Novel approaches to performance evaluation and benchmarking for energy-efficient multicast: empirical study of coded packet wireless networks

With the advancement of communication networks, a great number of multicast applications such as multimedia, video and audio communications have emerged. As a result, energy efficient multicast in wireless networks is becoming increasingly important in the field of Information and Communications Technology (ICT). According to the study by Gartner and Environmental Protection Agency (EPA) report presented to United State Congress in 2007,energy consumption of ICT nodes accounts for 3% of the worldwide energy supply and is responsible for 2% of the global Carbon dioxide (CO2) emission. However, several initiatives are being put in place to reduce the energy consumption of the ICT sector in general. A review of related literature reveals that existing approaches to energy efficient multicast are largely evaluated using a single metric and while the single metric is appropriate for effective performance, it is unsuitable for measuring efficiency adequately. This thesis studied existing coded packet methods for energy efficiency in ad hoc wireless networks and investigates efficiency frontier, which is the expected minimum energy within the minimum energy multicast framework. The energy efficiency performance was based on effective evaluation and there was no way an inefficient network could reach a level of being an efficiency frontier. Hence, this work looked at the position of how true efficiency evaluation is obtained when the entire network under examination attains their efficiency frontiers using ratios of weighted outputs to weighted inputs with multiple variables. To address these challenges and assist network operators when formulating their network policies and performing network administrations, this thesis proposed novel approaches that are based on Data Envelopment Analysis (DEA) methodology to appropriately evaluate the efficiency of multicast energy and further minimizes energy transmission in ad hoc wireless networks without affecting the overall network performance. The DEA, which was used to study the relative efficiency and productivity of systems in Economic and Operational Research disciplines, is a non-parametric method that relies on linear programming technique for optimization of discrete units of observation called the decision making units (DMUs)

Efficient Frontier and Benchmarking Models for Energy Multicast in Wireless Network Coding

This chapter introduces efficiency frontier and benchmarking concepts to evaluate the efficiency performance of wireless networks for multicast energy. These concepts are efficiency models based on the data envelopment analysis (DEA) technique. The DEA framework allows network administrators to evaluate the technical efficiency and determine how the inefficient wireless networks will attain a targeted efficiency frontier. In order to achieve efficiency frontier and benchmark by a wireless network, this chapter presents several models including the envelopment and the slack. The envelopment model evaluates the technical efficiency scores of each wireless network, while the slack model shows how the inefficient wireless network achieves efficiency frontier. The benchmark model evaluates the efficiency reference set and the lambda values of each network. The efficiency frontier algorithm has shown that many of the wireless networks sampled are inefficient. However, the algorithm has capability to help the inefficient wireless networks to achieve efficiency frontier and benchmark with their peers that are fully efficient

Green Wireless Internet Technology

YesIET Editorial: In the future communications will be pervasive in nature, allowing users access at the “touch of button” to attain any service, at any time, on any device. The future device design process requires both a reconfigurable RF front end and back end with high tuning speed, energy efficiency, excellent linearity and intelligence to maximise the “greenness” of the network. But energy efficiency and excellent linearity are the main topics that are driving the designs of future transceivers, including their efforts to minimise network contributions to climate changes such as the effect of CO2 emissions: the minimisation of these is a requirement for information and communication technology (ICT) as much as for other technologies. Recently, information and communication technologies were shown to account for 3% of global power consumption and 2% of global CO2 emissions, and hence far from insignificant. The approach towards energy conservation and CO2 reduction in future communications will require a gret deal of effort which should be targeted both at the design of energy efficient, low-complexity physical, MAC and network layers, while maintaining the required Quality of Service (QoS). There is also a need, in infrastructures, networks and user terminals, to take a more holistic approach to improving or achieving green communications, from radio operation, through functionality, up to implementation. The increasing demand for data and voice services is not the only cause for concern since energy management and conservation are now at the forefront of the political agenda. The vision of Europe 2020 is to become a smart, sustainable and inclusive economy, and as part of these priorities the EU have set forth the 20:20:20 targets, whereby greenhouse gas emissions and energy consumption should be reduced by 20% while energy from renewables should be increased by 20%

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF

Investigation of mobile devices usage and mobile augmented reality applications among older people

Mobile devices such as tablets and smartphones have allow users to communicate, entertainment, access information and perform productivity. However, older people are having issues to utilise mobile devices that may affect their quality of life and wellbeing. There are some potentials of mobile Augmented Reality (AR) applications to increase older users mobile usage by enhancing their experience and learning. The study aims to investigate mobile devices potential barriers and influence factors in using mobile devices. It also seeks to understand older people issues in using AR applications