Ant Routing Research Articles

Sink Node Placement Strategies for Wireless Sensor Networks

In wireless sensor networks (WSNs), all the data collected by the sensor nodes are forwarded to a sink node. Therefore, the placement of the sink node has a great impact on the energy consumption and lifetime of WSNs. This paper investigates the energy-oriented and lifetime-oriented sink node placement strategies in the single-hop and multiple-hop WSNs, respectively. The energy-oriented strategy considers only the minimizing of the total energy consumption in the networks, while the lifetime-oriented strategy focuses much more on the lifetime of the nodes which consume energy fastest. Using a routing-cost based ant routing algorithm, we evaluate the performances of different placement strategies in the networks. Simulation results show that the networks with lifetime-oriented strategy achieve a significant improvement on network lifetime.

Ant Routing Algorithm Parameters Optimization Based on Velocity over Mobile P2P Networks

Regarding ant routing algorithm parameters selection over mobile p2p networks, a basic ant routing algorithm is proposed and related parameters are analyzed. They are the power function of pheromone and evaporation time interval. They obey some parabolic functions of the velocity of nodes. A new method of ant routing parameters optimization based on an independent variable is proposed. Simulation results show that this algorithm selects the parameters effectively.

Do wood ants learn sequences of visual stimuli?

The visually guided foraging routes of some formicine ants are individually stereotyped, suggesting the importance of visual learning in maintaining these routes. We ask here whether the wood ant Formica rufa learns a sequence of visual features encountered at different stages along a route, as reported for honeybees. We trained ants in several simple mazes to follow two alternative routes. Along each two-stage route, the ants first encountered one of two priming stimuli. The identity of the priming stimulus determined which of two choice stimuli was rewarded in the second stage of the route. As stimuli we used ultraviolet and yellow/green light panels, and two black-and-white patterns. Did ants learn to pair each colour with the appropriate black-and-white pattern? Ants learnt readily to discriminate between the two coloured stimuli or between the two black-and-white patterns. They could also pair coloured and black-and-white patterns, provided that the two were presented simultaneously. The ants' behaviour with sequential stimuli varied according to whether the priming stimulus was a coloured stimulus or a black-and-white pattern. When the priming stimulus was coloured, ants seemed to learn the two sequences, but tests showed that their success was probably caused by the after-effects of colour adaptation. With a black-and-white priming stimulus and a coloured second stage stimulus, robust sequential learning could not be demonstrated, although under certain experimental conditions a tiny proportion of ants did acquire the sequence. Thus, ants perform conditional discriminations reliably when priming and choice stimuli are simultaneous, but they usually fail when the stimuli are sequential.

Basic ant routing algorithm in mobile Ad Hoc networks

Concerning that the resource in mobile Ad Hoc network is limited and the existing ant routing algorithms are complex,a basic ant routing algorithm was proposed.Based on the analysis of ant routing process,it only maintained basic ant routing mechanism,without any extra overhead,discussed pheromone update and pheromone use which were two key components of the algorithm;moreover,it analyzed their impact on performance by simulation.Finally,the experiment results show that it can get a performance closed to other routing protocols under a lower overhead.

A new ant colony-based routing algorithm with unidirectional link in UV mesh communication wireless network

Aiming at the unidirectional links coming from nodes with different transmitting power and the obstacle blocking in UV mesh wireless communication network and the traditional ant colony algorithm only supporting bidirectional links, a new ant colony based routing algorithm with unidirectional link in UV mesh communication wireless network is proposed. The simulation results show that the proposed algorithm can improve the overall network connectivity and the survivability by supporting the combination of unidirectional link and bidirectional link.

Performance Comparison of HA POSANT and RADAR POSANT Routing Algorithm for Mobile Ad-hoc Networks

Two different position based routing algorithms are proposed for the mobile ad-hoc networks in the present work. The present work considers a fleet of fishing trolleys which are the nodes of the mobile ad-hoc networks. The HA POSANT routing algorithm is the home agent position based ant colony routing algorithm. It is executed by the home agent associated with the home network of the fishing trolleys. The Google Map is installed in the home agent to view the real life image of each fishing trolley in the fishing area. The home agent draws a graph by joining the real life image of the fishing trolleys. It uses depth first search algorithm to select an optimal route from the graph of trolleys. The RADAR POSANT routing algorithm is the radio detection and ranging position based ant colony routing algorithm. It is executed by the trolley which is the source node of a session. The source node forwards ant packet towards destination. The destination node selects the optimal route using the ant packets received by it and sends the optimal route to the source node. Both the algorithms select an optimal route for a session before starting transmission of data packets associated with that session. They use route maintenance algorithm to detect whether a trolley associated with an existing route is going out of the communication range during the ongoing session in advance before the existing route fails completely. Such consideration helps to reduce the data packet loss for both the algorithm. The performances of the proposed routing algorithms are compared with the performance of the basic POSANT routing algorithm. The simulation results show that the proposed algorithms have lesser initial path set up time, average packet delay and packet loss than the basic POSANT routing algorithm

Biological inspired secure autonomous routing mechanism for wireless sensor networks

The field of wireless sensor network (WSN) is an important and challenging research area today. Advancements in sensor networks enable a wide range of environmental monitoring applications. Multihop routing in WSN is affected by new nodes constantly entering/leaving. Moreover, secure routing is a difficult problem due to the resource limitations in WSN. Thus, biological inspired algorithms are reviewed and enhanced to tackle the problems. Ant routing and human security system have shown excellent performance. Certain parameters as energy level, velocity, packet reception, dropping, mismatch rates and packet sending power are considered while making decision. The decision will come up with the optimal route and also to take best action against security attacks. In this paper, the design and initial work of BIOlogical Inspired Secure Autonomous Routing Protocol (BIOSARP) is presented. The proposed bio-inspired mechanism will meet the enhanced WSN requirements, including better delivery ratio, less energy consumption and routing overhead.

An Optimal Resource Allocation Method for Aircraft Subassembly System Based on Graphic Ant Colony Algorithm

Subassembly has an important role in aircraft manufacturing. There are different shop orders at different periods, so subassembly system need to adjust production capacity through reallocation of resources. Based on graphic ant colony algorithm(GACA), an optimal resource allocation method was proposed on aircraft subassembly system. In the method, first, a Petri Net(PN) model was used to describe the logic of tasks and resources in system. Next, to support solving process of GACA, structure graph for ant routing was designed by PN model. Then according to features of aircraft subassembly, solving rules and procedures of GACA are discussed in detail, which can obtain an optimal resource allocation scheme by requirement of production capacity adjustment. In addition, an example on flaperon assembly was introduced and the result demonstrated the functionality of the method.

Image-matching during ant navigation occurs through saccade-like body turns controlled by learned visual features

Visual memories of landmarks play a major role in guiding the habitual foraging routes of ants and bees, but how these memories engage visuo-motor control systems during guidance is poorly understood. We approach this problem through a study of image matching, a navigational strategy in which insects reach a familiar place by moving so that their current retinal image transforms to match a memorized snapshot of the scene viewed from that place. Analysis of how navigating wood ants correct their course when close to a goal reveals a significant part of the mechanism underlying this transformation. Ants followed a short route to an inconspicuous feeder positioned at a fixed distance from a vertical luminance edge. They responded to an unexpected jump of the edge by turning to face the new feeder position specified by the edge. Importantly, the initial speed of the turn increased linearly with the turn's amplitude. This correlation implies that the ants' turns are driven initially by their prior calculation of the angular difference between the current retinal position of the edge and its desired position in their memorized view. Similar turns keep ants to their path during unperturbed routes. The neural circuitry mediating image-matching is thus concerned not only with the storage of views, but also with making exact comparisons between the retinal positions of a visual feature in a memorized view and of the same feature in the current retinal image.

Application of Improved Ant Colony Algorithm in QoS Routing Optimization

QoS routing for the characteristics of the ant colony algorithm is improved. First of all defect from the ant colony algorithm and to increase Network Node bound, As well as to speed up global convergence analysis of the three areas of the ant colony algorithm to improve thinking; And by improving the ant colony algorithm in the QoS routing optimization application, the details of the ant colony algorithm to improve the design and implementation steps. Finally, simulation results show that: According to the algorithm for solving the problem and get the optimal solution of the ratio of full proof of improvement in the ant colony algorithm QoS routing optimization on the effectiveness and stability.

Simple ant routing algorithm strategies for a (Multipurpose) MANET model

A Mobile Ad-hoc Network has limited and scarce resources and thus routing protocols in such environments must be kept as simple as possible. The Simple Ant Routing Algorithm (SARA) offers a low overhead solution, by optimizing the routing process. Three complementary strategies were used in our approach: during the route discovery we have used a new broadcast mechanism, called the Controlled Neighbor Broadcast (CNB), in which each node broadcasts a control message (FANT) to its neighbors, but only one of them broadcast this message again. During the route maintenance phase, we further reduce the overhead, by only using data packets to refresh the paths of active sessions. Finally, the route repair phase is also enhanced, by using a deep search procedure as a way of restricting the number of nodes used to recover a route. Thus, instead of discovering a new path from the source to the destination, we start by trying the discovery of a new path between the two end-nodes of the broken link. A broadest search is only executed when the deeper one fails to succeed. We simulated our proposal and we tuned it to the optimal performance. We also compared it with the classical approach of AODV and other biological routing approaches. The results achieved show that SARA offers the smallest overhead of all the protocols under evaluation and presents an overhead reduction of almost 25% of the value achieved by the other proposals. SARA also presents the best goodput, specially for TCP traffic, but it needs more time to discover the routes.

An Improved Ant Colony Broadcasting Algorithm for Wireless Sensor Networks

Recent advances in nano-technology have made it possible to develop a large variety of Micro Electro-Mechanical Systems (MEMS)-miniaturized low-power devices that integrate sensing, special-purpose computing, and wireless communications capabilities. A sensor network is a collection of many small devices, each with sensing, computation, and communication capability. It has many potential applications, such as building surveillance and environmental monitoring. Broadcasting is defined that communications between one source and all receiver in networks, and it is widely used in Wireless Sensor Networks. Therefore, how to design an efficient and scalable broadcast protocol is challenging and meaningful work. Ant Colony Algorithm is a bionic optimization algorithm which simulated insect behavior of ants swarm intelligence. It is robust, has excellent advantages of distributed computation mechanism, and it has been widely applied to the solution of TSP, production scheduling, Graph Coloring Problem, and other combinatorial optimization problems. Broadcasting in wireless sensor networks is very similar with the ants routing problem. The target nodes and source nodes for broadcasting can be respectively compared to food and ant nests, and broadcasting problem can be regarded as the issue that ants find the optimal path between food and nests. When using ant colony algorithm for broadcasting, ants pheromone is calculated to select the path in accordance with the transition probability, and each ant in the path does not need access to all the information before starting, thus this will avoid the calculation of overhead of the initialization of parameters information in all nodes and the paths, and achieve the goal of multi-path data broadcasting in large-scale sensor networks. In this paper, we describe the broadcasting problem in sensor networks and introduce a network model, and after considering energy efficient broadcast in such networks, combined the characteristic of Wireless Sensor Networks and the properties of ant algorithm to quickly identify an optimal path, we introduce and evaluate an improved ant colony broadcasting algorithm (IACBA), which can adaptively find the optimal broadcasting path without global network information. In our simulation, the notion of network density is introduced in order to identify classes of network topologies with similar characteristics. We mainly analyze energy depletion, minimum distance, and network lifetime, and the results show that out algorithm can reduce energy depletion dramatically and decrease communication distances in broadcasting, and our algorithms can prolong network lifetime by about 10%-20% compared with BIP, and about 30%-50% compared with flooding.

A hybrid ACO/PSO algorithm and its applications

In this paper, we present a hybrid optimisation algorithm with particle swarm optimisation (PSO) and ant colony optimisation (ACO). Unlike the conventional ACO or PSO, the new optimisation scheme makes full use of the attributes of both algorithms. In the proposed algorithm, the PSO is used to optimise the parameters in the ACO, which means that the selection of parameters does not depend on artificial experiences or trial and error, but relies on the adaptive search of the particles in the PSO. We also make an optimised implementation of ACO, by which the running time of ants routing is largely reduced. The results of the simulated experiments show that the improved algorithm not only reduces the number of routes in the ACO, but also surpasses existing algorithms in performance for solving large-scale TSP problems. Simulation results also show that the speed of convergence of ACO algorithm could be enhanced greatly.

Routing algorithm based on ant colony optimization

This paper presents an approach bases on ant colony optimization route algorithm of mobile Ad Hoc networks. It can effectively bear network load in Ad Hoc networks by using the self-adaptability of that ant colony algorithm. The simulations in NS-2 show that it performs very well on Ad Hoc environments, especially in throughput, average latency, and delivery ratio. The performance is better than that of Ad Hoc On-demand Distance Vector routing (AODV) and Dynamic Source Routing (DSR).

Swarm Intelligent Power‐Aware Detection of Unauthorized and Compromised Nodes in MANETs

Characteristics of mobile ad hoc networks (MANETs) such as lack of central coordination, mobility of hosts, and limited availability of resources make quality of service (QoS) provisioning very challenging. Limited resource availability such as battery power and insecure medium is one of the major QoS issues to be dealt with. In this paper, we have proposed a new secure power‐aware ant routing algorithm (SPA‐ARA) for mobile ad hoc networks that is inspired from ant colony optimization (ACO) algorithms which are a swarm intelligent technique. In this algorithm, we have introduced a new metric, next-hop availability, which is a combination of two metrics. It maximizes path availability and minimizes travel time of packets, and therefore it offers a good balance between selection of fast paths and a better use of network resources. The protocol also incorporates a trust model which helps in detection of unauthorized and compromised nodes in MANETs.

Hierarchical Wireless Multimedia Sensor Networks for Collaborative Hybrid Semi-Supervised Classifier Learning.

Wireless multimedia sensor networks (WMSN) have recently emerged as one ofthe most important technologies, driven by the powerful multimedia signal acquisition andprocessing abilities. Target classification is an important research issue addressed in WMSN,which has strict requirement in robustness, quickness and accuracy. This paper proposes acollaborative semi-supervised classifier learning algorithm to achieve durative onlinelearning for support vector machine (SVM) based robust target classification. The proposedalgorithm incrementally carries out the semi-supervised classifier learning process inhierarchical WMSN, with the collaboration of multiple sensor nodes in a hybrid computingparadigm. For decreasing the energy consumption and improving the performance, somemetrics are introduced to evaluate the effectiveness of the samples in specific sensor nodes,and a sensor node selection strategy is also proposed to reduce the impact of inevitablemissing detection and false detection. With the ant optimization routing, the learningprocess is implemented with the selected sensor nodes, which can decrease the energyconsumption. Experimental results demonstrate that the collaborative hybrid semi-supervised classifier learning algorithm can effectively implement target classification inhierarchical WMSN. It has outstanding performance in terms of energy efficiency and timecost, which verifies the effectiveness of the sensor nodes selection and ant optimizationrouting.

On ant routing algorithms in ad hoc networks with critical connectivity

This paper shows a novel self-organizing approach for routing datagrams in ad hoc networks, called Distributed Ant Routing (DAR). This approach belongs to the class of routing algorithms inspired by the behavior of the ant colonies in locating and storing food. The effectiveness of the heuristic algorithm is supported by mathematical proofs and demonstrated by a comparison with the well-known Ad hoc On Demand Distance Vector (AODV) algorithm. The differences and the similarities of the two algorithms have been highlighted. Results obtained by a theoretical analysis and a simulation campaign show that DAR allows obtaining some important advantages that makes it a valuable candidate to operate in ad hoc networks and the same method helps in the selection of the algorithm parameters. Since the approach aims at minimizing complexity in the nodes at the expenses of the optimality of the solution, it results to be particularly suitable in environments where fast communication establishment and minimum signalling overhead are requested. These requirements are typical of ad hoc networks with critical connectivity, as described in the paper. Thus the performance of the proposed algorithm are shown in ad hoc networks with critical connectivity and compared to some existing ad hoc routing algorithms.

Jumping ant routing algorithm for sensor networks

Enterprises that may rely on critical equipments which are constantly moving around, for example, hospitals – need to ensure they can know the current location of vital but mobile assets. Besides, the sensor node on each device should inform us whether those relative equipments are functional or does not work at all in a hazardous environment, so that vital equipments can be maintained or fixed beforehand. A sensor network is a potential solution to this kind of application, because every node can be found through the routing processes. Due to its working environment and the mobility of sensor node, this kind of sensor network is very similar to MANETs (Mobile Ad-hoc Networks). This study proposes a jumping ant routing algorithm (JARA) which combines the advantages of reactive and proactive routing to speed up the route discovery time and reduce the route discovery overhead in sensor network.

Visual Cues for the Retrieval of Landmark Memories by Navigating Wood Ants

SummaryBackgroundEven on short routes, ants can be guided by multiple visual memories. We investigate here the cues controlling memory retrieval as wood ants approach a one- or two-edged landmark to collect sucrose at a point along its base. In such tasks, ants store the desired retinal position of landmark edges at several points along their route. They guide subsequent trips by retrieving the appropriate memory and moving to bring the edges in the scene toward the stored positions.ResultsThe apparent width of the landmark turns out to be a powerful cue for retrieving the desired retinal position of a landmark edge. Two other potential cues, the landmark's apparent height and the distance that the ant walks, have little effect on memory retrieval. A simple model encapsulates these conclusions and reproduces the ants' routes in several conditions. According to this model, the ant stores a look-up table. Each entry contains the apparent width of the landmark and the desired retinal position of vertical edges. The currently perceived width provides an index for retrieving the associated stored edge positions. The model accounts for the population behavior of ants and the idiosyncratic training routes of individual ants.DiscussionOur results imply binding between the edge of a shape and its width and, further, imply that assessing the width of a shape does not depend on the presence of any particular local feature, such as a landmark edge. This property makes the ant's retrieval and guidance system relatively robust to edge occlusions.

Probability routing algorithm for mobile ad hoc networks' resources management

This paper introduces a resource management application of a probabilistic-based ant routing algorithm for mobile ad hoc networks (ARAMA) that is inspired from the ant's life . Mobile ad hoc networks (MANETs) are highly dynamic, self-configured and self-built networks. The goal of this paper is to present ARAMA ability to manage MANET's resources by achieving fair network resources distribution, while considering the dynamic characteristics of MANETs and the need for low control overheads. This paper provides a description for the algorithm. In this algorithm, the nodes' (node's energy, processing power, ...) and links' (bandwidth, ...) parameters are measured and collected in the nodes' indices. A path index is used to measure the path total resources and serves to minimize the forward control packet (ant) size. The concepts of negative backward ant destination trail are introduced to enhance the performance of the algorithm. The simulation results show the potential of ARAMA to achieve fair energy usage across the network nodes as an example of the network resource management. More, the results show the general ability of the algorithm to solve MANET's routing problem.