Desynchronization of simulation and optimization algorithms in HPC Environment

Need for scalability of an algorithm is essential, when one wants to utilize HPC infrastructure in an efficient and reasonable way. In such infrastructures, synchronization affects the efficiency of the parallel algorithms. However, one can consider introducing certain means of desynchronization in order to increase scalability. Allowing for omitting or delaying certain messages, can be easily accepted in the case of metaheuristics. Furthermore, some simulations can also follow this pattern and handle bigger environments. The paper presents a short survey of desynchronization idea, pointing out already obtained results or sketching out the future work focused on scaling the parallel and distributed computing or simulation algorithms leveraging desynchronization

'Extremotaxis': Computing with a bacterial-inspired algorithm

We present a general-purpose optimization algorithm inspired by “run-and-tumble”, the biased random walk chemotactic swimming strategy used by the bacterium Escherichia coli to locate regions of high nutrient concentration The method uses particles (corresponding to bacteria) that swim through the variable space (corresponding to the attractant concentration profile). By constantly performing temporal comparisons, the particles drift towards the minimum or maximum of the function of interest. We illustrate the use of our method with four examples. We also present a discrete version of the algorithm. The new algorithm is expected to be useful in combinatorial optimization problems involving many variables, where the functional landscape is apparently stochastic and has local minima, but preserves some derivative structure at intermediate scales

Optimized cross-organizational business process monitoring: Design and enactment

Organizations can implement the agility required to survive in the rapidly evolving business landscape by focusing on their core business and engaging in collaborations with other partners. This entails the need for organizations to monitor the behavior of the partners with which they collaborate. The design and enactment of monitoring, in this scenario, must become flexible and adapt as the collaboration evolves. We propose an approach to flexibly design and enact cross-organizational business process monitoring based on Product-Based Workflow Design. Our approach allows organizations to capture monitoring requirements, optimize such requirements, e.g. choosing the monitoring process with lowest cost or highest availability, and enacting the optimal monitoring process through a service-oriented approach. Optimization, in particular, is made efficient by adopting an Ant-colony optimization heuristic. The paper also describes a prototypical implementation of our approach in the ProM framework

Self-organizing Routing Algorithm fo Wireless Sensors Networks (WSN) using Ant Colony Optimization (ACO) with Tinyos.

This paper describes the basic tools to work with wireless sensors. TinyOShas a componentbased architecture which enables rapid innovation and implementation while minimizing code size as required by the severe memory constraints inherent in sensor networks. TinyOS's component library includes network protocols, distributed services, sensor drivers, and data acquisition tools ? all of which can be used asia or be further refined for a custom application. TinyOS was originally developed as a research project at the University of California Berkeley, but has since grown to have an international community of developers and users. Some algorithms concerning packet routing are shown. Incar entertainment systems can be based on wireless sensors in order to obtain information from Internet, but routing protocols must be implemented in order to avoid bottleneck problems. Ant Colony algorithms are really useful in such cases, therefore they can be embedded into the sensors to perform such routing task

Energy Efficiency Based Load Balancing Optimization Routing Protocol In 5G Wireless Communication Networks

A significant study area in cloud computing that still requires attention is how to distribute the workload among virtual machines and resources. Main goal of this research is to develop an efficient cloud load balancing approach, improve response time, decrease readiness time, maximise source utilisation, and decrease activity rejection time. This research propose novel technique in load balancing based network optimization using routing protocol for 5G wireless communication networks. the network load balancing has been carried out using cloud based software defined multi-objective optimization routing protocol. then the network security has been enhanced by data classification utilizing deep belief Boltzmann NN. Experimental analysis has been carried out based on load balancing and security data classification in terms of throughput, packet delivery ratio, energy efficiency, latency, accuracy, precision, recall

Bioinspired Principles for Large-Scale Networked Sensor Systems: An Overview

Biology has often been used as a source of inspiration in computer science and engineering. Bioinspired principles have found their way into network node design and research due to the appealing analogies between biological systems and large networks of small sensors. This paper provides an overview of bioinspired principles and methods such as swarm intelligence, natural time synchronization, artificial immune system and intercellular information exchange applicable for sensor network design. Bioinspired principles and methods are discussed in the context of routing, clustering, time synchronization, optimal node deployment, localization and security and privacy

On infrastructure network design with agent-based modelling

We have developed an agent-based model to optimize green-field network design in an industrial area. We aim to capture some of the deep uncertainties surrounding infrastructure design by modelling it developing specific ant colony optimizations. Hence, we propose a variety of extensions to our existing work, first ideas on how to realize them and three cases to explicate our ideas. One case is the design of a CO2 pipeline network in Rotterdam industrial area. First simulation results have shown the relevance of the approach. Keywords-Infrastructure design, network planning, ant colony optimization, deep uncertainty, socio-technical systems

Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation