SwarMAV: A Swarm of Miniature Aerial Vehicles

As the MAV (Micro or Miniature Aerial Vehicles) field matures, we expect to see that the platform's degree of autonomy, the information exchange, and the coordination with other manned and unmanned actors, will become at least as crucial as its aerodynamic design. The project described in this paper explores some aspects of a particularly exciting possible avenue of development: an autonomous swarm of MAVs which exploits its inherent reliability (through redundancy), and its ability to exchange information among the members, in order to cope with a dynamically changing environment and achieve its mission. We describe the successful realization of a prototype experimental platform weighing only 75g, and outline a strategy for the automatic design of a suitable controller

Graph Theory and Networks in Biology

In this paper, we present a survey of the use of graph theoretical techniques in Biology. In particular, we discuss recent work on identifying and modelling the structure of bio-molecular networks, as well as the application of centrality measures to interaction networks and research on the hierarchical structure of such networks and network motifs. Work on the link between structural network properties and dynamics is also described, with emphasis on synchronization and disease propagation.Comment: 52 pages, 5 figures, Survey Pape

Scale invariance in natural and artificial collective systems : a review

Self-organized collective coordinated behaviour is an impressive phenomenon, observed in a variety of natural and artificial systems, in which coherent global structures or dynamics emerge from local interactions between individual parts. If the degree of collective integration of a system does not depend on size, its level of robustness and adaptivity is typically increased and we refer to it as scale-invariant. In this review, we first identify three main types of self-organized scale-invariant systems: scale-invariant spatial structures, scale-invariant topologies and scale-invariant dynamics. We then provide examples of scale invariance from different domains in science, describe their origins and main features and discuss potential challenges and approaches for designing and engineering artificial systems with scale-invariant properties

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

The influence of topology and information diffusion on networked game dynamics

This thesis studies the influence of topology and information diffusion on the strategic interactions of agents in a population. It shows that there exists a reciprocal relationship between the topology, information diffusion and the strategic interactions of a population of players. In order to evaluate the influence of topology and information flow on networked game dynamics, strategic games are simulated on populations of players where the players are distributed in a non-homogeneous spatial arrangement. The initial component of this research consists of a study of evolution of the coordination of strategic players, where the topology or the structure of the population is shown to be critical in defining the coordination among the players. Next, the effect of network topology on the evolutionary stability of strategies is studied in detail. Based on the results obtained, it is shown that network topology plays a key role in determining the evolutionary stability of a particular strategy in a population of players. Then, the effect of network topology on the optimum placement of strategies is studied. Using genetic optimisation, it is shown that the placement of strategies in a spatially distributed population of players is crucial in maximising the collective payoff of the population. Exploring further the effect of network topology and information diffusion on networked games, the non-optimal or bounded rationality of players is modelled using topological and directed information flow of the network. Based on the topologically distributed bounded rationality model, it is shown that the scale-free and small-world networks emerge in randomly connected populations of sub-optimal players. Thus, the topological and information theoretic interpretations of bounded rationality suggest the topology, information diffusion and the strategic interactions of socio-economical structures are cyclically interdependent

Investigation of Team Formation in Dynamic Social Networks

Team Formation Problem (TFP) in Social Networks (SN) is to collect the group of individuals who match the requirements of given tasks under some constraints. It has several applications, including academic collaborations, healthcare, and human resource management. These types of problems are highly challenging because each individual has his or her own demands and objectives that might conflict with team objectives. The major contribution of this dissertation is to model a computational framework to discover teams of experts in various applications and predict the potential for collaboration in the future from a given SN. Inspired by an evolutionary search technique using a higher-order cultural evolution, a framework is proposed using Knowledge-Based Cultural Algorithms to identify teams from co-authorship and industrial settings. This model reduces the search domain while guiding the search direction by extracting situational knowledge and updating it in each evolution. Motivated from the above results, this research examines the palliative care multidisciplinary networks to identify and measure the performance of the optimal team of care providers in a highly dynamic and unbalanced SN of volunteer, community, and professional caregivers. Thereafter, a visualization framework is designed to explore and monitor the evolution in the structure of the care networks. It helps to identify isolated patients, imbalanced resource allocation, and uneven service distribution in the network. This contribution is recognized by Hospice and the Windsor Essex Compassion Care Community in partnership with the Faculty of Nursing. In each setting, several cost functions are attempted to measure the performance of the teams. To support this study, the temporal nature of two important evaluation metrics is analyzed in Dynamic Social Networks (DSN): dynamic communication cost and dynamic expertise level. Afterward, a novel generic framework for TFP is designed by incorporating essential cost functions, including the above dynamic cost functions. The Multi-Objective Cultural Algorithms (MOCA) is used for this purpose. In each generation, it keeps track of the best solutions and enhances exploration by driving mutation direction towards unexplored areas. The experimental results reach closest to the exact algorithm and outperform well-known searching methods. Subsequently, this research focuses on predicting suitable members for the teams in the future, which is typically a real-time application of Link Prediction. Learning temporal behavior of each vertex in a given DSN can be used to decide the future connections of the individual with the teams. A probability function is introduced based on the activeness of the individual. To quantify the activeness score, this study examines each vertex as to how actively it interacts with new and existing vertices in DSN. It incorporates two more objective functions: the weighted shortest distance and the weighted common neighbor index. Because it is technically a classification problem, deep learning methods have been observed as the most effective solution. The model is trained and tested with Multilayer Perceptron. The AUC achieves above 93%. Besides this, analyzing common neighbors with any two vertices, which are expected to connect, have a high impact on predicting the links. A new method is introduced that extracts subgraph of common neighbors and examines features of each vertex in the subgraph to predict the future links. The sequence of subgraphs\u27 adjacency matrices of DSN can be ordered temporally and treated as a video. It is tested with Convolutional Neural Networks and Long Short Term Memory Networks for the prediction. The obtained results are compared against heuristic and state-of-the-art methods, where the results reach above 96% of AUC. In conclusion, the knowledge-based evolutionary approach performs well in searching through SN and recommending effective teams of experts to complete given tasks successfully in terms of time and accuracy. However, it does not support the prediction problem. Deep learning methods, however, perform well in predicting the future collaboration of the teams

The influence of topology and information diffusion on networked game dynamics

This thesis studies the influence of topology and information diffusion on the strategic interactions of agents in a population. It shows that there exists a reciprocal relationship between the topology, information diffusion and the strategic interactions of a population of players. In order to evaluate the influence of topology and information flow on networked game dynamics, strategic games are simulated on populations of players where the players are distributed in a non-homogeneous spatial arrangement. The initial component of this research consists of a study of evolution of the coordination of strategic players, where the topology or the structure of the population is shown to be critical in defining the coordination among the players. Next, the effect of network topology on the evolutionary stability of strategies is studied in detail. Based on the results obtained, it is shown that network topology plays a key role in determining the evolutionary stability of a particular strategy in a population of players. Then, the effect of network topology on the optimum placement of strategies is studied. Using genetic optimisation, it is shown that the placement of strategies in a spatially distributed population of players is crucial in maximising the collective payoff of the population. Exploring further the effect of network topology and information diffusion on networked games, the non-optimal or bounded rationality of players is modelled using topological and directed information flow of the network. Based on the topologically distributed bounded rationality model, it is shown that the scale-free and small-world networks emerge in randomly connected populations of sub-optimal players. Thus, the topological and information theoretic interpretations of bounded rationality suggest the topology, information diffusion and the strategic interactions of socio-economical structures are cyclically interdependent