In this paper we describe AntHocNet, an algorithm for routing in mobile ad hoc networks. It is a hybrid algorithm, which combines reactive route setup with proactive route probing, maintenance and improvement. The algorithm is based on the Nature-inspired Ant Colony Optimization framework. Paths are learned by guided Monte Carlo sampling using ant-like agents communicating in a stigmergic way. In an extensive set of simulation experiments, we compare AntHocNet with AODV, a reference algorithm in this research area. We show that our algorithm can outperform AODV on different evaluation criteria. AntHocNet’s performance advantage is visible over a broad range of possible network scenarios, and increases for larger, sparser and more mobile networks. AntHocNet is also more scalable than AODV.

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In this paper we describe AntHocNet, an algorithm for routing in mobile ad hoc networks based on ideas from the Nature-inspired Ant Colony Optimization framework. The algorithm consists of both reactive and proactive components. In a reactive path setup phase, multiple paths are built between the source and destination of a data session. Data are stochastically spread over the different paths, according to their estimated quality. During the course of the session, paths are continuously monitored and improved in a proactive way. Link failures are dealt with locally. The algorithm makes extensive use of ant-like mobile agents which sample full paths between source and destination nodes in a Monte Carlo fashion. We report results of simulation experiments in which we have studied the behavior of AntHoc-Net and AODV as a function of node mobility, terrain size and number of nodes. According to the observed results, AntHocNet outperforms AODV both in terms of end-to-end delay and delivery ratio. 1.

Although the ant metaphor has been successfully applied to routing of data packets both in wireless and fixed networks, little is yet known about its applicability to the task of query routing in peer-to-peer environments. This work presents SemAnt, an algorithm for distributed query routing based on the Ant Colony Optimization meta-heuristic. The experimental results show that the algorithm produces robust results and converges fast. Based on the results gained so far, the goal for the Ph.D. thesis is to extend the algorithm to include strategies for self-adaptation to volatile networks where nodes may leave or join at any time.

Biological systems present remarkable adaptation, reliability, and robustness in various environments, even under hostility. Most of them are controlled by the individuals in a distributed and self-organized way. These biological mechanisms provide useful resources for designing the dynamical and adaptive routing schemes of wireless mobile sensor networks, in which the individual nodes should ideally operate without central control. This paper investigates crucial biologically inspired mechanisms and the associated techniques for resolving routing in wireless sensor networks, including Ant-based and genetic approaches. Furthermore, the principal contributions of this paper are as follows. We present a mathematical theory of the biological computations in the context of sensor networks; we further present a generalized routing framework in sensor networks by diffusing different modes of biological computations using Ant-based and genetic approaches; finally, an overview of several emerging research directions are addressed within the new biologically computational framework.

Abstract An analytical justification is proposed for the design and global routing performance of three pheromone update methods proposed for use in Termite, a swarm intelligent routing algorithm for mobile wireless ad-hoc networks. A simple model is used in order to determine the average amount of pheromone present on a link, as well as some basic aspects of the pheromone dynamics. This includes a tendency towards a one-zero pheromone distribution favoring the better link. The pheromone update methods are investigated with the perspective that link pheromone is more an estimate of link utility than simply a routing heuristic. This allows the routing solution to be rephrased from a biological analogy to a more traditional best-metric routing terminology. A signal estimation perspective is suggested. 1.

Abstract. In this paper we propose SemAnt, a novel ant-based algorithm designed for query routing in taxonomy-based peer-to-peer environments. We introduce the reader to the pheronome trail-laying-andfollowing behaviour observed from natural ants and show how it can be applied to query routing in peer-to-peer networks. Our proposed algorithm accounts for network parameters such as bandwidth and latency and optimizes the pheromone trails to results for a given query depending on the query’s popularity. If a query is common, its pheromone trails will converge and lead to the nodes that offer the most results for the given query. Pheromone trails that are used rarely will evaporate over time. In addition, the proposed algorithm accounts for the inherent dynamics of peer-to-peer networks by adjusting pheromone trails when peers join or leave the network. 1

Abstract. A hybrid Orthogonal Scheme Ant Colony Optimization (OSACO) algorithm for continuous function optimization (CFO) is presented in this paper. The methodology integrates the advantages of Ant Colony Optimization (ACO) and Orthogonal Design Scheme (ODS). OSACO is based on the following principles: a) each independent variable space (IVS) of CFO is dispersed into a number of random and movable nodes; b) the carriers of pheromone of ACO are shifted to the nodes; c) solution path can be obtained by choosing one appropriate node from each IVS by ant; d) with the ODS, the best solved path is further improved. The proposed algorithm has been successfully applied to 10 benchmark test functions. The performance and a comparison with CACO and FEP have been studied. 1

We review the applications of artificial life (ALife), the creation of synthetic life on computers to study, simulate, and understand living systems. The definition and features of ALife are shown by application studies. ALife application fields treated include robot control, robot manufacturing, practical robots, computer graphics, natural phenomenon modeling, entertainment, games, music, economics, Internet, information processing, industrial design, simulation software, electronics, security, data mining, and telecommunications. In order to show the status of ALife application research, this review primarily features a survey of about 180 ALife application articles rather than a selected representation of a few articles. Evolutionary computation is the most popular method for designing such applications, but recently swarm intelligence, artificial immune network, and agent-based modeling have also produced results. Applications were initially restricted to the robotics

Abstract: Wireless multimedia sensor networks (WMSN) have recently emerged as one of the most important technologies, driven by the powerful multimedia signal acquisition and processing abilities. Target classification is an important research issue addressed in WMSN, which has strict requirement in robustness, quickness and accuracy. This paper proposes a collaborative semi-supervised classifier learning algorithm to achieve durative online learning for support vector machine (SVM) based robust target classification. The proposed algorithm incrementally carries out the semi-supervised classifier learning process in hierarchical WMSN, with the collaboration of multiple sensor nodes in a hybrid computing paradigm. For decreasing the energy consumption and improving the performance, some metrics 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 inevitable missing detection and false detection. With the ant optimization routing, the learning process is implemented with the selected sensor nodes, which can decrease the energy consumption. Experimental results demonstrate that the collaborative hybrid semisupervised classifier learning algorithm can effectively implement target classification in hierarchical WMSN. It has outstanding performance in terms of energy efficiency and time cost, which verifies the effectiveness of the sensor nodes selection and ant optimization routing.