Dominance Orders in Animal Societies: The Self-organization Hypothesis Revisited

Bonabeau, Eric; Théraulaz, Guy; Deneubourg, Jean‐Louis

doi:10.1006/bulm.1999.0108

Cited by 85 publications

(80 citation statements)

References 60 publications

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“…The general idea behind the concept of hierarchy can be stated as the emergence of a tree-like structure in a top-down fashion in the order of their ranks further depicting a specific relationship. Earlier studies on dominance relationship in animal societies, Bonabeau et al suggest a process of self-organization of nodes depending on their roles and importance [10]. This lead to the identification of important or 'leader' nodes within a community.…”

Section: Background and Prior Workmentioning

confidence: 99%

The Hierarchy Structure in Directed and Undirected Signed Networks

Maktoubian¹,

Noori²,

Amini³

et al. 2017

IJCNS

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The concept of social stratification and hierarchy among human dates is back to the origin of human race. Presently, the growing reputation of social networks has given us with an opportunity to analyze these well-studied phenomena over different networks at different scales. Generally, a social network could be defined as a collection of actors and their interactions. In this work, we concern ourselves with a particular type of social networks, known as trust networks. In this type of networks, there is an explicit show of trust (positive interaction) or distrust (negative interaction) among the actors. In a social network, actors tend to connect with each other on the basis of their perceived social hierarchy. The emergence of such a hierarchy within a social community shows the manner in which authority manifests in the community. In the case of signed networks, the concept of social hierarchy can be interpreted as the emergence of a tree-like structure comprising of actors in a top-down fashion in the order of their ranks, describing a specific parent-child relationship, viz. child trusts parent. However, owing to the presence of positive as well as negative interactions in signed networks, deriving such "trust hierarchies" is a non-trivial challenge. We argue that traditional notions (of unsigned networks) are insufficient to derive hierarchies that are latent within signed networks.

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Section: Background and Prior Workmentioning

confidence: 99%

The Hierarchy Structure in Directed and Undirected Signed Networks

Maktoubian¹,

Noori²,

Amini³

et al. 2017

IJCNS

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“…Self Organization 2. Stigmergy Self-Organization: The concept of Self Organization was defined by Bonabeau et al [11,12] in 1999 as -Self Organization is a set of Dynamic Mechanisms whereby structures appear at the global level of a system from interactions of its lower-level components". Self organization lays the foundation of three important characteristics: Structure, Multi-Stability and State Transition.Structure: It is founded from a homogeneous start-up state.…”

Section: Swarm Intelligencementioning

confidence: 99%

Performance Analysis of TBC-ACO Routing Protocol with Existing Routing Protocols of Wireless Sensor Networks

Radhika

Haritha²

2017

IJARCSSE

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I. INTRODUCTIONIn 21st Century, Wireless Sensor Networks [1] has gained huge amount of attention of researchers across the world with regard to tons of theoretical, practical and implementation challenges. The research progress in Wireless Sensor Networks has explored diverse novel applications enabled by large-scale networks comprising of thousands of sensor nodes capable of sensing environmental information, processing it in efficient manner and transmitting the information back to base locations for further analysis. Wireless Sensor Networks comprise of large number of low-cost, low power and multi-functional capable wireless sensor nodes [2,3] for sensing different information's, equipped with wireless communication transmission mediums and computational capabilities. In sensor nodes, different types of sensors in form of mechanical, biological, chemical, optical can be attached to acquire real-time data from environment and transmit it back after processing. The sensor nodes forming Wireless Sensor Networks [4,5] have limitation in terms of low memory, less processing speeds. A radio is integrated on nodes to transmit the information back to sink node. The nodes are powered using battery to act as main power source. As the batteries are the only and primary power source, it becomes difficult to replace and charge the batteries once nodes are deployed which remains the foremost challenge in front of researchers to enhance the Energy Efficiency of sensor nodes. The characteristics of Wireless Sensor Network [1,6,3] are Deployment of sensor nodes; Less power; less computation capability; Limited memory; Unreliability. Therefore, the characteristics open wide range of challenges for the development and real-time implementations of wireless sensor network in the world. Sensor nodes once deployed in the real world are grouped into clusters, and each cluster has a node named as -Cluster Head‖. All the nodes in the cluster forward their sensed data to the cluster head and it is the duty of Cluster Head to route the information back to Sink Node (termed as -Base Station‖) via multi-hop wireless communication as shown in Fig. 1. With regard to various limitations cum shortcomings, Wireless Sensor Networks requires a collection of network protocols for implementing network control and other managerial functions in terms of synchronization, localization, security, routing, clustering etc. Various routing protocols proposed till date for routing the packets among nodes suffer from energy-constraints. Researchers [2,5] have combined various allied field like Neuro-computing, evolutionary computing, fuzzy logic computing, reinforcement learning and swarm intelligence based techniques in WSN. The ultimate goal is to improvise the Routing Efficiency in Wireless Sensor Networks. Researchers have successfully deployed Swarm Intelligence based techniques to address varied challenges existing in area of Wireless Sensor Networks and able to successfully deploy WSN in varied applications. The aim of this research paper is to expl...

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“…The research done in this area is also known as swarm intelligence and has been applied to solve a huge number of problems (from telecommunications routing to robot control) [11]. This research builds on two different aspects: on one hand, a natural phenomenon that allows task partition in animal and insect societies through competitions among members [12] and, on the other, the work done by [13] on chaos in distributed systems and the model developed as a result.…”

Section: An Ecosystem Based Model For Physical Agentsmentioning

confidence: 99%

Learning and adaptation in physical heterogeneous teams of robots

Rosa¹,

Muñoz²

2007

jopha

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Abstract-In this paper we present a novel approach to assigning roles to robots in a team of physical heterogeneous robots. Its members compete for these roles and get rewards for them. The rewards are used to determine each agent's preferences and which agents are better adapted to the environment. These aspects are included in the decision making process. Agent interactions are modelled using the concept of an ecosystem in which each robot is a species, resulting in emergent behaviour of the whole set of agents. One of the most important features of this approach is its high adaptability. Unlike some other learning techniques, this approach does not need to start a whole exploitation process when the environment changes. All this is exemplified by means of experiments run on a simulator. In addition, the algorithm developed was applied as applied to several teams of robots in order to analyse the impact of heterogeneity in these systems.

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Dominance Orders in Animal Societies: The Self-organization Hypothesis Revisited

Cited by 85 publications

References 60 publications

The Hierarchy Structure in Directed and Undirected Signed Networks

The Hierarchy Structure in Directed and Undirected Signed Networks

Performance Analysis of TBC-ACO Routing Protocol with Existing Routing Protocols of Wireless Sensor Networks

Learning and adaptation in physical heterogeneous teams of robots

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