Advances in processor, memory and radio technology will enable small and cheap nodes capable of sensing, communication and computation. Networks of such nodes can coordinate to perform distributed sensing of environmental phenomena. In this paper, we explore the directed diffusion paradigm for such coordination. Directed diffusion is data-centric in that all communication is for named data. All nodes in a directed diffusion-based network are application-aware. This enables diffusion to achieve energy savings by selecting empirically good paths and by caching and processing data in-network. We explore and evaluate the use of directed diffusion for a simple remote-surveillance sensor network.

Advances in processor, memory and radio technology will enable small and cheap nodes capable of sensing, communication and computation. Networks of such nodes can coordinate to perform distributed sensing of environmental phenomena. In this paper, we explore the directed diffusion paradigm for such coordination. Directed diffusion is datacentric in that all communication is for named data. All nodes in a directed diffusion-based network are application-aware. This enables diffusion to achieve energy savings by selecting empirically good paths and by caching and processing data in-network (e.g., data aggregation). We explore and evaluate the use of directed diffusion for a simple remote-surveillance sensor network analytically and experimentally. Our evaluation indicates that directed diffusion can achieve significant energy savings and can outperform idealized traditional schemes (e.g., omniscient multicast) under the investigated scenarios.

Advances in microsensor and radio technology will enable small but smart sensors to be deployed for a wide range of environmental monitoring applications. The low per-node cost will allow these wireless networks of sensors and actuators to be densely distributed. The nodes in these dense networks will coordinate to perform the distributed sensing and actuation tasks. Moreover, as described in this paper, the nodes can also coordinate to exploit the redundancy provided by high density so as to extend overall system lifetime. The large number of nodes deployed in these systems will preclude manual configuration, and the environmental dynamics will preclude design-time preconfiguration. Therefore, nodes will have to self-configure to establish a topology that provides communication under stringent energy constraints. ASCENT builds on the notion that, as density increases, only a subset of the nodes are necessary to establish a routing forwarding backbone. In ASCENT, each node assesses its connectivity and adapts its participation in the multihop network topology based on the measured operating region. This paper motivates and describes the ASCENT algorithm and presents analysis, simulation, and experimental measurements. We show that the system achieves linear increase in energy savings as a function of the density and the convergence time required in case of node failures while still providing adequate connectivity.

This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies’ foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the ACO metaheuristic are defined. In the second part of the article a number of applications of ACO algorithms to combinatorial optimization and routing in communications networks are described. We conclude with a discussion of related work and of some of the most important aspects of the ACO metaheuristic.

Ant algorithms are multi-agent systems in which the behavior of each single agent, called artificial ant or ant for short in the following, is inspired by the behavior of real ants. Ant algorithms are one of the most successful examples of swarm intelligent systems [3], and have been applied to many types of problems, ranging from the classical traveling salesman

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We present HAS-SOP, a new approach to solving sequential ordering problems. HAS-SOP combines the ant colony algorithm, a population-based metaheuristic, with a new local optimizer, an extension of a TSP heuristic which directly handles multiple constraints without increasing computational complexity. We compare different implementations of HASSOP and present a new data structure that improves system performance. Experimental results on a set of twenty-three test problems taken from the TSPLIB show that HAS-SOP outperforms existing methods both in terms of solution quality and computation time. Moreover, HAS-SOP improves most of the best known results for the considered problems.

Mobile ad hoc networks are infrastructure-less networks consisting of wireless, possibly mobile nodes which are organized in peer-to-peer and autonomous fashion. The highly dynamic topology, limited bandwidth availability and energy constraints make the routing problem a challenging one. In this paper we take a novel approach to the routing problem in MANETs by using swarm inteligenceinspired algorithms. The proposed algorithm uses Ant-like agents to discover and maintain paths in a MANET with dynamic topology. We present simulation results that measure the performance of our algorithm with respect to the characteristics of a MANET, the varying parameters of the algorithm itself as well as performance comparison with other well-known routing protocols.

. Hybrid metaheuristics have received considerable interest these recent years in the field of combinatorial optimization. A wide variety of hybrid approaches have been proposed in the literature. In this paper, a taxonomy of hybrid metaheuristics is presented in an attempt to provide a common terminology and classification mechanisms. The taxonomy, while presented in terms of metaheuristics, is also applicable to most types of heuristics and exact optimization algorithms. As an illustration of the usefulness of the taxonomy an annoted bibliography is given which classifies a large number of hybrid approaches according to the taxonomy. The bibliography provides 129 references on the use of hybrid metaheuristics. Keywords: Taxonomy, Combinatorial optimization, Metaheuristics, Hybrid algorithms, Parallel algorithms. 1. Introduction Computing optimal solutions is computationally intractable for many combinatorial optimization problems, e.g., those known as NP-hard. In practice, we are usu...

This paper describes how multiple interacting swarms of adaptive mobile agents can be used to solve problems in networks. The paper introduces a new architectural description for an agent that is chemically inspired and proposes chemical interaction as the principal mechanism for inter-swarm communication. Agents within a given swarm have behavior that is inspired by the foraging activities of ants, with each agent capable of simple actions and knowledge of a global goal is not assumed. The creation of chemical trails is proposed as the primary mechanism used in distributed problem solving arising from self-organization of swarms of agents. The paper proposes that swarm chemistries can be engineered in order to apply the principal ideas of the Subsumption Architecture in the domain of mobile agents. The paper presents applications of the new architecture in the domain of communications networks and describes the essential elements of a mobile agent framework that is being considered fo...