Ant Routing Research Articles

Petal Region Analysis of Improved Petal Ant Routing for Mobile Ad Hoc Networks

Petal Region Analysis of Improved Petal Ant Routing for Mobile Ad Hoc Networks

Joint Clustering and Routing Optimisation for Low-power Wireless Sensor Networks

Wireless sensor networks (WSNs) have been one of the fields that have attracted a lot of attentions from many scientific researchers in recent years. The sensor nodes of the network are fixed or moved to detect the environment and impart the data accumulated from the remote monitored regions via wireless connections. It is indicated in complex environments such as forests, deep seas, urban areas, etc., the sensor nodes in WSNs are usually tiny and battery-driven devices. Thus, energy-effective data accumulation methods required to improve the network’s lifetime are very necessary. In this paper, we propose a joint technique of fuzzy clustering and heuristic ant routing (FCHAR) to save the energy for low-power WSNs. Simulation results are shown to demonstrate the benefits of the proposed FCHAR compared to other conventional ones.

Tripartite Transmitting Methodology for Intermittently Connected Mobile Network (ICMN)

Mobile network is a collection of devices with dynamic behavior where devices keep moving, which may lead to the network track to be connected or disconnected. This type of network is called Intermittently Connected Mobile Network (ICMN) . The ICMN network is designed by splitting the region into `n' regions, ensuring it is a disconnected network. This network holds the same topological structure with mobile devices in it. This type of network routing is a challenging task. Though research keeps deriving techniques to achieve efficient routing in ICMN such as Epidemic, Flooding, Spray, copy case, Probabilistic, and Wait, these derived techniques for routing in ICMN are wise with higher packet delivery ratio, minimum latency, lesser overhead, and so on. A new routing schedule has been enacted comprising three optimization techniques such as Privacy-Preserving Ant Routing Protocol (PPARP), Privacy-Preserving Routing Protocol (PPRP), and Privacy-Preserving Bee Routing Protocol (PPBRP) . In this paper, the enacted technique gives an optimal result following various network characteristics. Algorithms embedded with productive routing provide maximum security. Results are pointed out by analysis taken from spreading false devices into the network and its effectiveness at worst case. This paper also aids with the comparative results of enacted algorithms for secure routing in ICMN.

Pheromone Incentivized Intelligent Multipath Traffic Scheduling Approach for LEO Satellite Networks

Low Earth Orbit (LEO) satellite networking has been an indispensable and promising concept for extending the Internet coverage of future space-air-ground integrated networks to oceanic and remote airspace. However, the topology dynamics of the LEO Satellite Network (LEO-SN) for network state perception (Li <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2019) and the intermittent nature of the Inter-Satellite Links (ISLs) for multipath routing discovery (Wang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2019, Jiang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2019) both induce new complicated challenges to multipath traffic scheduling for the sake of improving the utility of the LEO-SN facility (Song <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2014, Zhang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2018, and Yang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2020). Motivated by these challenges, this paper aims to develop an AI aided intelligent multipath traffic scheduling approach for bolstering autonomous and efficient communications of LEO-SN. To achieve this, we formulate the multipath traffic scheduling problem into a pheromone incentivized Markov Decision Process (MDP) by considering ant routing protocol and adapting pheromone to LEO-SN. Employing enhanced pheromone characterizing network state, we propose ant-inspired multipath routing discovery, which is capable of promptly discovering routing paths available in the dynamic topology. To improve the utility of these discovered routing paths, we employ deep deterministic policy gradient into the pheromone-incentivized MDP-based scheduling problem to derive an intelligent multipath traffic scheduling strategy. The experimental results are further presented to show the achievable performance improvement.

Ship pipe route design using improved multi-objective ant colony optimization

In view of various objectives and complex constraints in ship pipe route design (SPRD), the previous studies often convert the problem into a single objective optimization consisting of several weighted routing objectives, which cannot generate rich references for designers. In addition, the current CAD systems only provide an environment for manual design and simple automatic route design, resulting in low work performance and a huge workload. Therefore, this paper presents a new ship pipe route design method based on multi-objective ant colony optimization (MOACO). Firstly, the mathematical model and multi-objective definition of SPRD are formulated, and then the key technologies of the modified algorithm are introduced in detail, e.g., ant moving direction, heuristic information, pheromone update rules, tabu search and step backward strategy et al. On this basis, the MOACO framework and its internal ant routing process are presented. To route multiple pipes and branched pipes, a combination method based on Pareto solutions and human choice is given. Finally, the feasibility and efficiency of the proposed method are verified by the simulation and actual routing cases.

Research on Communication Optimization of Power Carrier Sensor Control Network Based on Ant Colony Algorithm

In order to optimize the communication research of dynamic carrier sensing control network based on ant colony algorithm, this paper proposes an improved ant colony routing algorithm. Firstly, this paper establishes the experimental model, sets up the relevant experimental parameters, and builds the MATLAB experimental platform. The simulation results show that compared with the basic ant colony algorithm, the improved algorithm performs better in the final jumps. The average residual energy of the sensor node is more. The total energy consumption is less, and the consumption in each round is relatively stable. The residual energy fluctuation range of the sensor node is small, and the energy consumption of each node is relatively balanced. Therefore, the improved ant colony algorithm is important for improving the survival cycle of the WSN. The algorithm comprehensively considers the factors such as convergence speed, node communication distance, node residual energy, and optimal solution and can finally find the path with low node energy consumption and high node residual energy.

An adaptive threshold of remaining energy based ant colony routing algorithm

Because the node energy distribution among wireless sensor networks is not balanced and their routing algorithm is trapped in local optimal solution, this paper proposes the ant colony routing algorithm for wireless sensor network based on the adaptive residual energy threshold (ATRE-ARA). It introduces the search angle correction pheromone heuristic function to limit the search path and to reduce node energy costs. The residual energy threshold of the node is adaptive; the formula of pheromone increment is improved. The upper and lower limits of pheromone concentration are set; the pheromone updating strategy is optimized, thereby improving the accuracy of pheromone increment and the global optimization capability and balancing the residual energy of nodes in the wireless sensor network. The simulation results show that the ant colony routing algorithm reduces its average energy consumption by 15.12% and 11.68% in two environments and that its optimal path length is shortened by 1.47% and 1.59%, respectively, thus proving that the algorithm can effectively balance the global network energy consumption, improving the capability of the algorithm to search for global optimal solutions and extending the life cycle of the wireless sensor network.

A Cognitive Knowledged Energy-Efficient Path Selection Using Centroid and Ant-Colony Optimized Hybrid Protocol for WSN-Assisted IoT

In a WSN-assisted IoT environment, the sensors are resource constrained. The energy, computing and storage resources of deployed sensors in the sensing area are limited. A hybrid protocol named as an Energy Efficient Centroid-based Ant colony Optimization (EECAO) hybrid protocol is proposed in this paper to improve the performance of the sensor network in WSN-assisted IoT environment. This protocol uses a concept of centroid based clustering to gather the information of local clusters and ant colony optimization to relay the same to the base station. The energy level of deployed cognitive sensors is considered as a key parameter for defining the position of centroid in this protocol. The proposed protocol has a new distributed cluster formation design which includes multiple clustering factors such as energy cost, channel consistency and cognitive sensor throughput to select cluster heads. In the proposed protocol, the selection of the super cluster head is based on the energy centroid position for a defined coverage area. The path optimization between the super cluster heads and the base station is carried out using an ant routing model. Our simulation results indicate that the proposed protocol performs better when benchmarked against existing ETSP and EECRP protocols. Also, it suits well for the sensor networks that requires long lifetime when the base station is placed at either center, border or outside the network.

Towards a Formal Validation of a Hybrid Multi-Agent Routing Protocol for Ad hoc Networks

In the authors' previous papers, a new ant routing protocol for ad-hoc networks inspired from ant colony optimization was presented. They introduced a new approach that decreases both node energy consumption and routing overhead within the network. The validation of the routing protocol was based on series of simulations. The results show that the new algorithm provides a significant improvement compared to other protocols. After the algorithm is defined and published, they found it important to validate formally each one of its components in order to avoid any conflict, lack, or misbehaving situations. This process requires in a first step a formal specification. This is the main concern in this paper where the authors propose in a first part a formal specification using inference systems based on logical rules. A formal validation using these inference systems is proposed in a second step in order to prove the correctness, the soundness, the completeness, and the optimality of the proposition.

Secure Mobile Ad Hoc Networks Based-on Ant Colony: A Comparative Study and State-of-the-Art Core-based Networks

Aim: This work evaluates the existing secure ant colony protocols of MANETs such as ASHFIK, AAMRP, and MANHSI with each other. Here, each node in the proposed ad hoc network protocols is known with a trust value. Each trust value represents the corresponding security level and a node with a higher trust value is authorized to monitor and lead its neighboring nodes as a core node. Background: Trusted critical MANET is a secure wireless network that is used in different critical applications. Military battlefields are an example of these applications that force on people a number of needed things, including robustness and reliable performance within high mobility situations and constraints (e.g. hostile attacks, cost, and battery limits). Objectives: In this work, the extremely important similarities between the features of critical MANETs and the core-based routing protocols that are based-on ant colonies are discussed. Methods: The metrics used in this evaluation are the availability, reliability, packet delivery ratio, and total overheads, while the performance is serious and stubborn because of the mobility of node, senders ' number, and size of a multicast group. Results: The results of the simulation show that ASHFIK provides better availability, reliability, Packet Delivery Ratio (PDR), and lower total overheads. In addition, the results show that ASHFIK has a consistent performance with a different group size of the network, which means that it is a trusted and scalable network of ant colony core-based routing protocols. Conclusion: Here, the existing different mobile ad hoc networks based-on ant colonies were discussed. According to our comparative study, the ASHFIK protocol can be used as a good routing protocol for critical MANETs that are based on ant colonies.

The routes of one-eyed ants suggest a revised model of normal route following.

The prevailing account of visually controlled routes is that an ant learns views as it follows a route, while guided by other path-setting mechanisms. Once a set of route views is memorised, the insect follows the route by turning and moving forwards when the view on the retina matches a stored view. We engineered a situation in which this account cannot suffice in order to discover whether there may be additional components to the performance of routes. One-eyed wood ants were trained to navigate a short route in the laboratory, guided by a single black, vertical bar placed in the blinded visual field. Ants thus had to turn away from the route to see the bar. They often turned to look at or beyond the bar and then turned to face in the direction of the goal. Tests in which the bar was shifted to be more peripheral or more frontal than in training produced a corresponding directional change in the ants' paths, demonstrating that they were guided by the bar. Examination of the endpoints of turns towards and away from the bar indicate that ants use the bar for guidance by learning how large a turn-back is needed to face the goal. We suggest that the ants' zigzag paths are, in part, controlled by turns of a learnt amplitude and that these turns are an integral component of visually guided route following.

Simulation of Sports Venue Based on Ant Colony Algorithm and Artificial Intelligence

In order to improve the congestion of the evacuation plan and further improve the evacuation efficiency, this paper proposes the priority Pareto partial order relation and the vector pheromone routing method based on the priority Pareto partial order relation. Numerical experiments show that compared with the hierarchical multiobjective evacuation path optimization algorithm based on the hierarchical network, the fragmented multiobjective evacuation path optimization algorithm proposed in this paper effectively improves the evacuation efficiency of the evacuation plan and the convergence of the noninferior plan set. However, the congestion condition of the noninferior evacuation plan obtained by the fragmented multiobjective evacuation route optimization algorithm is worse than the congestion condition of the noninferior evacuation plan obtained by the hierarchical multiobjective evacuation route optimization algorithm. The multiple factors that affect the routing process considered in the probability transfer function used in the traditional ant colony algorithm routing method must be independent of each other. However, in actual route selection, multiple factors that affect route selection are not necessarily independent of each other. In order to fully consider the various factors that affect the routing, this paper adopts the vector pheromone routing method based on the traditional Pareto partial order relationship instead of the traditional ant colony algorithm. The model mainly improves the original pheromone distribution and volatilization coefficient of the ant colony, speeds up the convergence speed and accuracy of the algorithm, and obtains ideal candidate solutions. The method is applied to the location of sports facilities and has achieved good results. The experimental results show that the improved ant colony algorithm model designed in this paper is suitable for solving the problem of urban sports facilities location in large‐scale space.

Ant Router: An efficient routing protocol for social opportunistic networks using ant routing

Social opportunistic networks are a subclass of opportunistic networks which rely upon the predictability and established patterns of human social behaviour to facilitate the sharing of information by performing message routing. While the forwarding step purely depends upon the movement paradigm of nodes in the network, the said movement makes the network unreliable due to frequent disconnections and delay, and spasmodically connected environment. Various protocols have been developed so far for routing in such networks, whose primary aim is to ensure efficient and reliable message delivery. This study proposes a heuristic-based scheme for routing messages from the source to the destination using the Ant Algorithm. Problems related to frequent disconnections and inefficient routing are overcome in the proposed protocol through features such as pheromones. This protocol is thoroughly examined via simulation and analysis to assess the performance with other routing protocols in social opportunistic networks under various parameters. The performance criterion for the comparisons includes overhead ratio, average residual energy, average latency, number of dead nodes and average buffer time. The examinations have shown that the authors algorithm is superior to protocols such as Prophet, Epidemic and ProWait on the basis of average latency and buffer time.

An Ant Colony Optimization Based Data Update Scheme for Distributed Erasure-Coded Storage Systems

Owing to the high availability and space-efficiency of erasure codes, they have become the de facto standard to provide data durability in large scale distributed storage systems. The update-intensive workloads of erasure codes lead to a large amount of data transmission and I/O. As a result, it becomes a major challenge to reduce the amount of data transmission and optimize the use of existing network resources so that the update efficiency of the erasure codes could be improved. However, very little research has been done to optimize the update efficiency of the erasure codes under multiple QoS metrics. In this paper, our proposed update scheme, the Ant Colony Optimization based multiple data nodes Update Scheme (ACOUS) employs a two-stage rendezvous data update procedure to optimize the multiple data nodes updates. Specifically, the two-stage rendezvous data update procedure performs the data delta collection and the parity delta distribution based on a multi-objective update tree which is built by the ant colony optimization routing algorithm. Under typical data center network topologies, extensive experimental results show that, compared to the traditional TA-Update scheme, our scheme is able to achieve a 26% to 37% reduction of update delay with convergence guarantee at the cost of negligible computation overhead.

A secure and energy efficient ant routing algorithm

In Mobile Ad hoc Networks (MANETs), setting up an ideal and proficient route linking the conveying bodies is the essential objective of the routing protocols. But any assault during the routing stage may upset the communication, paralyzing the whole network. So, providing security in routing for a protected communication between nodes has become a prime concern. In the present study, we propose a Secure Energy Efficient Ant Routing Algorithm (SEEARA) based on Ant Colony Optimization (ACO) algorithm and cryptographic primitives that exercises on power control and secure routing between a pair of network nodes and increases the performance and longevity of the network. Also, it can be realized during simulation studies that SEEARA shows a better solution in comparison with the previously proposed routing protocols.

Canny edge detector improvement using an intelligent ants routing

Canny operator provides simple technique to extract the useful edge information from image. However, it suffers from two problems: a difficulty to choose the thresholds values and the presence of broken edges. This paper proposes to improve Canny detector in two steps: First, adaptive Otsu threshold is used to select appropriate thresholds. Then, a new variant of ant colony optimization (ACO) algorithm is used to recover missing edges. From binary image, extracted edge endpoints are used as starting pixels for an intelligent ants routing mechanism. Ants are directed to suitable contour areas and missing edges are retraced via the pheromone traces. Proposed improvements are evaluated with entropy and kappa methods. Experimental results are good and assert approach’s ability to recover most of the broken edges, particularly in noisy clinical images.

Increased Routing Algorithm for Mobile AdHoc Networks

A Mobile Ad-hoc Network (MANET) is an accumulation of portable hubs which impart over proportion. These systems have an essential preferred standpoint; they don't require any current foundation or focal organization. Portable specially appointed systems are appropriate for transitory correspondence joins. One of the significant issues in MANET is steering because of the versatility of the hubs. Directing means the demonstration of moving data over a web work from a source to a goal. The Enhanced Ant Routing Algorithm (EARA) depends on subterranean insect calculations. This calculation is enlivened from the insect states life. Forward bundles are utilized to gather data about the system and in reverse parcels are utilized to refresh the directing data in the hubs. EARA has two stages course disclosure and course support and furthermore utilities the idea of backtracking when the parcels are achieves goal hub. Re-enactment comes about accomplish better parcel conveyance proportion and lessen the normal end-to-end defer as contrast with its partner

Reinforcement learning-based real time search algorithm for routing optimisation in wireless sensor networks using fuzzy link cost estimation

Internet of things is a technological advancement of wireless sensor networks (WSNs) which are characterised by highly complex, large scale, heterogeneous, dynamically changing and asymmetric networks. Such constraints make routing in WSNs a difficult task. This paper introduces fuzzy link cost estimation-based real time search routing algorithm (fuzzy RTS) in which link cost estimation is obtained from physical and MAC layer parameters like residual energy, packet drop rate and RSSI. Its performance has been evaluated with traditional reinforcement learning-based algorithms like real time search, adaptive tree, ant routing and constrained flooding algorithms on the basis of metrics like throughput, loss rate, success rate, energy consumption, energy efficiency and node battery life. The simulation results reveal that fuzzy RTS algorithm is most appropriate reinforcement learning-based routing algorithm among given algorithms for ensuring energy efficient and QoS aware routing in dynamically changing, asymmetric and unreliable environment of WSNs.

Reinforcement learning-based real time search algorithm for routing optimisation in wireless sensor networks using fuzzy link cost estimation

Internet of things is a technological advancement of wireless sensor networks (WSNs) which are characterised by highly complex, large scale, heterogeneous, dynamically changing and asymmetric networks. Such constraints make routing in WSNs a difficult task. This paper introduces fuzzy link cost estimation-based real time search routing algorithm (fuzzy RTS) in which link cost estimation is obtained from physical and MAC layer parameters like residual energy, packet drop rate and RSSI. Its performance has been evaluated with traditional reinforcement learning-based algorithms like real time search, adaptive tree, ant routing and constrained flooding algorithms on the basis of metrics like throughput, loss rate, success rate, energy consumption, energy efficiency and node battery life. The simulation results reveal that fuzzy RTS algorithm is most appropriate reinforcement learning-based routing algorithm among given algorithms for ensuring energy efficient and QoS aware routing in dynamically changing, asymmetric and unreliable environment of WSNs.

Contamination Route of Leaf-Cutting Worker Ants Analyzed Through a Fat-Soluble Tracer Dye in Toxic Bait.

Our study seeks to discover contamination routes of leaf-cutting worker ants during chemical control by formicide baits. To do so, toxic baits containing fat-soluble tracer dye were provided to colonies of three subspecies of Acromyrmex under laboratory conditions, in order to assess the proportion of dyed workers by size category, as well as dyed internal morphological structures. Results showed that nearly 50% of the workers come into contact with the active ingredient, since the internal structures of their bodies are dyed by the fat-soluble tracer dye within the first 24h from contact with the toxic bait. In addition, the three subspecies of leaf-cutting ants present a similarity as to the contamination of their workers, probably due to their specialized behavior performed during the growth of the fungus garden with the baits. We conclude that the workers' pattern of behavior during fungus garden growth was the main means for dispersion of a fat-soluble substance among approximately half of the nest mates in our experiments, serving as a model for further studies on contamination of worker ants with insecticides.