Characterization and optimization of the power consumption in wireless access networks by taking daily traffic variations into account

In this study, a power consumption model as a function of the traffic is developed for macrocell base stations based on measurements on an actual base station. This model allows us to develop energy-efficient wireless access networks by combining the Green radio access network design (GRAND) tool designed by the authors, which develops an always-on network with a minimal power consumption for a predefined area, and an algorithm that introduces power reducing techniques in the network such as sleep modes and cell zooming. Green-field deployments and optimization of existing networks are investigated. For a green-field deployment, it was found that introducing sleep modes and cell zooming in the network can reduce the power consumption by up to 14.4% compared to the network without sleep modes and cell zooming. Optimizing existing networks by applying GRAND (without sleep modes and cell zooming) results in a power consumption reduction of 34.5% compared to the original network. A careful selection of base station locations already results in a significant energy saving. Introducing sleep modes and cell zooming to the current networks results in a saving of 8%. Sleep modes and cell zooming are promising energy-saving techniques for future wireless networks

Performance Analysis of Adaptive Location Update Schemes for Continuous Cell Zooming Algorithm in Wireless Networks

To reduce the transmitted power of base stations in mobile wireless networks, continuous cell zooming algorithm is a feasible dynamic cell zooming algorithm. In this algorithm, location management is required in order to know the locations of users. Movement-based Update is not compatible and the application of Convention Periodic Update (CPU) scheme in continuous cell zooming algorithm can lead to a high signaling cost. Thus, aiming to highlight the effectiveness of newly proposed location update schemes, Time-Adaptive Periodic Update (TAPU) and Location-Adaptive Periodic Update (LAPU), a simulation-based performance analysis is conducted. Applying in continuous cell zooming algorithm, the performances of TAPU and LAPU are compared to that of Convention Periodic Update (CPU) scheme in terms of transmitted power ratio, outage ratio and the number of update messages. The performances of TAPU and LAPU are analyzed in a network with different number of users and in a network with different average moving speeds of users. The results show that compared to CPU, both TAPU and LAPU have no significant effect on power saving capability of continuous cell zooming algorithm in every scenario. Meanwhile, LAPU and TAPU give a significant reduction of update messages in every scenario. In terms of QoS effect, LAPU gives approximately the same outage ratio as CPU and a higher outage ratio occurs in TAPU

NaviCell: a web-based environment for navigation, curation and maintenance of large molecular interaction maps

Molecular biology knowledge can be systematically represented in a computer-readable form as a comprehensive map of molecular interactions. There exist a number of maps of molecular interactions containing detailed description of various cell mechanisms. It is difficult to explore these large maps, to comment their content and to maintain them. Though there exist several tools addressing these problems individually, the scientific community still lacks an environment that combines these three capabilities together. NaviCell is a web-based environment for exploiting large maps of molecular interactions, created in CellDesigner, allowing their easy exploration, curation and maintenance. NaviCell combines three features: (1) efficient map browsing based on Google Maps engine; (2) semantic zooming for viewing different levels of details or of abstraction of the map and (3) integrated web-based blog for collecting the community feedback. NaviCell can be easily used by experts in the field of molecular biology for studying molecular entities of their interest in the context of signaling pathways and cross-talks between pathways within a global signaling network. NaviCell allows both exploration of detailed molecular mechanisms represented on the map and a more abstract view of the map up to a top-level modular representation. NaviCell facilitates curation, maintenance and updating the comprehensive maps of molecular interactions in an interactive fashion due to an imbedded blogging system. NaviCell provides an easy way to explore large-scale maps of molecular interactions, thanks to the Google Maps and WordPress interfaces, already familiar to many users. Semantic zooming used for navigating geographical maps is adopted for molecular maps in NaviCell, making any level of visualization meaningful to the user. In addition, NaviCell provides a framework for community-based map curation.Comment: 20 pages, 5 figures, submitte

Dynamic quantized consensus under DoS attacks: Towards a tight zooming-out factor

This paper deals with dynamic quantized consensus of dynamical agents in a general form under packet losses induced by Denial-of-Service (DoS) attacks. The communication channel has limited bandwidth and hence the transmitted signals over the network are subject to quantization. To deal with agent's output, an observer is implemented at each node. The state of the observer is quantized by a finite-level quantizer and then transmitted over the network. To solve the problem of quantizer overflow under malicious packet losses, a zooming-in and out dynamic quantization mechanism is designed. By the new quantized controller proposed in the paper, the zooming-out factor is lower bounded by the spectral radius of the agent's dynamic matrix. A sufficient condition of quantization range is provided under which the finite-level quantizer is free of overflow. A sufficient condition of tolerable DoS attacks for achieving consensus is also provided. At last, we study scalar dynamical agents as a special case and further tighten the zooming-out factor to a value smaller than the agent's dynamic parameter. Under such a zooming-out factor, it is possible to recover the level of tolerable DoS attacks to that of unquantized consensus, and the quantizer is free of overflow

Bridging Between Computer and Robot Vision Through Data Augmentation: A Case Study on Object Recognition

Despite the impressive progress brought by deep network in visual object recognition, robot vision is still far from being a solved problem. The most successful convolutional architectures are developed starting from ImageNet, a large scale collection of images of object categories downloaded from the Web. This kind of images is very different from the situated and embodied visual experience of robots deployed in unconstrained settings. To reduce the gap between these two visual experiences, this paper proposes a simple yet effective data augmentation layer that zooms on the object of interest and simulates the object detection outcome of a robot vision system. The layer, that can be used with any convolutional deep architecture, brings to an increase in object recognition performance of up to 7{\%}, in experiments performed over three different benchmark databases. An implementation of our robot data augmentation layer has been made publicly available