORA) in Vehicular Ad hoc Networks (VANETs)

Vehicular Ad hoc Networks (VANETs) are one of the significant areas of research and this is also a subfield in Ad Hoc Networks. This is mainly focused on improving the safety of roads and reducing the total number of accidents. There is no central coordination to this network, nodes are mobile, dynamic topology, the routing process is a big challenge, and this is most responsible for the delivery message to the small overhead and delay. Routing is a tedious task that occurs huge changes in network topology and delivers the data packets in a limited period. In VANETs many existing routing protocols are introduced to overcome various issues but these are not efficient to overcome all the issues in routing. Routing shows a huge impact on other parameters such as data transmission rate (DTR), packet delivery ratio (PDR), Packet Drop Ratio (PDRatio), Average Propagation Delay (APD) and throughput. In this paper, the dynamic and optimized routing approach (DORA) is introduced in VANETs to overcome the various issues and improve the performance of IP by measuring the DTR, PDR and PDRatio. Comparisons among the Ant Colony Optimization (ACO), improved distance-based ant colony optimization routing (IDBACOR), and DORA is shown.

Incorporate ACO routing algorithm and mobile sink in wireless sensor networks

Today, science and technology is developing, particularly the internet of things (IoT), there is an increasing demand in the sensor field to serve the requirements of individuals within modern life. Wireless sensor networks (WSNs) was created to assist us to modernize our lives, saving labor, avoid dangers, and that bring high efficiency at work. There are many various routing protocols accustomed to increase the ability efficiency and network lifetime. However, network systems with one settled sink frequently endure from a hot spots issue since hubs close sinks take a lot of vitality to forward information amid the transmission method. In this paper, the authors proposed combining the colony optimization algorithm ant colony optimization (ACO) routing algorithm and mobile sink to deal with that drawback and extend the network life. The simulation results on MATLAB show that the proposed protocol has far better performance than studies within the same field.

An enhanced hybrid ant colony optimization routing protocol for vehicular ad-hoc networks

Vehicle ad-hoc networks (VANETs) are a subclass of mobile ad hoc networks (MANETs). The VANETs communication framework is used to provide communication between moving vehicles in highway and urban road scenarios. Dynamic properties of VANETs, such as high dynamic topology, frequent route failure, high mobility of nodes, and bandwidth constraints, reduce the efficiency of routing. The long length route between source and destination affects the efficiency of the protocol in the form of high overhead, frequent disconnections, high packet loss rate, low packet delivery rate, and low throughput. In this paper, we propose an Enhanced Hybrid Ant Colony Optimization Routing Protocol (EHACORP) to improve the efficiency of the routing process using the shortest path. The shortest path in the proposed protocol has low communication costs and the least number of hops between source and destination vehicles. The EHACORP has two phases. In phase 1, the EHACORP relies on a distance calculation method to compute the distance between vehicles. In phase 2, the source-based ant colony optimization is used to guide the ants to build a shorter path with the least number of hops to transmit data. The shortest path improves the efficiency of protocol in all aspects. The simulation results show that the EHACORP has improved packet delivery rate, throughput, end-to-end delay, routing overhead, and packet loss rate compared to Fuzzy based ant colony optimization (F-ANT), Ad hoc on-demand distance vector (AODV), ant colony optimization routing algorithm (ARA), and AntNet routing protocols.

RETRACTED ARTICLE: Heuristic ant colony and reliable fuzzy QoS routing for mobile ad hoc network

The furthermost important issues in mobile ad hoc network (MANET) are to provide a multi-path and multi-constraint qualify of service (QoS) routing for uninterrupted communication between the nodes. On one hand multi-constrained QoS routing deal with identifying paths that assure numerous autonomous QoS limitations and on the other hand, multi-path refers to the identification of multiple paths. Besides due to higher rate of topological changes and the energy constrained nodes in the network, literature survey provides us ant colony optimization (ACO), a unique variety of optimization procedure which was found to be extremely convenient for identifying reliable routing. To deal with following concerns, we suggest a multi-path multi-constraint energy-efficient routing method. In the projected method, calculate the metrics such as end to end delay and QoS monitoring agents collect, link stability, energy consumption, available bandwidth, and packet loss rate. The proposed method is called as the heuristic concurrent ACO and reliable fuzzy (HEACO–RF) QoS routing protocol for MANET. The HEACO–RF QoS routing protocol for MANET includes two phases. In the first phase, the ant colony optimization algorithm is modified to identify a deposit of candidate routes among a pair of source and destination. While identifying the candidate routes along with the QoS metrics, heuristic factors and reliable fitness functions are also considered. In the second phase, the reliability technique is used to measure each path and the paths with high reliability is selected by the ant agents using fuzzy logic based on the metrics link stability, residual energy, and packet loss rate. Simulation outcomes prove that HEACO–RF QoS routing protocol decreases the energy consumption and improves the packet delivery ratio, packet loss rate with minimum delay.

WITHDRAWN: Resilience of mobile ad hoc networks to security attacks and optimization of routing process

Abstract The need for efficient routing that can be used to avoid malfunctioning or malicious network known issues, While Mobile Ad-hoc Networks (MANETs) are becoming extremely prevalent and are exploited in various applications, including the application of the Vehicle Ad-hoc Network (VANET), wireless sensor network (WSN) environmental monitoring, and 5G device-to-device (D2D) connectivity. This research presents the Resilience of Mobile Ad hoc Networks to various security attacks and optimizes the routing Process. Moreover, its application to Ant Colony Optimization (ACO), a flippant is implemented, a logic-improved routing algorithm that addresses all such difficulties. To identify the linear nodes of complexity, the distributed fuzzy logic module eliminates them from the Safety Aware Fuzzy Enhanced Ant Colony Optimization (SAFEACO) routing process. The NS-3 simulator follows the SAFEACO routing protocol, and various tests in MANET and VANET scenarios have been carried out. SAFEACO's efficiency is correlated to other innovative and well-known solutions. SAFEACO performs superiorly for each metric of its application, such as the Packet Delivery Ratio (PDR) and the End-to-End delay. SAFEACO also offers greater strength against the black hole, Sybil and inundation attacks due to its ability to classify linear-complexity nodes.