Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Multimodal interactions in insect navigation

Animals travelling through the world receive input from multiple sensory modalities that could be important for the guidance of their journeys. Given the availability of a rich array of cues, from idiothetic information to input from sky compasses and visual information through to olfactory and other cues (e.g. gustatory, magnetic, anemotactic or thermal) it is no surprise to see multimodality in most aspects of navigation. In this review, we present the current knowledge of multimodal cue use during orientation and navigation in insects. Multimodal cue use is adapted to a species’ sensory ecology and shapes navigation behaviour both during the learning of environmental cues and when performing complex foraging journeys. The simultaneous use of multiple cues is beneficial because it provides redundant navigational information, and in general, multimodality increases robustness, accuracy and overall foraging success. We use examples from sensorimotor behaviours in mosquitoes and flies as well as from large scale navigation in ants, bees and insects that migrate seasonally over large distances, asking at each stage how multiple cues are combined behaviourally and what insects gain from using different modalities

Computational Chemotaxis in Ants and Bacteria over Dynamic Environments

Chemotaxis can be defined as an innate behavioural response by an organism to a directional stimulus, in which bacteria, and other single-cell or multicellular organisms direct their movements according to certain chemicals in their environment. This is important for bacteria to find food (e.g., glucose) by swimming towards the highest concentration of food molecules, or to flee from poisons. Based on self-organized computational approaches and similar stigmergic concepts we derive a novel swarm intelligent algorithm. What strikes from these observations is that both eusocial insects as ant colonies and bacteria have similar natural mechanisms based on stigmergy in order to emerge coherent and sophisticated patterns of global collective behaviour. Keeping in mind the above characteristics we will present a simple model to tackle the collective adaptation of a social swarm based on real ant colony behaviors (SSA algorithm) for tracking extrema in dynamic environments and highly multimodal complex functions described in the well-know De Jong test suite. Later, for the purpose of comparison, a recent model of artificial bacterial foraging (BFOA algorithm) based on similar stigmergic features is described and analyzed. Final results indicate that the SSA collective intelligence is able to cope and quickly adapt to unforeseen situations even when over the same cooperative foraging period, the community is requested to deal with two different and contradictory purposes, while outperforming BFOA in adaptive speed. Results indicate that the present approach deals well in severe Dynamic Optimization problems.Comment: 8 pages, 6 figures, in CEC 07 - IEEE Congress on Evolutionary Computation, ISBN 1-4244-1340-0, pp. 1009-1017, Sep. 200

Mathematical models for chemotaxis and their applications in self-organisation phenomena

Chemotaxis is a fundamental guidance mechanism of cells and organisms, responsible for attracting microbes to food, embryonic cells into developing tissues, immune cells to infection sites, animals towards potential mates, and mathematicians into biology. The Patlak-Keller-Segel (PKS) system forms part of the bedrock of mathematical biology, a go-to-choice for modellers and analysts alike. For the former it is simple yet recapitulates numerous phenomena; the latter are attracted to these rich dynamics. Here I review the adoption of PKS systems when explaining self-organisation processes. I consider their foundation, returning to the initial efforts of Patlak and Keller and Segel, and briefly describe their patterning properties. Applications of PKS systems are considered in their diverse areas, including microbiology, development, immunology, cancer, ecology and crime. In each case a historical perspective is provided on the evidence for chemotactic behaviour, followed by a review of modelling efforts; a compendium of the models is included as an Appendix. Finally, a half-serious/half-tongue-in-cheek model is developed to explain how cliques form in academia. Assumptions in which scholars alter their research line according to available problems leads to clustering of academics and the formation of "hot" research topics.Comment: 35 pages, 8 figures, Submitted to Journal of Theoretical Biolog

Mechanisms for the Evolution of Superorganismality in Ants

Ant colonies appear to behave as superorganisms; they exhibit very high levels of within-colony cooperation, and very low levels of within-colony conflict. The evolution of such superorganismality has occurred multiple times across the animal phylogeny, and indeed, origins of multicellularity represent the same evolutionary process. Understanding the origin and elaboration of superorganismality is a major focus of research in evolutionary biology. Although much is known about the ultimate factors that permit the evolution and persistence of superorganisms, we know relatively little about how they evolve. One limiting factor to the study of superorganismality is the difficulty of conducting manipulative experiments in social insect colonies. Recent work on establishing the clonal raider ant, Ooceraea biroi, as a tractable laboratory model, has helped alleviate this difficulty. In this dissertation, I study the proximate evolution of superorganismality in ants. Using focussed mechanistic experiments in O. biroi, in combination with comparative work from other ant species, I study three major aspects of ant social behaviour that provide insight into the origin, maintenance, and elaboration of superorganismality. First, I ask how ants evolved to live in colonies, and how they evolved a reproductive division of labour. A comparative transcriptomic screen across the ant phylogeny, combined with experimental manipulations in O. biroi, finds that reproductive ants have higher insulin levels than their non-reproductive nestmates, and that this likely regulates the reproductive division of labour. Using these data, as well as studies of the idiosyncrasies of O. biroi’s life history, I propose a mechanism for the evolution of the first colonies. It is possible that similar mechanisms underlie the evolution of reproductive division of labour in other superorganisms, and of germ-soma separation in nascent multicellular individuals. Second, I ask how ant workers assess colony hunger to regulate their foraging behaviour. I find that workers use larval signals, but not their own nutritional states, to decide how much to forage. In contrast, they use their nutritional states, but not larval signals, to decide how much to eat, suggesting that in at least some ant species, foraging and feeding have been decoupled. This evolution of colony-level foraging regulation has occurred convergently in hymenopteran superorganisms, and is analogous to the evolution of centralised regulation of foraging behaviour in multicellular animals. Finally, I ask how an iconic collective foraging behaviour – the mass raids of army ants – evolved. I find that O. biroi, a relative of army ants, forages collectively in group raids, that these are ancestral to the mass raids of army ants, and that the transition from group to mass raiding correlates with expansion in colony size. I propose that the scaling effects of increasing colony size explain this transition. It is possible that similar principles underlie the evolution of disparate collective behaviours in other animal groups and among cells within developing animals. Together, these studies illuminate the life history of O. biroi, and suggest mechanisms for the evolution of core aspects of cooperative behaviour in ant colonies. I draw comparisons to the evolution of superorganismality in other lineages, as well as to the evolution of multicellularity. I suggest that there may be additional similarities in the proximate evolutionary trajectories of superorganismality and multicellularity

On the role of stigmergy in cognition

Cognition in animals is produced by the self- organized activity of mutually entrained body and brain. Given that stigmergy plays a major role in self-organization of societies, we identify stigmergic behavior in cognitive systems, as a common mechanism ranging from brain activity to social systems. We analyze natural societies and artificial systems exploiting stigmergy to produce cognition. Several authors have identified the importance of stigmergy in the behavior and cognition of social systems. However, the perspective of stigmergy playing a central role in brain activity is novel, to the best of our knowledge. We present several evidences of such processes in the brain and discuss their importance in the formation of cognition. With this we try to motivate further research on stigmergy as a relevant component for intelligent systems.info:eu-repo/semantics/acceptedVersio

An Intelligent Mobility Prediction Scheme for Location-Based Service over Cellular Communications Network

One of the trickiest challenges introduced by cellular communications networks is mobility prediction for Location Based-Services (LBSs). Hence, an accurate and efficient mobility prediction technique is particularly needed for these networks. The mobility prediction technique incurs overheads on the transmission process. These overheads affect properties of the cellular communications network such as delay, denial of services, manual filtering and bandwidth. The main goal of this research is to enhance a mobility prediction scheme in cellular communications networks through three phases. Firstly, current mobility prediction techniques will be investigated. Secondly, innovation and examination of new mobility prediction techniques will be based on three hypothesises that are suitable for cellular communications network and mobile user (MU) resources with low computation cost and high prediction success rate without using MU resources in the prediction process. Thirdly, a new mobility prediction scheme will be generated that is based on different levels of mobility prediction. In this thesis, a new mobility prediction scheme for LBSs is proposed. It could be considered as a combination of the cell and routing area (RA) prediction levels. For cell level prediction, most of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shape cells. These models are not suitable for certain LBSs where the objectives are to compute and present on-road services. Such techniques are the New Markov-Based Mobility Prediction (NMMP) and Prediction Location Model (PLM) that deal with inner cell structure and different levels of prediction, respectively. The NMMP and PLM techniques suffer from complex computation, accuracy rate regression and insufficient accuracy. In this thesis, Location Prediction based on a Sector Snapshot (LPSS) is introduced, which is based on a Novel Cell Splitting Algorithm (NCPA). This algorithm is implemented in a micro cell in parallel with the new prediction technique. The LPSS technique, compared with two classic prediction techniques and the experimental results, shows the effectiveness and robustness of the new splitting algorithm and prediction technique. In the cell side, the proposed approach reduces the complexity cost and prevents the cell level prediction technique from performing in time slots that are too close. For these reasons, the RA avoids cell-side problems. This research discusses a New Routing Area Displacement Prediction for Location-Based Services (NRADP) which is based on developed Ant Colony Optimization (ACO). The NRADP, compared with Mobility Prediction based on an Ant System (MPAS) and the experimental results, shows the effectiveness, higher prediction rate, reduced search stagnation ratio, and reduced computation cost of the new prediction technique