A Survey of Augmented Reality Technologies, Applications and Limitations

Abstract-DemandResponse is a mechanism used in power grids to manage customers' power consumption during critical situations (e.g. power shortage). Data centres are good candidates to participate in Demand Response programs due to their high energy use. In this paper, we present a generic architecture to enable Demand Response between Energy Provider and Data Centres realised in All4Green. To this end, we show our three-level concept and then illustrate the building blocks of All4Green's architectural design. Furthermore, we introduce the novel aspects of GreenSDA and GreenSLA for Energy Provider-Data centre sub-ecosystem as well as Data centre-IT Client sub-ecosystem respectively. In order to further reduce energy consumption and CO2 emission, the notion of data centre federation is introduced: savings can be expected if data centres start to collaborate by exchanging workload. Also, we specify the technological solutions necessary to implement our proposed architectural approach. Finally, we present preliminary proof-of-concept experiments, conducted both on traditional and cloud computing data centres, which show relatively encouraging results. I. OVERVIEWWith the energy consumption of ICT mushrooming for some decades, and data centres at the heart of this development, a lot of research has been dedicated to this huge problem for environmental health and resource depletion. However, it turns out that data centres are not only part of the problem but also one key to its solution because the energy challenge is both, a problem of energy consumption and a problem of power consumption: In times of low supply and high demand, extra power needs to be provided at high environmental cost, in times of high supply (e.g. through wind and sun) and low demand, superfluous energy suppliers are cut off the electricity net. The project All4Green1 shows that data centres with their huge power hunger can play a role in solving this challenge. To this end, the data centre is viewed as part of an ecosystem consisting of ICT users deploying services in the data centre, electrical power providers, and data centres cooperating in a federated way. By establishing a collaborative scheme within this eco-system through green contracts supported by an underlying signalling technology, All4Green tackles both goals: It aims at saving CO 2 emissions by enabling a cleaner energy mix for the energy consumption of a data centre. And additionally it will reduce this energy consumption by 10%.All4Green relevant actors in the system, as illustrated in Fig. 1 The All4Green approach is based on a three-levels-concept

The vehicle routing problem is a classical combinatorial optimization problem. This work is about a variant of the vehicle routing problem with dynamically changing orders and time windows. In real-world applications often the demands change during operation time. New orders occur and others are canceled. In this case new schedules need to be generated on-the-fly. Online optimization algorithms for dynamical vehicle routing address this problem but so far they do not consider time windows. Moreover, to match the scenarios found in real-world problems adaptations of benchmarks are required. In this paper, a practical problem is modeled based on the procedure of daily routing of a delivery company. New orders by customers are introduced dynamically during the working day and need to be integrated into the schedule. A multiple ant colony algorithm combined with powerful local search procedures is proposed to solve the dynamic vehicle routing problem with time windows. The performance is tested on a new benchmark based on simulations of a working day. The problems are taken from Solomon’s benchmarks but a certain percentage of the orders are only revealed to the algorithm during operation time. Different versions of the MACS algorithm are tested and a high performing variant is identified. Finally, the algorithm is tested in situ: In a field study, the algorithm schedules a fleet of cars for a surveillance company. We compare the performance of the algorithm to that of the procedure used by the company and we summarize insights gained from the implementation of the real-world study. The results show that the multiple ant colony algorithm can get a much better solution on the academic benchmark problem and also can be integrated in a real-world environment.

This work introduces Autonomous selection in EAs to escape the need for some central control during the selection phases of an EA. The results demonstrate that this is a viable idea that needs further investigation.The main idea is to make the decisions about (de)selection on local level (by the individuals) in a decentralized manner (without global coordination), in such a way that individuals with above/below average fitness have a high/low probability of surviving and producing offspring. The proposed mechanism is based on 1) information about the population's average fitness available at each individual, 2) a function that determines (de)selection probabilities, based on the individual's own fitness and the population's average fitness. This study concentrates on the selection mechanism, and assumes that the average fitness is locally available to all individuals. Indeed, in P2P networks, it is possible to gather an approximation of such statistics without any central control. However, in order to study the selection mechanism in its pure form, it is assumed that an "oracle" provides individuals with the actual average.The parental and survival probabilities of a given individualx are (sigmoid) functions of its fitness deviation from average Δf (x), and depend on two parameters each, sa and ma (with a=s or f, for survival or reproduction). The shift s determines where the transition from low to high probability takes place, and increasing s will increase the selection pressure. The multiplier m determines how sharp the transition from very low to very high probability is. More formally, the probability Pa thatx survives or reproduces isEach individual in turn dies, or survives and reproduces, stochastically with the corresponding probabilities. The population size can hence greatly vary during evolution, as offspring are created locally without any information about global population size.

In this paper we apply three Neuro-Evolution (NE) methods as controller design approaches in a collective behavior task. These NE methods are Enforced Sub-Populations, MultiAgent Enforced Sub-Populations, and Collective Neuro-Evolution. In the collective behavior task, teams of simulated robots search an unexplored area for objects that are to be used in a collective construction task. Results indicate that the Collective Neuro-Evolution method, a cooperative co-evolutionary approach that allows for regulated recombination between genotype populations is appropriate for deriving artificial neural network controllers in a set of increasingly difficult collective behavior task scenarios.